Databases Reference
In-Depth Information
targeted service offerings with a greater level of sophistication and certainty. Additionally,
with the help of a big data analytics platform, banks are now developing complete profiles
of their customers, mapping customer life events such as a marriage, childbirth, or a
home purchase, which can help banks introduce opportunities for more targeted services
and offerings.
Banks have heavily leveraged customer segmentation analytic techniques to devise
innovative marketing and sales strategies. However, in the Internet age, the same customer
segmentation techniques are turning out to be inadequate. Simplistic segmentations by
annual income or funds on deposit are becoming outdated. Two households that exhibit a
lot of similarity relative to deposits may actually turn out to be totally different with respect
to home equity, credit cards, prepaid debit, etc. Thus, segmentations have to be done at
micro level to obtain a much more accurate prediction of needs, attitudes, and buying-
spending behaviors. In order to do the fine-grain segmentation, you will need to collect,
organize, and correlate a variety of data sources consisting of customer transaction data
and customer interaction data. You need to find the right balance between data derived
internally and externally. External data can be used to identify customers' financial
triggers, pinpointing those who might be new to a geographic area or in the market for
a particular financial product. There are data markets that provide balances held at all
financial institutions down to the household level and across a variety of categories:
deposits, investable assets, investment balances, net assets, mortgage balances, etc. You
need leverage of these data sources to develop sophisticated segmentation models to
quantify the market share and target the right households to cross-sell.
Let's discuss how the big data analytics platform shown in Figure
3-6
below can help
in optimizing operations and improving insights from data. As we move from batch to
real-time process integration in financial services, the requirement for real time analytics
and an enterprise-wide view of banks operations becomes more acute. Operations
managers need to know about the state of operations regardless of client, transaction
type, delivery channel, or (in the case of payments) settlement method. In addition
to reliable and timely transaction processing, the data produced from the transaction
can be more valuable and provide key business insights for operations, marketing, risk
management, etc. However, collectively if we look across the various business operations
a bank does, the data is very-large volume, often exists in business silos, is structured and
unstructured, and exists inside the bank and on the internet (as evident from the various
data sources shown in Figure 3-6.
