Yum! Manufacturers’ secret Domo sauce: Jupyter Workspaces
For the reason that COVID period started and prevented folks for an extended time frame…
For the reason that COVID period started and prevented folks for an extended time frame from eating in at eating places, shoppers in every single place have more and more relied on restaurant ordering and supply apps to place meals on the desk for themselves and their households.
To deal with the shake-up in food-consumption dynamics, Yum! Brands’ digital and expertise groups invested considerably within the growth or enhancement of such apps for our eating places, together with KFC, Pizza Hut, Taco Bell, and The Habit Burger Grill.
For KFC-United States particularly, the idea of getting a restaurant ordering app was comparatively new. To encourage KFC prospects to obtain and use the app, we wanted to make sure that it was “related, simple, and distinctive”—or, RED, as our earlier CEO, Greg Creed, favored to say.
However to actually be sure that it was RED, we wanted metrics. We wanted to know if the app was certainly making the method of ordering fried rooster simpler. Had been folks glad with the app? Had been there recurring patterns amongst prospects who cherished the app (or didn’t love the app)? Did sure app launch variations carry out higher than others?
These had been among the many questions we needed to discover solutions to. Though each Apple and Android present entry to client rankings and opinions, they don’t present a deep dive into what opinions imply for a product. So, we turned to Domo, and the instrument that has grow to be our secret sauce: Jupyter Workspaces.
Jupyter Workspaces offers us the flexibility to entry and analyze this qualitative information. In my expertise with different enterprise intelligence platforms, textual content evaluation has been restricted to phrase counts and phrase clouds.
Jupyter Workspaces, however, takes textual content evaluation to the following degree, permitting practitioners to mix Python’s superior Pure Language Processing (NLP) capabilities with datasets proper inside Domo. It additionally permits Jupyter Notebooks to be scheduled as DataFlows to routinely refresh your information. By utilizing Python and Domo in tandem, KFC can now do the next:
| Python | Domo |
| Import buyer opinions instantly from Apple and Android shops and mix them right into a single dataset | Schedule the Jupyter Pocket book to routinely refresh each day |
| Use Pure Language Processing fashions to determine the shopper’s emotion towards the app in every evaluation | Create a dataset that may be shared throughout the group |
| Extract necessary metrics equivalent to when the evaluation was written and the consumer’s star-level score | Illustrate outcomes and metrics in a charming approach, utilizing firm branding and interactive visuals |
All of those options contribute to deriving insights for KFC’s cellular app crew. Now, the crew can determine what works for purchasers and what doesn’t, and domesticate concepts for future app enhancements—which all goes to point out that when KFC prospects converse, we hear. And that, after all, is essential to long-term model and product success.
