AI in Action: How Restaurants Can Boost Profits with the Right Data Analytics Tool
When the phrase ‘Artificial Intelligence’ is mentioned in relation to restaurants, most people imagine robots making pizzas, autonomous vehicles handling pickup and delivery, and other very grandiose scenarios. While some of these are being trialed, they are yet to become a reality among the wider restaurant community. In fact, it could take some time before such technology reaches the regular neighborhood restaurants.
Lost in the hype of these flashy manifestations of AI is the data science that is readily available today. Because there is so much noise surrounding these glamorous gizmos, the widely available and easy-to-use data analytics is often overlooked. Stemming from the origins of AI, machine learning, and data science, AI-driven data analytics is a highly underestimated and severely undervalued tool that can help restaurants optimize operations, rev up revenue and spur success. It is available to all eating and drinking places, from the high-end fine dining restaurant in the heart of the central business district to the family-run cafe at the corner of the street.
Actionable insights that boost performance
AI-driven data analytics is perhaps the most convenient and effective way to leverage restaurant data. It offers restaurateurs valuable insights into the state of business, revealing what drives success and what could be holding them back. It provides a clear understanding of seasonal shifts and customer preferences. It also shows patterns of increase and decrease in sales and connects these trends with the variables that impact them.
The observations made can help restaurant operators take appropriate and beneficial action. Knowing what demand is like will help plan inventory and staffing more accurately, reducing wastage and cutting costs. By monitoring changes in flavor preferences, menus can be tweaked to include sought-after innovations and unusual variations. Promotions, offers, specials, and discounts can be arranged based on predictions of customer behavior. Overall, AI-driven data analytics will provide actionable insights that translate into high functionality and increased profits.
The four fundamentals
Of course, trying to select a data analytics tool can be bewildering. There are so many solutions out there, with each focusing on different forms of analytics. The best data analytics tool is one that covers the four cornerstones of data science: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
- Descriptive analytics
Descriptive analytics is retrospective, presenting the occurrences of the past up to the present moment. Data is visualized in a descriptive format - graphs, charts, reports, and dashboards. In this way, restaurant operators can get a picture of the current performance of their establishments and can compare this to the previous week or month, or year. The information on the increase and reduction in sales is precise and accurate. Moreover, these details can be categorized based on time, region, and other variables which give context to an occurrence so that sense can be made of it. Data visualization can foreground areas of high performance as well as areas of concern, highlighting the operational and production aspects that need attention. Sometimes simply learning about what is going well and what is not can be just what is required to make key decisions.
Take a small neighborhood cafe, for instance. By using descriptive data, they can look at how their cakes have been selling. The visualizations will help them identify the surge in demand for their chocolate cake over the last three months. Seeing this increase, they can very easily bake more of this popular variety and less of the other options. By making visible the rise in sales of that specific flavor, descriptive analytics can help the cafe owners provide the right flavors of cake and cater to changing customer needs.
- Diagnostic analytics
Also retrospective in nature, diagnostic analytics has to do with understanding the ‘why’ of a situation. Root causes of an occurrence can be identified by overlaying graphs, connecting variables, and revealing relationships. This is particularly useful in troubleshooting. Diagnostic analytics makes links to lay bare the factors that have contributed to a specific event so that the right elements can be addressed to fix matters.
Going back to the small neighborhood cafe, they could discover that, while the individual slices of chocolate cake seem to be flying off the shelves, there are very few requests for the full chocolate cakes. When they look carefully at purchase patterns, they notice that patrons add on a slice of chocolate cake to their order of coffee and they then begin to understand why the individual servings are more desirable. With this knowledge, they could simply cut up the whole cake into individual pieces or run a special coffee and cake promotion. Alternatively, maybe they could consider reducing the weight of the full cakes to make them appealing to small groups.
- Predictive analytics
As the name suggests, predictive analytics makes forecasts about the future. These projections are based on the information of the descriptive and diagnostic analytics, vesting them with a high degree of reliability. The probabilities and trends presented by predictive analytics make it easier for restaurant managers to plan inventory, staffing, and other matters.
Say that it is nearing the Christmas holiday season and everyone is gearing up for some fun and excitement. The predictive analytics of that neighborhood cafe could forecast a further hike in the sale of chocolate cake as diets and eating restrictions fly out the window, just as they did the previous festive season. The cafe operators can stock up on the requisite ingredients and bake more cakes, more or less assured that these will sell.
- Prescriptive analytics
Taking things a step further, prescriptive analytics makes recommendations that aim to either capitalize on a trend or resolve a problem. These suggestions could cover multiple actions - marketing campaigns, new marketing channels, happy hours, promotions, special offers, alternative menu items, to name just a few. What happens is that a number of possible actions are presented along with potential outcomes. Restaurant operators can weigh each of these and choose what they should do.
After some serious overindulging at Christmas, many people make a New Year’s resolution to eat healthy again. This means that, in line with what has been happening in the past, that neighborhood cafe will most likely see a significant dip in the sale of those delicious chocolate cakes once the festive season ends. Prescriptive analytics can offer a range of prospective steps - run a coffee and cake promotion, have a happy hour for just the cake, reduce the number of chocolate cakes baked and introduce a healthier variety, or launch a sugar-free version of the chocolate cake. The cafe owners can implement one or multiple of these so that sales are not affected by the anticipated change in lifestyle.
Data analytics is vital for the success of any business and the food and beverage industry is no exception. As there is a dearth of professional data analysts, hiring experts can be prohibitively expensive. Furthermore, manual data analytics is time-consuming. AI-driven data analytics is so much faster and has the ability to process higher quantities of both structured and unstructured data. Therefore, it is more practical and cost-effective for eating and drinking places to use AI-driven data analytics. However, in order to be able to gain the full benefits of AI, machine learning, and data science, restaurateurs need to be prudent and select a tool that incorporates all four core categories.
About the Author
Sayanthan Balathasan is the Director of Operations at Applova Inc. He brings 14+ years of leadership roles in the tech industry. He specializes in Business development strategy, social entrepreneurship, business modeling, software literacy, and telecommunications.