We use cookies to personalize content, to provide social media features and to analyze our traffic. We also share information about your use of our site with our social media, advertising and analytics partners. For information on how to change your cookie settings, please see our Privacy policy. Otherwise, if you agree to our use of cookies, please continue to use our website.

Arthritis Foundation Increases Net Revenue

Revolutionize your fundraising

Results

  • 42%

    Reduction in annual list cost investment

  • $4

    Increase in average gift by donors

  • 71%

    Rate of donors giving above minimum threshold, up from 54%

  • 14%

    Rate by which net direct response budget was beat in 2014

Download Case Study

Challenge

While shifting organizational goals from high volumes of donor acquisition (many of whom were low-dollar donors) to a focus on net revenue, Arthritis Foundation was looking for all opportunities to cut unnecessary costs.

Approach

Merkle developed a unique List Source Optimization (LSO) approach that leveraged individual-level data and advanced analytics to make list decisions. The basic concept is to stop paying for duplicate names.

Analytics and optimization are at the heart of the List Sourcing Optimization solution. At a high level, Arthritis Foundation’s LSO strategy was organized into three interconnected analytical components:

  1. Optimization score creation
  2. Simulation and optimization
  3. Recommendation and measurement

Merkle created an optimization score for each individual record within each list. This score was essential for reducing list overlap and determining the optimal mailing depth for each list source. The multi-step process included determining unbiased response rates at the list level. Merkle moved Arthritis Foundation away from a traditional “credited on” attribution approach and focused on an unbiased “appeared on” response attribution , essentially assigning response to each of the lists on which an individual appears instead of the list to which an individual is randomly credited.

The final optimization score was calculated based on a formula consisting of both individual-level response score and data cost.

Discover how we did it. Contact us today.