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A look at how the calibration of survey-based choice models can make a substantial difference in predicted demand and revenue resulting from price changes.Calibration of brand part-worth utilities based on in-market data such as that derived from store scanner data can deliver more accurate measurement of price elasticity and better market predictions of demand response due to price changes.
However, coming up with and developing great product ideas that will be talked about can be a daunting task.
Additionally, how can a marketer know that an idea is really “great” and will be talked about by consumers?
Many marketing executives know quite a bit about category management, since it’s a core concept in retailing, or distribution through retail stores or online venues.
The purpose of this article is to share some research and analytic ideas that might prove useful to stimulate your thinking about ways of improving the process of Category Management.
Choice modeling makes it possible to simulate the shopping and decision-making process, with all of the important variables carefully controlled by rigorous experimental design, so that the new product's sales revenue can be accurately predicted.
Equally important, choice modeling helps marketers understand the many variables that underlie that forecast.
Segmentation approaches can range from throwing darts at the data, to human judgment, to advanced cluster modeling.
We will explore four such methods: factor segmentation, k-means clustering, Two Step cluster analysis, and latent class cluster analysis.
The focus of this paper is to examine the major decision-making models, strategies, and theories that underlie the decision processes used by consumers, and to provide some clarity for marketing executives attempting to find the right mix of variables for their products and services.
Most word-of-mouth (WOM) marketers would agree that having a great product is a major key to the success of a word-of-mouth campaign.