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Wahoo Fitness: Segmentation and Data Ins...

Venkatesan, Rajkum...

Case

Wahoo Fitness: Segmentation and Data Insights

Venkatesan, Rajkumar; Lopes, Henrique; Yemen, Gerry

M-1025 | | 16 pages Case

Collection: Darden School of Business

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This field-based case describes Wahoo Fitness’s (Wahoo’s) data-collection process, focusing especially on the customer survey it used to gain insights into its current and potential customers. One month before businesses across the United States closed due to COVID-19 lockdowns, Wahoo launched the Elemnt Rival GPS smart watch, which catered specifically to triathletes. The team at Wahoo was excited about the potential of this new category. To decide on next steps, the company turned to its roots: data insights. The team started with quantitative data surveys to identify customer segments and extract insights into other market opportunities. The data uncovered potential sports activity customer segments—competitive, social, and leisure. The team wondered: Was now the time to engage with cycling hobbyists or leisure cyclists? Or did it make sense to expand on the initial success of the smart watch and offer more products in the running segment? The case includes a student spreadsheet with survey data, as well as R code scripts for analyzing customer data and noncustomer data. Students utilize Excel to perform a K-means cluster analysis on the survey data, then use the analysis to determine a product expansion strategy and present their findings as if they were on the management team of Wahoo. It is a practical application of a data-driven process for tailoring product offerings and marketing strategy. This case has been taught at Darden in the Master of Science in Business Analytics program and in second-year MBA electives. It would also be suitable in graduate-level marketing or analytics courses, in addition to Executive Education programs.

1) Understand how to use customer segmentation to explore new markets. 2) Consider the different attributes by which customers can be segmented. 3) Conduct a profile analysis and K-means cluster analysis. 4) Determine when data are and are not sufficient to perform customer segmentation.