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Unit 4: Customer Retention, Acquisition and Expectation

that are custom-produced for each customer is moving closer to reality. Existing channels such Notes
as the Internet or outbound telemarketing also allow you to be more specific in the ways you
target the exact wants and needs of your prospective customers. A significant drawback of the
modelling of individual response behaviour is that the analytical processing power required
can grow dramatically because the data mining process needs to be carried our multiple times,
once for each response behaviour that you are interested in.
How you handle negative responses also needs to be thought out prior to the data analysis
phase. As discussed previously, there are two kinds of negative responses: rejections and
non-responses. Rejections, by their nature, correspond to specific records in the database that
indicate the negative customer response. Non-responses, on the other hand, typically do not
represent records in the database. Non-responses usually correspond to the absence of a response
behaviour record in the database for customers who received the offer.
There are two ways in which to handle non-responses. The most common way is to translate all
non-responses into rejections, either explicitly (by creating rejection records for the
non-responding customers) or implicitly (usually a function of the data mining software used).
This approach will create a data set comprised of all customers who have received offers, with
each customer’s response being positive (inquiry or purchase) or negative (rejections and
non-responses).
The second approach is to leave non-responses out of the analysis data set. This approach is not
typically used because it throws away so much data, but it might make sense if the number of
actual rejections is large (relative to the number of non-responses); experience has shown that
non-responses do not necessarily correspond to a rejection of your product or services offering.

Once the data has been prepared, the actual data mining can be performed. The target variable
that the data mining software will predict is the response behaviour type at the level you have
chosen (binary or categorical). Because some data mining applications cannot predict non-
binary variables, some finessing of the data will be required if you are modelling categorical
responses using non-categorical software. The inputs to the data mining system are the input
variables and all of the demographic characteristics that you might have available, especially
any overlay data that you combined with your prospect list.
In the end, a model (or models, if you are predicting multiple categorical response behaviour)
will be produced that will predict the response behaviour that you are interested in. The models
can then be used to score lists of prospect customers in order to select only those who are likely
to response to your offer. Depending on how the data vendors you work with operate, you
might be able to provide them with the model, and have them send you only the best prospects.
In the situation in which you are purchasing overlay data in order to aid in the selection of
prospects, the output of the modelling process should be used to determine whether all of the
overlay data is necessary. If a model does not use some of the overlay variables, you might want
to save some money and leave out these unused variables the next time you purchase a prospect
list.

When Prospects Become Customers

As the focus of your program shifts from acquisition to retention, the goals become those of
establishing loyalty, advancing the relationship and building a sense of community, participation
and affinity. As with prospecting, however, the data strategy should also help determine whether
customers do or don’t meet the company’s criteria for retention.
Look for factors that will feed back into the acquisition cycle to reduce marketing costs, increase
success rates or both. Look for trends in the length of customer relationships and determine if

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