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. The ability to access and analyze data from many cultures and geographic regions is one way the current machine learning platforms can produce models that deal with global human behavior. In the past, when we were dependent on subject matter experts, predictions were often limited by geography and culture.
Predictive analytics will help you to make more meaningful analysis with Big Data and allows you to make forward-looking business decisions. Before starting a 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:
Contribution Margin 1
-Acquisition and Retention costs
Contribution Margin 2
Contribution Margin 3
Customer segmentation and churn modeling are important underlying tasks in order to make relevant predictions in the telcommunication sector. See example:
• 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.
Trying to measure the success of a group of company based on a limited number of KPI will not lead to a fair, motivational decision. It will lead to an oversimplified judgement of a situation and to shortsighted conculsions.
What can be done in order to improve a decision making process of a group:
• Use as many data sources as you have (big data, data lake)
• Use external data and research data – to bring the external view in the decision making process
• Find patterns relevant for your group or company – the patterns are different for all companies (you can’t guide companies with the same five KPI).
• Look at trends and personal feedback – build a story around the data
• Use IT extensively to measure and analyse data.
• Do not measure the KPI in a traditional financial form – the new KPI is explained in multidimensional ways.
• Automated processes are welcome, but be aware, that for an holistic picture you need good human analysts.
With today’s tools you can evaluate complexity much better than years ago. It’s possible to make better decisions in a more sophisticated world. 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.