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Restaurants tap big data for growth, customer engagement | 7wData

Last updated: 12-06-2018

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Restaurants tap big data for growth, customer engagement | 7wData

Restaurants tap big data for growth, customer engagement
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Big data is making a significant impact on decisions made in the restaurant industry. A saturation of restaurants, combined with less loyalty from Gen Y and Z, and the ubiquity of foodservice in the retail and omnichannel environment are contributing to an industry that is highly competitive. It’s an environment in which apparent customer traffic growth for one restaurant is only achieved at the expense of another, and one in which customer and transaction data analysis has become more valuable.
But how can all this data be tracked? Mounds of data are pouring in from point of sale systems, mobile app platforms, email campaigns, social media channels, review sites, in addition to real-world experiences and feedback, to name just a few.
New Gen Apps, an international IT solutions provider with offices in Mumbai and New York, has identified several uses of big data analysis for foodservice and food retailers. There are several high-impact uses: to analyze data to customize the guest experience, to create offers and marketing campaigns with a higher value proposition, to understand social sentiment and to learn from such feedback using text data analysis, and to create predictive analysis to create better food delivery experiences for customers.
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Predictive analysis can also improve the drive-thru experience, enhancing preparation for large orders, based on previous drive-thru lane traffic patterns. Data analysis that can be used to customize a mobile ordering experience, for example, can be personal and automated so that customers’ next most desirable item is offered to them automatically on a future mobile app transaction. This type of interaction is done through a market-basket analysis and can be part of recurring programming.
One of the most common uses of data analysis has been social sentiment analysis. Natural language processing, for example, is a common tool applied to this type of data collection and review. Multi-unit operators can learn much from social sentiment, and the analysis can be scaled to fit the size of the restaurant chain. In today’s world, customers are easily able to review a restaurant and be a critic. For restaurants, the popularity of review sites and the large presence of feedback tools within search engines and aggregator websites makes the task of analyzing data quite daunting.
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“Forget the dread of a famous restaurant critic’s piece hitting the newsstands. Restaurant brands now must treat every diner as a restaurant critic. When you wow them, they’ll light up the social landscape with praise,” notes a Social Intelligence Restaurants 2018 report from NetBase, a social analytics technology provider. While some vocal restaurant owners have felt outrage and scorn at the types of comments they are seeing on review sites, and this reaction has prompted many a news headline, other savvy managers are extracting that information to operate better restaurants.
NetBase notes that this type of online information is freely available and can be used as a tool to give customers exactly what they want and culling widely available feedback and sentiment. Additionally, it can also be used for benchmarking and evaluating competitor performance.
Sentiment analysis can also be used to more easily make big changes to a restaurant operation.
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