Modeling and Predictive Research

When modeling and predictive research makes sense

Modeling and predictive research is used to forecast trends and predict behaviors such as attentiveness, actual consumption, and/or satisfaction to uncover the right product, price, or concept when it is not possible or practical to measure actual reactions in real time. This research is especially useful for product/service optimization, pricing strategy, demand forecasting for new products, market segmentation, and understanding competitive markets.

Discrete Choice Modeling is a powerful analytical technique for understanding what customers want and why they buy.  It uses a modeling technique that reveals the relationship between the probability of choosing an alternative and the attributes or benefits that characterize that alternative.

How modeling and predictive research works

Modeling and predictive research uses mathematical models and simulation techniques to predict behavior.  In Discrete Choice Modeling, the mathematical model is used to predict the change preferences for a particular product or service as a function of changes in feature and price.  Utility values for the attributes and benefits derived from the modeling process are incorporated into a market simulator that allows you to perform “what if” scenarios.  You can change pricing, features, and add or delete products from the competitive frame to determine the impact of a wide range of marketing actions on customer choice behavior.

 

Included in modeling and predictive research are:

  • Conjoint analysis
  • Discrete Choice Modeling