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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
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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