Markets are becoming more aggressive, in order to achieve better growth rates, organizations are moving from a reactive to a proactive approach for predicting business trends. Their key focus is on understanding customer behavior,
analyzing their buying propensities and offering the correct product. Building Data Mining models using the historical data and available attributes, helps in achieving these goals leading to an increased market share.
With a CRM (Customer Relationship Management) implementation the focus of an organization is driven towards building strategy for managing and strengthening customer relationships and getting loyal and long-lasting customers. The
approach hence has to be customer-centric, based on customer insight leading to ''personalized'' handling of customers as distinct entities through the identification and understanding of their differentiated needs, preferences
The two main objectives of CRM:
Customer retention through customer satisfaction.
Customer development through customer insight.
Data Mining models are used to better address the CRM objectives and deliver the right message to the right customer. It involves the assessment of the value of customers - understanding and predicting their behavior.
It is about analyzing data patterns to extract knowledge for optimizing customer relationships.
Data Mining in a CRM framework
Data Mining can provide customer insight, which is vital for establishing an effective CRM strategy. It can lead to personalized interactions with customers, hence increased satisfaction and profitable relationships
through data analysis.
Data Mining can support individualized and optimized customer management through all phases of the customer lifecycle, from the acquisition and establishment of a strong relationship to the prevention of attrition
and the winning back of lost customers. Marketeers strive to get a better market share and a greater share of their customers. In simple words - they are responsible for attracting, developing and retaining
Support for marketing activities
Value-based segmentation: Customer ranking and segmentation according to current and expected/ estimated customer value.
Behavioral segmentation: Customer segmentation based on behavioral attributes.
Value-at-risk segmentation: Customer segmentation based on value and estimated voluntary churn propensity scores.
Targeted marketing campaigns
Voluntary churn modeling and estimation of the customer's likelihood/ propensity to churn.
Estimation of the likelihood/ propensity to take up an add-on product, to switch to a more profitable product or to increase usage of an existing product.