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Yes!
I thought I should get the answer to the title question out of the way, not that it wasn’t obvious what the answer would be. I violated one of the cardinal rules of market research in this case and asked a biased question. Having said that, let’s use a methodical approach to prove that this answer is indeed the correct one. Let’s start by first considering what market research is, and what it is not.
Market research is:
Market research is not:
Small and medium sized enterprises (SMEs) do not have market research departments; they often don’t even have one single employee dedicated to market research. Why do you think that is? In my view, it’s probably because they believe that other investments closer to sales are needed more than market research. Of course this doesn’t mean that their marketing department won’t buy the occasional syndicated research report or even commission some custom research every now and then.
According to OECD a small enterprise typically generates up to 10 million euros and a medium enterprise up to 50 million euros of annual turnover. Companies even larger than that don’t have market research departments. If I had to guess I would say a company would have to be over 250 million euros for a market research department to be the rule rather than the exception.
Consequently we have two types of companies to consider:
It is safe to say that all blue-chip multinationals belong to group A. Most of them treat market research with respect, especially the FMCG manufacturers. P&G is probably the biggest market research spender in the world. Their ability to swiftly turn information into action is legendary. I will venture say that this is one of the main reasons they are the biggest FMCG company in the world. They are an insights driven organisation through and through.
The rest of the organisations (that belong to group B.) in most cases have the marketing department deal with carrying out or buying market research when they need it. If they have access to lots of data they may give it to the Business Intelligence department (if it exists that is) which is more about analysing owned data and not collecting new - especially customer opinions. Now within a marketing department, depending on company size, we have a CMO or Marketing Director, and the rest of the positions and functions are all over the map: Brand Managers, PR Managers, Social Media Managers, Digital Marketing Managers, Communications Managers (internal and external).
For all the things that market research is and is not, every person in a marketing department - all things being equal - would prefer to be called something other than a ‘market research manager’. A market research manager is not on the front line heroically battling competition helping the organisation sell more… they are an ancillary service in the absence of which the heroic marketing employees will make decisions based on their experience and gut feeling. Without data, some will get it right a few times and they will make sure it is known by everyone and will be celebrated; in most cases they will get it wrong or not entirely wrong but without great results, and they will find ways to explain it away (i.e. shove it under the rug) and move on. In such occasions market research is actually the enemy because it can show exactly what the marketers did wrong, or even worse for them it can show why they should not have launched that campaign or change that product messaging or package. The market research method they should have used is called pre- and post campaign evaluation. It can be carried out using social intelligence and online survey methodologies.
Last November I was speaking at the first social intelligence conference of its kind - probably in the world. It took place in London and it was about social media listening and how to turn the findings into useful intelligence. A few of the pundits represented the opinion that social intelligence should be its own discipline and not be part of the insights function (the slightly sexier way of saying market research). When I asked why, the answer was: no-one in a marketing department wants to be called a market research manager…..thus “market research” are dirty words for marketers; case closed. I would love an opportunity to discuss with you, the readers of this post, if you have other thoughts on this subject or (even better) if you are in agreement. Please write to me on Twitter @listening247AI or send me an email.
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There is a relatively simple formula which describes “weak” or “narrow” artificial intelligence: AI = ML+TD+HITL. To be more specific, this is the definition of supervised machine learning, which is the most common method to produce artificial intelligence. The acronyms in the formula stand for:
Strong artificial intelligence - as defined by the Turing test - is when a human has a conversation with a machine and cannot tell it was not a human, based on the way it responds to questions. The optimists believe that strong AI is 10-15 years away whilst the realists/pessimists say not before the end of this century.
Over 90% of all human knowledge accumulated since the beginning of time, is unstructured data. That is text, images, audio, or video. The other 10% are numbers in tables which is what quantitative market researchers usually use. The qualies, they are the ones using unstructured data, but the volume is limited to a few pages or a few video clips that a person can read/watch in a couple of days.
