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AI adoption is exploding with over 61% of Americans having used AI tools in the past six months, and 19% engaging with AI every day. Globally, that’s nearly 2 billion users and growing fast (source).
But most people aren’t looking for science-fiction breakthroughs. They want practical AI that helps them:
So which AI tools are best to improve work and creativity in 2025? We’ve compiled a list of seven practical AI applications that our team at listening247 has tested and have shown significant time saving benefits. From coding, to design, to video creation, each tackle a different part of our business workflow.
Sintra.ai positions itself as a “team of AI coworkers.” It offers pre-trained helpers such as recruiters, sales managers, and copywriters that can assist with tasks that would normally require additional hires. Sintra's "helpers" come pre-trained, and as you provide more information about your business, they get smarter and tailor responses to suit your company needs.
Your AI-powered coworkers.
More: Sintra.ai
Midjourney is an AI image generation tool that turns any computer in a design studio. It’s quickly become a creative staple widely used for:
It doesn’t replace designers, but it accelerates “what if” visual thinking.
More: Midjourney
Most of us spend way too much time in PowerPoint trying to create the perfect slide. Gamma changes that. Drop in your content or ideas and it generates clean, modern presentations and even lightweight websites.
It’s slide creation, simplified.
More: Gamma.app
Adobe AI integrates intelligent tools into Adobe Acrobat to help you quickly understand and navigate PDFs.
Smarter PDFs, faster insights.
More: Adobe Acrobat AI
We’ve all hit roadblocks while coding: digging through Stack Overflow threads, rewriting boilerplate, or debugging for hours. GitHub Copilot changes that by living inside coding environments (IDEs) and generating code in real time with you.
Code smarter, not harder.
More: GitHub Copilot
Video is the language of the internet, but producing it has always been slow and expensive. Lumen5 creates short-form videos quickly by combining your own uploaded scripts or prompted text into fully built out scripts with visuals, music, and voiceovers.
Video creation at the speed of ideas.
More: Lumen5 | Best Free AI Tools
All of these tools are impressive on their own (ideation, slides, code, images or videos), but what happens when you combine AI with the ability to listen, analyze, predict and create all in one place. That's listening247.
It combines the above to help brands act fast by turning online chatter into visual marketing campaigns with content that is authentic to its audience.
It’s not just monitoring, it’s social listening + action in one.
More: listening247
AI adoption is high, but fragmented. People jump between Midjourney, Copilot, Lumen5, and others depending on their needs. That’s powerful, but it also creates friction.
The future isn’t just single-purpose AI apps. It’s platforms like listening247 that bring listening, analysis, prediction, and creation into one seamless workflow. It takes the best of what's out there and weaves it into seamless brand conversations. Because in a world this chaotic and noisy, listening isn't enough. You have to filter, respond, and engage in real time.
Now if only AI could also make my morning coffee. Maybe in next quarter's update.
What other AI tools should we check out?
With Valentine’s Day on the horizon, the chocolate industry is in full swing to make their mark and drive sales and brand engagement. Using listening247’s Social Listening and Analytics, we analysed 24,930 posts from Instagram across multiple regions and languages, including English, Italian, Korean, and Indonesian. Our goal? To uncover the most influential conversation drivers around chocolate, love, and seasonal promotions.
Among the posts analysed:
If there’s one thing chocolate lovers adore more than indulging, it’s winning free chocolate. Giveaway promotions dominated the conversation, accounting for 14,884 of the total posts analysed. Brands like That’s It and Choczero sparked engagement through interactive contests, encouraging users to tag, share, and follow for a chance to win exclusive treats. Lindt and Baci Perugina took things further, tying their giveaways to limited-edition Valentine’s chocolates, ensuring their brand stayed top-of-mind as shoppers browsed for the perfect gift.
Takeaway: Giveaways don’t just create buzz; they build brand affinity and amplify visibility across social media. Tying contests to seasonal events maximises impact.
Valentine’s Day isn’t the only reason people talk about chocolate; seasonal occasions accounted for 6,085 posts, reinforcing how deeply chocolate is woven into celebrations. Brands like Lindt and Baci Perugina successfully capitalised on holiday excitement with heart-shaped boxes, themed promotions, and limited-edition releases.
Beyond promotions, people are passionate about their chocolate preferences. 3,801 posts discussed chocolate types, from dark and milk varieties to unique flavours. Discussions on discontinued favourites like mango and cream truffles gained traction, highlighting opportunities for brands to reintroduce nostalgic flavours.
Takeaway: Nostalgia sells. Revisiting past favourites or launching limited-edition throwback collections can rekindle consumer excitement.
Valentine’s Day-specific promotions accounted for 807 posts, with Lindt’s Pick & Mix selections and Baci’s signature love-note chocolates standing out. While consumers embraced these festive offerings, some concerns emerged around pricing. A Valentine’s loyalty programme could be a strategic move to balance premium appeal with affordability.
Takeaway: Limited editions fuel demand, but pricing strategies should ensure accessibility without compromising brand value.
With 493 posts, chocolate emerged as more than just a treat; it’s a symbol of affection. Baci Perugina’s multilingual “Love Note” campaign was a standout, adding a personal touch that deepened emotional connections. Lindt’s Pink Mixed Bar Bouquet and Lindor chocolates, often paired with roses, reinforced the role of chocolate in heartfelt gifting.
Takeaway: Thoughtful packaging and personalised messaging enhance emotional appeal and gift desirability.
Although less frequently mentioned, events and gifting traditions made their mark, with 275 posts discussing chocolate’s role in group celebrations and gifting culture. The Valentine’s Chocolate and Wine Walk was a particular highlight, proving that immersive brand experiences leave a lasting impression.
Takeaway: Experiential marketing, such as chocolate pairing events, can deepen consumer engagement beyond traditional advertising.
Valentine’s Day remains a key moment for chocolate brands.The top-performing strategies? Giveaways for engagement, personalised packaging for emotional appeal, and nostalgia-driven product revival to spark consumer excitement. Brands like That’s It, Lindt, and Baci Perugina demonstrated how interactive campaigns and thoughtful promotions can turn seasonal shoppers into lifelong customers.
As brands prepare for the next big occasion, one thing is clear: chocolate is more than just a treat; it’s a storytelling tool, a memory-maker, and the ultimate symbol of indulgence and love.
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The festive season is upon us, and Christmas 2024 promises a celebration that blends cherished traditions with fresh, modern interpretations. Using listening247’s Social Listening and Analytics, we analysed over 50,117 posts from Twitter, TikTok, and Instagram between 5th October and 8th December. Keywords such as Christmas Gifts, Secret Santa, Christmas 2024, Holiday Season 2024, Gift Ideas, Holiday Shopping, Christmas Shopping, Festive Gifts, and Holiday Gifts were used to categorise posts.
These posts shed light on the key conversation drivers and trends that brands can leverage to create unforgettable holiday experiences.
Holiday cheer dominates online discussions, with a significant 68% (34,358 posts) expressing positive sentiment. Neutral conversations accounted for 29% (14,384 posts), while negative posts were low at 3% (1,375 posts). This overwhelming positivity prompts brands to connect with customers through festive messaging.
With 36,816 mentions, occasions emerged as the leading theme this holiday season. Consumers prefer shared experiences over material gifts, marking a cultural shift towards valuing time spent with loved ones.
This trend highlights the importance of framing campaigns around moments that matter for brands. Think event-driven promotions, experiential pop-ups, or even hosting holiday workshops to tap into the emotional core of Christmas. By aligning products and services with these experiences, brands can foster a deeper connection with their audience.
The allure of winter wonderlands - complete with sparkling lights, frosty landscapes, and cosy settings - is pulling audiences. This theme reflects a universal desire for escapism, nostalgia, and the joy of immersive holiday environments.
Brands can capitalise on this by transforming store displays into magical winter settings or curating themed collections inspired by the festive season. Even digital spaces can incorporate "winter wonderland" shopping experiences using AR-powered (augmented reality) winter effects.
Christmas carols with traditional melodies are being offered in interactive formats like virtual sing-alongs, community carol events, and life performances. Carols act as a unifying force, bringing families and communities together during the holidays.
Brands can harness this trend by sponsoring carolling events, incorporating carol-themed marketing campaigns, or creating playlists to complement the shopping experience.
In 2024, exceptional customer experiences are the ultimate differentiator. Shoppers seek seamless, personalised interactions at every touchpoint, from festive packaging to in-store ambience and online convenience.
This was never about selling products but building an emotional connection with the customer. Brands should focus on creating memorable experiences, such as personalised gifting services, festive loyalty rewards, or interactive in-store events that leave a lasting impression.
Sustainability continues to shape consumer behaviour. From eco-friendly holiday decor to gifts with purpose, shoppers are drawn to brands that reflect their values. Many are engaging in charitable giving, seeking ways to give back through donations and community drives.
Brands can respond by offering sustainable products, recyclable packaging, and charity collaborations. Highlighting eco-conscious initiatives in holiday campaigns resonates with consumers and strengthens brand loyalty.
The season is no longer confined to a single location. Many are opting for unique travel experiences, whether snow-covered retreats or tropical getaways. At its heart, this trend revolves around the desire to connect with loved ones, bridging geographical gaps to create cherished memories.
This opens up opportunities for brands to market travel-friendly products, such as compact gifts, travel kits, or digital gifting options. Partnering with travel companies or creating holiday content tailored to travelling audiences can further amplify relevance.
Christmas 2024 is a season of connection, creativity and purpose. Consumers are looking for experiences that resonate emotionally, whether through the carols, the escapism of winter wonderlands, or the shared joy of meaningful occasions.
The message is clear for brands: adapt to these evolving preferences by focusing on experiences, sustainability, and personalisation. With data-driven decisions, brands can create memorable and meaningful campaigns - ensuring Christmas 2024 is one to remember for customers and brands alike.
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As Black Friday 2024 draws closer, retailers worldwide are gearing up for one of the busiest shopping days of the year. With billions spent in a single day, it’s an unmissable opportunity to connect with consumers, boost sales, and build brand loyalty. Thanks to listening247’s Social Listening and Analytics, we’ve uncovered the key trends and conversation drivers shaping this year’s Black Friday, helping retailers align with consumer behaviour and stand out in a crowded market.
Over 15,719 social media posts in English from platforms like Twitter, TikTok, and Instagram between 20 September and 15 November were analysed, to provide a wealth of data on what shoppers are saying, what they want, and how they feel about Black Friday 2024. Keywords such as Black Friday, Black Friday 2024, online shopping, in-store shopping, gift shopping, and Christmas shopping were used to track and categorise posts.
The buzz around Black Friday remains overwhelmingly positive:
Shoppers are excited, and motivated by the promise of unbeatable deals and the chance to save on holiday purchases. Positive sentiment is around tech deals, personalised promotions, and early access discounts, while negative sentiment is minimal, often tied to delivery delays or website crashes.
Using listening247, we identified the dominant topics shaping consumer conversations:
1. Technology – 12,881 posts: Tech products remain the crown jewel of Black Friday, accounting for nearly half of purchases. Televisions, laptops, gaming consoles, and smartwatches are the most wanted items. Major retailers like Amazon and Best Buy are expected to lead the charge with deep discounts, making this a key category for shoppers and brands.
2. Price – 9,313 posts: Pricing is a decisive factor in consumer decision-making. With financial pressures rising, shoppers are thoroughly comparing deals, looking for the best value, and waiting for Black Friday to make high-ticket purchases.
3. Discount Deal Promotions – 9,224 posts: Shoppers are actively seeking pre-Black Friday sales and exclusive member rewards. Brands offering early access or time-limited offers are seeing increased engagement, highlighting the effectiveness of targeted, exclusive promotions.
4. Gift Ideas – 7,116 posts: Black Friday is a prime moment for gift shopping. Consumers are turning to curated gift guides and social media for inspiration, with jewellery, apparel, and tech products topping the list of preferred gifts.
5. Shopping Platform – 2,094 posts: More than half of shoppers prefer online platforms to avoid crowds and enjoy convenience. Social media-driven e-commerce is particularly popular with Gen Z and Millennials, who rely on mobile-first shopping experiences.
1. Tech-Takeover: Electronics are projected to dominate Black Friday spending, driven by promotions from retailers like Walmart and Target. Smart home devices, gaming consoles, and high-end TVs are leading the wish lists. The influence of younger shoppers, particularly Gen Z and Millennials, is steering purchases online, where seamless digital experiences win the day.
2. The Power of Price: Pricing remains king. Consumers have set aside budgets specifically for Black Friday, emphasising its role as a cornerstone shopping event. The focus isn’t just on discounts but on true value, with shoppers scrutinising every deal to ensure it’s worth the spend.
3. Discount-Driven Decisions: Early-bird promotions and tiered discounts are becoming standard. Successful brands are going beyond basic sales by offering “spend-and-earn” rewards or bundling discounts to entice shoppers. This maximises short-term revenue and builds customer loyalty for future events.
4. Gifts Variety: Black Friday is the ultimate inspiration hub for gift-givers. Retailers curating well-targeted gift guides see higher engagement and conversions, as consumers look for meaningful yet budget-friendly options.
Black Friday 2024 provides a golden opportunity for retailers to connect with their audience through strategic pricing, tailored promotions, and a strong digital presence. Here’s how to capitalise on these insights:
Black Friday 2024 is more than just a sales event—it’s a cultural moment that reflects shifting consumer priorities and behaviours. By understanding what shoppers want, why they’re excited, and how they plan to spend, brands can tailor their strategies to exceed expectations. Thanks to listening247’s powerful analytics, retailers have the insights they need to navigate this high-stakes season and come out on top.
Let’s make this Black Friday a win for your business and your customers.
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As the countdown to Halloween 2024 begins, it’s clear that this year’s celebrations are set to echo a blend of nostalgia and innovation, thanks to insights from listening247’s Social Listening and Analytics. Over 25,727 social media posts across Twitter, TikTok, and Instagram have been meticulously analysed to reveal what costumes will dominate and the sentiments surrounding this spooky season.
Our data reveals a robust positivity surrounding Halloween, with a striking 71% of posts radiating enthusiasm. Here’s a snapshot of the sentiment breakdown:
• Positive Sentiments: 18,336 posts (71%)
• Negative Sentiments: 1,589 posts (6%)
• Neutral Sentiments: 5,802 posts (23%)
This positivity is largely driven by families looking forward to trick-or-treating—a time-honoured tradition that promises joy and a bounty of memories for parents and their children.
