Compass Data - Bring Your Data to Life

By: Compass Data  09-12-2011
Keywords: Data Mining

  1. Identify 'at-risk' segments.   Analyze data to find any at-risk customer segments, and quantify the financial damage caused by retention issues.  In this phase we can guarantee that you will learn something new about your customers and their relationship to your company. 

  2. Get the customer's perspective.  Gain valuable insights into customers' decision to leave or discontinue purchasing.  We seek out the customers' own words to reduce reliance on internal anecdotes and hypotheses.   Conduct interviews, surveys, or questionnaires of lapsed customers. 

  3. Place a value on retention.  We design a simple model to show the cost of acquiring versus maintaining customers in your business.  The cost of losing customers is often hidden - we bring it to light so options to improve retention can be prioritized.

  4. Design actions to solve retention problems.

  5. Implement. 

Actions to solve retention problems are usually selected, designed, and implemented by our customers.  We assist however by validating the selected course through customer feedback, and by tracking results to plan.

We are always looking for the best technologies or methods to identify retention issues and receive feedback from customers. 

  • Data analysis - data mining, statistical analysis, financial analysis, segmentation.  We bring together all kinds of data so that it tells us a story. 

  • Customer interviews - whether phone based, in-person, or via web based survey, we pick up the subtle clues into how your customers 'vote with their feet'. 

  • Market Analysis - reviewing your competitor's offerings to understand the available options your customers act on.

Some specific technologies we are using currently include:

Business Intelligence (BI) Systems 

  • SQL Server:  Integration Services (ETL) , Analysis Services (OLAP) , Data Mining

  • Microsoft Access:  user interfaces, Visual Basic programming

  • OLAP User Software:  Proclarity, Crystal Analysis, Crystal Enterprise

Statistical analysis, data mining

  • SPSS:  Decision Trees & base components

Keywords: Data Mining