Other than reading, listening to, or viewing unstructured data, 15 years ago there was no other way to discover their content and understand their meaning. Thankfully (especially if we are dealing with big data) today there is a way to discover and understand the information hidden in mega-, giga-, tera- or n-ta-bytes of data; you guessed it, it is AI. Machine learning allows us to create models that can process large files of text or images in seconds, and annotate sentences, paragraphs, sections, objects, or even whole documents with topics, sentiment and specific emotions. Sentiment and semantic analysis are the two most popular ways to analyse and understand unstructured data with the use of machine learning or a rules based approach. When the unstructured data to be analysed is in text format, the discipline falls under Computer Science (not linguistics funnily enough) and is called Natural Language Processing (NLP) or Text Analytics.
Semi-supervised-, unsupervised- and deep-learning are other forms of machine learning, used to a smaller extent in a market research context, even though deep learning implementation is picking up speed - especially for image analytics.
There is a multitude of users, data sources and use cases within an organisation. Let’s take a look at relevant data sources first:
ESOMAR mainly caters to the market researchers in organisations globally, but there are many more users of text and image analytics solutions sitting in different departments, that can benefit from using AI to understand unstructured data. Here is a combined list of users and use case examples for each one, which is not exhaustive by any means:
If we agree that social intelligence is currently the most popular application of AI in research and insights then it does make sense to review possible questions that can be answered using it.
If you are amenable to a bold statement such as “social intelligence may replace some traditional market research methods used to solicit consumer opinions” then here is a list to consider:
Of course whether social intelligence can replace them altogether or enhance them depends on the country, language and product category. If you have not embraced the use of AI yet, to tap into the wealth of unstructured data available to us everywhere, then at least keep an open mind and keep asking questions that will help you make an informed decision when the right time comes.
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This may not be a 100% original idea. Other people have thought of a version of it in the past, like the Russian news site City Reporter. The site brought positive news stories to the front of its pages and found any and all silver linings in negative stories - “No disruption on the roads despite snow,” for example.
Nevertheless, we posit that launching a news channel that will only report good news will have a positive impact on humanity. It’s all in the execution. The same idea can be executed well or really badly... if in the case of City Reporter it was the latter we should give the idea another chance.
Here is an open invitation to the powers that be in the news industry: the CNNs and the BBCs of this world to consider a global initiative and launch a TV and/or online News Channel that will only report the good news, and ignore the bad ones. We are not suggesting spinning the bad news to make them sound like good ones, just ignore them. In this respect this may be an original idea after all.
The news industry is defined by the saying: If it bleeds it leads.
Here are some excerpts from a Guardian article by Steven Pinker for more context:
In a BBC article by Tom Stafford, an academic experiment is described around how people deal with negative vs positive news. This is an excerpt from the article:
“The researchers present their experiment as solid evidence of a so called "negativity bias", psychologists' term for our collective hunger to hear, and remember bad news.
It isn't just schadenfreude (from the German words : Schaden=damage + Freude=joy, it means: pleasure derived by someone from another person's misfortune - bracket is not part of the excerpt), the theory goes, but that we've evolved to react quickly to potential threats. Bad news could be a signal that we need to change what we're doing to avoid danger.”
No one can say it better than Steven Pinker in his genius article on The Guardian:
“Make a list of all the worst things that are happening anywhere on the planet that week, and you have an impressive-sounding—but ultimately irrational—case that civilization has never faced greater peril.”
The subconscious stores everything even if we don’t know it.
According to 26 experts our subconscious stores every event, occurrence, emotion or circumstance from before we were born (i.e. from the womb... nothing metaphysical). It also fails to distinguish between real and imagined. If we keep contaminating our subconscious with negativity it will inform our future decisions influenced by this content, be it real or the product of a movie. It records everything without judgement but everything in our subconscious is part of who we are.
