For the reason that COVID period started and prevented individuals for an extended time period 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 handle the shake-up in food-consumption dynamics, Yum! Manufacturers’ digital and know-how 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 wanted to make sure that it was “related, simple, and distinctive”—or, RED, as our earlier CEO, Greg Creed, appreciated to say.
However to really be certain that it was RED, we wanted metrics. We would have liked to know if the app was certainly making the method of ordering fried hen simpler. Have been individuals 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 had 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 device that has change into 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.
Pattern of a Domo/Jupyter Pocket book mission carried out on Doordash Critiques
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 robotically refresh your information. By utilizing Python and Domo in tandem, KFC can now do the next:
|Import buyer evaluations immediately from Apple and Android shops and mix them right into a single dataset||Schedule the Jupyter Pocket book to robotically refresh every day|
|Use Pure Language Processing fashions to determine the shopper’s emotion towards the app in every assessment||Create a dataset that may be shared throughout the group|
|Extract essential metrics comparable to when the assessment was written and the person’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 group. Now, the group can determine what works for patrons 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, in fact, is vital to long-term model and product success.