Retail Banking

Retail Banking

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The next step to big data: Could Lady Gaga teach banks a lesson?

What do banks and Lady Gaga have in common?

Answer: both see the huge potential of big data to serve their interest. Only Lady Gaga is much better at exploiting it than banks are. In July 2012, her (now ex-) manager Troy Carter and his team compiled the data from more than 31 million Twitter followers, 51 million Facebook fans and from ticket sales to create a database of “hardcore” fans targeted to join the community. This new social media community, 100% focussed on the star, was used by Carter to collect new information about this representative sample of the star’s fan base in order to increase sales.

What lessons can banks learn from this example on how to use customer information?

Lesson 1 – Realise your big data potential
  • In terms of scale of accessible data banks have nothing to envy Lady Gaga. They have by far the biggest potential, holding more data than Google or Amazon, kings of customer intelligence! (Source) Think about it: your bank holds information about everything you buy, when you buy it, where you buy it, how you buy it (i.e. online vs shops).
  • Moreover, banks have probably even more interest than Lady Gaga in using data. Beyond the obvious sales potential through knowing their customer better and offering more targeted services, banks could also cross-reference data to build customer profiles to identify fraudsters and manage credit risks. Bank of America has started to use “Big Data” (a term coined by computer analyst John Mashey in the late 1990’s) to understand their customers across all channels and interactions and detect high-risk accounts. This approach notably enabled the bank to accelerate significantly loan default calculation time: 96 hours to 4 hours for a £10m mortgage book! (Source)
  • Yet in spite of the obvious benefits, and even if banks have started to embrace the world of big data and analytics, they are still lagging behind. Way behind. A Capgemini publication on banks and big data (Big Data Alchemy: How can Banks Maximize the Value of their Customer Data?) states that “only 37% of banks have hands-on experience with live big data implementations, while the majority of banks are still focusing on pilots and experiments”. So let’s ask ourselves: what is holding them back

Lesson 2 – Understand the limits of big data
  • The key obstacle for banks is that they simply don’t have the tools to leverage this huge amount of aggregated data, ranging from operations and loyalty programs data, to social media information. In other words, a lot of data doesn’t necessarily mean better data and is often adding to the difficulty of massive, heterogeneous, scattered, irrelevant or complex data.
  • Carter understood this very well. His idea of creating came from a simple observation: Whilst compiling terabytes of data across various sources has some undeniable value, 10 million likes on Facebook doesn’t equate to 10 million album sales. Actually, what does this data even mean? With such a vast user base, it could mean anything really, from the I’ll-definitely-go-and-buy-the-album-right-now to the I’m-liking-it-so-my-friends-and-followers-can-see-how-cool-I-am-#Iamawesome. Troy Carter therefore knew that he would have to be smarter than this in order to transform these terabytes of data into cold, hard cash.
Lesson 3 – Learn how to ‘milk’ big data
  • In an interview to the South China Morning Post, Carter explains that building a more intimate community of a million of the most loyal ‘little monsters’ is worth much more in dollars and publicity than tens of millions of Twitter and Facebook contacts. enabled the collation of new, first hand, structured information about a representative sample of Gaga’s most loyal fans.
  • Surfing on the Internet, I find that more and more sector experts are predicting the rise of specialised social networks that will rule the world of big data (Source) by providing an incomparable level of insight.
  • What if banks were following the same model?
  • Banking club memberships, which used to be the prerogative of the very wealthy customers of private banks (e.g. Coutts & Co), are now the rising trend amongst high street banks.
  • Could they leverage these existing clubs of like-minded customers to create smaller communities on which to collect data?
The banking industry’s difficulties, and their attempts to make the most of the customer information they have, illustrate a new push in the area of big data. Could it be that the power of the almighty Big Data can be outweighed – or at least limited - by our incapacity of processing it? Will we need to go one step further and be smarter about it? Could smaller communities like become the new ‘Little gurus’ of Big Data?
Also read our Capgemini publication: Cracking the Data Conundrum: How Successful Companies Make Big Data Operational.

Banking Innovation

The sponsor for Banking Innovation at Capgemini Consulting UK is Anuj Kumar, Head of Risk and Regulatory Compliance Consulting. Connect with Anuj via his LinkedIn profile here.

About the author

Christine Chanier
Christine Chanier
Christine is a senior consultant in the Operating Model & Performance Improvement capability of Capgemini Consulting UK. She has five years of consulting experience across sectors (public sector, finance, CPR) and specialises in operating model design. Before joining Capgemini Christine worked for one year for Societe Generale as an equity research intern for the banking sector.

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