We started this retrospective by looking at how industry challenges and regulatory responses have been increasing analytics capabilities in Financial Institutions in Part I of this series.
Let’s now layer on the introduction of analytics enabling
capabilities with the growth of customer insight over the same period. When we
do distinct phases of advancement in business insight over these three decades
emerge.
Bef
ore the 80s banks ran on volume metrics in branches, with practically no
technology enabled insights into customer relationships. This was followed by a
product-centric management trend (thanks partly to FTP, partly to the influx of
consumer packaged goods marketers in the 80s) and the evolution of the
geography-product management matrix that still prevails in most banks today.
With Basel
I we got better at assessing credit exposure and that information, coupled with
FTP and ABC costing gave us the basic ingredients for understanding customer
"profitability" (more properly Customer Value). There was a big push
in the 90s to develop a 360 degree view of each customer relationship (the Customer
Information File or CIF) and start leveraging FTP, credit loss exposure and ABC
cost to measure customer value to the bank. The insights produced were
profound, enabling us to understand the value dimension of the customer base
for the first time, revealing huge disparities in contribution of different
customer groups. These observations spurned development of target marketing and
marketing strategies that are still dominant today.
Leading firms started to look at transaction streams when
AML requirements (and card fraud losses) forced investment in streaming data
analysis. Banks started to apply business rules to identify anomalous
transactions – statistically outside of a customer’s normal volume, frequency,
location etc. – in an attempt to identify appropriate interventions in
response. These efforts were fruitful, enabling retention and cross selling
interventions to be identified in real time, dramatically increasing the
relevance of the response to events detected by the monitoring tools.
In parallel predictive analytics – forecasting future events
based on history – has grown in importance. Originally used to predict loan
defaults (credit scoring) the same statistical techniques have extended to
identify next likely sale, probability of offer conversion, probability of
account and customer defection and the like. More sophisticated methods of
analysis such as price optimization based on price elasticity of demand at the
micro-segment and individual customer level have been meeting with significant
success in recent years.
All of these techniques have led us to better understand our
customers better. The new frontier in Financial Institution customer analytics
is an extension of these insights to get past understanding the probability of
a customer doing something or reacting quickly to new or unusual transactions,
to understanding why the customer is doing what they are doing.
Understanding “why” is the key to being relevant. This
is the domain of customer behaviour analysis, which requires a new way of
thinking about our data, our tools and our objectives in using analytical
tools.... and this time advances are being driven not by regulators, but by the need to create competitive advantage.
NEXT: Beyond What: understanding customer behaviour
- Dave McNab
