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direct channels in order to prevent churn (attrition) and to drive customer acqui-
sition and purchase of add-on products. More specifically, acquisition campaigns
aim at drawing new and potentially valuable customers away from the competition.
Cross-/deep-/up-selling campaigns are implemented to sell additional products,
more of the same product, or alternative but more profitable products to existing
customers. Finally, retention campaigns aim at preventing valuable customers
from terminating their relationship with the organization.
When not refined, these campaigns, although potentially effective, can also
lead to a huge waste of resources and to bombarding and annoying customers with
unsolicited communications. Data mining and classification (propensity) models
in particular can support the development of targeted marketing campaigns. They
analyze customer characteristics and recognize the profiles of the target customers.
New cases with similar profiles are then identified, assigned a high propensity
score, and included in the target lists. The following classification models are used
to optimize the subsequent marketing campaigns:
Acquisition models: These can be used to recognize potentially profitable
prospective customers by finding ''clones'' of valuable existing customers in
external lists of contacts,
Cross-/deep-/up-selling models: These can reveal the purchasing potential
of existing customers.
Voluntary attrition or voluntary churn models: These identify early churn
signals and spot those customers with an increased likelihood to leave volun-
tarily.
When properly built, these models can identify the right customers to contact
and lead to campaign lists with increased density/frequency of target customers.
They outperform random selections as well as predictions based on business rules
and personal intuition. In predictive modeling, the measure that compares the
predictive ability of a model to randomness is called the lift. It denotes how much
better a classification data mining model performs in comparison to a random
selection. The ''lift'' concept is illustrated in Figure 1.2 which compares the results
of a data mining churn model to random selection.
In this hypothetical example, a randomly selected sample contains 10% of
actual ''churners.'' On the other hand, a list of the same size generated by a data
mining model is far more effective since it contains about 60% of actual churners.
Thus, data mining achieved six times better predictive ability than randomness.
Although completely hypothetical, these results are not far from reality. Lift values
higher than 4, 5, or even 6 are quite common in those real-world situations that
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