Improving customer value by applying predictive models, segmentation, experimental design, and strategy optimization.
Most companies don’t understand their customers. Which ones will remain loyal? Which ones will defect? Which ones will be most valuable in the future? Companies can begin to answer these questions by data mining and by building predictive models that forecast customer behavior. While these firms have taken a great first step, they still have not identified the best ways to retain and cultivate their most valuable customers.
Companies have massive amounts of customer data, yet they struggle to leverage this information into concrete business benefits. Data analysis efforts are often ad hoc, producing “interesting facts” that typically fail to yield actionable and directly measurable improvements.
Firms can dramatically improve their customer loyalty and profitability by establishing an advanced analytics environment, comprising:
- robust analytical tools
- repeatable analytical processes
- a structured analytics organization
The advanced analytics environment promotes sustained, measurable, and continuous improvement through:
- Data mining and knowledge discovery
- Customer metrics dashboards
- Predictive customer modeling (e.g., churn, value, propensity-to-buy, response, risk)
- Customer segmentation
- Customer strategy development
- Customer strategy testing and optimization
- Operational staffing analysis and resource allocation
- Higher revenues per customer
- Reduced churn for high-value customers
- Increased marketing response rates
- Reduced write-offs and delinquencies
- Decreased customer service costs
- Improved bottom line profitability