“For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%” – Andrew Hogue

Did you know this insight?

Imagine if you have this and tens of other such insights that have a direct impact on your business. This is the power of restaurant analytics and the impact it can have on your business. The commoditization of hardware and software has made capture and analysis of data easy and economical. Also, due to increasing competition and rising customer expectations, it has become increasingly important to capture data and personalize the customer experience and use data for business performance improvement.

The restaurant industry is one such industry where competition has increased and at the same time, customers have become even more demanding. With the onset of cloud kitchens, increasing investment from private equity and emergence of food delivery apps, the competition in the space has become fierce for all sorts of players in the market, be it full-service restaurant, quick service restaurant or a well-recognized chain. In such a scenario, data analytics can play an important role in creating a differentiating factor for a restaurant. Better knowledge of business, personalized customer experience, reduced wastage and pilferage and targeted marketing are some of the key areas where data analytics can help in either increasing revenue opportunities or reduce the expenses.

We have listed down 8 top data analytics use cases that are essential for any restaurant business for data-driven decision making.

1. Forecasting for Restaurant Business:

Forecasting is an essential part of any business. Based on the sales forecast and customer inflow, things need to be planned to provide a smooth experience to customers. In the restaurant business, forecasting can be done at three levels:

A) Sales Value:

Forecasting how much are you going to earn in the next three days or next week or next month is essential to plan your budgeting activities and manage your P/L effectively. Usually, this is high-level forecasting for the business as a whole. There are multiple forecasting techniques one can use, the simplest of them being linear regression. The more complex are non-linear regression models, neural networks and others.

B) Customer Visits:

This is again essential to understand when (day of the week and time of the week) your customers prefer to visit. You need to understand what day of the week and what time of the day is preferred by your customers. You can optimize your staffing requirements and inventory accordingly. In the restaurant industry, this would mean forecasting table turns by day of the week and time of the day.

Below chart gives an idea about the visit of customers – split by day of the week and time of the day.

The above analysis provides us with insights into the visit patterns of customers. What day and what time is preferred by customers – combining this data with order details and customer details [Refer Use Case 8 – Customer Segmentation in this post], you can go a level deeper and identify which segment of customer prefer kind of food and at what time they prefer to visit.

C) Delivery Orders:

With the emergence of food delivery apps, most of the restaurants are trying to improve their top line by encashing on the orders placed by customers through these food delivery apps. Predicting how many and what items will be ordered in any given hour will help in reducing the overall order time, thereby improving customer experience and hence, customer loyalty.

We have discussed different kinds of forecasting which are useful for the restaurant business. One important point to note here is that there are too many external factors that could impact the accuracy of your forecasting model. Some of the external factors that could have a significant impact on your restaurant business are the weather, office holidays, school/college holidays, exams, weekday/weekend, among others. Due care should be taken to include all such factors in your forecasting model.

2. Menu Engineering:

Menu engineering refers to creating the right mix of menu keeping into consideration the customers’ choices and preferences, and profit margins. Based on order history, customer feedback, pricing and other factors, the restaurant may identify the food items ordered and liked by customers. Menu engineering can be done to the level where you identify which items are sold in breakfast, which are sold in lunch or dinner. The menu can be altered in a manner that the items which are not sold or sold rarely during dinner time are removed. This will help in reducing the wastage of food at the end of the day, thereby, reducing cost. Below is a chart which shows a similar kind of analysis.

Key insights that can be derived from the above analysis:

S. No.	Insight	Key Questions to Answer to Act
1	Sale of Risotto is extremely low throughout the day	Is it leading to a lot of wastage and hence, loss? Should we discontinue the item?
3	Sale for Bruschetta, Caesar Salad, Pasta, Pizza and Virgin Mojito is very during the period 9:00 AM to 12:00 Noon	Should we discontinue these items during these timings? If these are high margin items, can we give discounts or promotions to customers to order more of these items?
4	Omelette, Sandwiches and Coffee are preferred items during the morning time	Should we create a combo of omelette and coffee or sandwich and coffee? Should we add more of egg dishes in the breakfast menu?

There are tons of other useful insights which could be gathered from this chart. That’s what this analysis one of the most important analysis for any restaurant business.

3. Customer Feedback Analysis:

Analyzing customer feedback in the restaurant business is as essential as anything else in the entire operations. Customers tend to post photos and reviews on Instagram, Facebook and restaurant-aggregator websites. These reviews need to be properly mined and analyzed for customer’s preferences, feedback, likings and dislikings. You may have an account on Facebook, Instagram, Twitter and others but are you reaching out to right customers? Are you listening to your customers? There are multiple tools available in the market which can help you carry out detailed analysis on social media data.