There are some people who avoid watching the news for this exact reason. What if we could give these people a news channel they can watch?
listening247 lives and breathes agile product development. In the world of agile a prototype is created first, to serve as a proof of concept. If the prospects seem good, then with multiple iterations it gets improved into an Alpha-, then Beta-version, and ultimately it is launched in production mode.
This is exactly what we suggest we do in this case as well. This article is almost like an open strawman proposal to all news media.
How about listening247 starts by doing what it does best: find good news online. We can create a social media daily harvester of posts with positive sentiment, in a few different languages, using our proprietary Generative AI.
We will then implement an automated stage of curation based on topics and report them on a daily newsletter and micro-site in a number of fixed columns as well as top stories and features. Here are some assumptions on the columns and features:
Let’s first see the kind of content we will get from social media listening and whether we think it has potential as a Digital TV channel. Should that be the case then maybe we can go to a VC fund or a like-minded charity foundation with this business idea and give it a go. Please contact us on X or email me with your thoughts.
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listening247 answer:
listening247 is a technology company in the market research sector offering platform access as well as end-to-end market research services to Agencies, FMCG, Retail, Financial Services, Telecoms, Tourism & Hospitality, Healthcare, Automotive, Government & NGOs.
listening247 answer:
listening247 answer:
The pricing for social intelligence is based on product category, language (not country) and period covered. A rule of thumb is that an average product category is defined by up to 12 competitive brands. These 12 brands are used as keywords for harvesting from the web. The frequency of reporting and the delivery mechanism also have an impact on cost.
The pricing for any text or image analytics processing and annotation through an API, regardless of data source, is charged per annotated post or image.
listening247 answer:
Yes, for many different product categories and languages and in different formats e.g. PDF decks, infographics, one pagers and demo dashboards.
listening247 answer:
For Social Intelligence listening247 harvests data from social media and any public website such as Twitter, Blogs, Forums, Reviews, Videos, News and also Facebook and Instagram with some limitations that apply to all data providers.
The listening247 text and image analytics technology is source agnostic and can therefore ingest client data from open ended questions in surveys, transcripts of qualitative research, call centre conversations or any other source of unstructured data.
listening247 answer:
For social intelligence listening247 uses all the available methods to harvest data from public sources i.e. direct APIs, Aggregator APIs, Custom crawlers and scrapers, RSS feeds etc. When doing so listening247 abides by the ESOMAR code of conduct, the law and the Terms & Conditions of the sources.
For client data - see answer to Q6 - the client can share its own data by email, on FTP, on cloud drives or through APIs.
listening247 answer:
For social intelligence yes - as long as the posts still exist online at the time of harvest.
listening247 answer:
Text, images, audio and video can be harvested from the web or taken from other sources (see answer to Q5). listening247 - the listening247 software - does offer the capability of data harvesting from online sources. It provides buzz (word counts), sentiment, 7 pairs of opposite emotions such as ‘Love Vs Hate’, and semantic (topic) analysis. The topic analysis provided is inductive (bottom-up) and top down. Topics can be broken down in sub-topics and sub-topics in attributes and so on. listening247 can also analyse images for objects, brand logos, text (extraction) and image theme (aption). It uses 3rd party technology to turn audio to text, followed by its own text analytics capability to analyse for sentiment, emotions and topics.
listening247 answer:
The listening247 software represents the implementation of years of R&D funded by the UK government and the EU. It includes supervised, semi-supervised and unsupervised machine learning as well as deep learning for data “cleaning”, sentiment, emotions, topics and image annotations. For data “cleaning” and topic annotations listening247 uses a combination of engineered approaches and machine learning. All listening247 custom models and set-ups continuously improve their accuracy. The user can also provide improvements to the supervised machine learning models by adding training data any time.
listening247 answer:
The text analysis is done at document, paragraph, sentence, phrase, or keyword mention
level. This is the choice of the client. The analysis extracts named entities, pattern-defined expressions, topics and themes, aspects (of an entity or topic), or relationships and attributes – and it offers feature resolution, that is, identifying multiple features that are essentially the same thing as the example in the guidance (Winston Churchill, Mr. Churchill, the Prime Minister are a single individual.)
