Using Data Analysis and Natural Language Processing to Optimize Success in New Restaurants

Posted on Oct 10, 2022

TLDR

Restauranteering is a tough business to get into since it has higher than average failure rates. Here we identify characteristics of highly rated restaurants to optimize chances of success in new restaurants. EDA reveals that population demographics of the location of the restaurant are vital for the success of a certain cuisine type (i.e. education positively correlated with Japanese cuisine). Topic modeling shows that people valued atmosphere more than decor, and most appreciated fresh ingredients for meat, fish, and alcoholic beverages. Customer service and taste were the two most central topics in a 5-star restaurant. Freshness and Vibe/atmosphere were not too related, meaning if a restaurant provides high quality ingredients, they can get away with not having the perfect atmosphere, and if a restaurant has a fun and lively atmosphere, they did not need to rely as heavily on good quality ingredients to score a 5 star review.Ā 

 

 

Figure 0: Ā (Top) Distance map of topics andĀ  their relativity to each other. (Bottom) Tokens that make up each topic.

 

OverviewĀ 

 

Restaurants are one of the toughest businesses to go into, with 26.16% of restaurants failing within the first year, anda cumulative failure rate around 60% by year 3 Ā¹. It has been shown that yelp rating has an effect on revenue, with a 1 star increase leadingĀ  to a nearly 10% increase in revenue Ā². We can use the yelp dataset to identify characteristics of highly rated restaurants to optimize chances of successĀ  when opening new restaurants. Here, weā€™ll look at 5 cities in Arizona with the highest number of 5-star restaurants.Ā 

 

EDAĀ 

 

When exploring the data, we can ask a few intuitive questions: What cuisines do well in each city, and how do theĀ  demographics of each city affectĀ  the success of that cuisine? Figure 1 gives us a baseline of demographics of 5 cities in Arizona. The cuisines with theĀ  highest number of 5-star restaurants in these 5 cities areĀ  BreakfastĀ  & Brunch, Japanese, and Mexican. We can explore each ofĀ  these cuisine categories individually to pick out trends.Ā 

Figure 1: Demographics of Phoenix, Scottsdale, Mesa, Tempe and Chandler Arizona. Mesa and Chandler have less 5-star restaurants per capita than the other cities, with statisticalĀ  significance.Ā 

 

Studies have shown that ā€œBrunchā€ has become wildly popular amongst demographics of young, middle class, white citizensāµ ā¶. If we look at our demographics baseline in Figure 1, we couldĀ  hypothesize that Tempe would be the city most likely to have the highest percentage of successful brunchĀ  restaurants. FigureĀ  2 confirms this.Ā 

 

Figure 2: Ā Percentage of brunch restaurants in Tempe, Chandler, Mesa, Scottsdale andĀ  Phoenix.

 

We can make similar predictions using literature reviews for Japanese restaurants. There is a positive correlation between education levels and Japanese restaurant density. In fact, theĀ  best indicator for the prevalence of Japanese restaurants in America isĀ  education density.Ā  Ethnic composition and income did not have a significant effect on restaurant density ā·. When we look at figure 3, however, we can seeĀ  that TempeĀ  again has the highest percentage of Japanese restaurants- even though Scottsdale has theĀ  highest density of educated residents. Upon inspecting the demographics,Ā  we can see aĀ  clear confounding variable: Scottsdale has much older residents on average. Studies have shown that older AmericansĀ  are much less likely to consume sushi āø,Ā  so when we adjust for this it actuallyĀ  makes senseĀ  that Tempe would have the highestĀ  density of Japanese restaurants.

Figure 3: Japanese restaurant density in Tempe, Chandler, Mesa, Scottsdale and Phoenix.

 

Education levels haveĀ  shown to haveĀ  a negative correlation with density of Mexican restaurantsā·. This is confirmed in Figure 4, with statistical significance. Phoenix and Mesa have the lowest education density, andĀ  the highest Mexican restaurant density.

Figure 4: Mexican restaurant density Tempe, Chandler, Mesa, Scottsdale and Phoenix.

Itā€™s clear that itā€™s important toĀ  take location demographics into account when looking into opening a new restaurant.

 

Natural Language Processing

 

The Yelp data set includes restaurant reviews/comments from users. We can clean and pre-process the review data (removing punctuation, capitalization) and turn the data into tokens. Stemming and lemmatization could be considered, but here it is suggested that might do more harm than good. We can also remove stop words, which wonā€™t be useful to the analysis. Now that we have our cleaned tokens, we can add bigrams to give the tokens more meaningful interpretation (i.e. ā€œgoodfoodā€ vs ā€œgoodā€).Ā 

 

Once the data is cleanedĀ  and preprocessed, we can continue to topic modeling shown in Figure 5. The closer two topics are on the distance map, the more related they are to each other. We can draw some meaningful conclusions from this:Ā 

 

It seems like when it comes to freshness, the most important ingredients to have fresh are meats, fish, and alcoholic beverages.

 

Service with good managersĀ  and good serves was the most vital.

 

People prioritized vibe/atmosphereĀ  over restaurant decor.

 

It was important to have a high range of flavors (spicy, sweet, sauce).

 

Service and taste were pretty central topics in high reviews, but what's interesting is that freshness and vibe were not. Vibe is most strongly related to Service and staff. This could be interpreted that if restaurants have great staff, great taste in their food, and a great atmosphere they might not needĀ  to have superĀ  high quality ingredients (i.e. a dive bar with live music and great wings). The same is true for the freshness topic, if a restaurant has very fresh and high quality ingredients, they might need to rely as much on having a fun vibe and great atmosphere.

Figure 5: (Top) Distance map of topics andĀ  their relativity to each other. (Bottom) Tokens that make up each topic.

 

Insights/Suggested ActionsĀ 

 

Demographics(age, race, education) play a heavy role in rating of a restaurant. Therefore, itā€™sĀ  important to select a location with a highĀ  density ofĀ  peopleĀ  in your target demographic that would be attracted to the type of cuisine you are selling.Ā 

 

Focus budget on building a great customer service team and excellent taste in food. Then, select to prioritize either quality of ingredients or curating a fun atmosphere. This may depend on your cuisine- sushi restaurants should prioritize freshness, while brunch restaurants may wantĀ  to prioritize atmosphere. If your restaurant offers meat, fish, and drinks be sure that these are the ingredients you get fresh.

 

References

 

https://journals.sagepub.com/doi/pdf/10.1177/0010880405275598Ā 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1928601

https://bestneighborhood.org/race-in-phoenix-az/

www.census.govĀ 

https://books.google.com/books?hl=en&lr=&id=igYHBAAAQBAJ&oi=fnd&pg=PA93&dq=brunch+and+college+towns&ots=cXazrTwBSM&sig=bhiTyWZobBte7ZsA9OH4RXXmo6g#v=onepage&q&f=false

https://journals.sagepub.com/doi/full/10.1177/0001699313498263

https://www.tandfonline.com/doi/pdf/10.1080/15528014.2017.1337390?needAccess=true

https://www.keltonglobal.com/recognition/pei-wei-releases-sushi-survey-results-adds-new-rolls/Ā 

https://journals.sagepub.com/doi/pdf/10.1177/0010880405275598

https://journals.sagepub.com/doi/pdf/10.1177/0010880405275598

https://medium.com/broadhorizon-cmotions/nlp-with-r-part-3-using-topic-model-results-to-predict-michelin-stars-ba8ec1b182c2

 

Github

 

https://github.com/LaurenTomlinson/R_Project

About Author

laurentomlinson

Master's student studying Bioinformatics at NYU with a passion for Data Science
View all posts by laurentomlinson >

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