4. Location Identification for New Store Opening:

Rising real estate costs combined with mushrooming of different restaurants have made it difficult to identify and afford the prime locations for your stores [Real estate cost for a restaurant business is between 5-10% of gross revenue]. In such a situation, it becomes essential to identify alternate locations where your target customer segment is present, as well as rental expenses, are not as high as prime locations. For example, if your restaurant targets office goers who have their lunch outside on a daily basis, then you should identify locations where new offices are opening up or new business parks are being set up. Initially, the population in that area may not as high but the benefits of the low rental cost and low competition will take care of it.

5. Sales Cannibalization Analysis:

If I open a new store within 2 miles of an existing store, will it impact the sales of my existing store? If yes, how much? Answering these two questions is the crux of sales cannibalization. It’s not just analyzing the impact of new stores, but also considering the impact of new product launches on existing products. There are no specific models to carry out cannibalization analysis, but you can use statistical approaches (such as t-test) to compare the KPIs (such as sales, average value per order, average number of orders per day, etc.) to see if there is statistically significant difference before the new store and after opening of new store.

6. Staff Optimization:

Staff cost for a restaurant business is around 20-30% of gross revenue – that’s huge. Almost one-third to one-fourth of your gross revenue goes into labour cost. Imagine if we were to optimize this cost and lower it by 2-3% with better planning (and not impacting customer service), it has a direct impact on your EBITDA (bottom-line). There are multiple considerations into optimizing staff, most important being full-time vs contractual workforce. This is more of a business decision and depends on the kind of restaurant service you are into – fine dining vs quick service vs café. No matter what it is, you need to play to both kind of workforce and maintain a balance that your cost is controlled, and customer service is not impacted.

7. Bundling of Items:

You must have seen McDonald’s offering a combo pack or Happy Meal – that’s nothing but the bundling of items based on their likelihood of being sold together. The idea here is to sell a combination of items at a discounted price as compared to the sum of individual prices of the items. It may look like we are losing out on revenue by selling on discount, but in reality, it’s increasing the share of wallet of customers. For example, if we were to have two items – burger (costing $ 1.5) and soft drink (costing $ 1) – and if we create a combo for the two and sell it as $ 2 then the chances of people buying both will be way higher. Though this may slightly reduce gross margins, the overall impact due to increased revenue takes care of margin reduction.

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8. Customer Segmentation Based on Restaurant Visits:

Understanding your customers is the key to any business; the restaurant business is no different. In fact, it is even more important to understand customers, their demographics, their liking/disliking, choices, preferences, what time they prefer to visit, and further details. Think of yourself as a customer and you visit a restaurant where you have already visited in the past. The restaurant offers you a complimentary cheese pizza because they know you love pizza [How they know you love Pizza and not something else? They have captured your order history where you have ordered pizza 5 times in your last 6 visits.]. Wouldn’t that make you happy? Wouldn’t that increase your loyalty to that restaurant? Let’s understand the customer visit pattern to a restaurant by gender.

A) Day-of-the-week and Time-of-the-day Analysis

The above chart shows a pattern of customer visits to the restaurant. This does not split by gender or any other customer attribute. We can see that we have a high number of customers visiting the restaurant during certain time periods on certain days. Let’s break the same analysis by gender and see if there’s any difference in the trend for men and women.

In the above two charts, we can see the difference in the visit pattern between men and women. Key insights and actions that should be taken based on those insights are as below:

• Men and women, both tend to visit on Sunday afternoon. Promote family/couple lunches on Sunday by offering a customized menu or discounts on table reservations.
• Women prefer to visit on weekdays (Tuesday/Wednesday/Thursday) during lunch hours (1:00 PM to 4:00 PM); while the visits by men during that time is not high as compared to their visits during other times. Check if any promotion was rolled-out only to women customers which resulted in an increase in women customers during this period. If yes, promote such offers during other time periods as well. If not, reach out to women with similar demographics.
• Men prefer to visit on Friday evenings, possibly after office-hours. Look for order details during this time. Are they majorly drinks? Can food items be combined with their drinks order? Can there be certain promotions for couple entries?

Do you know anyone who runs a restaurant business? You can reach out to them and ask them to provide you with data in return for a wonderful analysis. You will get a good hands-on practice in return.

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