The sentiment or emotions analysis is ascribed to each of the resolved features or at some other level; the user may choose the resolution of e.g. sentiment/emotion and semantic annotation.
listening247 answer:
listening247 provides document level data with the capability to drill through to the posts/verbatims, making it possible for users to verify the accuracy of all the annotations made by the models.
listening247 answer:
In literally all languages, including the likes of Arabish (Arabic expressed in Latin characters) and Greeglish (Greek expressed in Latin characters), since the automated analyses are done using custom models specifically created for the particular product category and language. The only trade-off is that it takes 1-3 weeks to create the set-up that guarantees the accuracy as advertised.
listening247 answer:
listening247 uses its own proprietary software and models to produce all the analyses. It provides fully configured customised models; the end user is not responsible for that training but has the option to participate or improve if they wish to do so.
listening247 answer:
When it comes to social intelligence, limited demographics are available in the meta-data of normally harvested posts - see Q6. Any and all demographics can be inferred/predicted using a custom machine learning model which is trained to classify authors based on the way they write. The accuracy of prediction can be validated by testing it on new annotated data that was not used to train the model.
listening247 answer:
For social intelligence listening247 typically harvests and reports all the posts from all the keywords and sources included. This is called census data as opposed to sample data. Data sampling is only done at the training data generation part of the process when the approach used is supervised machine learning. A random sample of 10% or up to 20,000 posts whichever is smaller is used as training data annotated by humans.
When it comes to sources other than the web, lower samples are needed to train the machine learning algorithms in order to reach the minimum accuracy.
listening247 answer:
listening247 was originally designed for market research purposes (in any language) thus the focus is on data accuracy and data integration with other sources such as surveys and transactional/behavioral data for insights. A few years down the line, it is now also being used for sales lead generation and identification of micro/nano influencers.
listening247 answer:
For social intelligence, listening247 uses a combination of boolean logic and machine learning models to eliminate irrelevant posts due to homonyms. The priority and focus during the set-up period of a social listening tracker is to include all the synonyms (also misspellings, plurals etc) and exclude all the homonyms. Typically the data processed is over 90% relevant i.e. only a maximum of 10% is noise.
listening247 answer:
listening247 offers a money back guarantee for the following precisions in any language:
Recall is usually at similar levels but it is not deemed as important as precision for market research purposes because if we end up with say 50% of all the data (50% recall) the sample is still hundreds if not thousands of times higher than the samples we use to represent populations in surveys.
For image captioning the committed Bleu-1 score is >75%
listening247 answer:
Yes
listening247 answer:
Different users have different definitions of spam. These are identified at the beginning of the project and eliminated during the set-up process described under Q17 by using a combination of boolean logic queries and custom machine learning models. Clients are also enabled to flag and remove spam themselves should they find any.
listening247 answer:
Yes absolutely. Even more than that since listening247 complies with the ESOMAR code of conduct which is stricter than the local laws.
listening247 answer:
listening247 abides by the ESOMAR code of conduct and not only stays informed about changes with the laws and terms & conditions of specific sources it actually gets actively involved in making sure the clients/users of these services stay well informed (e.g. the initiative to create this document under the auspices of ESOMAR). listening247 uses the highest standards of security in storing and transmitting data.
listening247 answer:
The codes of conduct and industry standards including the ICC/ESOMAR International Code on Market, Opinion and Social Research and Data Analytics; the Market Research Society in the UK (MRS).
listening247 answer:
By abiding to the codes of conduct mentioned in Q23. In the occasions when an author of a post is contacted by listening247 the etiquette of the medium where the post was found is strictly followed and the medium/platform allows such contact and is usually expected by the authors of such posts. No offers are made unless the author indicates acceptance in the process of following the contact etiquette.
listening247 answer:
Only data from public sources are shared with users without masking. If the data is not from a public source then it is only offered in aggregated form or masked.
listening247 answer:
Most of the data in social intelligence is public but in the occasions when the data is owned by the client or is sourced from a non-public source cutting edge security measures are used. listening247 uses secure sites and encrypted transmissions to protect the data in its custody.
