Ben-Mercier-Barclays

Benjamin Mercier is Vice President, Senior Digital Analytics Manager in the Retail division of London-based bank Barclays. 10 years ago, he began his career by helping launch the classifieds ads site Vivastreet, before continuing on as a consultant for two different analytics companies (one of which was AT Internet!) in France and in the United Kingdom. Strongly influenced by Anglo-Saxon, “data-driven” culture, he took over the Digital Analytics department at Barclays, where he manages a team of about 10 web analysts and consultants. For Benjamin, big data is not just a passing trend – it’s a concept that must be mastered in order to truly be discussed. Its analytic implications? He sees big data taking hold at the executive level in a few years. In short, Benjamin is a true data expert who shares with us today his multi-faceted experiences – read on.

Your professional experience is impressive. What key elements have guided your choices?

My first adventure in digital at Vivastreet gave me the opportunity to do a little of everything: marketing, affiliation, SEO, ad serving, with analytics at the heart of these different activities. My first experience in this field was therefore very positive.

I must say, from the start, I was captivated by Anglo-Saxon culture following my time in London and New York. It was then and there that I discovered the “data-driven” business model, where you permanently reassess and question things (as opposed to in Southern European countries, where decision-making is based more on intuition). In the Internet era, where a concept is practically obsolete as soon as it’s born, it seems absolutely vital to constantly adapt your business to respond to demand.

After that, the consultant role truly forged my experience. I was able to touch various industries – Finance, Retail, Publishing – in markets that are more or less mature: UK, US, Russia, UAE, Asia… The analytics approach is so different from one country to another that working across different markets is naturally very enriching. With the passing years and diversifying projects, I encountered both operational and strategic challenges. In the end, this great variety of missions, and my desire to drive an analytics project over the long term, led me to where I am today.

What are the particularities of the digital analyst role in the Banking sector?

There are 2 means by which banks directly generate value via their digital channels: sale of financial products (loans, accounts, insurance, bonds, etc.), and reduction of operational costs. Today, the majority of banks concentrate many of their services on the web. This digital shift – if poorly navigated – can nonetheless be risky, as it can have a disastrous effect on service quality, and therefore on the bank’s financial results. It’s in this key area where the analyst’s work is essential. We must systematically reason in terms of service quality and “P&L” (Profits and Loss). Let’s take a very simple example: If a client transfers funds online instead of by phone, it’s seen as a positive outcome, in theory. But for us (analysts), the fundamental question remains: Do we offer the same quality of service in this channel? In the long term, will this affect sales of another financial product? These parameters are important to take into account with each and every one of our analytics recommendations.

The second aspect particular to this industry: everyone needs a bank, no matter their age, gender, or profession. This means that our digital properties (websites, mobile apps) must be relevant to different population segments that are very dissimilar to one another online. At Barclays, we’ve therefore broken down traditional segments to better adapt them to digital activity. This targeting strategy, driven by analytics, comes into play in particular with people just starting to use the Internet for their banking needs. The goal here is to encourage them to use our online resources.

The last aspect particular to Banking: multichannel activity. Our clients are not uniquely online or uniquely offline. Each client interacts with us by way of multiple channels (bank branch, telephone, cash points, website, mobile applications). The analytics approach here is much more overarching than with traditional web analytics. Studying multichannel behaviour allows us to improve customer relations, but also to discover new service opportunities.

How do you see big data: as a true opportunity? or as a phenomenon whose applications remain largely abstract?

If you ask me, big data is not just a passing trend. We hear a lot about it because certain technologies have become very accessible, financially speaking. Today, thanks to products like Amazon Web Services, any company can store data in the Cloud and have access to different storage models allowing for calculation of significant volumes of data. But I still think a misunderstanding of the phenomenon exists with the market players. For many of them, big data sounds simply like “lots of data, therefore more analytic precision, therefore faster performance via the new available technology”. In reality, to master the basic concepts of big data’s 3V (Volume, Velocity and Variety), we have to go much further in our understanding of this phenomenon and determine a few caveats in advance:

  • Rely on analyst knowledge in order to understand big data’s concrete business applications.
  • Call on technical expertise to master data formats and their calculation.
  • Define strategy and goals ahead of time.

