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Want
to understand what differentiates one group of customers from another?
Or
responders from non-responders?
Or buyers of product 'Y' from buyers of product 'X'...
The answer can be
found by profiling and scoring your customers and prospects
One of the
age old problems of customer analysis, segmentation
and predictive (or response) modelling, is being able to apply the results
back to your marketing database to improve targeting and
hence grow customer value.
Another point is to make sure that all relevant information is used in the
analysis process - even where data is
scattered across different systems and locations.
Fortunately
we have an ideal solution. Read on...
What is
scoring?
For those
of you new to the concept of using scores for database segmentation,
profiling and to then select
individuals for targeting, the following introduction should help.
Most
people have at some time applied for an insurance policy or credit of some
kind - a loan, mortgage, finance for a new car, etc. - and during the
application process have been asked questions such as age, length of
residency, how many credit cards, health history, etc.
For each of the
answers given, data will have been entered into a computer program that
assigns a number (or score) to each piece of information. This is usually
termed a score card. The way the numbers are then added together will depend
on the formulae used, but will greatly influence the decision to give you
what you are requesting.
The
important thing to remember is that whilst each separate piece of
information may in its own right not be seen as important and might otherwise be ignored, only when these items are
combined does the resulting information and its score become significant and
predictive.
A
predictive model would usually be created by a statistician and used to
compare the characteristics (or profile) of individuals who are known to
represent, for example, a good vs. bad risk. This produces a formulae and
computes a value (or score) for each individual. Depending on how high or
low the final score is, the lender or insurer can determine the most
appropriate offer for that individual and be fairly confident in the degree
of risk.
The
picture to the right shows an example of how important various pieces of
information are in predicting who might respond to a specific type of
mailing or email campaign.
Direct Marketing
In direct
marketing, to find the most likely responders for your offer could well
involve making hundreds of separate or combined counts and selections, and
then only using
only the information you believe is important - i.e. maybe missing
key predictors!

However, by using a scoring process, the selection task
can be greatly simplified. Each individual can be allocated a single score
based on their propensity to take the kind
of action you desire.
There will never be a single model or formulae that meets all your targeting
or profiling needs however.
A separate model and scoring formulae
may need to
be created to optimise the targeting and response to each different offer you
make, or to achieve the results you desire. And remember, customer needs' change over
time as do the activities of your competitors. What works well today may not work so well in the
future, so the art is to continually test, learn and refine to get the most
from your marketing spend!
Profiles
Profiling in market targeting is used to
compare the distribution of a group of individuals - based on their
purchasing, behaviour, etc., characteristics -
relative to other
individuals on your
customer or prospect
database.
Once a
profile has been created, a marketer can then build predictive models
based on the results of that profile.
Predictive models are used
to score and segment your marketing database to, for example, find prospects
that ‘look like’ your existing high value customers. This process helps you
test and purchase only the most responsive lists.
The model reports to the
right
illustrate potential gains, revenues and profits.
Applications
Typical
applications for scoring and profiling
include:
Selection of
look-alike prospects (i.e. prospects who match the profile of selected
customers)
Using scoring to grade and
select the best prospects from third party lists. See also profiling
with
TRAC geo-demographic data
Using scoring to grade
prospects to prioritise contact strategies by mail, e-mail, telemarketing
or face to face
Using different analysis
selections, base marketing priorities on: financial
risk or likelihood to churn
(lapse or defect), respond, purchase, upgrade, etc.
Profiling responders against
the base of all those marketed to in a test campaign, and score prospects
in order to prioritise who to select for the roll-out campaign
How Tech4T can help you
Developing
models, profiles and scores typically requires extensive analytical or
consultancy projects, but Tech4T have the technology and skills to greatly
simplify the task.
We can merge and consolidate your data which can be supplied to us in almost
any format, undertake the analysis, present the results, and importantly
append the outcome to your database as a set of 'easy to use' indicators
which can then be used to make targeting selections. We can also
provide an on-line 'Targeting
Workbench' where you can conduct your own analysis and
then turn the results into new strategic and tactical direct mail and e-mail
campaigns.
Targeting Workbench Step-by-Step
Analysis Guide
Analysis Services
See also...
Customer Reactivation
Data
Planning / Data-driven Marketing
Free
Calculators
Animated E-Surveys
TRAC geo-demographic data profiling
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