All the communication from and to listening247 happens through a Secure Sockets Layer (SSL) to ensure the encryption of communication client-server. In addition our hosting partner has successfully completed multiple SAS70 Type II audits, and now publishes a Service Organization Controls 1 (SOC 1), Type 2 report, published under both the SSAE 16 and the ISAE 3402 professional standards as well as a Service Organization Controls 2 (SOC 2) report. In addition a PCI (Payment Card Industry) DSS (Data Security Standard) Level 1 certificate has also been received. The users are welcome to carry out their own audits.
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The missing link in CX measurment is...Social Intelligence!
CX stands for customer experience for those of you who are not familiar with the acronym. There are more related acronyms that are sometimes used interchangeably: EFM (Enterprise Feedback Management), CEM or CXM (Customer Experience Management or Measurement). Measurement happens first, management follows. Titbit: managing the experience without measuring it first is like driving a car in complete darkness.
Forbes says that customer experience is the "cumulative impact of multiple touchpoints" over the course of a customer's interaction with an organisation.
A Wikipedia definition for EFM is: “Enterprise feedback management is a system of processes and software that enables organizations to centrally manage deployment of surveys while dispersing authoring and analysis throughout an organization…
…Modern EFM systems can track feedback from a variety of sources including customers, market research, social media, employees, data collection, vendors, partners and audits in a privatized or public manner.”
This article is definitely about modern EFM systems, and the main point here is that it is not enough to use surveys as the operative word in the EFM definition above.
There are many sources for customer experience feedback but they can be classified in three main groups; these are data from:
The 3rd group is what we refer to as Social Intelligence.
The mainstream customer experience measurement vendors focus on surveys after each experience type; the really good ones also measure visits or sales, recording feedback on digital kiosks, using call centers, chat apps and even face to face interactions to record, measure and integrate.
Here is what the excellent ones do: they do a 360 degree measurement by including and integrating social intelligence on top of everything else. Although social Media is mentioned as a source of information by most, it is very rarely included in a customer experience measurement program. When it is actually included, it is limited by their language analytics capability, as the few tools that do this can only carry out text analysis - for sentiment in particular - in English, or translate another language into English and then annotate the data with sentiment. Most importantly, when they offer it their sentiment and topic accuracy is lower than 60%.
EFM is another case of disruption of a very specific part of market research: the stakeholder assessment. Unfortunately, the market research sector has been very slow in adapting to change, with the result being that tech companies have mushroomed in the areas of DIY, Social Media Monitoring, Mobile and EFM/CXM.
An organisation cannot replace their customer loyalty and employee engagement programmes (run by a market research agency), with a flashy software platform from one day to the next. Our suggestion to users is to always ‘connect the dots’ to combine multiple sources of information, i.e. “marry” state of the art technology with experienced analysts and data scientists; only then, can true insights be synthesized. A machine cannot do that on its own - even if the best machine learning algorithms are employed, utilising the best methods of predictive analytics.
Some multinational market research agencies that decided to fight back for what has been theirs are Nielsen, Ipsos, Maritz and Kantar. The tech companies that are leading the push and growth in this sector are the likes of: CloudCherry, Medallia, Qualtrics, Evaluagent, Usabilla, Aptean, Critizr, Verint and so on.
Where should all the feedback from all the different sources “live” so that it can fulfill its destiny? Its destiny being to drive customer commitment and loyalty that is. Typically it should “live” on an online dashboard. Is it straightforward to integrate social intelligence to surveys? Nope. It takes a good thorough understanding of how customer satisfaction/loyalty and experience surveys work alongside the unsolicited online posts. Simple things are misunderstood and lead to confusion if the vendor is not experienced in all data sources. For example, someone who posts online is labelled as a “respondent” and their post is labelled as a “response” - which implies there was a question to begin with, when it is rather about an author and a post expressing an unsolicited opinion or fact.
