Consumer Clustering

Key benefits

  • Segments based on relevant features or similarities that may not be otherwise apparent.
  • Takes advantage of the user’s knowledge and expertise and does so quickly, without taking months to develop special algorithms.
  • Ensures that nothing significant is overlooked.
  • Produces a unique, easy-to-understand visualization of how individuals are grouped relevant to the search criteria

Possible applications

  • Identifying patients who might be good candidates for a particular drug or treatment protocol
  • Examining the characteristics of an online store’s visitors to separate browsers and spenders
  • Analyzing consumer behavior to predict which customers will have the most long-term value…or be the biggest short-term purchasers
  • Analyzing the database to identify what drives the behavior or target groups.

Icosystem’s clustering algorithms identify relevant similarities in consumer characteristics and decision-making behavior from data and discover new, high-potential consumer segments. Traditional clustering techniques are reaching the limits of their usefulness as the wealth of data that companies have about their customers grows exponentially. We’ve solved many of the problems of existing clustering technologies by incorporating an expert human evaluation of similarity between consumers. While our algorithms generate thousands of possible clusters, a domain expert interacts with the system in real time to guide the algorithms about characteristics and behavior that are important.

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