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How analytics and data science can help restaurants become more profitable

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Most of the time, restauranteurs cannot put a finger on whether it is the food or the ambiance or the crowd or the service or the mood in the restaurants that makes them popular. But data science can decipher what works for your business.

ave you ever wondered why out of the two seemingly similar restaurants with similar kind of menu and location, one does extremely well while the other struggles to survive? Every restaurant owner wants to unlock that magic combination of menu, timing, price, customer engagement, staff and supplies in order to keep clicking and generating incremental profits, while delighting the customers each time. But as the fast and casual dining landscape evolves, it has become harder to do business without understanding your customers deeply and connecting with their core values. Analytics and data sciences can now make behavioural insights actionable, and with digital targeting and personalisation capabilities, you can make your guests like you more than your competition.

How can you put advanced analytics to use to understand customer expectations and gratify them?

How can restauranteurs put their time, energy and investments in the right places?

Here are some top tips:

Infer the motivation behind each visit and target the right segments of customers

No customer visit to a particular restaurant outlet, in a particular street, at a particular time is by fluke; it is a result of a combination of various touchpoints that the customer experienced consciously or sub-consciously before they chose to step into your restaurant and not the one across the street. As you must know by now, one size does not fit all. Restauranteurs must collect, analyse, and test options to ensure customers keep walking in. This requires analysis of customer behaviour, so that you can create behavioural segments that isolate the causal drivers behind their visits such as timing, menu choices, spends, and promotion response patterns.

Let’s study Harry. He visits a quick service restaurant thrice a week on an average, usually orders breakfasts and lunches on weekdays and occasional dinners, finishes the food in under 20 minutes, spends $7 average per weekday visit and $12 average per weekend visit. Given the above information, we can infer that he is probably single, works somewhere nearby where it’s convenient for him to catch a quick meal and probably needs to get back in time after breakfast or lunch break. As you already know, Harry is not the only one with such attributes. Segmentation classifies behaviour like Harry’s across all your customers and identifies people with similar patterns, and therefore similar needs and life/work situations. This segment of people can be targeted with similar engagement strategies and tactics to generate more powerful results.

Analyse gaps in customer need fulfillment to uncover new opportunities

Data science can help you identify needs that are not fulfilled by your present offerings. Apart from improving your current operations and practices, you can also study how to get the most of what you already have. Is there any scope to improve your margins with the same set of customers? What is the size of this unrealised opportunity? What configuration of your business can achieve this? Would it be profitable to do so?

Let’s look at a leading US fast food chain. They traditionally served lunch and dinner only. Analysis showed that there was a large number of loyal guests (in restaurants around office areas) who visited during evenings for a takeaway order that served one person. The insight that there were many single customers who packed dinner from the restaurant, encouraged the restaurant chain to offer another meal option to capture more share of the palate, and so they decided to launch the Breakfast Menu in select restaurants frequented by such customers. The restaurant chain streamlined the menu, meal combinations, labour and the supply chain to meet the breakfast needs. And, luckily for the restaurant chain, they saw a 12 percent revenue increase that could be attributed to this new meal coverage. But would you really call that luck?

Analyse gaps in your menu offering and optimise it for your audience

A perfect menu is the holy-grail of any restaurant. Picking the most appropriate items, pricing them right, creating combinations that work well with the customers, modifying the menu according to seasons, and adding new items frequently to keep the menu fresh are some of the important challenges that data sciences can solve. Analytics can help look at the patterns in ordering and consumption data to identify new ways to package existing menu offerings or targeting/promoting a new occasion, in order to uncover new demand.

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