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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.

Why Banks?

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 done SI banking reports in the past, in 2011 and 2012 in the UK and the US respectively and already had a good understanding of the category and the semantic analysis set-up in English 
• In addition to the English reports they had already produced a report in Traditional Chinese for banks in Hong Kong 
• This is a sector that has B2C and B2B services
• Within this vertical there are multiple sub categories that are business units with a P&L and consequently have their own budget to invest in business intelligence such as: retail, corporate, wealth management, credit cards, insurance etc.
• They started an R&D project with an academic insitution on how ESG and especially the G in ESG i.e. governance impacts the business performance of banks. If you are not familiar with the acronym you should read up on it as it is becoming something everybody talks about in the financial services sector especially. E stands for environmental and S for social.  
• The legacy banks in this sector lack innovation and are being disrupted by challenger banks, blockchains and AI. They feel  the urgency (or at least they should) to make some drastic changes to the way they operate.

The Scope of the first Social Intelligence Report:

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.

Post volume justifies tracking bank performance online:

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.

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The first presentation:

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.

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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.

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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?

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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%.

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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!