Our analysis has pinpointed several key costume trends, shaped by discussions on purchase intentions, celebratory occasions, and peer recommendations:
1. Sexy Costumes: Embracing boldness and body positivity, sexy costumes are trending as symbols of empowerment. This trend highlights a shift towards costumes that celebrate individuality and self-confidence.
2. Nostalgic Revivals: A wave of nostalgia is bringing back favourites from the '80s and '90s. Expect to see a resurgence of characters from cult classics like “Beetlejuice,” alongside beloved Disney icons such as Mickey Mouse and Cinderella, tapping into the hearts of both new and lifelong fans.
3. Budget-Friendly Finds: Economic uncertainties have steered the trend towards cost-effective costumes. With a spike in searches for “budget-friendly costumes,” it's clear that affordability is top of mind for many this season.
4. Musical Homages: From rock legends like Freddie Mercury to pop sensations like Madonna, and current stars like Sabrina Carpenter, costumes inspired by musical icons are allowing fans to embody their favourite artists.
5. Couples Costumes: Doubling the fun, couples are choosing to coordinate their outfits, showcasing their relationships through creative and complementary ensembles.
6. Eco-Conscious Choices: Reflecting a growing dedication to sustainability, there’s an increased interest in eco-friendly costumes and decorations made from recyclable or biodegradable materials.
The capability of listening247 to drill down into the specifics of social media chatter has not only revealed what people will be wearing but also why certain trends are taking hold. For instance, the popularity of sexy costumes correlates with a broader cultural movement towards embracing body positivity and self-expression.
Moreover, the detailed sentiment analysis provides brands with a clear understanding of consumer attitudes towards Halloween, empowering them to align their marketing strategies with real-world preferences and expectations.
As brands look to capitalise on Halloween, the insights provided by listening247’s Social Listening and Analytics offer a strategic advantage by unpacking the complexities of consumer behaviour and trending topics.
This Halloween, equipped with data-driven insights, brands are better positioned to meet consumer desires head-on, ensuring a celebration that’s as delightful as it is insightful.
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Just like every other buzzword, it means different things to different people. Is there a simple definition of AI that everyone can understand? You bet! AI can be classified as weak or strong AI.Weak AIThe majority of current AI use cases - such as social intelligence using text analytics (NLP) - fall under weak AI. It usually involves supervised machine learning, though we are increasingly seeing use cases where semi- or unsupervised machine learning is being used. For the time being, let’s define (weak) AI with this simple formula:
Strong AI
Strong, full or general AI is something different. For most people it is defined by Alan Turing’s test whereby according to Wikipedia “A machine and a human both converse sight unseen with a second human, who must evaluate which of the two is the machine, which passes the test if it can fool the evaluator a significant fraction of the time”. The optimists among pundits claim it will be with us in 10-15 years from now, the pessimists say by 2100.
Some people talk about machines with consciousness. To paraphrase the well known author Yuval Noah Harari: a taxi driver needs to only take us from A to B, we are not interested in how he feels about the latest Trump news or the sunset; thus an autonomous car has a good enough AI to do the job without needing to feel or having a consciousness. The same applies to so many other aspects of work and life. Will machines ever be able to feel? Is that necessary for strong Ai to exist?
AI’s impact on humanity
“Two billion people will be unemployed by 2050.”
“Humanity is in danger of being taken over by machines”
“This could spell the end of the human race” said the late Stephen Hawking. Elon Musk and Bill Gates are also often quoted expressing a similar opinion. The flipside of the coin is that humanity should choose to see a positive version of the future, and then strive to make it happen. Rather than worrying about unemployment, we should be looking forward to spending more time on the beach, pursuing our passions and hobbies to perfection. I dream of days philosophising in a circle of close friends (also fellow philosophers) about the meaning of life… and not just human life as this has been covered by Plato, Aristotle and others; maybe we will be focussing on the lives of robots who can fall in love, or superhumans with chips in their brains that are “a-mortal” - as opposed to immortal (stuff for another blog post).
Doing market research using AI is a close second to philosophising.
Universal Basic Income (UBI)
The best idea floating around when it comes to managing unemployment brought on by the impact of AI is the UBI. Having said that, being the best idea so far does not necessarily make it a great idea. Too many people with some means and lots of time on their hands may ultimately become a curse for humanity. Possible outcomes include:
Artificial intelligence for text and image analytics has been around for years, and listening247 has been carrying out R&D for the social intelligence use case since 2012. Strangely, we haven’t yet published our view on the impact that AI will have on humanity in the near to medium term future. It is time to rectify this!
We need new laws and we need to figure out what moral compass we want to ingrain in the autonomous machines of the future - if that is at all possible.
We want to ideally avoid scenarios described in science fiction films such as Ex Machina and I, Robot.
As per Isaac Asimov’s laws about robots from the previous century:
• A robot may not injure a human being or, through inaction, allow a human being to come to harm.
• A robot must obey the orders given to it by human beings, except where such orders would conflict with the First Law.
• A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
At a first glance they seem reasonable; he certainly thought that if these principles were to be applied, humanity would be safe from destruction by AI powered machines.
Apparently he is wrong.
What we will need is machine ethics for super-intelligence, and the laws or principles cannot be chauvinistic as Asimov’s are. Humans cannot realistically expect to be the boss of machines that are a million times smarter than them.
Ray Kurzweil the author of ‘The Singularity Is Near: When Humans Transcend Biology’ predicts that the singularity moment will happen around 2045. Ray said in his book:
“I regard someone who understands the Singularity and who has reflected on its implications for his or her own life as a ‘singularitarian’.”
I guess thinking about and writing this article - I will not claim I understand the Singularity - may make me a singularitarian! What do you think?
There is an impressive and humbling definition of this moment that I heard from a fellow YPO member, who among other things is the Executive Director of Singularity University Germany, Nikolaus Weil, at a CEO (organisation) recruitment event:
I will paraphrase but the gist is: “It is the moment in time at which machines will acquire the same knowledge and capability as the whole of humanity and in the next 5 minutes by continually improving themselves will become millions of times smarter than humans”.
AI = Augmented Intelligence
Some people like to challenge the notion of artificial intelligence so they decided to give the acronym AI a new interpretation namely “Augmented Intelligence”.
Vernor Vinge came up with these 4 options of the singularity manifestation:
The development of computers that are "awake" and superhumanly intelligent.Large computer networks (and their associated users) may "wake up" as a superhumanly intelligent entity.Computer/human interfaces may become so intimate that users may reasonably be considered superhumanly intelligent.Biological science may find ways to improve upon the natural human intellect.[9]
Options 3 and 4 may involve the augmentation of human intelligence to a superintelligence.
The problem with these two options is that only super rich people will be able to afford them; thus ending up with some serious inequality issues between new castes or should we say new breeds of humans.
I think this article is running away from me. It feels like one thing leads to another, one thought to the next; there is no end and I feel like getting a beer,so I will end it - abruptly - here.
My fitbit is asking me to start preparing to go to bed … futurism is a very tiresome business!
In an increasingly digital world, social media platforms have transformed the way individuals connect, share information, and express opinions. Banks can leverage the power of social media listening to manage loan default risks effectively. By gathering and analysing online posts about the companies to which they have extended loans, banks can conduct continuous commercial due diligence and identify early warning signals when debtors encounter financial difficulties. This post explores how banks can utilize social media listening as a strategic tool to proactively manage loan default risks and enhance their risk management practices.
Social Listening & Analytics involves monitoring and analysing online conversations, mentions, comments, and reviews across various social media platforms. It allows banks to extract valuable insights and gain real-time information about the financial health, market reputation, and business activities of the companies they have extended loans to. By employing machine learning for natural language processing techniques, banks can effectively identify potential red flags indicating financial difficulties and anticipate loan defaults.
A. Proactive Risk Assessment:
Traditional due diligence processes primarily focus on pre-loan assessment, often failing to capture evolving risks that borrowers may face after obtaining the loan. By incorporating social media listening into their risk management framework, banks can conduct continuous commercial monitoring in addition to their due diligence during the loan underwriting or credit extension phase. This enables them to monitor ongoing developments, industry trends, and financial indicators related to their borrowers, providing a more comprehensive risk assessment. These non-traditional indicators of credit quality and borrower’s abilities to service their debts and obligations give a much fuller picture than simple financial statements which are backward looking and often don’t provide the full story. This alternative data can also be a better predictor of borrower behaviour than historical financial statements.
B. Real-Time Insights:
Social media platforms act as virtual marketplaces where individuals freely share their experiences, opinions, and concerns. By monitoring online posts about borrower companies, banks can gain real-time insights into their operations, financial stability, customer sentiment, and market perception. Any notable shifts, negative sentiments, or concerning patterns identified through social media listening can serve as red flags, prompting banks to investigate further and take necessary actions. As we have seen recently with the collapse of Silicon Valley Bank and First Republic Bank in the US, depositor sentiment played a striking role in their demise. Due to adverse online sentiment that spread very rapidly, customers and depositors caused a digital run on the bank that had never been seen or experienced before. In the case of Silicon Valley Bank, deposits were leaving at the rate of $1 million per second for 10 hours (or $41 billion).
A. Detecting Financial Difficulties:
Social media listening allows banks to identify early warning signals of potential financial difficulties faced by their borrowers. By analysing online conversations, comments, and reviews, banks can detect signs of operational challenges, supply chain disruptions, declining customer satisfaction, or negative market perception. These signals can help banks proactively engage with borrowers, assess their financial health, and take appropriate measures to prevent loan defaults.
B. Amplifying Existing Risk Indicators:
Social media listening augments traditional risk indicators with additional insights derived from user-generated content. For example, a decline in positive sentiment towards a borrower company may coincide with a decrease in revenue, an increase in customer complaints, or a deteriorating market position. By integrating social media listening into their risk management framework, banks can enhance their ability to identify and act upon early warning signals, thereby mitigating loan default risks.
“Effective Early Warning Systems (EWS) reduce loan loss provisions by 10%-20% and required regulatory capital by 10%”
Galytix paper in association with PWC
A. Leveraging Advanced Tools:
To effectively analyse vast amounts of online data, banks can employ machine learning and sentiment analysis tools. These tools enable banks to filter and categorize information, identify patterns and trends, and extract meaningful insights. By leveraging sentiment analysis, banks can assess the overall market sentiment towards borrowers and gauge the impact of external factors on their financial health.
B. Enhancing Risk Models:
Integrating social media listening insights into risk models can strengthen banks' loan default risk assessments. By combining traditional financial indicators with sentiment analysis and social media data, banks can improve the accuracy and predictive power of their risk models. This holistic approach allows for a more comprehensive evaluation of borrower creditworthiness and provides a deeper understanding of the potential risks associated with loan defaults.
A. Privacy and Data Protection:
As banks engage in social media listening, it is crucial to prioritize data privacy and protection. Banks must ensure compliance with relevant data protection regulations and implement robust security measures to safeguard the information collected. Respecting user privacy, obtaining consent, and anonymizing data if necessary are essential steps to maintain ethical practices. This is of particular importance when used for due diligence and for the monitoring of individuals and their transactions, as is required of banks by Know-Your-Customer rules and regulations imposed upon them by the authorities.
B. Noise and Information Overload:
The sheer volume of online information can pose challenges in effectively filtering and interpreting relevant data. Banks can employ sophisticated filtering techniques and analytical tools to address information overload. Machine learning algorithms and natural language processing can help identify key topics or themes, prioritize relevant content, and provide actionable insights to manage loan default risks efficiently.
By harnessing the power of social media listening, banks can conduct continuous commercial due diligence and effectively manage loan default risks. Monitoring online posts about borrower companies enables banks to gather real-time information, detect early warning signals, and anticipate financial difficulties. However, banks must navigate ethical considerations, prioritize data privacy, and address information overload challenges. When implemented strategically, social media listening empowers banks to proactively manage loan default risks, enhance risk management practices, and ensure more informed lending decisions.
“Banks that fail to improve their EWS will also face significant regulatory pressures. The European Central Bank (ECB) has highlighted the huge variation in the quality of early warning systems and how credit assessment at a micro as well as macro level is core to risk management and processing.”
Galytix paper in association with PWC.
In today's fiercely competitive business landscape, companies are constantly seeking ways to gain an edge over their rivals. Among the various capabilities that contribute to success, unstructured data analytics capability stands out as indispensable for survival in the face of intense competition. This post explores the significance of text and image analytics specifically and argues that no company can thrive without harnessing the power of these capabilities. There is of course also audio and video analytics to consider but once the tech is available to analyse text and images the rest can be handled with voice-to-text and image-to-text technology. More details on this below.
1. Uncovering Insights: Text and image analytics enable companies to extract valuable insights from vast amounts of textual and visual data. By employing sophisticated algorithms and machine learning techniques, businesses can analyse customer feedback, reviews, social media posts, and other textual data sources. This allows them to identify emerging trends, preferences, and sentiment patterns, leading to informed decision-making and strategic planning. Similarly, image analytics empowers companies to understand visual content, enabling them to recognize brand logos, product placements, and consumer behaviour from images shared on social media platforms. The ability to uncover such insights provides a competitive advantage by allowing businesses to stay ahead of the curve.
2. Enhancing Customer Experience (CX): Text and image analytics play a crucial role in enhancing the customer experience, which is a key differentiator in today's market. By leveraging these capabilities, companies can gain a deep understanding of customer needs, preferences, and pain points. Through sentiment analysis of calls, chats, emails and social media posts, businesses can assess customer satisfaction and promptly address any concerns, improving overall customer experience and loyalty. Furthermore, image analytics can identify visual cues and sentiment from images shared by customers, helping companies gain insights into how customers engage with their products or services. By proactively addressing customer needs, businesses can establish a stronger foothold in the market and build long-lasting relationships.
3. Competitive Intelligence: Text and image analytics applied on publicly available information online also serve as powerful tools for competitive intelligence. Companies can monitor competitor activities, track mentions, and analyse customer sentiment related to competitors through textual data. This information provides valuable insights into competitor strategies, product offerings, and market positioning. Similarly, image analytics can help identify visual elements associated with competitors, such as logos or brand imagery, aiding in assessing market share and brand perception. Armed with this knowledge, businesses can adjust their own strategies, differentiate their offerings, and better position themselves to gain a competitive edge.
