James Buckley, Vice President and Director for Europe, Infosys Finacle
Following in Amazon’s footsteps, enterprises from every industry are using insights from analytics to improve the customer experience. The push marketing of old has been replaced by a market-of-one approach, where businesses ascertain a customer’s needs and try to fulfill them using micro segmentation and data-driven insights for contextual and timely intervention. In addition to customer-specific insights, businesses can offer recommendations and pitch solutions based on data and preferences of other customers with similar buying habits, needs, and interests. For the digital customers of the millennial generation in particular, this Amazonified experience hits the sweet spot.
In financial services, the closest parallel is the experience provided by Personal Financial Management (PFM) solutions. From primitively classifying account transactions into buckets, PFM has come a long way, and current solutions use comparative insights based on consumer history, attitude and behavior extensively to provide personalized financial advice.
Another important analytics use case in financial services is the systemic digitisation of customer experience. Here, analytics helps to improve sales and target the right proposition to the right customer at the right time. But more importantly, it provides intelligence to ensure that the customer can be positively contained within the digital experience planned, without needing to drop into costly interaction channels.
Increasingly, financial service organisations are turning to analytics to prevent digital churn, directing the customer into higher contact channels only when the bank wants to interact. Analytics is playing a huge role in reducing customer attrition; this is highly desirable for the bank since retaining an existing customer takes far less money and effort than finding and onboarding a new one.
Banks are going the same way as telecom companies which replaced their high street operations in Europe and the United States with an online presence that relied on digital distribution and self-service. Take the example of Telefonica and Tesco Mobile in the UK – both have large teams working with active analytics software to specifically mitigate customer churn and contain costs.
Onboarding has a huge bearing on the quality of banking experience and is a key focus area for banks looking to benefit from the convergence of analytics, artificial intelligence and automation for improving experiences. Together, these technologies are helping reduce the onboarding time significantly – to below three minutes – by digitising and automating the entire process, from document collection to customer authentication. Caveat: while this is driving down the dropout rate during onboarding for the most part, it may also backfire if there is friction in the process.
On the flip side, using analytics to improve experience has become more complicated in the last two years, after the General Data Protection Regulation (GDPR) came into effect. Financial service enterprises, which could freely aggregate “anonymised” information, must now seek explicit agreement from customers on how and where to use their data. In such circumstances – where a customer may accept or reject a data sharing agreement depending on context – banks will find it more difficult to build a systemic data analytics and aggregation platform that standardises data aggregation across customers. In the absence of a standardised analytics approach, banks may be forced to add layers of intelligence to understand what they can and cannot access to build a customer profile.
It is clear customers use different financial service providers for different needs – credit card, mortgage, insurance, investment and so on – which means that even so-called “primary” banks don’t have complete customer data to create an aggregated view. This limits their ability to harness the full potential of analytics. On the other hand, because banks, especially in Europe and the United Kingdom, are becoming more open and ecosystem-driven, they can harness external data and third-party relationships to advance their entry into the customer journey to the point of primary need (when they’re still looking for a house or car, for example).
Banks are looking to increase their relevance by providing lifestage platforms. One such West European bank is servicing the needs of an ageing population by developing a platform for end-to-end healthcare requirements of the elderly. By joining up pharmacies, hospitals, healthcare centres, and specialist gerontology facilities together with transport, the platform is a one-stop-shop serving all the needs of an individual.
This type of “life stage banking” will mature in the next five to ten years as banks try to remain relevant to consumers. In fact, banks don’t have a choice because there are hordes of non-bank providers, from FinTech and BigTech, bringing both disruption and disintermediation. To escape that fate, banks must gather deep insights into customer need, behaviour and context and respond with highly personalised, customer-centric propositions sourced from the best providers in the ecosystem. Analytics will not only play a big role in supplying these insights but also in identifying the best-fit products and services available in the market.
In the case of corporate customers, banks can also leverage analytics to inform next best actions, or to compare and contrast alternative funding and liquidity options, such as overdraft, short-term loan or sweep to tide over a shortfall for example. Five to seven years from now, analytics and AI will not only automate most banking operations, but also have the potential to automate switching between different financial service providers, making the relevance of value added services ever greater and leading to commoditisation of manufacturing in financial services.
