Data Mining in CRM - Data Mining Techniques in CRM: Inside Customer Segmentation - page 11

Table 1.1 The CRISP-DM phases.

1. Business understanding 2. Data understanding 3. Data preparation

• Understanding the business
goal
• Situation assessment
• Translating the business goal
into a data mining objective
• Development of a project
plan

• Considering data
requirements
• Initial data collec-
tion, exploration, and
quality assessment

• Selection of required data
• Data acquisition
• Data integration and formatting
(merge/joins, aggregations)
• Data cleaning
• Data transformations and
enrichment (regrouping/binning
of existing fields, creation of
derived attributes and key per-
formance indicators: ratios, flag
fields, averages, sums, etc.)

4. Modeling 5. Model evaluation 6. Deployment

• Selection of the appropriate
modeling technique
• Especially in the case of pre-
dictive models, splitting of
the dataset into training and
testing subsets for evaluation
purposes
• Development and examina-
tion of alternative modeling
algorithms and parameter
settings
• Fine tuning of the model
settings according to an
initial assessment of the
model's performance

• Evaluation of the
model in the con-
text of the business
success criteria
• Model approval

• Create a report of findings
• Planning and development of
the deployment procedure
• Deployment of the data mining
model
• Distribution of the model results
and integration in the organiza-
tion's operational CRM system
• Development of a
maintenance-update plan
• Review of the project
• Planning the next steps

An outline of the basic phases in the development of a data mining project
according to the CRISP-DM (Cross Industry Standard Process for Data Mining)
process model is presented in Table 1.1.
Data mining projects are not simple. They usually start with high expectations
but may end in business failure if the engaged team is not guided by a clear
methodological framework. The CRISP-DM process model charts the steps that
should be followed for successful data mining implementations. These steps are as
follows:

1. Business understanding: The data mining project should start with an under-
standing of the business objective and an assessment of the current situation.
The project's parameters should be considered, including resources and