Predictive Analytics Services & Case Studies
Our Analytics Services Our Analytics Services include full customer cycle modeling. Predictive/Adaptive modeling Full customer cycle Modeling including acquisition, retention, upsell/crossell, risk, reacquisition etc. Predictability analysis Multi-perspective data mining Dynamic data mining Customer profiling and segmentation Media/Campaign effectiveness analysis Data collection strategies Data quality assessment Analytics readiness assessment Analytics opportunity assessment and road mapping Analytics tools evaluation Implementation
Analytics and Predictive Modeling Case Studies
New Student Acquisition Improving ROI in For-Profit Education A multi-campus vocational/technical institute knew it was overspending to get qualified students enrolled and was losing too many between enrollment and start date A set of models that modeled the full student lifecycle from acquisition of optimal leads through enrollment, commencement of classes and graduation. Solution separated out low performing lead sources, leading to millions of savings in student acquisition cost. A dynamic, cutting-edge model to adjust start probability based on student-counselor interaction, leading to better use of admissions counselor time, better forecasting to capacity and a significant improvement in the start right of high quality enrollees
Single-line Retailer - Does Our Radio Promotional Strategy Move Product? A medium-sized chain of nutritional products stores wished to understand if radio advertising drove incremental sales for featured products and for all products. Cross-analyzing ad frequencies, station formats and day parts with product sales led to findings that radio moved the needle for one gender segment, but not the other, and that the ad spend would be more effective if it better fit the seasonality of the business. The analysis established a platform for testing whether the better choice was to reduce overall ad spend, or reinvest it into the most productive time slots and station formats.
Grocery Chain - Using Buyer Behavior Analysis to Direct Growth Strategy A leading grocery chain was struggling to get actionable information from their data, which was housed in silos and limited their ability to make decisions. In just eight weeks, we were able to integrate the data, create an effective customer segmentation and increase the accuracy of customer behavior predictions by 15%. The model outputs were used by the client to alter their expansion plans, saving millions in potentially bad product and store set decisions.
Major Toy Retailer - Does Loyalty Equate to Profitability? A large toy retailer was losing share to larger, full-service general merchandisers and wanted to better understand customer loyalty. Combining attitude & usage research data, syndicating industry information and transactions data samples, we identified that highly loyal customers not only spent less than the average toy consumer in their stores, but were also price sensitive and therefore susceptible to poaching by lower-priced retailers. The client used the insights to reevaluate their ad targeting strategy to begin to stem the tide.
Catalog Customer Acquisition Using Lifetime Value Metrics to Guide List Purchasing A consumer catalog retailer with a multi-million customer housefile was using a single customer lifetime value assumption to measure the performance and guide the purchase of rental lists for new customer acquisition. We combined our database management skills with our understanding of direct marketing financials to build a multi-dimensional lifetime value reporting and assessment tool. This allowed for scoring of list sources and estimation of the future lifetime value of the prospects presented. The company was able to better eliminate non-productive list source and improve their negotiating position with brokers.
Insurance A major international insurer sought to improve customer lifetime value through. Their existing business model failed to meet even modest sales goals, let alone achieve cross-selling objectives. Rich, actionable profiles to effectively identify the right customer segments, and predict behavior within them. Models identified those most likely to purchase a given type of policy. The client realized a 26% improvement in sales conversion over previous best practices, lowered cost of acquisition and increased retention rate.
Health Care The USA 's leading provider of health care risk models was exposing itself to unnecessary risk because its actuarial approach was allowing high risk policyholders to slip through. They needed a solution to better predict which individuals could be classified into the top 0.5% of the most likely to lead to high medical costs. Our models enabled senior management to improve predictions with 53% accuracy a significant improvement over the old method. The models provided a direct impact to the company s bottom line - a net savings over $11 million.
Technology Distribution A Fortune 500 technology solutions company sought to reduce high selling costs associated with constant re-quoting of opportunities Our model drew out the characteristics associated with successful and profitable quotes and was able to identify 85% of quotes that would go on to close from just 30% of those scored
Identity Theft Protection A leading provider of identity theft protection services was experiencing a high attrition rate among its subscribed member base We built an early-warning model that identified over 90% of potential attriters within the top 50% of scored cases This allowed the company to focus retention team resources on the most problematic cases, and projected to avoid $10 million in annual subscription losses