Customer Value and Profitability- Business Intelligence
The key areas of analytics are to validate the co-relation between the customer parameters and customer value, building predictive analysis on impact of your actions on customer value and finally tracking the actual impact of your actions on customer value.
Customer Value and Profitability KPIs
- Customer Value (current value, long-term value, lifetime value) against expectations.
- Customer Profitability against expectation.
- Customer Value Distribution against expectations.
- Customer Profitability Distribution against expectations.
- Sales value and volume with new vs. existing customers.
Measures-Facts related to Customer Value and Profitability
- Customer Value (Long-term, Current and lifetime)
- Customer Cost
- Customer profitability
- Sales volume to existing customers
- Sales value to existing customers.
- Number of Up-sell and Cross-Sell Transactions
Dimensions for Customer Value and Profitability
Following are the Foundation Dimensions and attributes which you can use to create your analytics and reporting base.
- Product-wise customer value and profitability
- Channel-wise customer value and profitability
- Customer segment
- Customer Value Band
- Customer Profitability band
- Tenure band of customer since acquired
- Tenure band of customer since last transaction
Analytics for Customer Value and Profitability:
Key analytical insights you can have on customer value and profitability:
- Time Trend Analysis of customer value enhancement: It tells us how well are we moving up the value enhancement. You can do time trending analysis on dimensions of customer-segment, product-segment, location and channel.
- Validating your actions to customer value: Once you have taken your actions to enhance the customer value, you would like to validate the actual impact on the ongoing basis.
Business Modeling/Data mining examples on customer value and profitability:
- Validating the association between a customer parameter and the customer value: This is a subject of data mining OR analytics, whereby you come out with the level of co-relation between a customer characteristic and its impact on the outcome. Before you come out with a business model to project life time OR long-term value, you will need to have this validation done. This can be done by analytics (taking the parameters one by one OR in simple combinations) OR using a data mining tool, to come out with the associations.
- Data mining on identifying actions to impacts on customer value: You need to find association between your actions and predicted impact on customer value. For example- the expected customer behavior of high value customers, if you increase OR decrease price.