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Predictive Analytics Tackles Yelp Restaurant Reviews

restaurantRestaurants who have struggled with Yelp reviews may find a silver lining in the results of the Yelp Dataset Insight competition. The winners, Eunkwang Joo, James Huang, and Stephanie Rodgers applied predictive analytics to Yelp reviews to produce a series of actionable insights for restaurant owners who want to get better reviews.

The Berkley School of Information Blog explains what the students learned:

The team…conducted their analysis on a dataset of 229,907 Yelp reviews of 11,537 businesses, written by 43,873 different users.

The analysis pinpointed fifty different topics discussed by restaurant reviewers, including service, food, decor, healthiness, value, and wait time. Overall, service was the most important element, appearing in nearly 9% of the reviews.

They made several discoveries as part of their research:

Ratings for food quality and service tended to be positively correlated, suggesting that reviewer’s perception of one may be influenced by their experience with the other.

Reviewers are ten times more likely to mention Groupon in a negative review than a positive one.

Restaurants are rated lower by 2/5ths of a star when the reviewer visited during their busiest time period.

When reviewers discuss restaurant service, Asian restaurants–and especially Thai restaurants–tend to be the most polarizing, with both strongly positive and strongly negative reviews.

All of these provide plenty of ways for restaurants to improve their business, from ceasing to offer Groupon deals to focusing on service.

If you want actionable insights like this for your business, you don’t have to rely on college projects. With the proper training you can launch your own data analytics projects and profit from actionable insights that make your business better.

Ready to get started? Check out The Modeling Agency’s free webinar or predictive analytics training courses, and join the growing list of companies that are making the most of their data.

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