James Buckley, Vice President and Director for Europe, Infosys Finacle
Following in Amazon's footsteps, enterprises of all the 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 The push marketing of old has been replaced by a market-of-one approach, where businesses ascertain a customer's needs and try to fulfill them using micro segmentation and />
Another important analytics use case in financial services is the systemic digitisation of customer experience. Here, analytics helps to improve sales and concentrate on the right proposition to the right customer at the right time. But more importantly, it provides intelligence to make sure that the customer can be positively contained within the digital experience planned, without needing to drop into costly interaction channels.
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 crucial role in reducing customer attrition; this
is highly desirable for the bank since retaining an existing customer takes far
less cash and effort than finding and onboarding a replacement.
are going the same way as telecom companies which replaced their high-street
operations in Europe and also the United States with an online presence that relied
on digital distribution and self-service. Consider for example Telefonica and
Tesco Mobile in the UK – have large teams working with active analytics
software precisely to mitigate customer churn and contain costs.
has a huge bearing on the quality of banking experience and it 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 along the way.
the flip side, using analytics to improve experience has become more
complicated within the last two years, after the General Data Protection Regulation
(GDPR) arrived to effect. Financial service enterprises, which could freely
aggregate “anonymised” information, must now seek explicit agreement from
customers 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. Even without the
a standardised analytics approach, banks may be forced to add layers of
intelligence to know what they can and cannot access to develop a customer
is clear customers use different financial service providers for different
needs – credit card, mortgage, insurance, investment and so forth – which means
that even so-called “primary” banks do not have complete customer data to create
an aggregated view. This limits remarkable ability to harness the full potential of
analytics. However, 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).
are looking to increase their relevance by providing lifestage platforms. One
such West European bank is servicing the needs of an ageing population by
creating a platform for end-to-end healthcare requirements from the elderly. By
joining up pharmacies, hospitals, healthcare centres, and specialist
gerontology facilities along with transport, the platform is a one-stop-shop
serving all the needs of an individual.
kind of “life stage banking” will mature in the next five to ten years as banks
try to remain relevant to consumers. Actually, banks don't have a choice because
you will find hordes of non-bank providers, from FinTech and BigTech, bringing both
disruption and disintermediation. To flee that fate, banks must gather deep
insights into customer need, behaviour and context and respond with highly
personalised, customer-centric propositions sourced in the best providers in
the ecosystem. Analytics will not only play a big role in supplying these
insights but additionally in identifying the best-fit services and products available
in the market.
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, for example overdraft, short-term loan or sweep to tide on the shortfall
for example. Five to seven years from now, analytics and AI won't
automate most banking operations, but additionally have the potential to automate
switching between different financial providers, making the relevance
of worth added services ever greater and resulting in commoditisation of
manufacturing in financial services.