4. Operational Efficiency and Risk Mitigation: Text and image analytics contribute to operational efficiency by automating processes that would otherwise be time-consuming and error prone. For instance, text analytics can automate the categorization and tagging of large volumes of textual data, reducing manual effort, and improving data accuracy. Similarly, image analytics can automate the identification and classification of visual content, streamlining tasks such as quality control or identifying counterfeit products. By improving operational efficiency, companies can reduce costs, optimize resource allocation, and respond quickly to market demands, ensuring survival in a competitive environment.
At listening247, we leverage voice-to-text and image-to-text technology to efficiently process all forms of unstructured data through our social listening and analytics platform platform. This enables us to label the data with custom machine learning models, ensuring the highest possible accuracy, regardless of the original language. In contrast, some vendors offering multilingual text labelling solutions rely on translating everything to English before labelling, which is not an optimal or accurate approach.
Lately, many individuals have inquired about how the listening247 sentiment and topic labelling approach compares to GPT-4 or Bard. The answer is: the listening247 approach is unequivocally better. For a less biased and more objective perspective, I encourage you to refer to this paper. Here is an excerpt from the paper summary:
“The preliminary study shows that ChatGPT and GPT-4 struggle on tasks such as financial named entity recognition (NER) and sentiment analysis, where domain-specific knowledge is required, while they excel in numerical reasoning tasks.”
This subject deserves its own article with a proper gap analysis between LLMs and the proprietary and custom ML models that listening247 creates.
Text, voice and image analytics have become indispensable capabilities for any company striving to survive and thrive amidst fierce competition. The ability to extract insights, enhance the customer experience, gain competitive intelligence, and improve operational efficiency makes these capabilities vital for success. Companies that neglect to harness the power of unstructured data analytics will find themselves at a significant disadvantage, missing out on crucial insights, falling behind competitors, and failing to meet evolving customer expectations. Therefore, to remain competitive in the modern business landscape, organizations must prioritize the adoption and utilization of text, audio and image analytics to secure their long-term survival.
This statement, which I have shared numerous times in previous articles, encapsulates the essence:
“Over 90% of all human knowledge recorded throughout history exists in the form of unstructured data. If your company solely focuses on analyzing and comprehending structured data, it implies that you are utilizing less than 10% of the available data to inform your decision-making processes.”
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“Pan metron ariston” (παν μέτρον άριστον) is a quote in ancient Greek which was coined by Kleovoulos o Lindios in the 6th century B.C. and means “everything in moderation”. Some believe that the original quote was “Metron Ariston” which means “moderation is best”. Whatever the quote, ancient Greeks believed that you should live your life choosing the mean and avoid the extremes on either side, as much as possible.
Talking about extremes, I have always been fascinated by continua, I think it’s because of the order they bring to chaos and complexity. Almost every ideology or idea that matters in life, can be expressed on a continuum. A continuum has two extremes - let’s think of them as black and white with many shades of grey in between.
Here are two more official continuum definitions which are quite similar:
I do not consider myself qualified to improve on wisdom that transcended centuries (26 centuries since Lindios said “everything in moderation”) but I do have an opinion about quotes that include the words “everything” or “nothing”, “always” or “never”; incidentally these two pairs of opposite words can be the extremes of two continua; very few things are absolute, this is why the quote “everything in moderation... even moderation” may be just short of genius.
There is no doubt that being an extremist has mainly negative connotations: a fascist, a racist, a sexist, a religious fanatic, a communist… There are also some other examples like “feminist” or “atheist” that would create a debate with certain groups - as to whether they have negative connotations - that I am cowardly avoiding to mention at this time (see how I did this :)?).
Let’s first review a few random continua to familiarise ourselves on what they could look like, and after that we will go ahead and discuss the usefulness of looking at an issue through the lens of a continuum. Take the eating continuum below for example, isn’t it amazing how many types of diets there are? It has an impressive 13 elements in addition to the 2 extremes; a total of 15 elements. Kangatarian (I bet you can guess what these people eat :)) is the one that cracks me up with cannibal being a close second! I am also intrigued by how vegetarians managed to be the mean nowadays, they have come quite far from being an extreme in the not too distant past. And in case you are not familiar with ahimsa fruitarians, they only eat fruit that falls off a tree and they call pulling a carrot from the earth murder!
The God continuum with probabilities on God’s existence is not as harmless as the eating one; it is one that has been the basis for so many debates, civilised and uncivilised - and when I say uncivilised I mean the killing type if you think of the Crusaders (even though in their case it was more of a “my God is better than yours” rather than about its existence).
The selfishness continuum comes straight out of the Vedanta Treatise, a Hindu approach to life.
The colour coding means red is bad and green is good for most people.
Disclaimer: this does not always represent the author’s opinion. We will discuss more the groupings or segments of continua in the next chapter.
The continuum below communicates a thesis of mine that most people disagree with. I believe that being a patriot is the beginning of a proverbial “slippery slope”. It could progressively lead to someone becoming a nationalist and then a jingoist which is what you have to be to vote for Brexit or for someone like Trump.
The nationalism continuum can be integrated with the selfishness one at the point of loves all humans which is another way to say world citizen. One can then make interesting connections and draw conclusions about love and nationalism.
Those of you who have read other articles of mine may be wondering what all this has to do with market research, social intelligence, customer insights etc. Well the nice thing about continua is that you can conjure one out of nothing about almost anything. Case in point, digital transformation is something closer to home for a company with a name like ours; listening247. It was a sensible name 10 years ago to communicate specialism in digital market research; today however, when almost everything is digital, a name like this loses its meaning. It’s like calling a car a horseless carriage when in this day and age it is quite obvious that a car does not need horses to move (unlike the 1920s when Ford T1 was launched). But I digress... if you replace physical with ‘brick & mortar’ then this continuum becomes about retail, and if you replace it with ‘analogue’ it could be about equipment.
For market research, physical could mean in-person or telephone interviews, whilst digital means online surveys or unsolicited opinions found on social media using social listening tools.
Nothing easier than creating a 5 point continuum. The one below is about ways of gathering the opinions of customers and other stakeholders. Asking questions refers to surveys and focus group discussions whilst listening refers to unsolicited posts of people online. The discipline of harvesting these posts and analysing them is what we call social intelligence and it is mainly based on machine learning models that annotate posts for topics and sentiment in an automated way.
When you take some time to absorb the 6 examples shared above, you will realise that not all continua are created equal.
Here are some ways to differentiate them:
1. both extremes are bad (ahimsa fruitarian AND cannibal)
2. both extremes are acceptable (asking AND listening)
3. One extreme is really bad the other is really good (fascist Vs world citizen)
4. The mean is a combination of the extremes (asking & listening)
5. The mean is just a standalone option that has nothing to do with the extremes (vegetarian)
So what are they good for? They are philosophical tools that can help organise thought, clear the fog, visualise relationships, pinpoint and explain movements and trends.
Ancient Greeks believed that you should live your life choosing the mean and avoid the extremes on either side, as much as possible. Is this a good principle to follow though? If we consider the various types of continua described in the previous chapter sometimes the best choice is to adopt one of the extremes, sometimes it is indeed the mean like our ancient progenitors preached.
Thinking about moderation, can one be too much of a world citizen or too loving for all creatures
When a continuum describes progress over time it is more likely that the most recent extreme is the best place to be. Even so, living it in moderation is probably a sound piece of advice.
I do subscribe to the notion that life is not black or white, it is mostly grey. Most of our lives are lived in the grey, only very few of us live on the extremes - sometimes by choice, but mostly not. Extremists must always be on edge, in contrast to leading a happy life, laid back, going with the flow, accepting the things they cannot control. Do let me know how you feel about continua and “pan metron ariston” @listening247_CEO or via email.
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‘How does market research rank on usefulness for human life’
As businesses seek to understand their standing in the digital conversation, listening247's Net Sentiment Score (NSS™) emerges as a pivotal metric. This proprietary formula quantifies online sentiment towards brands, transforming raw social media data and unsolicited customer opinion into actionable insights.
Let’s deep dive into the NSS™ Score and explore its origins, how it operates, and its significance in today's digital-first world.
The Net Sentiment Score™ was developed by listening247 to fill a crucial gap in social media analytics. In a landscape saturated with diverse opinions, it provides a standardised way to assess and compare brand sentiment. This metric is the result of advanced machine learning models that meticulously annotate sentiments as positive, negative, or neutral, ensuring a nuanced understanding of the digital conversation landscape.
Simplicity lies at the heart of the NSS™'s effectiveness. By focusing on the balance between positive and negative mentions and considering the volume of discussions, the NSS™ offers a comprehensive snapshot of a brand's online health. This approach allows for the aggregation of vast amounts of data into a digestible, numerical format, empowering businesses with the clarity needed to navigate the complexities of online reputation management.
This seemingly simple calculation belies the complexity and sophistication of the technology behind it. listening247's algorithms analyse vast amounts of online content, from tweets and blog posts to forum discussions and reviews, employing natural language processing (NLP) and machine learning to accurately capture and categorise sentiments.
The process involves more than just keyword recognition; it delves into the nuances of language, picking up on context, irony, and even regional dialects to ensure the sentiments are accurately interpreted. The result is a score that ranges from -100 (entirely negative) to +100 (entirely positive), offering a clear, quantifiable measure of online sentiment.
The implications of the Net Sentiment Score™ for businesses are profound. It serves as a vital indicator for assessing the impact of social media conversations on brand perception. Here's why NSS™ is indispensable:
The Net Sentiment Score™ by listening247 represents a significant advancement in the analysis of social media sentiment. Its creation marks a strategic move towards a more informed, data-driven approach to understanding digital conversations. As businesses continue to operate in increasingly digital environments, the NSS™ offers a vital metrics for navigating the complex dynamics of online brand perception. Through its precise, insightful analysis, businesses are better equipped to foster positive engagements, adapt to consumer needs, and ultimately, drive success in the digital age.
To explore the depth of insights that the Net Sentiment Score™ can bring to your brand, we invite you to reach out and request access to your brand health dashboard. The brand health tracking dashboard offers a comprehensive view of your brand's digital presence and sentiment, enabling you to make informed decisions that drive your brand forward.
Whether you're looking to enhance your social media strategy, improve customer engagement, or simply gain a better understanding of your brand's position in the digital landscape, our team is here to guide you through the insights our dashboard can provide. Contact us today to unlock the full potential of your brand's digital narrative.
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On September 7th we gave the report below to our PR agency and asked them to publish what our analysis of online posts was telling us about Liz Truss.
Sadly, the Queen passed away the next day, so the news cycle moved on from the PM’s election.
The report below was never made public but we decided to post it on our blog and in the form of a Medium article almost a month and a half later as it is an illuminating illustration of the kind of robust AI driven “social intelligence” that is now possible – you can check, at the bottom of the report, the actual posts that were shared.
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This is the 4th report produced by listening247 with data gathered from social media and other public online sources between July 11 and September 6 2022 on Politicians. The 1st report was based on data collected between Sept. 15 and Dec. 14, 2021. The 2nd Dec. 15, 2021 to March 15, 2022 and the 3rd March 16 to July 10, 2022.
The sources of posts included in this report are Twitter, news, blogs, forums, reviews and video.
It is important to make a strong distinction between polls/surveys which are based on a sample of respondents – plenty of those exist and they are not necessarily accurate - and social intelligence which is not based on a sample - this is a unique report and the first of its kind. The data collected by listening247's CXM as well as Social Intelligence and Text Analytics platform are based on the universe of all the posts about the names included in the research (not a sample of posts) and it is the unsolicited opinion of the people who posted these posts – in contrast to polls they were not paid to answer questions, so they had no incentive to cheat or write an opinion that was not theirs.
The main KPIs used to rank the subjects of this research were:
In the table below the 3 politicians are ranked based on total number of posts from all sources.
It is now the 3rd time that the total number of posts or share of voice is predictive of who will win an election.
In the previous report we published Rishi was leading in this metric and he was the one elected with the highest number of MPs. Now from July 11 to September 5th the tables turned. Liz has more than double as many posts as Rishi and won the vote of the 170,000 conservative members.
In terms of net sentiment score during the 24 hours post Liz’s election the ranking is almost turned on its head.
Keir has positive 3% whilst Rishi Sunak is closer to him with negative 3% and Liz has negative 8% a whole 11% worse score in public sentiment than the leader of the Labour Party.
The examples of posts below are a representative sample of what most people post about the next national election in January 2025:
It is quite clear that they all think Liz was the worse PM to go against Keir.
Three numbers are very important to keep in mind and understand what they represent:
The two final candidates (Liz+Rishi) of the conservative PM race were selected in multiple voting iterations by ~350 conservative MPs.
On September 5th ~170,000 conservative members voted and elected Liz Truss as the Prime Minister.
The total electorate for parliamentary votes in the UK has over 46 million voters.
Our report reflects the opinions of the 46 million voters; thus, the ranking may be able to predict what would happen if the vote for the election of a conservative PM was put to a national vote yesterday. To better interpret our rankings above we should keep in mind that in 2022 around 85% of the UK population are social media users.
From that we can infer that the online posts gathered from various sources between March and September this year may impact up to 85% of the voters; it could be a bit less because the older population who do not have access to social media are all voters whereas the 85% (people with access to social media) includes children below 18 who are not voters yet. Having said that online News is one of the sources (the media) which is editorial and impacts the opinions of everyone who are exposed to the media.
The discussion in our previous report about possible scenarios was inferring that the conservative members should pay attention who was the most likely candidate to win the national election in January 2025 and let that inform their decision.
Unfortunately, they did not do that.
The social media data indicates that Rishi would have a better chance to beat Keir.
The question now is if this data was predictive for the last two finalists and for the September 5th vote - which it was - will it also be predictive for the national election results in 2025? It looks like it is when 2 out of 2 times the share of voice predicted the outcome!
Stay tuned for more data from listening247 on the subject.
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This is what we wrote on September 7th, 2022. I think for the 4th time unsolicited citizen opinion from social media and other online sources proves to be much more predictive than polls which are based on samples of respondents (who sometimes lie and sometimes forget what they said or did a week ago) that may not be representative.
We think that it is about time for social media listening and analytics to take its rightful place in the political forecasting business.
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AI can boost Call Centre Efficiency and Customer Satisfaction.
Call centres are a vital part of many businesses, providing customer support and assistance. In recent years, the use of artificial intelligence (AI) has become increasingly prevalent in the call centre industry, and for good reason. By leveraging AI, call centres can increase revenue and improve their overall performance.
Throughout the rest of this blog post we will use “Contact Centres” instead of “Call Centres” as it is a more appropriate description of what these organisations do. They do not just respond to calls but also to chat messages, emails and sometimes even social media posts.
