Case Study: YUM! Brands

See how xAPI and Watershed's technology enabled Yum! Brands to explore their learning data to determine how employee training impacts store performance.

Business Challenge

Yum! Brands is one of the world’s largest restaurant companies with more than 41,000 restaurants in more than 125 countries and territories. With a focus on developing innovative tools and improving the capabilities of their workforce, the global learning team at Yum! was interested in how they could use xAPI to benefit their overall learning and development programs.

Yum!’s learning team needed to engage in a proof-of-concept to explore how xAPI and a learning record store (LRS) could fit into their overall training and development strategy and, ultimately, help reinforce their business.

Data Challenge

Because Yum! didn't have the technology to fully explore their learning data, they also couldn't determine if employee training was impacting store performance. To test xAPI and Watershed, Yum! created a pilot project to pull in data from three data sources relating to KFC employees at individual stores:

  1. completion of know-how resources (i.e., employee training materials)
  2. usage of the TeamKFC portal
  3. store performance metrics as represented in an internal system called MERIT

The premise of the project was to investigate any connection between employees using know-how resources and the impact on store performance.


Watershed worked with KFC to outfit the TeamKFC portal to send xAPI statements to Watershed in real time. Data about know-how resources and store performance was pulled into Watershed via a CSV import prior to analysis of the data.

This data was presented in a range of reports investigating usage of know-how resources and the TeamKFC portal versus store performance. Reports looked at data across all KFC stores and compared an individual store’s performance in terms of usage and achievement of performance metrics.


By the end of the proof-of-concept, the Yum! Team not only experienced xAPI’s ability to collate data from a range of sources into an LRS, but also how an LRS can create meaningful insights from that same data.

Now that Yum! has proved the concept, they’re evaluating next steps and considering ways to utilize xAPI and Watershed for future high-level projects.

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