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, Inventory and product distribution optimization,  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

case-studies

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
  1. 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
  1. 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
  1. The Challenge
  • Fashion retail chain facing challenges with demand forecast and implement assortment based on each store characteristics and demand peculiarities
  • Operational challenges due to high volume of data and inventory to analyze
  • Planning and forecasting challenges with large number of sku’s with different not interlinked attributes

The results

  • Develop a Cloud Analytics platform for Demand Planning and Inventory Planning & Replenishment that provides forward looking analysis to support the Category Manager/ Product Manager to do following:
  • Place a right-size order well before time
  • Optimize the stock levels for individual products & sub-products across different cities/ stores Plan distribution & re-distribution
  1. The Challenge
  • Large furniture rental / leasing with hundreds of locations and over 1,000 SKU’s needed to better optimize their forecasting of SKU’s by location based on demand or in resale and end of life optimization

The Results

  • Improved forecasting results by 20% through our forecasting model, identifying which product attributes such as item, price bracket, fabric, shape, color contribute to the demand of the products.
  • Created a Capital Budgeting Optimization Model for the resale of the unused SKUs in the aftermarket.

Our key areas of focus and expertise are:

  1. Customer Retention models identifying Customer Lifetime Value and Propensity to Quit
  2. Inventory optimization and SKU forecasting
  3. Customer Up-sell and Cross Sell models.  Identifying the Next Product to Buy
  4. Margin Pressure and Optimization.  Defining price optimization and implementing what if Monte Carlo Analysis
  5. 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.

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