In this lecture, two representative techniques of product analytics will be introduced to students because they help a manager with his or her product management decisions.
In the first session, I will introduce K-means cluster analysis because it is flexible and can easily accommodate multiple variables for segmentation. Exploiting customer heterogeneity and differences across segments is a characteristic of a good resource-allocation strategy.
In the second session, I will introduce conjoint analysis because this technique enables a manager to identify product features that appeal to consumers, which can be connected to managers’ product development and product line resource-allocation decisions. By utilizing the obtained partworths or utilities of product attributes and levels, I will introduce three applications – (1) quantifying the trade-offs customers or potential customers are willing to make among the various attributes or features that are under consideration in the new product design, (2) predicting the market share of a proposed new product, given the current offerings of competitors, and (3) determining the importance of any individual attribute in the consumers’ decision processes.
The focus of this lecture is on interpreting the obtained analysis results and applying them into strategic product management decisions rather than conducting in-depth analytics.