Page 97 - DMGT308_CUSTOMER_RELATIONSHIP_MANAGEMENT

Customer Relationship Management

Notes For example, assume that your prospect list (that you purchased from a list broker) was composed
of men over age 30 who recently purchased a new car. If you were to market to these prospective
customers and then analyze the results, any patterns found by data mining would be limited to
sub-segments of the group of men over 30 who bought a new car. What about women or people
under age 30? By not including these people in your test campaign, it will be difficult to expand
future campaigns to include segments of the population that are not in your initial prospect list.
The solution is to include a small random selection of customers whose demographics differ
from the initial prospect list. This random selection should constitute only a small percentage of
the overall marketing campaign, but it will provide valuable information for data mining. You
will need to work with your data vendor in order to add a random sample to the prospect list.
More sophisticated techniques than random selection do exist, such as those found in statistical
experiment design and Multi-Variable Testing (MVT). Deciding when and how to implement
these approaches is beyond the scope of this unit, but there are numerous resources in the
statistical literature that can provide more information.
Although this circular process (customer interaction, data collection, data mining, and customer
interaction) exists in almost every application of data mining to marketing, there is more room
for refinement in customer acquisition campaigns. Not only do the customers that are included
in the campaigns change over time, but the data itself can also change. Additional overlay
information can be included in the analysis when it becomes available. Also, the use random
selection in the test campaigns allows for new segments of people to be added to your customer
pool.

Evaluating Test Campaign Responses

Once you have started your test campaign, the job of collecting and categorizing the response
behaviour begins. Immediately after the campaign offers go out, you need to track responses.
The nature of the response process is such that responses tend to trickle in over time, which
means that the campaign can go on forever. In most real-world situations, though, there is a
threshold after which you no longer look for responses. At that time, any customers on the
prospect list that have not responded are deemed “non-responses.” Before the threshold,
customers who have not responded are in a state of limbo, somewhere between a response and
a non-response.
Building Data Mining Models using Response Behaviour

With the test campaign response data in hand; the actual mining of customer response behaviour
can begin. The first part of this process requires you to choose which behaviour you are interested
in predicting, and at what level of granularity. The level at which the predictive models work
should reflect the kinds of offers that you can make, not the kinds of responses that you can track.
It might be useful (for reporting purposes) to track catalogue clothing purchases down to the
level of colour and size. If all catalogues are the same, however, it really doesn’t matter what the
specifics of a customer purchase for the data mining analysis. In this case (all catalogues are the
same), binary response prediction is the way to go. If separate men’s and women’s catalogues
are available, analyzing response behaviour at the gender level would be appropriate. In either
case, it is a straightforward process to turn the lower-level categorical behaviour into a set of
responses at the desired level of granularity. If there is overlapping response behaviour, the
duplicates should be removed prior to mining.
In some circumstances, predicting individual response behaviour might be an appropriate course
of action. With the movement toward one-to-one customer marketing, the idea of catalogues

92 LOVELY PROFESSIONAL UNIVERSITY