In industries with longstandig customer relationships, you want to know how customer-churns are impacting the customer value. The better you can analyse the future churns within or outside your company, the better you can react to these changes. Typical industries for this analysis are: Telecommunications, Banking, etc.
Predictive analysis will help you to make more meaningful analysis with Big Data and allows you to make forward-looking business decisions. Before starting the predictive analysis, you need to know the components of your value drivers in details. E.g. the churn rate is one important value driver and the financial contribution margin analysis includes the following categories:
Net Revenues
-Direct Costs
Contribution Margin 1
-Acquisition and Retention costs
Contribution Margin 2
-Network Costs
Contribution Margin 3
Customer segmentation and churn modeling are important underlying tasks in order to make relevant predictions.
Churn within your company (within price plans):
In order to predict the churn-likeliness of customers you need to take into consideration the Social Data and Usage Data of your customers. The usage data includes trends of the following pricing components: Voice-Call-Patterns, Data-Usage, Roaming, Wholesale, etc. New market-developments like new handset models need also to be incorporated in your analysis. Social Data includes social network data (comments, recommendations) of your customers. Both (social and usage data) need to be merged and analysed (time line, clusters, regression, etc.).
Churn outside your company:
To get a better accuracy of churns outside your company you need to include the pricing developments of your competitors in your analysis.
Predictive Analysis helps you bringing your Business Intelligence to the next (analysis-)level and predicts how your customers are going to react in your price plans in the near future.
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