Top
CPG Loyalty Solution Increases Usage - Data Square
6868
portfolio-item-template-default,single,single-portfolio-item,postid-6868,mkd-core-1.0.3,highrise child-child-ver-1.0.0,highrise-ver-1.5,,mkd-smooth-page-transitions,mkd-ajax,mkd-grid-1300,mkd-blog-installed,mkd-follow-portfolio-info,mkd-header-standard,mkd-no-behavior,mkd-default-mobile-header,mkd-sticky-up-mobile-header,mkd-dropdown-slide-from-bottom,mkd-light-header,mkd-full-width-wide-menu,mkd-header-standard-in-grid-shadow-disable,wpb-js-composer js-comp-ver-6.5.0,vc_responsive

CPG Loyalty Solution Yields over 200% Consumption Increase 

Challenge                                                                                                                                                                     

CPG is a multi-billion dollar international consumer package goods company. Due to increased competition and volatility in many of its markets, CPG experienced 5 percent erosion in profit. In response, the company embarked on a 5 year restructuring effort to streamline its operations and shrink its product lines. The restructuring plan consisted of eliminating a significant number of unprofitable brands and a focus on driving growth of leading brands, like Brand A, in new markets and enhancing brand loyalty among existing customers. Specifically,  CPG was looking for ways to gain incremental customer revenue for Brand A, while minimizing marketing costs.

Solution

The solution was to identify CPG Brand A users most likely to increase consumption in response to a special relationship marketing program designed to increase loyalty. This was done by creating a test group that received the relationship marketing communications and a control group that did not receive the program. The two groups were equalized based on usage levels derived from volumetric data from supermarket frequent shopper panels across the country. Next, a predictive model was built to rank consumers on their incremental usage based on demographic and geo-demographic characteristics. Profile Analyses were conducted to further understand the composition of this most easily influenced customer set.

Results

The solution enabled CPG to a) Prioritize households on the CPG database for program investment based on their expected change in usage; b) Evaluate expected relative performance of different name sources; c) Target communications to potential high-performance households ranked off of coop or mass media vehicles at minimum cost.