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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: |
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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