Case Studies – Data

Every hour enough Information and Data is consumed by Internet traffic to fill 7 million DVDs

Companies today are capturing more customer information than ever before

Companies are struggling to make sense of all this data.  What insights can be derived?

What changes should you be making to your marketing campaigns based on this data?

How should your call center interact with customers with all this insight?

The application of Analytics provides you the necessary insights as to what product or service to sell to whom and when.  It will also help identify when an intervention is needed to prevent a customer from leaving your company

Over the past several years we have worked extensively with Customers (Canada, US and UK) and implemented Predictive Analytics Models that are used to identify Customer Lifetime Value, Propensity to Quit as well as Upsell and CrossSell opportunities

For example, using a customer segmentation model, companies through the development of Predictive Analytic Models can identify customers using predictions based on measurements of their past responses, potential revenue, and flight risk they represent.  They can then standardize actions based on a Customers value to your company.  So the most valuable customers might get a personal visit, the next most valuable a sales call and the third most valuable a sales email


The following is a sample of the case studies resulting from these engagements:

1. The Challenge

  • Payment Processor experiencing significant customer churn
  • Needed to identify customers propensity to quit and allow for intervention before they churn

The Results

  • 200% ROI achieved. $2.5MM

2. The Challenge

  • Medical Lab Company needing to increase share of uninsured products to their revenue mix
  • Need to identify  which tests to promote to specific physicians

The Results

  • Increased Revenue Opportunity of $1.9MM

3. The Challenge

  • Services company wishing to reduce churn and optimize revenue
  • Needed to identify customer’s propensity to quit and allow for intervention
  • Revenue optimization  by minimizing risk of churn  and maximizing revenue

The Results

  • Increased Revenue Opportunity of  £2.1MM

Our key areas of focus and expertise are:

  1. Customer Retention models identifying Customer Lifetime Value and Propensity to Quit
  2. Customer Up-sell and Cross Sell models.  Identifying the Next Product to Buy
  3. Margin Pressure and Optimization.  Defining price optimization and implementing what if Monte Carlo Analysis
  4. Customer Service.  Understanding the mound of unstructured data and translating it into actionable insights

Data and Predictive Analytics: a great tool to achieve a competitive advantage.