Enhance Marketing Strategy with Shopper Persona Segmentation Using MaxDiff, Text Analytics, and Cluster Analysis

CHALLENGE

The client wanted to better understand attitudes and behaviors associated with grocery shoppers in the U.S. The results would be used to create marketing segments to repurpose the insights into content, infographics, and website materials to further the agency’s knowledge and thought leadership with grocery retailers.

Least Agree	Statement	Most Agree
	I look forward to grocery shopping
	I’m more likely to visit a grocery store with extensive sanitization procedures
	Organic and natural foods are my first choice
	Religious or cultural beliefs influence the groceries I buy

APPROACH

A national consumer segmentation study:

The study was conducted based on MaxDiff rankings for 50 grocery shopper attributes
20 choice sets for the MaxDiff
Five alternatives (options) for respondents based on survey length and respondent fatigue
Designed R Code: 30 choice set solution for ideal survey “power”
Analysis used K-means cluster analysis to identify commonalities and create clusters
Text analytics were used to understand drivers and key terms used to describe the ideal grocery shopping experience

IMPACT

The outcome was the creation of detailed personas of grocery shoppers to aid with marketing and strategy. A total of 6 segmentation profiles were created from the cluster analysis offering distinct profiles of shoppers with shared psychographic, behavioral, and attitudinal data points. The segments were utilized to enhance the agency’s expertise for future marketing contracts with retailers and aid in prospect/client conversations. The study roll-out aimed to earn backlinks and PR around the topic to assist with journalists and SEO.

3 years ago

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