Case Study: Determine Consumption Habits and Preferences for Candy Manufacturer

StatGenius has recently launched “Relate” – a powerful yet easy-to-use module that helps users find hidden relationships across variables. Statisticians might call this “chi square” or “Pearson correlation”, but with StatGenius, you only need to know how to click the Relate button. Our platform does the rest.

Recently, a project manager (“John W.”) at a prominent candy manufacturer was determined to establish a new product line of chocolate candies – a difficult task in today’s health-conscious environment. The researcher needed to determine if any eating habits and preferences relate and combine with each other, in a way that might suggest a marketable product.

Using StatGenius, this project manager opened up the Relate window, and simply selected five variables that were collected during a recent market research project. Those variables were:

  • Amount of Sugar Preferred
  • Preferred Flavor
  • Calories
  • Income
  • Marital Status]

Typically, John would have to pay a statistician to run chi-square and Pearson correlation across the interested variables. That statistician would run the analysis, and report back the results within 24 hours. However, using StatGenius, John was able to run this analysis himself, and have the results and insights back in minutes. StatGenius took all of the variables that were selected, and instantly presented the most notable insights.

John noticed that there was a statistical relationship between the Amount of Sugar and the Flavor that the consumer preferred. For instance, consumers that preferred Watermelon flavor, also preferred a low amount of sugar. At the same time, consumers that preferred chocolate, preferred and amount of sugar that was neither high nor low. Finally, John could also debunk one myth based on true data: the income level or the marital status of the consumer, will not define anything about flavor or sugar preference.