Call centres can leverage AI in a variety of ways to reduce cost and even increase their revenue. By automating customer interactions, using predictive analytics, improving call routing, and analysing customer feedback, call centres can improve their efficiency, reduce costs, and provide better customer service. As AI technology continues to advance, the opportunities for call centres to use it to increase revenue will only grow.
listening247 uses proprietary machine learning models on its AI platform that are customised for each subject or product category, achieving a minimum accuracy of over 80% each time, in any language. Often, the accuracy is over 90%, depending on the amount of training data used for the custom models.
While there are ML models available for anyone to use (e.g. open source, Google, AWS and Microsoft), free or paid, the problem with those is that they are generic to a language, which means not specific to a product category. Thus, they can never reach acceptable accuracies without custom training data as top-up. Typically, their accuracies linger below 70% at best and usually around 50%-60%.
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According to research by Forrester, 53% of companies worldwide have a dedicated CX department, while the remaining companies may integrate CX responsibilities into other departments, such as marketing or operations, or may not have a CX function at all. In some cases, customer care or customer service may be the only CX related function, but this setup often falls short of the lofty goals of optimizing the overall customer experience.
Customer experience (CX) and insights are both critical components of understanding and improving customer satisfaction. While they may be closely related, they are distinct disciplines that require different approaches and skill sets. Therefore, it's essential to have clarity about the ownership and responsibilities of these functions, particularly when it comes to measuring customer experience with NPS trackers and by analysing customer calls and messages.
CX is about creating and delivering an exceptional experience for the customer throughout their journey with the company. CX teams focus on understanding customer needs, pain points, and behaviours to design and optimise the customer journey. They collect and analyse data from various sources, such as surveys, customer feedback via contact centres, and predictive analytics, to identify areas of improvement and create strategies to enhance the customer experience.
CXM – a popular acronym used in this context - stands for customer experience measurement or customer experience management. When it comes to the latter there is no doubt that the CX team is responsible for it. When it comes to measuring though the Insights team is well positioned to offer support or even own it.
Customer Experience (CX) teams are primarily focused on identifying actionable insights at the individual customer level. They typically rank customer pain points based on their frequency of occurrence and then identify both proactive and reactive solutions to address them.
On the other hand, insights teams are responsible for gathering and analysing data to generate insights that can drive business decisions. Insights teams use a wide range of data sources, including customer data, market research, and internal business data to identify trends and patterns, support new product development, monitor business performance, and generally inform decision-making.
Insights teams are primarily focused on discovering strategic insights that are actionable at the total market level, rather than the individual customer level.
The process of discovering a true market insight is not straightforward. It requires multiple sources of data to be integrated, an actionable hypothesis supported by synthesised data, and a little intuition and gut feeling.
When it comes to NPS trackers, the lines between CX and insights can get blurred. NPS (Net Promoter Score) is a widely used metric for measuring customer loyalty and satisfaction. It involves asking customers how likely they are to recommend the company to others, on a scale of 0 to 10. The NPS score is calculated by subtracting the percentage of detractors (0-6) from the percentage of promoters (9-10) while ignoring the passives (7 & 8). The score provides a benchmark for how well the company is meeting customer needs and expectations.
Both CX and insights teams can benefit from NPS data. CX teams can use the score to understand how customers perceive the company and its products/services and identify areas for improvement in the customer journey. Insights teams can use the data to track overall customer satisfaction and loyalty, compare the company's performance against competitors, and identify factors that influence customer behaviour.
So, who should own the NPS tracker if CX is a separate department? The answer may vary depending on the company's size, structure, and culture. In some cases, CX and insights functions may be combined, and one team may be responsible for both functions. In other cases, the teams may be separate, and the ownership of the NPS tracker may depend on the purpose and goals of the survey or simply where the budget sits.
CX teams, if they have the skillset, could take the lead in designing and implementing NPS surveys since they are more closely related to the customer experience. Dedicated CX teams should have the expertise and experience to design surveys that capture customer feedback effectively, analyse the results, and translate them into actionable insights for the business.
However, insights teams can also play a crucial role in analysing and interpreting NPS data. Insights teams have a broader perspective on the business and can provide valuable insights into how customer satisfaction and loyalty relate to other business metrics. Insights teams can also identify trends and patterns in the data that can inform strategic decisions.
At listening247 we published a lot of articles on the importance of not just relying on a sample of customers who agree to take a survey but listening to all customer interactions using AI for natural language processing.
Ultimately, the success of a company's CX and insights functions depends on collaboration and communication between the teams. Both functions are essential for understanding and improving the customer experience, and both have a role to play in measuring customer satisfaction with NPS trackers. The roles of the two functions have to be well defined to avoid confusion and misunderstandings.
By working together, CX and insights teams can ensure that the NPS data integrated with all the other customer interactions tagged with sentiment (such as phone calls, chats, emails etc.) are used effectively to drive business decisions that benefit both the customer and the company.
Social media for over a decade now have established themselves as a powerful tool for marketers to reach out to their target audience and promote their brands. With the rise of social media platforms such as Facebook, Twitter, Instagram, YouTube, Reddit and TikTok businesses have found new ways to reach out to their customers. However, the success of social media campaigns can be difficult to measure. In this post, we will discuss the best way of evaluating the performance of social media campaigns.
Traditionally, brands have used tracking surveys to evaluate frequent campaigns. For ad-hoc campaigns these surveys were conducted before and after to measure changes in brand awareness, perception, and loyalty. While these methods can be useful, they are time-consuming and expensive. Moreover, it can only provide a limited understanding of the impact of a campaign.
One of the main limitations of using surveys for evaluating social media campaigns is that they are based on a sample of respondents. In other words, only a small group of people are asked to provide feedback on the campaign. Some of them agree and some don’t. This can lead to biased results and make it difficult to draw meaningful conclusions about the campaign's impact on the broader population.
In contrast, social media listening and analytics allows for a more comprehensive analysis of the campaign's impact. This method involves monitoring all the posts and mentions related to the campaign, rather than relying on a small sample of respondents. This provides a more accurate and representative view of how the campaign is being received by the public.
Social media listening involves monitoring social media platforms for mentions of a brand, product, or service. This method can provide real-time feedback on the effectiveness of a campaign. Machine learning for text analytics, on the other hand, can help analyse large volumes of data and identify patterns and insights that would be difficult to detect manually.
One of the most effective ways of evaluating social media campaigns is by tracking engagement metrics. Engagement metrics include likes, comments, shares, and clicks. By monitoring these metrics, brands can determine how well their content is resonating with their target audience. Moreover, engagement metrics can help brands identify which platforms and types of content are most effective for their audience.
Another important metric to track is conversions. Conversions refer to the number of people who take a desired action, such as making a purchase, after seeing a social media post. By tracking conversions, brands can determine the ROI of their social media campaigns.
Furthermore, social media listening and analytics can help brands identify patterns and insights that would be difficult to detect through traditional surveys. Machine learning algorithms can analyse large volumes of data and identify trends and themes that may be missed by manual analysis. For example, sentiment analysis can help brands identify whether the overall tone of the conversation about their campaign is positive, negative, or neutral, and adjust accordingly.
It is important to measure the reach of social media campaigns. Reach refers to the number of people who have seen a post. By tracking reach, brands can determine how far their message is spreading and identify opportunities for growth.
Finally, social media listening and analytics can be more cost-effective than traditional surveys. While surveys can be time-consuming and expensive to conduct, social media listening and analytics tools are often more affordable and accessible. This makes it easier also for smaller companies to monitor their campaigns and make data-driven decisions.
Surveys and social media listening produce different metrics for evaluating social media campaigns. While surveys can provide valuable insights into how customers perceive a brand, social media listening can offer a more comprehensive and real-time view of a campaign's impact. Here's a comparison of some key campaign evaluation metrics produced by surveys versus social media listening:
My usual stance is that social media listening, or the practice of monitoring unsolicited customer opinions, can serve to enhance and complement survey results, which rely on solicited customer opinions. However, there are instances where I strongly believe that surveys are not the most effective way to gather feedback. For example, when assessing the impact of an advertisement on social media, it doesn't make sense to rely solely on a small group of survey participants who agreed to give their opinion for a fee. Instead, we can leverage social media listening to gain insights from all the individuals who actually saw the ad online and freely expressed their thoughts about it. By doing so, we can obtain a more accurate and comprehensive understanding of the ad's reception among the target audience.
While traditional methods of evaluating social media campaigns can still be somewhat useful, there are now more effective and efficient ways to measure performance. Social media listening and machine learning for text analytics have made it easier to track engagement, conversions, sentiment, and reach. This method provides a more comprehensive analysis of the campaign's impact, allows for real-time feedback, and can be more cost-effective. By using these metrics, brands can gain a better understanding of the impact of their campaigns and make data-driven decisions to improve their brand strategies.
“You don’t have to be a major multinational brand to be able to afford social media listening, as a matter of fact such an approach is way cheaper than the traditional one.”
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The discovery of emerging trends has become increasingly important in recent years. Product development and innovation executives are constantly searching for ways to predict what consumers will want before their competitors. In this post, we will explore how trend discovery was done in the past, and more importantly, we will highlight cutting-edge Natural Language Processing technology that can help you identify emerging trends before they become mainstream.
The process of discovering consumer trends has undergone a massive transformation over the past 15 years, primarily due to the advent of social media and Artificial Intelligence.
In the past, companies had to rely on traditional market research methods that were time-consuming and expensive. However, with the rise of social media listening tools, it has become much easier for companies to track and analyse consumer behaviour, preferences, and opinions. In this post, we will explore the challenges faced by companies in discovering consumer trends 15 years ago, compared to the ease with which it can be done now.
To be more specific, in the past, companies relied primarily on surveys and focus groups to understand their customers. These traditional methods were often expensive, time-consuming, and had a limited sample size. Companies had to go through a rigorous process of recruiting participants, conducting the survey or focus group, analysing the data, and then interpreting the findings. This entire process could take weeks or even months to complete, making it difficult for companies to innovate and be competitive.
Moreover, surveys and focus groups were often limited to a specific geographic area or demographic, making it difficult to get a broad understanding of consumer behaviour. This lack of data often led to companies making assumptions about their customers' preferences, which could result in costly mistakes.
According to various studies, the failure rate for new products is estimated to be between 70% and 90%. In other words, most new products that are launched fail to achieve their business objectives, such as generating sufficient revenue or profitability. This underscores the importance of conducting thorough market research, testing, and analysis before launching a new product to increase the chances of success.
With the rise of social media, companies now have access to a wealth of data that can be used to uncover consumer trends. Social media platforms like Facebook, Twitter, and Instagram have billions of users, and each one of them is creating content, sharing opinions, and engaging with brands. Social media listening tools have made it easier for companies to monitor these conversations and extract meaningful insights.
Social media listening tools allow companies to track specific keywords and hashtags related to their brand or industry. These tools analyse the data and provide valuable insights, such as sentiment analysis, conversation drivers, engagement metrics and virality. This information can be used to identify emerging trends, monitor brand reputation, and engage with customers in real-time.
In addition, social media listening tools allow companies to track their competitors' activities, which can provide valuable insights into their marketing strategies and product development. By monitoring their competitors, companies can identify gaps in the market, and create products or services that meet the needs of their customers.
Furthermore, social media listening tools have made it possible for companies to connect with their customers in a more personalized way. By monitoring social media conversations, companies can identify individuals who are influential in their industry or have a large following. These individuals – the influencers - can be targeted to become brand advocates or ambassadors and to propagate offers, which can lead to increased engagement and more sales.
listening247 has developed a proprietary approach to discovering emerging trends that involves the following steps:
By using this approach, listening247 provides valuable insights into emerging consumer trends that can help companies stay ahead of the competition and better understand their customers.
The process of discovering consumer trends has evolved significantly over the past 15 years, thanks to the rise of social media listening tools such as our Social Listening and Analytics Solution. These tools have made it easier for companies to monitor and analyse consumer behaviour, preferences, and opinions. They have also provided valuable insights into competitors' activities and enabled companies to connect with their customers in a more personalized way. With the help of social media listening tools, companies can stay ahead of the competition and create products or services that meet the evolving needs of their customers.
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They say a picture is worth a thousand words. Perhaps even more judging from the graphs below and the compelling story they tell on bank performance!
In October 2021, listening247 carried out a social intelligence (SI) project about banks in Portugal, it’s 5th project in the industry, to illustrate the value of unsolicited customer opinion to a bank’s management.
The opinions were “unsolicited” in the sense that no one asked anyone a question; the only source used was online sentiment as expressed on Twitter, Facebook, blogs, forums, videos, reviews and the news.
25,758 unique posts were gathered about 13 banks from all these sources from September 1st , 2020 to August 31st, 2021.
Novo Banco has the highest share of voice at 54% followed by MIllenium BCP with 40% and Santander with 18%. On the other end of the positive-negative spectrum, we have four banks each with fewer than 200 posts in an entire year.
The question is: is it a good thing to have the highest share of voice (SoV) in a competitive market?
Not necessarily…it depends!
In the case of Novo Banco, the SoV is bad news. Most of the posts about them express negative sentiment – the red bars on our graph express a net sentiment scoreTM (NSSTM).
The NSSTM basically means that there are more negative posts than positive, for 8 out of the 13 banks included in the analysis.
Caixa Central has the worst NSSTM at -46% followed by Novo Banco with -42%.
So, what is the reason for the negative sentiment?
Well, it is mainly about the customer experience – a whopping 16,881 posts - and Novo Banco leads with 51% share of negative sentiment, considerably more than the corresponding score for Millenium BCP (24%). The 3rd highest share of negative CX was for Santander.
Interestingly, Santander is one of the 5 banks with positive NSS overall, perhaps on account of their active Facebook presence and higher customer engagement.
However, if we just look at the news, Santander’s NSS is slightly negative at -4% albeit still way better than the corresponding figure for Novo Banco (-45%).
In Fig.4 below we compare the NSS for Portugal’s main banks with other industries and product categories as well as with selected global banks.
What transpires is that banking in Portugal is among the three worst sectors in terms of negative sentiment along with public transportation in the UK and telecommunications in the Netherlands. Even within banking, Portugal’s lag in performance (compared to other countries or regions) is starkly evident.
Fig 5. below - for 11 Global Banks - is the equivalent of Fig. 2 where we show the NSS ranking for Portuguese banks. The difference could not be starker; the red is replaced by green – which means that 10 out of 11 banks have positive NSS albeit recorded one year earlier than for Portugal.
