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DATA ANALYSIS - DEVELOPING CUSTOMER INSIGHT
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Key to building and
maintaining a relationship with your customers is the need to
record and analyse all information that stems from that business
relationship.
Assuming the first
hurdle of bringing all relevant data together into a single
customer view has been undertaken, then the next step is to
correctly analyse your data, interpret the results and apply
your findings (the insight) to develop more appropriate and
more highly targeted data-driven marketing strategies. These
will deliver a host of benefits to your business including a
greater return on marketing ROI and a substantial increase in
profitability.
Customer insight
will also keep your business on its planned course and help
spot reasons for any deviation.
Changes in customer
behaviour can often signify deeper business reasons for the
change - a new website, new competition, different suppliers,
customer service issues, etc. - and provided indicators are
set to record key events, then the knowledge gleaned from in-depth
customer analysis will help you keep your marketing and your
business on the right track.
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SO, WHY ANALYSE YOUR DATA?
A marketing
database would usually contain records of all your customers
together with all of the transactions they have made
with your organisation over time. This highly valuable
resource would typically be enhanced with marketing,
profile, survey, suppression and prospect data to provide
you with the deepest customer understanding possible.
However, left incomplete your customer vision will be
blurred!
The saying
that 'the whole is greater than the sum of the parts'
is never truer than in database marketing, as when viewed
in its entirety, this information will help to maximise
the potential of your business and uncover otherwise
hidden sales opportunities that will drive increased
profitability. By using data mining and statistical
analysis, you will be able to:
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Is this how you see your customers?
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Improve your
understanding of the real value and profitability of your
customers,
how it changes and why
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Segment your
customers using their trading history to maximise cross-sell
and up-sell opportunities
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Identify the
most responsive prospect lists and best customers to target
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Increase retention
- spot the factors that predict churn and then develop a
corrective retention strategy
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Spot changes
in customer behaviour to drive 'adaptive' campaign targeting
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Plan more appropriate
communication strategies, with selective messaging to strengthen
customer loyalty
However, customer
analytics is not just a one-off exercise. To reap the rewards,
data analysis needs to be carried out on an on-going basis to
keep track of change and allow you to take the appropriate action
on a timely and a proactive basis. But to analyse your data
effectively and reap the rewards, you need complete and reliable
data, the right know-how and some easy-to-use data interrogation
tools.
Plus - and perhaps
this is the most important part - you need to first define some
SMART marketing objectives.
SMART stands for - specific,
measurable, achievable, realistic and time-bound.
For instance, first create a customer development road map such
as...
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Recruit (acquire
/ win) 'X' new customers within 'Y' months with an average
order value
of 'Z' for 'ABC' products or services
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Improve retention
of high-value customers by 'Y' % over the next 'Z' months
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Increase average
order value by 'Z' %, margin by 'Y' % and average order
frequency by 'X' %
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Reactivate 'X'
inactive / lapsed high-value customers at a rate of 'Z'
per month
... and then develop for each of these an appropriate segmentation
scheme coupled with offers
that you can establish and test
by studying past purchase and behaviour patterns.
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