The original piece, "THOUGHT FOR FOOD: Analyzing the performance of a leading restaurant...", appeared originally on the CrowdANALYTIX blog.
The restaurant industry in the US has changed dramatically over the years, and the need to understand and respond to changing customer preferences is more important than ever. As a result, many restaurants have become adept in collecting and tracking sales and customer survey data to optimize pricing, food quality and guest experience. But is that enough?
More and more customers are speaking their minds on social media and other open channels, sharing their preferences directly through a review or implicitly through location-based applications. This information is more valuable than any data the restaurant can collect internally, primarily because it’s unbiased and allows direct comparison with competitors.
However, although the consumers reveal terabytes of data every day, most of what they share is not necessarily actionable. Knowing what to act upon and knowing whether all possible sources of information have been considered isn’t easy. Social media monitoring tools are good at aggregating information in more appealing visuals, but they don’t tell you what strategic decisions should be taken.
Here, we present a case where we were able to address the following strategic questions for Olive Garden and potential investors in Darden Restaurants:
Our approach included a restaurant level “bottom-up” model where we gathered data across 800+ stores at the ZIP level, covering 200+ factors from various categories, such as:
We posed the problem to the CrowdANALYTIX solver community by launching a contest called Olive Garden Restaurant Comparison Analysis, where we provided the RAW public data we collected. 262 solvers worked over a period of 3 weeks to submit multiple approaches and derive insights.
One of the many challenges with this data was the large number of factors across very few time intervals. To address this, data scientists had to implement methods to reduce the data dimensionality, such as:
Transformation of the data and variables in this manner enabled us to uncover robust and valuable business insights. Further using an inductive approach, we analyzed correlations and conducted business sanity checks, whittling down factors for building an “Industry and Macro-economic Model”.
Unfortunately, we can't discuss the final results here, but we can say that the analysis confirmed some long-held hunches and also revealed some irreversible shifts.