To be fair, the sentiment towards banks seems to change drastically depending on the economic situation. Negative sentiment appears to get a boost during an economic downturn or a recession. It improves when times get better.
Most traditional bankers are very slow to adopt innovation perhaps because they are trained to be risk averse. Unsurprisingly, when we showed Fig. 2 to a number of Portuguese bankers their reaction was to immediately question the validity of the data. What is more, they asked to see proof that negative NSS has bottom line implications for those banks.
Peter Nathanial the Board Chairman of DMR and former Group Chief Risk Officer of the Royal Bank of Scotland said about the report: “Social intelligence sourced insights seem to polarise board members and top executives of banks everywhere; at one end, people remain unconvinced that social intelligence provides any insights and want to see proof or causality with their business performance, whilst at the other end, people believe that this data is very powerful and definitely needs to be an important part of their future decision-making process. If the first group is right, then the second group is moving too soon. However, if the second group is right, the first group – and their institutions - will be left behind.”
We do not have proof of a causal link to their bottom-line performance yet, but we do have the next best thing: data that shows extremely high correlation of sentiment expressed in news and social media with the banks’ stock price.
Check out the last figures below (Fig. 6 & 7) from our report with 11 Global Banks in 2019, which also serve as the conclusion of this post.
I am tempted to say I rest my case. What do you think?
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Most people prefer order to mess, hardly a surprising conclusion.
A score that enables ranking in multi-player environments provides order and the ultimate gamification. Ideally it should be a composite score. This is a score that combines multiple metrics in one all-encompassing index.
Gamification does not mean turning a serious activity into a game; it is using gaming techniques to provide participant motivation and make the activity more fun overall.
I do like to think of business as a game. Some people take it too seriously, to the extent that it has a pathological effect on them – it affects their health in a negative way. Business is not a life-or-death endeavour. Winning is fun, losing is dreadful (worse for bad losers like myself) but it is not the end of the world.
So, let us break down how the SPS is calculated and its benefits for brands and for individuals; starting with the business perspective.
Brands and their parent organisations are always looking for the ideal KPIs that will drive their performance. NPS was once hailed as the single metric a company needed to measure and predict its future performance. NPS stands for Net Promoter Score and it is produced via a single question to customers, usually delivered via a survey: On a scale from 0-10 how likely is it that you would recommend Brand X to your friends and colleagues?
All those who provide a score between 0-6 are considered detractors, whereas the 9s and 10s are considered promoters; the 7s and 8s are polite negatives or passives at best.
The calculation of this composite score is as follows NPS=((promoters-detractors)/all respondents) X 100, with scores ranging from -100 to +100.
A similar idea applies to the Social Presence Score (SPS) for brands; however, its calculation is not as straightforward. Several social intelligence metrics have been considered by our data scientists and our conclusion was to use the following:
The SPS can have a value between 0 and 1 (see Fig.1 above); it is calculated, for a certain period of time, as one aggregated metric for multiple brand posts; it can be offered as a single score from all source types, or for individual ones such as Twitter, Facebook, Instagram, YouTube or Tik Tok (see Fig.2 below).
There is a secret sauce that even if I wanted, I would not be able to adequately describe in a mainstream article, and that is the weighting of the above-mentioned metrics in the SPS. Not only that but also the entire process, starting before the online posts are annotated, to eliminate irrelevant posts due to homonyms i.e.clean the data and remove the noise.
The benefit of having a score like this as a brand is, not surprisingly, the ability to:
The motivation to track a social presence score is not that dissimilar for individuals. Influencers and other high profile individuals want to know how their personal brand is doing compared to others and where they rank.
The metrics we use to create the composite Social Presence Score for individuals are similar but not all the same as for brands (see Fig.3 below):
The benefits for a person to know their SPS are:
It goes without saying that even if the SPS is perfectly synthesised with its composite metrics, the accuracy of the individual metrics included must be measurable and acceptable. It is always possible to reach over 80% accuracy for sentiment, topic and brand relevance annotations.
So, what do you think?
Would you like to know your SPS in relation to others?
I know I would; I miss having one since klout score ceased to exist.
Social presence score (SPS) comes to the rescue; it is the new single KPI for brands and individuals based on all online mentions - not just a sample – that can be used to measure the overall success of their marketing efforts.
Unlike one of my recent blog posts titled Social Listening - Social Analytics - Social Intelligence, the 3 bigrams in the sub-heading are not part of a continuum, they are synonyms.
Synonyms are words that do not necessarily look or sound alike, but they have more or less the same meaning, while homonyms are words which are spelled the same, although they mean different things.
For the social intelligence discipline, synonyms and homonyms are treated in a diametrically opposite manner: the former are included when gathering online posts whilst the latter must be excluded; failure to do so results in another bigram we so often use in the data analytics business: “Garbage-in…”!
Sometimes, depending on the popularity of the homonyms, more than 80% of the posts gathered - using a social media monitoring tool are irrelevant - referred to as “noise” (as opposed to signal). Only if we have a way to remove the noise can we avoid completing the popular saying mentioned in the previous paragraph with: “…Garbage-out”.
But I digress…
This post is about efficient and meaningful ways to integrate unstructured and eventually structured data sources as part of an organisation's customer experience (CX) measurement or customer management (CM) process - a relatively new more encompassing term gaining ground on CX - in order to discover actionable insights.
This new process of beneficial unstructured data fusion from multiple source types can be described in the following 8 steps:
First a quick reminder as to what constitutes unstructured data:
Text analytics is the easiest to perform (as opposed to audio analytics for example) hence the idea to transform all forms of unstructured data to text for easier manipulation.
One of the most useful sources of unstructured data for businesses is their call center audio recordings, with conversations between customers and customer care employees. These audio files can easily be transformed to text (voice-to-text) using specialised language specific machine learning models. An accuracy metric used for the transcripts produced is WER=Word Error Rate which should be lower than 10%.
Another popular source of insights are images e.g. posted on social media or shared on a business client community. A deep learning model adequately customised can produce a caption describing in text what is illustrated in each image (image-to-text).
When it comes to video, a combination of voice-to-text and image-to-text tech can be used.
When all sources of unstructured data are turned into text, they then need to be uploaded onto a text analytics platform, usually in the form of a JSON or CSV file.
If the same platform has the capability to provide data from additional sources, such as online posts (text and images) from Twitter, Facebook, Instagram, YouTube, reviews, forums, blogs, news etc. so much the better. It can serve as both a social intelligence and text analytics platform.
If needed, text from each source type can be uploaded or gathered and saved separately and merged at a later stage, so as to take a bespoke approach to cleaning and subsequently annotating the text using custom machine learning models for each source.
When it comes to client/user owned data they are all intrinsically clean (read relevant) since the source types are:
As for the data gathered from online sources – what is commonly known as social listening or social media monitoring – that is where a thorough data cleaning process is required. The problem as already indicated above is the homonyms. When a Boolean logic query is created to gather posts from social media and other public online sources, using a brand name like Apple or Coke or Orange as a keyword invites a lot of “noise” as you can imagine. The platform is required to offer easy ways to eliminate posts about apple the fruit, cocaine and orange the colour or fruit..
There are two ways to get rid of the irrelevant posts which sometimes make up more than 80% of all posts gathered.
If data cleaning is done properly, we can expect brand/keyword relevance over 90%.
Natural language processing is the umbrella discipline that takes care of this step in the process. Ideally the use of machine learning models to annotate text in any language works best, but sometimes a rules-based approach may be a shortcut to enhancing the annotation accuracy.
A good text analytics tool offers multiple options i.e. the ability to train generic & custom unsupervised machine learning models or using native language speakers as well as a taxonomy creation feature using a rules based approach.
Text can be annotated for sentiment, topics, relevance, age or other demographics of the author (if not otherwise obtainable), customer journey etc. A minimum accuracy of annotation should be declared and aimed for, and the users need to be able to easily verify the annotation accuracy themselves.
This step can happen before or after the merging of the various data sources, depending on their homogeneity.
For the longest time data fusion or integration or merging from different sources meant weeks or months of data harmonisation, so that the different sources could fit together and make sense. Merging 5-10 source types of unstructured data after steps 1-3 above only takes a few minutes, not months. It would take a few hours from start to fusion.
A powerful filtering tool is required for the user (data analyst) to be able to drill down into the data and discover interesting customer stories which might lead to actionable customer insights. For example, the user could first filter for negative sentiment, then for a specific brand, after that a topic and finally a source type before they start reading individual interactions to get an in-depth understanding of the WHY and the SO WHAT.
Once the data is cleaned, merged, annotated, and explored, it can be delivered in multiple ways such as:
Data visualisation via PowerPoint slides, drill down or query dashboards and alerts work best. Ideally the data formats should be flexible so that they can work with multiple data visualisation tools.
For now, data fusion included in a CX/CM program is a better fit for larger corporations, for two reasons:
Hopefully soon there will be versions of SaaS products that will make this process efficient and inexpensive enough for SMEs (SMBs) to be able to afford it.
That is what we call the democratisation of data analytics and market research.
The more data sources we integrate the more likely it is for a data analyst, the user of a tool such as listening247, to be able to synthesize actionable insights in their true meaning.
It seems that the biggest gain from this newly found ability to accurately annotate text in any language and fuse/integrate/merge from any source type in a matter of hours is in the discipline of customer experience (CX) measurement and management (CM).
CX and CM are increasingly seeking to encapsulate market research, business intelligence, customer care and other business disciplines and are meant to perfect the customer path to purchase, minimise brand defectors and maximise the number of advocates.
S-L-A-I
Social Listening, Social Analytics, and Social Intelligence - are they the same or are they integral parts of a sequential process, a so-called continuum?
Quite a few pundits have discussed this question in their articles, blogs, and essays. The most controversial of the three is social intelligence; if you Google it you will find its Wikipedia definition on first position explaining that it is “the capacity to know oneself and to know others”.
Of course, the alternative definition, the one that was coined after social media monitoring and analytics became popular only appears on page four of Google search - which you would only know if you are me and you search for social intelligence. In this secondary context, this bigram* means: the knowledge and insights that organisations can extract from online posts (mainly on social media platforms) published by their customers and other stakeholders.
The precondition to get to actionable insights is to avoid “Garbage-in” during the so-called data harvesting process, and to use appropriate machine learning models to maximise the accuracy of brand, topic, and sentiment annotation.
But let us look at the three bigrams one at a time,*combination of two words.
Social listening is short for social media listening, and to some a synonym of social media monitoring. Most people use social listening as an all-inclusive term for all online sources from which online posts can be gathered or harvested. However, in addition to popular social media platforms such as Twitter, Facebook, Instagram, YouTube, we also can harvest data from blogs, forums, news, and reviews. There are other sources that are country specific, such as Weibo for China and VK for Russia.
It is of paramount importance to harvest for all synonyms and avoid all homonyms (garbage-in) which can be over 80% of all harvested posts. A homonym is a word that is spelled the same way as the keyword for harvesting but means something entirely different, which makes it irrelevant for the project at hand. The classic example used to explain this problem is: wanting to harvest posts about Apple the company but ending up with lots of posts about the fruit or juice; let alone Apple Martin who is Gwyneth Paltrow’s daughter and if not excluded will invite a lot of noise in the dataset rendering it not only useless but dangerous for the user!
Social analytics is what happens after the posts are harvested from the various sources and irrelevant posts are removed as “noise”. For data analysis to take place, accurate intelligence needs to be added to the dataset using machine learning and/or rules-based NLP methods. The most common annotations added to each post or relevant snippet within the post (this is a longer story that requires its own blog post) are: brand, sentiment, emotions, and topics/subtopics/attributes.
These annotations can be added for text in any language, and the accuracy sought after – measured in precision, recall and F-score – should be over 75% in all cases. It is even possible to reach F-scores that are over 95% with focussed and context related training of suitable machine learning algorithms.
Social intelligence is the wisdom discovered by exploring the intelligent dataset. You see, adding brand, sentiment, emotion, and topic annotations to a dataset makes it “intelligent” but in order to find wisdom or “actionable insights” a lot more than just accurate annotations is required.
For the time being, an intelligent data cruncher and a powerful filtering or drill down tool is still needed to explore a dataset and find the gold nuggets.
We like to think of the listening247 unstructured data analytics platform also used for social listening, as the Google Maps of big data. It enables a user to navigate in a maze of millions of online posts - or other documents for that matter - safely and accurately from A to B; B being the destination or in our case the actionable insight. The path to finding the actionable insight is oftentimes a data story worth telling.
It was a lightbulb moment during lockdown; a few days after the team completed a piece of work, it suddenly hit me. Online posts from Twitter, Facebook, Instagram, blogs, forums, news, reviews and videos were fused with call centre audio files and survey verbatims in just 3 days, done for the first time and done right. Albeit from very different sources, involving both solicited and unsolicited opinion, this data had something in common - it was all unstructured data.
When I compare the amount of effort that is required to integrate structured data, with what we experienced integrating text and audio (unstructured data) during the “light bulb event” the contrast could not be more surprising!
If you are dealing with numbers in tables, you’re looking at column headings, product names, units and rigid time periods, so integrating various sources means that everything should be harmonised, for example:
Harmonising structured data to import it into one platform and then further manipulate it to integrate the various sources in order for meaningful analytics to be possible takes weeks, sometimes even months, compared to the 3 days to import, integrate, annotate, and explore unstructured data from various sources.
With unstructured data the integration process is simple; all data in text format can be annotated for relevance, brand, sentiment and topics in an automated way using machine learning models or taxonomies. Data in other formats (such as image or audio) can be converted into text in order for the same process to follow. This makes it possible to annotate call centre conversations or images from social media, just as easily as text in online posts and responses to open ended questions from surveys.
Fig. 1 Ingesting survey verbatims on listening247
The difference that makes all the difference (pun intended) when it comes to integrating structured vs unstructured data is that with the former the intelligence is already an added layer before the data fusion takes place, whilst with the latter the text is ingested and integrated before consistent intelligence is added to the dataset as a whole e.g. brands, sentiment/emotions and topics. Once the data is integrated it is already homogeneous (since it is all text) so it is straightforward to annotate it using custom or generic machine learning models and taxonomies - without having to worry about harmonisation.
Fig 2. Annotated online posts with brand topics and sentiment on listening247 Data Explorer
There are some obstacles to integrating and annotating unstructured data other than text such as audio that needs to be transcribed and images that need to be captioned with text; only when that happens can the accurate annotation of all the integrated data sources take place. There are even more obstacles if the data to be fused involves multiple languages.
