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How a Pet-Care Retailer Used MaxDiff to Optimize Its Cat Food Assortment

Quick summary

  • Customer: A pet-care retailer seeking to improve its cat food assortment, merchandising, and category growth.
  • Research need: Determine which formats shoppers truly prefer when required to make trade-offs, rather than relying on ratings where several options can all score well.
  • Compeers AI capability: Seamlessly guided a robust MaxDiff workflow by confirming seven choice sets, six options per set, and 11 formats; detecting tasks, attributes, and dataset columns; mapping Best, Worst, and Not Chosen values; and requiring review before analysis.
  • Analysis delivered: Preference shares, Best-Worst scores, zero-centered utilities, credible intervals, subgroup comparisons, and preference-based segmentation among 500 cat owners.
  • Main result: Soft treats led with a 27.8% preference share, 43.3% Best selections, and only 15.3% Worst selections. Kibbles, dry food, and wet food formed a clear second tier.
  • Retail implication: Give treat-like formats more assortment depth and visibility, retain familiar options selectively, and reserve low-preference formats for targeted needs rather than broad distribution.

What business question did the retailer need to answer?

The retailer wanted to determine which cat food formats deserved priority across its assortment, shelf space, digital merchandising, and promotions. Eleven formats were tested, including dry food, wet food, gourmet food,, different flavors.

MaxDiff, or best-worst scaling, was appropriate because it forced respondents to choose the most and least appealing options within each task. This revealed relative preference more clearly than a standard rating question.

How did Compeers AI support a robust MaxDiff project?

Compeers guided the user through setup before running the analysis. The workflow confirmed the study design, automatically detected the task-and-attribute structure, displayed the associated dataset columns for review, and asked the user to map the values representing Best, Worst, and shown but not chosen.

This validation reduced the risk of analyzing incorrectly defined tasks or response codes. After approval, Compeers generated preference shares, utilities, credible intervals, attribute rankings, demographic comparisons, and a preference-based segmentation.

What did the MaxDiff research reveal?

Wet food was the clear category leader. dry food captured 17.5% preference share, followed by soft chewable foods at 15.9% and hard gourmet foods at 12.1%. Other formats and pastes each received less than 4%, indicating limited broad-market appeal.

The research also identified two shopper segments. Traditional Format Seekers represented 46.8% and favored familiar, straightforward formats. Food-Driven Enthusiasts represented 53.2% and preferred formats that felt enjoyable and rewarding for the cat.

How could the retailer use the results?

The retailer could build its core assortment around soft treats, allocate growth space to dry food and chewables, and tailor merchandising by shopper segment. Less-preferred formats could be reserved for targeted use cases.

The wider study also supported a tiered price architecture: 48.2% of owners were willing to spend $25 to $34 monthly, while 55.4% were open to $35 or more. This provided a basis for mainstream and premium assortment tiers, targeted education, sampling, and more disciplined product-selection decisions.

A pet-care retailer used Compeers AI and a MaxDiff study of 500 cat owners to optimize product preferences, shopper segments, and pricing strategy.

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