Data Mining in CRM - Data Mining Techniques in CRM: Inside Customer Segmentation - page 7

Figure 1.2 The increase in predictive ability resulting from the use of a data mining
churn model.
were appropriately tackled by well-designed propensity models, indicating the
potential for improvement offered by data mining.
The stages of direct marketing campaigns are illustrated in Figure 1.3 and
explained below:
1. Gathering and integrating the necessary data from different data sources.
2. Customer analysis and segmentation into distinct customer groups.
3. Development of targeted marketing campaigns by using propensity models in
order to select the right customers.
4. Campaign execution by choosing the appropriate channel, the appropriate time,
and the appropriate offer for each campaign.
5. Campaign evaluation through the use of test and control groups. The evaluation
involves the partition of the population into test and control groups and
comparison of the positive responses.
6. Analysis of campaign results in order to improve the campaign for the next
round in terms of targeting, time, offer, product, communication, and so on.
Data mining can play a significant role in all these stages, particularly in
identifying the right customers to be contacted.
Market Basket and Sequence Analysis
Data mining and association models in particular can be used to identify related
products typically purchased together. These models can be used for market basket
analysis and for revealing bundles of products or services that can be sold together.