Fig. 3. Image caption example, image-to-text
Thankfully, technology is available to enable voice-to-text and image-to-text transformation, as well as accurate annotations. Without accurately adding layers of intelligence, big data and especially text is not only useless, but with the wrong labels also harmful.
A data analyst cannot be expected to read millions of online posts, but what they can do is use a smart filtering tool to drill down and explore the annotated documents (e.g. social media posts or call center threads) and discover the “gold nuggets”, the elusive actionable insights.
The future of unique and actionable insights lies in data fusion of unstructured + structured data. Some of this data will belong to the companies e.g. sales data, and some they will need to procure e.g. 3rd party online posts or survey results.
Integrating unstructured data is more effortless and straightforward than you might think. You only need a good unstructured data analytics tool.
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Everyone loves an underdog story, like the classic David and Goliath, or in this case, GameStop and Melvin Capital. Even if you’re not involved in investing, chances are that you heard about the GameStop story, which started on a Reddit community called Wallstreetbets, went viral and spread like a wildfire.
The story started with Gamestop ($GME) but then many other listed companies became part of the same saga i.e. hedge funds shorted them and groups of retail investors are egging each other on via social media to buy and hold them for as long as it takes to materially hurt the funds involved.
As expected, we were curious about the whole thing, so we decided to have a look on social media and other online sites for learnings that were not obvious and subsequently not in the news. We even dared taking a peek at the Dark Web.
listening247 used its proprietary social intelligence platform to gather 2.5 million posts from December 1st 2020 to January 30th 2021 from Twitter, forums, blogs, news, videos, and reviews, using the following Boolean logic query:
"gamestop" OR "robinhood" OR "melvin capital" OR (("GME" OR "AMC" OR "BB" OR "NOK" OR "EXPR" OR "PLTR”) AND ("stock" OR "stocks" OR "shares" OR "share price" OR "NYSE" OR "nasdaq" OR "wallstreet" OR "trade" OR "trading" OR "short"))
We also gathered the entire Wallstreetbets subReddit from January 23rd to January 30th 2021.
Once the data was gathered, we used machine learning models to annotate the relevant posts with sentiment and topics in a quick and efficient way, adding intelligence to the big dataset in a matter of minutes.
Up until 10-15 years ago, the only way we could have known what the content of the 2.5 million posts was about was to read each and every one of them. Thankfully, nowadays we have the means to understand big data in an easier way, and so after annotating the data with sentiment and topics, the entire dataset was visualised on a drill-down dashboard.
After a few hours of navigating the data and exploring the online conversations, here are 7 interesting things that came up:
The acronym for “you only live once” appeared nearly 55,000 times - mostly as a verb - by small investors communicating that they were betting all they had on GameStop and some other stocks, in some cases asking for advice or encouraging others to follow suit, and oddly enough, in some cases defying the end goal that’s usually behind an investment decision (i.e. to make a profit).
“At the moment, if I had a spare $50k cash to yolo on something, I'd throw it in GME shares or PLTR shares. PLTR for the long term, GME for the short term. Maybe split $20k GME / $30k PLTR, and once GME hits $150 or higher take my gains and dump them into more PLTR.” - Forums
“I just cleared my debts, i have $500, I want to go YOLO, do i buy GME at the price that its at?” - Reddit
“I bought GME at the top. Don't care about making a profit, fuck it. YOLO.” - Forums
“BRB gonna yolo everything into GME! It can only go up!” - Twitter
Even though we only included keywords or brand names for a handful of companies other than GameStop ($GME), even more companies such as Bed Bath & Beyond ($BBBY), American Airlines ($AAL), AgEagle Aerial Systems ($UAVS), and Pershing Square Tontine Holdings ($PSTH) came up in the data. As it turns out, these are some other stocks that the retail investors are strongly recommending to buy and hold for the same reason as $GME.
Net Sentiment ScoreTM (NSSTM)is a great way to rank brands or companies in order to measure brand health and possibly predict how their stock price will fluctuate. It is no surprise that in this case Melvin Capital has the lowest NSSTM at -17%
Some people - particularly on Twitter - believe that Elon Musk further pushed the $GME story with a tweet, which was perceived as him striking back at Melvin Capital because at some point in the past they had shorted Tesla, and apparently he hates them for that.
“Actually, Elon Musk got involved because once upon a time, Melvin Capital shorted Tesla stock. End of story.” - Twitter
“Elon Musk is shilling GameStop because Melvin Capital shorted Tesla a long time ago and bragged about it.” - Twitter
“Apparently Melvin Capital has been bearish on Tesla for a long time. Elon doesn’t forget. haha” - Forums
“He will be up another 5 Million tomorrow. He should thank Elon for his tweet by ordering 50 Teslas” - Reddit
Over 100,000 posts mention the once popular phone brand, as one of the stocks to keep an eye on and buy or hold so as to replicate the $GME effect. Their stock price peaked on January 27th at 6.55 USD.
“🚀🚀NOKIA (NOK) STOCK | MASSIVE POTENTIAL | ARE YOU BUYING?” - Videos
“Bit late for massive gains on $GME. People are saying NAKD, AMC, NOK, and BB are next” - Twitter
“bought massive amount of NOK, i just hope everyone else is holding it as well. :D” - Reddit
“I was late to GME, I’m waiting on today’s market start dip. I’m in on NOK rn.” - Twitter
Very high correlation between online buzz and stock price is observed for companies such as GameStop ($GME), AMC Entertainment ($AMC), and Express Inc. Causation is obvious in this case: online posts by people recommending buying these stocks lead to people actually buying the stocks and thus driving their value sky high.
Albeit just a small percentage of the entire dataset, it’s interesting that just like YOLO, the acronym for “fear of missing out” comes up in online conversations close to 2,000 times. It seems some of those behind the $GME story belong to the generation where fear of missing out is a significant motivation to buy.
“Am I the only one with FOMO buying GME today? I bought one share yesterday and am getting five when the market opens today...not the biggest loss if it goes badly I guess.” - Forums
“I will feel sad if anyone go broke because they FOMO GME.” - Reddit
“I believe there is definitely a strong element of FOMO with this stock, especially with what we’ve seen in stocks like GME and AMC.” - Forums
“@breakneck_tv I think I hopped into AMC a bit late, but fomo after staring at GameStop the last couple days made it so I couldn’t sit out anymore” - Twitter
That’s it for now in terms of interesting - and in some cases useful - titbits, but in ~2.5 million posts there is bound to be more... Stay tuned!
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I’m afraid I am pretentious, but do I have a choice?
It’s always good to provide a credible definition of the subject from the get-go; this is the Oxford dictionary definition of the word pretentious:
“Attempting to impress by affecting greater importance or merit than is actually possessed.”
The root of the word is the verb ‘pretend’, and in this context a pretentious person is someone who pretends to be someone or something she/he is not - which sounds even worse than the Oxford definition.
Social media has probably exacerbated this quality in many people because it makes it easy to pretend hiding behind a screen.
I am the founder of listening247, which as you know is a scale-up that developed an AI based data analytics platform for the integration of unstructured customer data from social intelligence and solicited customer opinion from private online communities. In full disclosure, some of my friends on the advisory board of DigitalMR accuse me of being too much of an engineer - which I objectively am (by education) - and not enough of a marketer.
I always thought of myself as a man of substance when it comes to business, not one to add fluff to a statement to make it sound better than it really is. Who knows how others perceive me...
More and more I am getting the feeling that this is not a quality appreciated in an entrepreneur.
The founder of a Venture Capital firm told me recently that my 5 year revenue forecast is not aggressive enough, and in the same sentence he said: “I like to halve the sales and double the cost of an entrepreneur’s forecast”.
I found this somewhat confusing. Should I boost my revenue forecast beyond what I believe is safe to meet - and yes ideally exceed? Is it a sign of weakness and risk aversion to offer conservative forecasts in order to increase the probability of meeting or exceeding them?
Some introspection might help to flesh this out about myself, and hopefully in the process you as readers will find some value for yourselves as well.
When I was growing up in Platres - a village on the mountains in Cyprus, I had grand aspirations of becoming an astronaut, spearheading humanity to discovering new worlds. As a teenager I was also very conscious of branded clothing and shoes. Aspirational brands were Lacoste, Fred Perry, Levi’s, Adidas etc. So when I got my hands on a t-shirt or polo shirt of the “right” brand, I am positive I came across as very pretentious wearing it at school. Especially so, because my high school catered for around 20 villages of the region, full of kids from peasant families.
I was also very conscious about the make of the car that my family owned. I pegged our Lancia Beta and VW Golf somewhere in the middle of the ranking order. I was not very happy that we did not own a BMW or a Mercedes but could still live with not being at the bottom of the food chain.
In my final year as a student in Germany, I managed to buy myself a damaged 1979 Porsche 924 for 5,000 Deutsche Marks (approx. 3,500 US$) and drove it all the way to Cyprus, where I had the body fixed and painted red. How much more pretentious can a young man be than driving a red - so called “housewife’s Porsche” - in ‘91 in Cyprus?
Vanity is a vice similar to pretentiousness. I guess I was guilty of that too. This is a contradiction to what I mentioned above, about who I think I am today when it comes to business relations. I guess I’m still working through who I really am :).
Nobody likes pretentious people, even if they seemingly “like” their pretentiousness on Facebook or Instagram just to brown-nose them (also known as ass-kissing).
So then why do we do it? Why do we engage in creating a better image of ourselves than is really the case?.
Disclaimer: I am no expert in sociology or psychology, this is just an attempt to interpret my own experiences - so similar to the 4 Pillars of a happy life, another theoretical experiment with one subject (myself).
When I have been vain and pretentious I think the motivation was to be liked, to get respect, maybe even admiration by some. In my case there was never a sinister agenda to make a product sound better than it is so that it can be sold or inflate a company’s forecasted sales so that it can get institutional funding.
What I have failed to see so far is: this approach delivers the exact opposite result with some people.
Maybe most people. Possibly all people.
The jury is still out about the business context and the acceptable marketing kind of embellishment - a grey area whereby the truth is bent to appear better, without lying per se.
Whether people see through the attempt to be liked and are turned-off by a person who appears to be needy or they resent it or they buy the boasting, they see it as such, and they are jealous.
In some cultures and even religions they believe in the “evil eye”! It is often explained as a negative energy emitted by a jealous person towards the one boasting - totally inconsequential whether the subject of boasting is factual or not. A girlfriend says to you: “I love your dress”. Next thing you know you spill tomato soup on it and not only the dress is ruined but you also burn yourself in the process. Whether you believe in a spiritual or scientific explanation of the “evil eye” or you consider it bogus one thing is for sure: if your words, appearance or actions elicit jealousy in people….this cannot be good for you.
Does he think he is better than me?
How come she can afford this handbag? Is it fake?
I wish I had a car like his.
She must be earning twice as much as I do….and she is so dumb.
Why can I not have a baby and she has two and complaints about it
Everyone thinks he is so handsome, what a great person he is….they should look closer
There are people who think this way about you; so what do you think they say to other people on the subject when they get the opportunity? Nice things? Probably not.
Can the things they say harm your career, family life, friendships? You bet!
My conclusion is that I should stop being on stage and just be who I really am, all the time; if anything, go the other way, never advertise facts about me that I am proud of. I always knew that nobody likes a boaster, a pretentious person, a navel gazer but never thought of myself as one - apparently I was wrong. Everyone admires a humble and modest person…
...unless they think it is the cunning attempt of a pretentious person to be liked and gain respect!
In any case, I'll end this introspection here. It's good to do this from time to time, but business calls, and I need to go back to thinking about social intelligence, digital brand equity, social brand performance, online communities, CX measurement, and many other areas we work in.
Stay healthy everyone!
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I posit that the four pillars of a happy life are encapsulated in these 4 verbs:
Let's call them the Big 4!
Before you go on reading I should clarify that I am not an expert in any of these four categories, I am just very interested in them thus I read and experiment a lot with a single research subject: myself.
Since my day job is running listening247, you may wonder what this subject has to do with data analytics and market research. In case you don't know much about what it is that we do: we developed an A.I. based data analytics & market research platform that helps our blue chip clients make data driven decisions. This capability delivers high fidelity unstructured data by integrating social intelligence with surveys and customer purchases or other transactional data.
On our blog, we have a category called ‘human connections’ and this is where this post belongs. The point of this category is to communicate that a consumer or a respondent is first and foremost a human being. In order for us as researchers to understand why consumers buy a brand, we need to have a deeper understanding of how their brain works and all the motivations that make a human tick.
Just over a year ago I published an article on whether market research adds value to the quality of human life. That article was about what humans need in order to stay alive and functional, with needs like love, sex, fulfillment, spirituality and entertainment falling under a super category called the pursuit of happiness. At the time I wasn’t sure what to do with health, and of course needs like air, water and food topped the list of 11 categories, with health coming in 5th place.
Twelve months, a couple of books and many articles later, I am seeing things from a different lense; a more holistic, more crisp view about the human condition. Let's discuss the big 4 individually and hope we get to some conclusions by the end of this article.
We are what we eat… quite literally, if by we, we mean our body. Many of you who frequent Medium may have seen the documentary Game Changers on Netflix, which strongly recommends a plant-based diet for humans. Many called it one-sided, which objectively it is - but this does not mean the message is wrong. Some called it vegan propaganda. The very fact that the producers chose a much less controversial description instead of Veganism - which sounds like a cult - and went with “plant based diet” was also considered a calculated PR move.
Now add intermittent fasting to a plant based diet and you have a super formula for a super healthy body including brain function improvements; at least this is what the result feels like the result with the one subject of my research (me). It has to be said that intermittent fasting is not for everyone, for example it is not suitable for people with eating disorders. On the positive side according to Dr. Mark Mattson, a professor of Neurology at John Hopkins University, fasting has been shown to increase rates of neurogenesis in the brain (Article of Dr Brady Salcido on Medium).
Our gut microbiome is credited with a lot of power over our wellbeing. The nervous system in our gut is constantly in communication with our brain letting it know the state of our body in real time. Our immune system has a strong dependence on the good bacteria in our gut. “Using your gut feeling” is not just a figure of speech, there is more to our gut than we think.
Here is a fun fact: Most Koreans eat kimchi (fermented cabbage) every day… can anyone guess why?
