A Case for Analytical Customer Relationship Management 15
good customers, i.e. ‘those likely to buy a product’, are much more likely to get a
solicitation than those who are not so good, the situation for the good customers is
even more dire. This is really testing the patience of many customers, and thus we
have witnessed a spate of customers signing up to be on ‘no solicitation’ lists, to
avoid being bombarded with unwanted solicitations..
From the viewpoint of the organizations, the situation is no better. Even though
the cost of unit customer communication has dropped dramatically; the impact of unit
communication has dropped even faster. For example, after a lot of initial enthusiasm,
it is now widely accepted that the impact of web page banner advertisements in
affecting customer opinion is practically negligible. On the other hand, the impact of
targeted e-mails, especially with financial offers, is quite high. In essence, each
organization is spinning its wheels in tying to target the same set of good customers,
while paying insufficient attention to understanding the needs of the ‘not so good
customers’ of today, and converting them into good customers of tomorrow. A clear
example of this mutual cannibalism of customers is the cellular phone industry, where
each service provider is constantly trying to outdo the others. “Customer churn” is a
well-accepted problem in this industry.
A well-accepted wisdom in the industry is that it costs five to seven times as
much to acquire a new customer than to retain an existing one. The reason is that the
organization already has the loyalty of existing customers, and all that is required for
retention is to meet the customer’s expectations. For customer acquisition however,
the customer must be weaned away from another organization, which is a much
harder task. Given this, it is crucial that the selection of customers to target is done
with care, and the right message be sent to each one. Given these needs, it becomes
important for an organization to understand its customers well. Thus, one can consider
customer relationship management to consist of two parts as follows:
CRM = customer understanding + relationship management
This equation is not new, since in the classical ‘neighborhood store’ model of
doing business, the store had a highly localized audience, and the store owner knew
practically everyone in the neighborhood – making it easy for him to meet the needs
of his customers. It is the big corporations, serving a mass customer base, that have
difficulty in understanding the needs of individual customers. The realization of this
gap of knowledge has been one of the driving factors for the rapid adoption of CRM
software by many corporations. However, the initial deployment of CRM software
has been for the second part of the CRM equation, namely ‘relationship
management’. As described above, relationship management efforts without an
understanding of the customer can be marginally effective at best, and sometimes
even counter productive.
The approach that resolves this dilemma is the use of data analytics in CRM,
with the goal of obtaining a better understanding of the needs of individual customers.
Improved customer understanding drives better customer relationship efforts, which
leads to better and more frequent customer response; which in turn leads to more data
collection about the customer – from which a more refined customer understanding
can be gained. This positive feedback cycle – or ‘virtuous loop’ as it is often called –
is shown in Figure 1.