To sum up, it’s about perfectly mastering the potential uses of relational and non-relational databases. For banking and insurance, predictive modelling has long existed for calculating risk rates. At Barclays, we’re testing robust solutions like Hadoop, but these initiatives are still rather preliminary. When it comes to Business Intelligence, there is still so much to take advantage of that we prefer to navigate this transition in a progressive manner. In other words: we want to learn to walk before we learn to run.

Personally, I fully understood big data once I understood the difference to Business Intelligence. On one hand, BI relies on analysing behaviour via highly detailed data, in order to study opportunities, difficulties, and trends for business and for driving strategy in real-time. On the other hand, big data counts on the Law of Large Numbers which lets us establish correlations between certain factors (outside of the business) and defined segments for a specific context and time period. BI measures past events and draws conclusions to improve the business, while big data predicts behaviours or opportunities based on recognisable patterns. When applied at the business level, big data functions based on BI learnings. Therefore, in my opinion, without knowing your audience, and without solid preliminary hypotheses, big data is useless. Nonetheless, we can always cite counterexamples like Walmart in the United States, which uses weather data to boost sales of strawberry Pop-Tarts before a hurricane hits. By crossing “Weather” and “Sales” dimensions, they noticed that the appetite for these products was 7 times higher than normal in the week preceding the storm. While there’s no real explanation, the fact remains that each forecasted storm represents a good opportunity to change aisle displays and replace beer with strawberry Pop-Tarts, and boost sales.

Privacy is a hot topic that’s shaking up the digital world. Do banks have a particular approach to data protection?

For banks, this is not a new subject. At Barclays, for example, we’ve collected data in our data warehouse since 1992, and we are under the microscope of the government, the European Union and the Financial Conduct Authority. Privacy is a key argument that we always highlight, and it is vital for a financial establishment. To give an example, Apple recently offered an emergency iOS update to compensate for a security problem with certain applications vulnerable to attack. Barclays was not affected, as we already had a strengthened encryption system in place. Another example: When we put our mobile application tracking in place, our legal team advised us to go much further than just the legal minimum – like setting the default to opted-out, for example.

On the topic of data security, we are extremely vigilant, even internally. As Senior Digital Analytics Manager, one of my prerogatives is to oversee and ensure good “data governance” – making sure that no member of my team can inject or collect personal data in or from our systems. Evidently, this is ensured through very strict processes and regularly testing individuals for sufficient knowledge levels.

What will the role of digital analyst look like in 10 years?

The profession of digital analyst will always remain closely tied to decision-making. And actually, digital analysts have fought hard for this. Today, companies realise that data is one of the most important vectors in decision-making. Analytics departments will therefore become the central point between business, decision-making, finance and operations. Today, certain companies’ decision-makers don’t have a “digital” culture. But tomorrow, the generations who have grown up with digital will take over those key roles. Analytics will establish itself at the highest levels within companies, because data is not meant to simply be collected and presented – it’s meant to be interpreted. Analytics stakeholders will also face increasingly complex technical challenges. I believe that in the end, technology will enable us to automate certain interpretations to generate insights more quickly. I strongly believe that tomorrow’s digital analysts will be represented more and more at the executive level – as are marketing or operations – by a dedicated position such as Chief Analytics Officer.

Any interesting stories to share?

Yes, we regularly work with our company’s BI department to match offline/online data and extract insights for general management. One time, prior to a meeting, we each prepared segmentation models based on our respective data. In comparing our results, we realized that our models were exactly the same! This really illustrates the shifting of our professions toward convergence and complementarity. The borders between digital analytics and Business Intelligence are disappearing.

 

A big thank you to Benjamin for accepting our interview.

You can find Benjamin at the eMetrics Summit in London in October, where he’ll be speaking. He also co-founded and actively contributes to the Digital Analytics Social Club, which you can discover on LinkedIn.

Author

Editorial Manager. Bernard is responsible for the content strategy of the AT Internet brand. He has almost 10 years’ experience in technical and marketing writing in the Software industry. A word specialist, Bernard’s job sees him working on many different mediums including blogs, white papers, interviews, business cases, press, infographics, videos etc. His specialist fields? Marketing and digital analytics content of course!

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