In order to recover and rejuvenate, a human needs a minimum of 7 hours of quality sleep every night. Most of it should be light sleep but we need at least 4-5 sleep cycles between Light Sleep, Deep Sleep and Rapid Eye Movement (REM). This is the time when muscles recover after exercise and the brain gets rid of harmful toxins that build up during awake time.
Without enough of it we get sick and it is actually possible to die due to lack of sleep.
In a pyramid of health often circulated in fitness circles, you will notice that neither exercise nor nutrition are at the base of the pyramid; sleep is!
In their book Younger Next Year Chris Crowley and Henry Lodge M.D. recommend going to the gym 6 days a week in order to turn back your biological clock.
Apparently after the age of 30, we lose 3-5% of our muscle mass every decade.
Adding muscle mass not only makes you stronger but it also improves your metabolism and passes on a message to the brain that not only are you not heading to your grave, but you are actually going in the opposite direction; getting younger like Crowley & Lodge posit in their book.
Now if we think about Alphabet’s Calico and the Human longevity projects which aim to extend human life (some say to reach 750 years which effectively means eternal), we have an extra incentive to exercise and be healthy so that we can reach “longevity escape velocity”. This is a concept of the life extension movement which implies that life expectancy is extended at a rate faster than the time passing e.g. for every year that passes we find ways to extend life for a year and 1 day or 1.5 years or longer. This has not happened yet.
It may be as simple as endorphin induced euphoria after a gym session, if only it wasn't short lived; the same feeling of euphoria can be replicated with the use of opioids by the way.
Scientists Daniel Goleman and Richard Davidson in their book “The Science of Meditation” lay out evidence that meditation can induce lasting positive traits in the human brain; from better attention and vigilance, to an improved immune system and reduced brain atrophy after the age of 50. There are still not enough MRI scans of the brains of Yogis from the Himalayas to provide solid proof for all the assumed benefits of meditation, but those brain scans that are available strongly hint that the benefits are real.
Permanently altered traits - including longer time functioning in gamma frequency which apparently has multiple health benefits - are accentuated once you achieve over 1,000 hours of lifetime meditation. Like in every other skill such as playing tennis, the violin or football you become world class (possibly a yogi) with over 10,000 lifetime meditation hours!
When you read what comes next you may be reminded of Jekyll & Hyde. So here goes my alter ego:
How could anyone think that happiness is as simple as 4 pillars which dictate black on white actions that can lead to plausible results?
Happiness is so elusive that even the founding fathers of the USA wrote about the “pursuit of happiness” in their constitution. They made it sound like it is a continuous chase of a mythical state that no one has ever achieved - similar to Buddha’s enlightenment.
One thing is for sure though, having no needs and expectations helps. The complete fulfilment of our needs ended when we exited our mothers’ womb. The perfect supermarket… whatever we needed was delivered to us at the blink of an eye. Everything went downhill on exit; we all tried our very best to tell the people in the room at the time, but no amount of crying made any difference.
It is simple really: if we want something and we can’t have it we are unhappy; when we get it we are happy for 2 seconds and then on to the next thing that we want but cannot have.
Some Indian yogis/sages talk about high thinking and simple living.
The 4 verbs, if that’s all we did in our lives would describe a simple life with low expectations and a higher probability of not being unhappy for a longer period of time. When the big 4 are applied to a complex life with high aspirations, sadly they are not sufficient for a happy life; they can lead to a less frantic life which is a step in the right direction, but what about the other 9 million steps to happiness?
The big 4 contribute toward a healthy life which is a precondition for happiness; but they are not enough. If you are sick, whatever else you have will hardly move the happiness needle - unless of course you are a stoic.
I think I am losing my own argument. It is obvious I need some help from professional philosophers...
I’m ending this article with the Stoic take on happiness which is very easy to understand and agree with, but extremely difficult to implement; in a nutshell:
“focus on what you can control accept what you can't”
“No person has the power to have everything they want, but it is in their power not to want what they don’t have, and to cheerfully put to good use what they do have.” – Seneca
“Curb your desire—don’t set your heart on so many things and you will get what you need.” – Epictetus
Maybe brands can play a role in giving humans what they need to be happy; especially if they fulfill one of the basic needs discussed here and if they elicit one of the 14 human emotions that the listening247 proprietary emotions detection model includes.
I think the conclusion is: the stoics get it and maybe a few friends from the Young Presidents Organization (YPO) as well the market researchers and data scientists at listening247.
What do you think?
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It is not the first time we’ve pondered the issue of whether market research needs a new name. In fact, as far back as 2016, we issued a blog post aptly named “Does the market research industry need a new name?”
This article is about a relatively simple idea but with a slightly convoluted explanation not so much about the name of our industry, more about what it really is becoming. Hopefully the conclusion will have enough clarity to make sense to most readers!
The tagline of the listening247. (est.2010) logo is Market Research Evolved. Not only living organisms like humans, animals and plants get to evolve, but so do ideas, industry verticals and disciplines; especially technology, which is practically a synonym of evolution in certain cases. The other interesting thing about technology is that not only it is a vertical itself, but as a business enabler it cuts across almost all other verticals . Hold this thought, it will all make sense a bit further down in this narrative.
So, 10 years ago we wanted to drive the evolution of market research. Hold this thought too.
Have you ever come across this pair of rhyming words in presentations:
“Evolution - Revolution”?
The presenters who use the pair (including Harvard Business School Profs.) usually want to differentiate between gradual - maybe linear - change/improvement, compared to radical/exponential change.
What about the sentence that has almost become a cliche in tech innovation circles:
“The pace of change will never be this slow again?”
Cliche or not, listening247 needs to change its tagline as a result; and it probably needs to change its name as well - there is no point calling something digital when almost nothing is analogue anymore. There is also no point calling something MR (for market research) when most of it is analytics. We will probably end up calling ourselves DMR and the acronym will have no current meaning, it will merely explain our legacy.
Could the logo tagline change from Market Research Evolved to …Market Research Revolted (from revolution not from disgust :))? That actually doesn’t make much sense even though it is symmetrical with the previous one; maybe Market Research Revolution; though a more appropriate name for this revolution is indeed … drum roll…
”Data Analytics” - powered by AI of course!
During the last 10 years, the pace of change was such that ESOMAR (the biggest global association of market research) is now including the revenues of companies like SAS, Adobe, SAP, and Salesforce in its newly defined market research market.
In the context of social intelligence, listening247 has always supported the notion that harvesting online posts is a commodity. Anybody with some basic programming skills and access to the cloud can harvest posts from Twitter or other public sites. The same applies to data collection in traditional market research which is essentially asking other people questions.
If market research = data collection + data processing + data analysis + reporting then it follows that market research - data collection = data analytics …pretty much.
If you put all the above points together, you will agree that market research started going through a revolution. This revolution is mainly driven by the progress in machine learning and cloud computing. The new face and possibly new name of MR is as the equation above shows Data Analytics. This is the beginning of a consolidation tsunami in the data analytics field marked by landmark acquisitions such as SAP acquiring Qualtrics at a 20 times revenue multiple.
listening247 had to go through a process that took 6 years of focused R&D, researching and ultimately developing tech that was good enough to annotate unstructured data accurately, in any language (and images for that matter), in order to analyse it, understand it and extract value from it - usually in the form of actionable insights.
It turns out the technology that was developed during all these years is not only applicable to market research but it can also be used to:
All these are adjacent markets to market research and they are another strong reason to call what we developed and what we now do …you guessed it…. “Data Analytics”.
We have mentioned this statistic in previous articles: 80-90% of documented human knowledge of all times is in the form of unstructured data; this definition includes text and audio in multiple languages, images and video clips/feeds. This only leaves around 10% of documented human knowledge being numbers in tables; what we would call structured data.
Integrating unstructured data with all the traditional data sources some of which businesses probably already own, has to be one of the biggest game changers of this new decade. A couple of years back the CMO of DIAGEO (on a call about a social analytics report that we were presenting) referred to this idea as the “holy grail”. Case in point WeLab a new virtual bank in Hong Kong, that raised hundreds of millions of dollars in funding, bases its entire risk management strategy in analysing mobile unstructured data.
This data integration can only work if we can ensure we are combining and synthesizing High Fidelity Data (HFD).
Data analytics seems to be a mega industry. According to Statista, the global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45% of professionals in the market research industry reportedly used big data analytics as a research method.
Market researchers have to move on to the next chapter, we need to build on what we brought to the table and combine the three data sources that matter most:
This is not just market research (customer opinion), it is certainly not just business intelligence (BI - historically analysing transactional data), it is what we now will simply call:
I know I’m repeating myself but I can’t say this often enough: in order for the data integration to not turn out to be useless - or even harmful when it comes to making business decisions, the data has to be as accurate as it can be. This is a simple concept, anyone who has experienced it before wants to avoid it, and it is called GIGO (Garbage in Garbage out).
If this quote is true: “The world’s most valuable resource is no longer oil, but data” The Economist Report in 2017 - and I believe it is - then during this next decade the balance of power might change dramatically on our little planet. With our new company name and tagline : listening247 - High Fidelity Data the future can be nothing but bright and promising!
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This is the story of a start-up that became a scale-up.
It will hopefully offer some helpful thoughts and tips to first-timer or aspiring entrepreneurs.
I always liked the expression "after ten years of hard work we became an overnight success!". Admittedly, it is self-serving if your company has been around for almost 10 years and it only experienced real traction in year nine going on ten.
The truth is we (listening247) have spent a lot of time and money on Research & Development funded by seven grants: six from Innovate UK - we could not be more thankful, and one from the E.U.. It took six years of focussed R&D to create listening247 in today’s manifestation: a Social Intelligence SaaS for market research power-users on its way to becoming a DIY SaaS for end-clients.
Some people called us grant junkies! No matter what anyone says or believes, those grants allowed us to stay away from institutional investors until today - I will come back to this later.
Another related (and probably cliché) phrase I like is "Timing is everything". The discipline I am referring to, kept changing names: first it was web listening then social media monitoring then social media listening then social listening & analytics and now social intelligence; whatever the name, this data source and insights discovery approach took what feels like forever to become mainstream for the market research function in organisations.
It took social intelligence spend eight or nine years to get to 3.4B US$ (Reuters) in 2017; it is predicted to be 9B US$ (listening247) by the end of 2020 and 16B US$ by 2023 (Reuters). Many people have published predictions about the size of this market in the past and they all overestimated it. They do say that humans overestimate the short term, and then (as a result) underestimate the long term. In other words, if we are conditioned that this market grows by a few hundred million US$ per year we will be taken by surprise when the proverbial “hockey stick” appears.
Well, this article is making sure its readers will not be surprised by the exponential growth of the social media listening and analytics market.
After all “a rising tide lifts all boats”!
I find proverbs, sayings, clichés and buzzwords quite curious linguistic phenomena. Where do they come from, who coined them, how many different interpretations do they have? Take the term ‘scale-up’ for example: “a business that is in the process of expanding”.
Yes, but by how much?
Is 20% enough?
Should it be over 100%?
What if the “expansion” is 300% of 1,000 US$ - does that count?
Whatever the definition, one of the big four accounting firms thought listening247 fits the profile of a scale-up and was invited to participate in an institutional fund raising program; our very first institutional round. The funds will allow us to accelerate our growth and the process will help us sharpen our focus and fine tune our business plan.
Staying away from institutional investors for so long has pros and cons.
The pros:
The cons:
The moral of our story is: perseverance will eventually get an entrepreneurial team to where they want to go... but I think the more succinct description of our state of mind all these years was stubbornness; and the belief that “whatever does not kill us makes us stronger”. Stubbornness may sound like a negative attribute to have, but it really is what kept us going.
Another interesting phrase I saw on the Skype account tagline of a teenager was
"Failure is not a motherf&%*!% option".
Quite inspirational, don’t you think?
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As one would expect, social media intelligence (or just social intelligence) came up as a subject at the “Social Intelligence World” conference in London back in November 2018. More specifically, it came up in the context: how does it differ from social media listening?
This question took me back several years, when we published our first eBook about “web listening”, our label of choice at the time which was a buzzword; its most popular version was “social media monitoring”. Social media intelligence did not come up at all back then, albeit in hindsight it is odd that it didn’t. I am not sure how we missed it then, but now, when someone asks what is the difference between intelligence and listening, the answer seems quite obvious!
Social media listening or social media monitoring is simply about harvesting the online posts and maybe even annotating them with a topic and/or sentiment. If the annotation is accurate then it answers questions like ‘what are people talking about online’ or ‘how do they feel about my brand’? Social intelligence on the other hand, is about understanding the deeper meaning of what people choose to post - although sometimes there isn’t one - and link it to a business question; notice how the term ‘actionable insights’ has not come up yet? Another buzzword that is overused in the market research sector, and another one for which we published numerous blog posts with our own - very concrete - definition of what it really is!
When we say ‘social media’ in this context we don’t just mean social media platforms, but rather any public online source of text and images which might express consumer or editorial opinions and/or facts. A side note: things would be a lot easier if we meant what we say in a literal way. People who coin phrases or titles or headings tend to take a lot of freedoms on the altar of “crispness” or “snappy creativity”!
listening247 - an aspiring state-of-the-art DIY SaaS looks at the world of social intelligence via four lenses:
We would be remiss if we didn’t mention text and image analytics as a standalone discipline when the source is not social media or other online sources. In such a case the only difference is that the source is not the online web but any other source of text and images. Perhaps if the source is not the online web it should just be called Business Intelligence, which is an old and very familiar discipline within organisations.
Back to the 4 modules, they have the power to generate intelligence derived from unstructured data - which make up 80-90% of the human knowledge, produced since the beginning of time. Structured data which are effectively numbers in tables or graphs only account for 10-20% of all our knowledge as a species.
Unstructured data can be harvested from the web and if we want to stay out of jail we will stick to public data (as opposed to private conversations or personal data). They can be harvested through APIs that the sites which contain the data make available for pay or for free, and through scrapers which can crawl a website and find specific consumer or editorial posts. Responses to open ended questions in surveys, transcripts of focus groups or even call centre conversations are also great sources of opinions and facts (i.e. unstructured data).
In order to make sense of big unstructured data, machine learning is a good place to start. Supervised machine learning requires humans to annotate a big enough sample of the available data. The annotated data-set is then used to train a machine learning algorithm to create a model that does a specific job really well; the aim is to get over 80% relevance, precision and recall. Unsupervised machine learning is making great strides but cannot replace the supervised approach currently.
