Personalizing key driver analysis: individualized insights for more engaging CUSTOMER Experiences
The CHALLENGE
A global manufacturer felt they were not getting full value out of their current key driver analysis (KDA) as results failed to drive an increase in customer engagement.
Seeking better personalization, Burke was enlisted to update the client’s KDA. By creating a machine learning based solution, our team generated individual-level importance scores and formed needs clusters. These enabled the client brand to craft personalized strategies to more effectively address pain points and create more engaging customer experiences.
The APPROACH
Burke leveraged a highly-predictive, random forest model and new advances in explanatory machine learning to provide a more accurate key driver analysis with personalized individual results. The team then parsed the KDA results by core business groups of interest and created a customized cluster analysis to find needs cohorts based on the individual-level results.
An in-depth excel simulator was created for ease of client interaction with the new key driver results. This interactive simulator, paired with a short storytelling deck, enhanced and expanded the client viewpoint by detailing overall core needs, the unique engagement opportunities for the needs cohorts, and opportunities for personalized engagement within key business groups of interest.
Burke’s approach ultimately provided a rich span of customer-based priorities, illuminating areas of interest that the client hadn’t initially identified.
The OUTCOME
Through the comprehensive KDA targeting insights, Burke demonstrated the validity of expanding beyond supply chain considerations. To the client’s surprise, more than four in five customers prioritized something other than the current priority area. Burke then assisted client decision-makers with creating a fresh, personalized targeting strategy to better address all customer needs and broaden their solution set.
FEATURED CASE STUDIES
