1. Analyzing customer preferences for Ready to Eat products

Objective: A retail (food industry) client wants to understand and report the impact of customer preferences due to recent modification in processing and other key parameters for existing line of ready to eat products. The survey data is available from CLT study on test and original product

Key challenge(s): ): Understanding Key KPIs for analyzing the food product


  • Step 1- Understand the different analysis (Just about right study, Penalty analysis, Sentiment analysis, liking score comparison etc.)
  • Step 2- Define the key attributes upon which choice of product depend
  • Step 3- Create the reporting layout for product attributes
  • Step 4- From above analysis mentioned, derive the findings for each product flavor
  • Step 5- Highlight the significant differences in product scores to arrive at choice of product

2. Cluster analysis to understand product substitutes and cannibalization

Objective: A top retail food chain wants to conduct cluster analysis on multiple product flavors in order to understand the product substitutes and potential cannibalization in sales.

Key challenge(s): ): Hierarchical Cluster Analysis/ Segmentation


  • Step 1 - Formulate the hypothesis for product substitute and identify the data requirement
  • Step 2 - Create the analytical data set by treating the respondent data and structure the data for cluster analysis
  • Step 3 -Identify the product cohorts with 2 or 3 or 4 flavors which indicates the product substitute with varying degree of substitution
  • Step 4 - Map the product intent with product flavor in order to understand the impact of product substitution
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