Fine-tuned dining

When this UK-wide chain of American-style diners needed to optimise its menu and offers, we helped it gain a true picture of what its customers really want and by doing so we were able to help increase footfall and maximise table bookings.

Challenge

Our client – which runs a network of 80 restaurants across the UK – wanted to exploit data to better understand restaurant use by type and location. It needed to improve the way it communicated to its customers based on real insight. It also wanted to boost profitability by increasing footfall and maximising table bookings. At the same time, our client wanted to improve restaurant front-of-house marketing with specific time-of-day offers.  We had the skills and the business vision to help.

Solution

We got started quickly. We looked at restaurant locations and gathered receipt data for the last twelve months. We then collected promotion data from a customer application and investigated all the ways consumer feedback is collected.

Next, we correctly classified the restaurant estate by groups based on site location including city centre, shopping centre, retail park and stand alone. We took the rest of the data sets, processed and linked them in a SQL database.

By successfully joining this information together, we quickly created a set of metrics by store type, day of week, time of day and promotion.  These metrics included average party size, average spend per person, percentage of repeat business, ratio of drinks to food and customer feedback measured through sentiment analysis. All presented in a dashboard that allowed the client to interrogate the cleansed data itself.

Impact

For the first time, the client was able to tailor its menus to suit the characteristics of a specific store type, helping it to maximise customer value and minimise the possibility of empty tables.  It was also able to tailor its promotions, so customers receive offers fine-tuned for them and the local restaurants. For the client and its hungry customers, it’s a win-win.

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