Data integration happens at multiple levels:
The feedback delivery mechanisms vary and it is best to use a combination of the following:
Everything described in this article boils down to one idea: delight is the sought after customer experience by the customers and the service organisations alike. It is rather simple when you think about it: understanding what the customer wants, needs and likes is a precondition to delight; without social intelligence an important piece and multiple experience touchpoints are missing from the full picture. As always please do reach out with your own feedback on X or by email.
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Article titles are very important, they can make or break an article, so I usually consider multiple before I choose one. Here are the ones that did not make it this time:
On to our topic, there is a phrase I first heard from a friend in Poland - who probably got it from Arthur H. “Red” Motley or Peter Drucker (the famous business author), or even IBM’s Thomas Watson: “nothing happens until someone sells something”. These were all business people and they obviously meant this phrase solely in a business context, but I think the phrase is true in a much broader sense.
Think about it; if you are a kid you sell how much you want that toy to your parents, if you are a teacher you sell the importance of education to your students, if you are a priest you sell your religion to your community, if you are a politician you sell your plan to the voters... you get the idea. “Selling” goes beyond trading products or services for money. So when Drucker says “nothing happens...” it looks like literally nothing happens; these business gurus have elevated themselves to deep philosophers by sharing this universal truth with the world, probably unknowingly.
This article is about generating good sales leads that easily convert to a sale. It is about finding leads deep in the sales funnel, ideally just one small nudge away from buying.
I usually try to provide some structure to make it easier to scan and decide what to read in more detail; In this piece I will describe the lead sources, then talk about the lead generation process, spend some time on conversion and finish off with how purchase intent on social media or on the public web works.
At the highest level there are two types of leads:
People often refer to inbound lead generation as “pull” marketing; in other words the lead finds an offer and proactively reaches out to a sales organisation inquiring about the product or service with the intent to buy. An inbound marketing plan involves great SEO (search engine optimisation), SEM (search engine marketing, otherwise known as paid search), affiliate marketing, brand ambassadors - ranging from nano influencers to celebrities, as well as other types of advertising (online and offline) and digital content sharing. This of course applies to online leads, the 100% offline purchase path is simpler: watch a TV ad, go to a brick and mortar store and buy the product.
Outbound lead generation, also known as “push” marketing, involves reaching out to the prospects with an offer, whether using email campaigns or cold calling; I personally prefer warm calls. Companies usually use their own CRMs to contact existing clients and leads, they may buy lists of possible leads who consented to being contacted, or they may even hire companies that already have access to relevant leads and pay them to contact them with their offering.
The cool way to say lead generation process: Lead Gen. Incidentally, I recently learned from a much younger person that it is not cool to say ‘cool’ anymore. Go figure.
The lead generation and conversion process is simple:
Admittedly finding, qualifying, and contacting a lead is the easy part; the difficult part is to nurture the lead and actually sell something.
I have seen claims that between 7 and 11 touch-points are needed to go from an unaware lead to a converted one to a customer. This is why multi-channel marketing makes a lot of sense.
Imagine a B2B lead receiving a cold email with an offer from an unknown company; they don’t open the email but the subject line and the company name sort of registers in their mind. Then on the same day a sponsored post appears in their Facebook newsfeed - now they are trying to remember why this company name is familiar; when the same post appears in their LinkedIn and Twitter feed they start wondering which company this is, and what they do exactly. Up until this point we have four touchpoints and counting. A week later they receive another email from the same company, only now they actually open it because they are curious… ’these guys are everywhere’ is the dominating thought in their mind. Touchpoints six and seven are articles that come up when the prospect “googles” the company name. In case you are wondering there is no magic in appearing in your leads’ social media feeds; it’s all a part of the advertising options each platform offers. All you need to find and target them is the email address of the lead (which you should already have if you included them in your email campaign) that will be matched with the email address they used to sign up.
