Restaurant Data Analytics – A Comprehensive Guide
In 2024, the restaurant industry is in the middle of a restructuring fuelled by data, AI, and analytics. The sector is favoring data-driven approaches and technologies that power up data analytics. The data-first mindset in the restaurant industry is gaining traction, especially after 2020. Today’s restaurant businesses are trying to find avenues to acquire insights such as restaurant location data, restaurant delivery analytics, and many more to decide future actions. This shift toward restaurant data analytics is healthy and proving to be highly profitable for restaurant owners relying on restaurant analytics to make key business decisions. The rise of third-party food delivery apps, cloud kitchens, and take-out-only restaurants (ghost kitchens) in the last few years points towards the shift from dining to delivery. To capitalize on this trend, even traditional restaurants have joined the delivery platforms to start online food deliveries. Many restaurant chains have started their own online platform where customers can place orders for online food delivery. However, amidst this evolution in the restaurant industry, all industry participants such as restaurant owners, food delivery apps, ghost kitchens, and restaurant chains require restaurant analytics to keep themselves ahead of the competition. From fine-tuning their food menu as per current customer demand to creating loyalty programs that match competitor offerings, restaurant data analytics offer insights that are critical for success in the restaurant industry. In this guide, we will cover all aspects of restaurant data analytics including its meaning, key metrics, collection sources, extraction methods, benefits, use cases, and best practices.
What is Restaurant Analytics?
Restaurant analytics are numbers or ratios in the form of key performance indicators (KPIs) that provide insights into the core processes of the restaurant business. These metrics or analytics include data insights that restaurant owners can use for making improvements in their business or making investment decisions. For instance customer food preferences analytics. Suppose a newbie restaurant wants to know the top 5 dishes that it should list on its website or a third-party food delivery partner app. It will need data that reveals the top 5 dishes that are ordered the most online from competitor restaurants listed on popular food and restaurant aggregator apps that serve the locale ((say regional restaurants in a radius of 5-10 miles). With such data in hand, the newbie restaurant can create targeted menus that resonate with the customer base in that particular locale. The restaurant can also create targeted marketing campaigns to promote those top dishes or cuisines that align with their customers’ tastes. The restaurant can make sure to keep inventory adequate to cook those top dishes. This will also reduce waste as the newbie restaurant can plan to eliminate some items that are unpopular and rarely ordered. Now, the above is just one example of how restaurant analytics work. With the right tools and restaurant data analytics solutions, restaurant businesses can track several important metrics to keep their business ahead of the competitors.Top Restaurants Data Analytics and Metrics to Track
Restaurant businesses must track the below analytics and KPIs to gain a competitive advantage, achieve higher profit margins, and increase resilience to disruptions.#1 Competitor Restaurant Analytics
There were 156,715 single-location full-service restaurant businesses and 349,000 chain restaurant businesses in the US alone in 2023. The competition is extremely high in the restaurant industry which is growing at 13.6% CAGR. By examining competitor details, a restaurant can know how many restaurants operate in a particular region, how many have dine-in and takeout facilities, how many only have takeout facilities, etc. The data also reveals direct and indirect competition among restaurants in a particular region. Direct Competition refers to restaurants that offer the same dishes to the same target market. In the restaurant industry, examples of direct competition include McDonald’s and Burger King: Both are fast-food chains offering similar types of food (burgers, fries, etc.) and targeting similar consumer demographics. Indirect Competition involves restaurants with different dishes but targeting the same customer preferences. For example: Subway and Jamba Juice: While one offers sandwiches and the other offers smoothies and healthy snacks, they both cater to health-conscious consumers looking for quick, convenient options. Knowing the competition that you will face when operating your restaurant is critical to building strategies to beat the same.#2 Menu Analytics
Competitive menu analytics means identifying and analyzing competitors’ menus (dishes offered, best sellers, specialties, etc). This data will help a restaurant to tailor its menu to meet local demand, offer everything that competitor restaurants serve, and add more items to keep their menu better than the competitors.#3 Promotional Analytics
This restaurant analytics focuses on finding the current offers, promotions, discounts, and loyalty points competitor restaurants offer. An analysis of competitor’s combo offers, free meals, complimentary items, etc. will help a restaurant business implement its own promotions and loyalty programs. 46% of US diners are a part of a loyalty program.#4 Marketing Analytics
People today search online before ordering from restaurants or visiting them for dine-in. Social media suggestions, videos by food influencers, and digital marketing campaigns by restaurants impact footfalls and orders. Restaurant businesses need to analyze these marketing campaigns and strategies to guide their marketing efforts.#5 Pricing Analytics
Competitor pricing analysis ensures that pricing is not discouraging customers from ordering. Determining the best pricing for menu items and understanding the cost and profit margin of each dish helps in setting prices that maximize profits while being acceptable to customers. Prices affect how customers perceive a restaurant. Analytics help in setting prices that match the restaurant’s desired brand image.| Perhaps that’s because variable pricing is as much a part of the business as knives and forks, says Peter Romeo, Editor at Large for Restaurant Business. |
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