Customer Intelligence
I have been receiving brochures
from a international training institution, from which I have never ordered
something, with an old employer & job title for over 18 months. Evidently this
institution has very limited view of my behavior. What a resource waste. Poor
customer view combined with a lack of any customer intelligence, can lead to
poor marketing & operations.
I recently took my car for
repair & service to my standard car service agency. They asked me whether
I needed a service and whether I had replaced the engine belt. I did not
remember details and the very busy chief engineer had to search for about 10
minutes in different company files (yearly as I understood) in order to
find out the car service history.
Customer intelligence is the
ability of an Enterprise to efficiently capture & exploit Customer data, in
order to produce actionable information, supporting its marketing & operations.
Though Customer intelligence is
an impressive term, considered by many to imply
sophisticated processes,
infrastructures & data processing techniques, in fact it is based on a few very
simple concepts.
A
CRM system is the infrastructure that enables the capture
of Customer information, in order to analyze it and develop Customer insight. In
small and medium Enterprises, capture of Customer information may require a
simpler infrastructure (a simple marketing database).
Past Customer behavior is
the strongest predictor of future Customer behavior. Concepts like
latency, recency, RFM (recency frequency monetary value),
form the framework which can be applied to all markets in order to develop
Customer Intelligence.
Traces of customer behavior are
left in the Enterprise in printed or electronic form. These traces when analysed
can improve Customer understanding and enable actions that will safeguard the
relationship with the profitable customers. Customer contact history via all
customer touch points should be recorded systematically, in order to allow for
all relevant behavior analysis.
In order to analyze Customer
behavior and produce actionable ideas, the Customer Lifecycle stages have to be
understood. The Customer Lifecycle differs for each Business and different
product.
Techniques like Customer value
ranking, can be used to develop a treatment of each individual Customer
according to his/her value to the Enterprise.
Recency is widely
recognised as a strong predictor of future behaviour.
Complex math has been proposed as a
solution to estimate parameters like the Customer Lifetime Value, or the Net
Present Value of a proposed marketing campaign. However many of the parameters
proposed are unknown in real world cases, rendering these approaches
infeasible.On the other hand, the estimation of a metric like Customer Ligetime
value is not the business goal but rather the means in achieving the business
goal which is: classifying the Customers according to their value to
the Enterprise.
The approach to identify simple techniques
in order to achieve the business goal is considered to be a critical success
factor in similar customer intelligence or database marketing projects. Jim Novo
proposes the calculation of metrics which meet the aforementioned business goal,
while calculating simple metrics: a relative Customer value instead of an
attempt to calculate the absolute Customer Lifetime Value.