Once we have a trained model and our data-set we need to process the latter and annotate it in its entirety. The data can be filtered and navigated in many ways. Structured data can be produced in the form of tables, making the analysis of the data-set possible. The goal here is of course to enable human analysts to uncover actionable insights - since machines are not there yet.
Data visualisation is typically done on dashboards or PPT presentations. The most appropriate types are drill-down and query dashboards. There are multiple delivery mechanisms and use cases, e.g.
Social media intelligence has multiple use cases for multiple departments as shown in the list below, annotated as multipurpose ‘intelligence’ or specific ‘actions’:
The many departments involved and the many use cases ultimately create a confusion as to who the owner should be within an organisation. Maybe Social Intelligence should simply be part of the Business Intelligence or the Market Research department, offering custom user interfaces to the various action players with only the information they need specifically to take action.
Having a Business Intelligence or Market Research Department is a privilege reserved only for large organisations. For small and medium enterprises (SMEs or SMBs) that do not have a business intelligence department a different approach and possibly nomenclature should be employed; but this is the stuff for another blog post. In the meantime let us know where you stand on all this by emailing us or tweeting to @listening247AI.
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Humans are inclined to think linearly. We overestimate the short-term and underestimate the long-term when we forecast. Quite often, linear trends are interrupted by “hockey sticks” that no-one could foresee. For example, no one predicted the exponential growth of computers and smartphones but we have been forecasting flying cars for over 30 years.
Credible forecasters in their quest to become better, look at their past predictions and measure their “batting rate”. This is what Greenbook asked me to do with my 2015 predictions about market research.
There is one dynamic about forecasting that needs to be explained. It may sound like an excuse for getting a prediction wrong but really it’s a compliment to the forecaster. When a prediction puts governments, companies or people in a bad place in the future, then the affected parties do their utmost to avoid that future. Case in point: blockchains were predicted to be the end of traditional banks as we know them due to their power of disintermediation; according to a FinTech rep at a conference in London, Bank of America owns 82% of all blockchain related patents in the US. This article does not mention the percentage but it quotes BofA as the leading company with the most patents in the field!!!
Happening: 4
Not Happening (yet): 4
Possibly happening: 2
Two years later - in 2017 - I published a new list of predictions. As you will see below the quality of my forecasting improved a lot.
Getting the hang of it!
Happening: 7
Not Happening (yet): 1
Possibly happening: 2
From 40% to 70% in two years…. not bad if I may say so myself :)!
One last prediction (from Reuters this time) which we endorse: The social analytics market will be US$16 billion by 2023.
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A few days after I registered listening247 on alternativedata.org (a spur of the moment kind of thing), companies I had never even heard of before started reaching out to explore cooperation. One of them was Bloomberg. Obviously they were an exception - I did happen to know them.
The unknown (to me) companies were mainly conference organisers fishing for alternative data providers, to bring them together with investment funds.. So we bit.
Our first question as you may imagine, was: what is alternative data? They said that there are many categories such as sentiment from social and news, app usage, surveys, satellite imagery, geo-location etc. and their main use is to give investors an edge in predicting stock prices.
Funnily enough, they all used the same example to bring their point home: satellite images of retailer parking lots, that depending on how full they are, can predict the retailer’s sales and share against competitors. I have to admit, even though it’s a bit out there it does make sense..
Traditionally investment funds and other traders use fundamentals to make their investment decisions. Even though alternative data and the ability to analyse it (using machine learning) have been around for over a decade, in the last 12 months - i have the impression - chatter about it is going through the roof.
I am thinking: “looks like we caught this wave quite early”.
One of my favourite business success analogies is “the surfer”; for the act of surfing, 3 things are required: a surfer, a surfboard and a wave. The surfer is the CEO of a company, the surfboard the company itself, and both are waiting for the mother of all waves to lift and accelerate them. Without the wave, even the best CEO with the best functioning company will not make it far.
Needless to say, we jumped in with both feet.
Next order of business was to figure out for ourselves to what extent our “alternative data” correlates with stock prices. It so happened that when all this interest became apparent we were considering to focus on social intelligence for the banking sector; so when a well known business school asked us if we wanted to investigate the correlation of Bank Governance stories in online news and social media to their business performance we knew exactly what needed to be done.
If you are a regular reader of our articles you will already know the scope of the social intelligence project we carried out:
Keywords for harvesting: 11 major brands including: HSBC, Barclays, RBS, Deutsche Bank etc.
Language: English
Geography: Global
Time Period: past 12 months
Data sources: Twitter, blogs, boards / forums, news, reviews, videos
Machine learning annotations: sentiment, topics, brands, and noise (irrelevant posts picked up due to homonyms)
The data scientists and researchers of listening247, after having cleaned the data from “noise” (resulting from homonyms) they annotated each post with topics and sentiment using custom machine learning models. The sentiment, semantic and brand accuracy were all above 80% as often advertised.
They then regressed the daily stock price of the banks against various time series derived from the annotated posts that were harvested.
The results were astounding!
For each of the 4 examples below I will describe the social intelligence metrics that were correlated with daily bank valuation. As with all R&D projects there was a lot of trial and error going on. What was impressive…….hmmm I will not give this away yet
1. For Societe Generale when we correlated ESG (Environmental, Social, Governance) posts only from News - which means editorial as opposed to consumer posts - regardless of sentiment, the correlation factor of monthly total posts and monthly valuation was R2 =0.79. With the exception of the red spike in the graph below, not bad I would say.
2. For the Royal Bank of Scotland (RBS) the correlation factor was even higher when we correlated the posts from News about ESG with positive and neutral sentiment: we got R2=0.87. In this case we used the 30 day rolling average for both variables. Also visually it looks really impressive - in the graph below.
3. Can it get any better? You bet!! Barclays - using almost the same parameters as for the RBS case but from all sources instead of just News, returned a correlation factor of R2=0.92. By the time I see the Barclays result I am thinking “unbelievable”.
Well, not really. Not only is there correlation between the two, but we also know which way causation goes. Traders are indeed influenced by what is circulating in the news and on social media when they trade.
4. Example number 4 is equally impressive even though the correlation factor is lower. For Deutsche Bank, we correlated negative posts about ESG against their stock price using a 30 day rolling average R2=-0.40. It turns out it makes perfect sense, when the red line (number of negative posts) goes up the DB stock price goes down and when the red line goes down the blue line goes up.
Amazing! Our alternative data turns out to be quite useful primarily to discretionary, and private equity and with a few adjustments to quantitative funds. It feels like the sky is the limit. We probably need to create a new business unit to deal exclusively with the 15 social intelligence metrics that we discovered to date.
Please do reach out and share your views or questions on X, mmichael@listening247.com if you find this interesting.
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Nope, not in this case!
Statements such as ‘XYZ ranks first on social media buzz’ can be quite misleading. In Social Intelligence, looking at the number of posts (i.e. buzz) about a brand or company is equally important as understanding the sentiment and topics expressed in these posts.
In the case of Deutsche Bank, they do indeed rank first among 10 other global banks included as part of the first listening247 banking report that listening247 launched in April this year, however many of these posts are negative and could in fact harm Deutsche Bank in the real world; in terms of valuation and bottom line impact that is.
In social listening & analytics, the starting date and the time period for which data is to be analysed is not restricted to the date one decides to carry out the project, like it would be in traditional market research (e.g. customer surveys), as we have the ability to harvest and analyse posts from the past. In this first report listening247 analysed English posts about 11 banks, found on X, YouTube, News, Forums, Blogs, and Reviews, during the 12 months of May 2018 – April 2019 inclusive.
As you can see below, Deutsche Bank with its 1.9 million posts across all sources, commands an impressive 48% share of voice among the banks.
Despite having the largest number of posts, Deutsche Bank is underperforming in ESG, which stands for Environmental, Social, and Governance. Interestingly, news on governance is the driving force behind negative posts about the bank.
In the table below you can see the Net Sentiment ScoreTM (NSSTM) for ESG by bank, where a negative NSSTM is observed in 4 out of 5 quarters for Deutsche Bank. NSSTM is a composite metric in the social intelligence world, that mirrors the well known Net Promoter Score (NPS) from surveys.
Unsurprisingly, the number of posts about ESG with negative sentiment has a high negative correlation with Deutsche Bank’s valuation based on its daily stock price. The negative correlation is even visible to the bare eye in the chart below: when the red line for negative sentiment about ESG goes up, the blue line for the bank’s value goes down.
It never ceases to amaze me how news, in particular negative news, about well known brands and people pick up and in a matter of a few hours become viral. In the case of Deutsche Bank, a jump of 5-10x can be seen literally from one day to the next (April 29/30), the main reason being that the Trump family was suing the bank.
Ideally Deutsche Bank and every other corporation should be able to track buzz around their corporate brand, all their product brands and senior people, so they can react immediately when a PR crisis is about to happen. Containment would be the key intent in cases like this, but the pre-condition is that the bank has access to a social intelligence solution such as listening247*. There are of course numerous other use cases of social intelligence for various bank departments; a couple of obvious ones are:
1. Operational issues can be brought to the attention of senior management in order to be addressed
2. Early warnings can be provided for any underlying problems before they get out of hand
*Using any social media monitoring tool is not good enough, buyers need to be informed on what is needed for accurate analysis and avoiding GIGO (garbage in…), and they need to have proof of the sentiment, brand, and topic annotation accuracy of the tool or solution before subscribing. A minimum of 75% accuracy is achievable in all three cases, in all languages.
Another useful feature for a social intelligence solution is to be able to look at topics (e.g. scandals) of conversations within brands; and not only that but to also be able to drill down into multiple levels of subtopics, as shown in the image below.
The real magic in a solution like listening247 actually happens when you “click here to view posts” once you have made all your selections on the drill down dashboard; this is where you actually get to see what people really said about ‘Trump suing Deutsche’ (examples in the screenshot below). What makes it even better is that when you click on any one of those posts you are taken to the original post on the platform where it was posted.
Stay tuned for more stories with findings from the social intelligence report for banks brought to you by listening247. In the next story we will analyse how banks can predict their future business performance expressed in their daily stock closing price using accurate social intelligence. In the meantime please do connect with me on X or email me at mmichael@listening247.com to ask questions or offer a view on this article.
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This is a short story about social intelligence (SI) and banks. The unique selling proposition of listening247, a social intelligence solution, is high multilingual accuracy for sentiment, topics and brands; unfortunately this is also one of the solution’s biggest obstacles to scale. This trade-off between accuracy and scale was consciously made by a team of people - they were market researchers and they do have tremendous respect for data accuracy, sometimes to their detriment - until one day, not too long ago, they realised scalability does not have to be a trade-off.
Normally it took 3 weeks to create new custom machine learning models every time they came across new categories and languages. The operative word is new in the previous sentence. That was their little secret on how to reach higher brand, sentiment and topic accuracy than their competitors. They realised that once they have the A.I. custom set-up (for a product category and language) done then they could be on the same footing as every other social media monitoring tool on scalability, but with a much higher accuracy. That’s when they decided to pick one industry vertical, create the necessary set-up and run with it.
The decision was not easy, there were too many variables; they created a strawman proposal and asked the question to the whole company and its advisors; after a couple of weeks and a lot of back and forth they picked the banking sector. There are many good reasons why this vertical deserves focus. They could have taken an FMCG product category or retail, healthcare, automotive or telecoms but they chose to enlighten the banks first, before they tackled the rest of the world (in their own words). Here are some of the reasons that influenced their decision:
They had to start somewhere so with the help from a high profile advisor from the industry they picked 11 major banks, mostly multinationals to use as keywords for post harvesting. Here is the rest of the scope:
Language: English
Geography: Global
Period: Past 12 months
Sources: Twitter, blogs, forums, news, reviews, videos
Machine Learning Annotations: Sentiment, Topics, Brands, Noise (irrelevant posts which contain homonyms)
Deliverables: annotated data in CSV and Excel, drilldown and query dashboards, powerpoint presentation.
For the ESG impact on bank performance for their R&D project with the University they also retrieved the daily valuations of each of the 11 banks from Yahoo/Google Finance.
They harvested 4.5 million posts for the 11 banks in English globally. The pie chart below shows the share of each source type. Twitter was by far the biggest source of posts followed by News which is the only non-consumer source, mostly editorials published by the banks by journalists or by the banks themselves.
For DB, HSBC, BNP Paribas, Santander and Credit Agricole, Twitter was the biggest source of posts. Consumers do talk a lot about their banks, especially when they have complaints. On the other hand for Barclays, SosGen, Unicredit and Intesa Sanpaolo News was the biggest source which means that their customers do not have complaints or they do not focus on engaging with them on social media.
The findings were presented for the first time to a group of board directors of banks from various countries who were taking part in the International Directors Banking Programme (IDBP) at INSEAD.
Here are some of the highlights of the report:
1, Deutsche Bank is ranked first in terms of Buzz (=total volume of posts) with 1.9 million posts from all sources. This represents 42% share of voice for DB which is followed by HSBC and Barclays, as you can see in the bar chart below.
2. The net sentiment score (NSS) was calculated for each bank and was used to rank them in the chart below. This is a trade marked score of DigitalMR and it combines all the positive, negative and neutral posts. RBS has the lowest score with a -3% whilst HSBC leads the pack with a +9% score. Considering other verticals or product categories the top NSS score of 9% recorded here, is quite low.
3. When it comes to topics of conversations online, financial events scored -8%. ESG scored +5% with the top topic being emotional connection. ESG seems to be a very hot topic around banks and other corporates.
4. The report can be quite granular in terms of topics and time periods. The table below shows a drill down into ESG by brand and quarter for net sentiment score. The colour coding makes it easy to pinpoint the problem areas. Deutsche Bank and RBS are the ones with the most quarters showing a negative NSS.
It looked as if the board level executives had never seen anything similar before, they viewed the results with some scepticism, they asked quite a few questions. Some of them wanted to drill down and understand more especially those of them who were with banks included in the project. The question is will they manage to get the management of their banks to integrate social intelligence in the other streams of data they have?
What makes this report credible is that we know its sentiment and topic accuracy is over 75%. This is not just a number thrown out there, it can be verified by anyone. You can extract a random sample of 100 posts, read through them, and verify with how many brand, sentiment and topic annotations you agree. By the time we publish the next short story on the banking report the machine learning models will improve themselves to accuracies over 80%.
In the next article you can expect to find out how news about governance impact the valuation of the banks. If you are wondering what other ways there are to create value for your bank from a social intelligence report like this, stay tuned; if you can’t wait two weeks reach out to me via X or email, Talk soon!
