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Yum! Brands’ secret Domo sauce: Jupyter Workspaces

Yum! Brands’ secret Domo sauce: Jupyter Workspaces

Because the COVID period commenced and prevented persons for a prolonged time period of time from dining in at eating places, consumers in all places have ever more relied on restaurant ordering and delivery applications to set foodstuff on the table for by themselves and their households.

To address the shake-up in meals-use dynamics, Yum! Models’ electronic and know-how teams invested significantly in the development or improvement of such apps for our dining establishments, together with KFC, Pizza Hut, Taco Bell, and The Behavior Burger Grill.

For KFC-United States in particular, the strategy of having a cafe purchasing app was relatively new. To inspire KFC customers to download and use the app, we essential to be certain that it was “relevant, simple, and distinctive”—or, Purple, as our prior CEO, Greg Creed, appreciated to say.

But to genuinely be certain that it was Crimson, we wanted metrics. We required to know if the application was without a doubt producing the system of buying fried rooster less complicated. Have been men and women contented with the application? Were being there recurring designs amid buyers who loved the app (or did not really like the application)? Did selected application launch variations execute improved than other individuals?

Those ended up between the concerns we had to obtain answers to. Even though both Apple and Android give accessibility to shopper ratings and testimonials, they do not offer a deep dive into what evaluations imply for a item. So, we turned to Domo, and the tool that has grow to be our secret sauce: Jupyter Workspaces.

Jupyter Workspaces provides us the means to accessibility and evaluate this qualitative data. In my encounter with other business intelligence platforms, textual content investigation has been minimal to term counts and phrase clouds.

Yum! Brands’ secret Domo sauce: Jupyter WorkspacesSample of a Domo/Jupyter Notebook challenge executed on Doordash Opinions

Jupyter Workspaces, on the other hand, takes text investigation to the upcoming stage, allowing practitioners to combine Python’s highly developed All-natural Language Processing (NLP) capabilities with datasets suitable inside of of Domo. It also enables Jupyter Notebooks to be scheduled as DataFlows to immediately refresh your knowledge. By using Python and Domo in tandem, KFC can now do the following:

Python Domo
Import consumer critiques straight from Apple and Android retailers and incorporate them into a one dataset Plan the Jupyter Notebook to instantly refresh every day
Use Natural Language Processing products to recognize the customer’s emotion towards the app in every critique Generate a dataset that can be shared across the firm
Extract crucial metrics these as when the assessment was prepared and the user’s star-stage score Illustrate effects and metrics in a captivating way, working with enterprise branding and interactive visuals

All of these features add to deriving insights for KFC’s cellular app team. Now, the staff can determine what functions for shoppers and what does not, and cultivate suggestions for future application improvements—which all goes to present that when KFC shoppers communicate, we hear. And that, of system, is important to very long-term brand and solution results.