This lead was nurtured to the point that it now becomes an inbound lead when they land on let’s say the listening247 website and request a free online consultation. From then on, a request for a proposal is solicited, one is sent, negotiated and closed. Job done!
A typical path to purchase or sales funnel starts with awareness, then interest, followed by consideration, intent, evaluation and purchase (see Fig. 2 below).
Google search, which is considered a source of qualified and mature leads, may indicate interest or consideration on behalf of the person searching. Both funnel stages come before purchase intent, and thus if there was a way to identify all the leads who intend to buy from a product category before intent is explicitly expressed, it brings us a big step closer to completing a purchase. The deeper we go in the sales funnel the more difficult it is to nurture and convert leads to the next stage; this is why if we can find a lead expressing purchase intent online it saves us tonnes of money and resources needed to nurture them from awareness to the next point in the funnel.
All you need is a social media listening tool that can accurately find people who express purchase intent online and has a machine learning capability to score the leads appropriately (see Fig. 1). Here is how it works:
As ever I am keen to engage in a conversation with you to compare notes, answer questions and ask some as well. Do contact me @L247_CEO or by email.
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Without a doubt it pays to be data driven. McKinsey Global Institute reports that data-driven organizations are now 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable as a result.
Some organisations have business intelligence and market research departments, and many others don’t. Those who don’t are typically driven by the decisions of their senior management. What this means is that sometimes a handful of people work together to arrive to a consensus decision, and sometimes a single person - the CEO or the Head of a Department - makes a call based on their judgement alone. Unless their name is Steve Jobs or Jack Welch (well known autocratic leaders who got more things right than they got wrong), chances are their judgement or intuition or gut feeling (call it what you like) will not get them optimal results.
CEOs come and go, some have great intuition, some less so. Some are extrovert and some are “level 5 leaders” to use a definition from Jim Collins’ book ‘Good to Great’, the sequel to ‘Built to Last’… which by his own admission should have been the sequel.
HBS Professor Michael Tushman says so. Being data driven encourages a culture whereby gut feelings and anecdotal information do not carry a lot of weight.
There are many sources and types of data. There are structured and unstructured data (such as text, images and video clips). There are facts and there are opinions. We can get opinions by asking questions in surveys and focus groups, preferably through online communities* or by analysing unsolicited opinions using social media listening, social intelligence or social media monitoring, however you prefer to call this new discipline. And then we have our own data from accounting; sales, profit, expenses; you get the idea.
If all these data are available to all employees and everyone’s goals (including the CEO’s) are measured using these data, then we get public accountability through transparency. This point alone is enough reason for a company to decide to become data driven!
*Did you know you can create and fully customise your own online community? Start your cost and commitment free trial.
When a business decision is based 80% on data and 20% on gut feeling then it will be fast and confident. Companies that take a long time to debate and decide on something, and then even longer to execute are overrun and crushed by their competitors.
When decisions are not based on mood and appetite but on data they tend to be consistent and inspire stability to all stakeholders.
Curiosity is a vital characteristic of innovative people. Data availability allows the curious to find answers to questions. The more the questions and answers the more the successes.
Abundance of data on its own will not do the trick. We need people to turn the data into information, then into knowledge and then into insight and hopefully foresight.
Talking about foresight, predictive analytics is what sometimes produces it. Without data predicting anything becomes a shot in the dark.
Becoming a data driven organisation is not possible from one day to the next. We need data, ways to analyse it and a data hungry culture with people that are data literate and buy into the concept. It takes commitment from the CEO and the management team and it takes perseverance. Unless there is objective data that supports a decision, regardless of how much we think we know what action to take, we should resist to take it and we should always ask the question: what data supports this decision?
