For the reason that COVID period started and prevented folks for an extended time period from eating in at eating places, shoppers in all places 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! Manufacturers’ digital and expertise groups invested considerably within the improvement or enhancement of such apps for our eating places, together with KFC, Pizza Hut, Taco Bell, and The Behavior 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 would have liked 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 certain that it was RED, we would have liked metrics. We wanted to know if the app was certainly making the method of ordering fried hen simpler. Have been folks happy with the app? Have been there recurring patterns amongst prospects who beloved the app (or didn’t love the app)? Did sure app launch variations carry out higher than others?
These have been among the many questions we needed to discover solutions to. Though each Apple and Android present entry to shopper rankings and evaluations, they don’t present a deep dive into what evaluations imply for a product. So, we turned to Domo, and the instrument that has change into our secret sauce: Jupyter Workspaces.
Jupyter Workspaces offers us the power 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, then again, takes textual content evaluation to the subsequent stage, 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 mechanically refresh your information. By utilizing Python and Domo in tandem, KFC can now do the next:
| Python | Domo |
| Import buyer evaluations immediately from Apple and Android shops and mix them right into a single dataset | Schedule the Jupyter Pocket book to mechanically refresh every day |
| Use Pure Language Processing fashions to determine the shopper’s emotion towards the app in every overview | Create a dataset that may be shared throughout the group |
| Extract necessary metrics similar to when the overview was written and the consumer’s star-level score | Illustrate outcomes and metrics in a fascinating means, utilizing firm branding and interactive visuals |
All of those options contribute to deriving insights for KFC’s cell app workforce. Now, the workforce can determine what works for purchasers and what doesn’t, and domesticate concepts for future app enhancements—which all goes to indicate that when KFC prospects communicate, we pay attention. And that, after all, is essential to long-term model and product success.
