Research Objective

• A leading healthcare technology company needed to identify existing product attributes within a product line that were not optimizing incremental customer reach.
• The focus was on both product features and offers.
• A survey would need to be designed, scripted, and fielded among healthcare professionals.

Scope & Methodology

• A MaxDiff and TURF analysis was required to identify the best/ideal set of product attributes and build an optimal model of product combinations/attributes that would reach the maximum number of unduplicated customers.
• After completing the analysis of the best product attributes, an interactive simulation of customer preferences was designed to observe all possible attribute combinations.

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Value Delivered

• As a result of these analyses, the client was able to build a more effective product line consisting of the most preferred product line while also maximizing customer reach.
• With this information, they determined that 90% of participants were very likely to purchase from this combination of product offerings.

Learn more

• Understanding Relative Importance of Healthcare Imaging Features for New Product Development – A survey + MaxDiff case study
• Optimizing Healthcare Technology Offerings to Avoid Cannibalization | A MaxDiff TURF case study
• Tips for the First-Time Conjoint Analysis Researcher

Research Objective

• A top food company wanted to identify the optimal set of soup flavors for incremental reach. They needed to prevent confusion from creating too many flavors while also preventing brand disloyalty from creating too few flavors.

Scope & Methodology

• A survey with a wide range of product attributes was designed. The optimum number of items per set, sets per participant, and number of versions was decided.
• Based on a MaxDiff analysis, the share of preferences for each potential set of flavors was identified.
• In addition, the TURF analysis identified the maximum reach for each set of flavors.

Value Delivered

• The client was able to understand how many customers would prefer each set of flavors, as well as how large the market could be for each set of flavors. They were also able to identify the incremental reach associated with each set of soup flavors.

Check out other food case studies

• Measuring Consumer Preferences for Successful Ready-To-Eat Development | A Sensory Research Case Study
• Benchmarking Brand Awareness and Competitive Brand Space – A consumer food survey case study
• Sentiment and Content Analysis of Qualitative Consumer Comments – A food survey + text analysis case study