Food Analytics - An Overview

Fast-casual restaurants are a relatively new industry segment that offers counter service and high-quality food in an upscale ambiance. They provide fast service with made-to-order but affordable food, and they are growing.

In fact, fast-casual restaurants are growing so fast that they are starting to eat into the market share of leading fast-food chains. According to Technomic’s 2014 Top 500 chain restaurant report, sales for fast-casual chains grew by 11%. However, even with all the success and popularity, fast-casual restaurants have been slow in adopting advanced analytics and building big data infrastructures – an exclusive resource once reserved for the market giants that can deliver the segment unprecedented ROI and growth opportunities.

With the advent of scalable cloud technology and the “analytics-as-a-service” industry, what once was too large and expensive for a growing fast-casual chain is now readily available and affordable. The fast-casual chains are finally able to glean insights beyond the business intelligence (BI) their point-of-sale (POS) systems provide and can now use advanced analytics and data science to drive performance.

BI is Not Analytics

The key difference between BI and advanced analytics is that BI provides information on what happened in the past but not the root cause correlative to the understanding of why things happened or their drivers. This “why” is key to learning how to predict using predictive analytics, which is closer to predicting the future than any other form of technology. In the past, a restaurant had to rely on instincts and experience. Today, it’s possible for a restaurant to get outside, unbiased, data-driven information about the future.

The Danger of Being Fooled by Averages

There’s a statistical joke that goes like this: A statistician confidently tried to cross a river that was one meter deep on average; he drowned. Restaurants can be fooled by averages from their POS system. Without slicing and dicing the data further and taking a multi-faceted, dimensional look, data can be deceiving.

For example, looking at average sales by product for the month or quarter might show significant results for a particular product. However, diving deeper might show that most of those sales are on promotion days at deep discounts. Thus, the sales volume might be high on average, but profits might be flat or even at a net loss when drilled down to a more granular view.

Moving up the Spectrum of Analytics

Restaurants collect and store their data in their POS systems, relational databases, and enterprise data warehouses. The first stage in the spectrum of analytics is reporting and descriptive analytics, which explains to the restaurants what has happened. For example, how many dinner entrees were sold last week, last month, and last quarter? The next level would be to dive deeper into root causes. For example, why are dinner entrees selling more on Wednesdays and not Tuesdays?

Fast-casual restaurants are now able to discover the root causes and factors that drive revenue and, by using predictive analytics, they can predict next month’s or next quarter’s revenue based on these underlying factors. By using insights based on root-cause analysis and predictive analytics, fast-casual restaurants can prescribe actionable recommendations to change business operations and strategies to optimise profitability.

What Fast-casual Restaurants are Learning

Restaurants drive performance by applying insights from advanced analytics in the following ways:

  • Menu optimisation. Menu optimisation through analytics can help with profitability and selection. For example, when the menu is too broad, service maybe slow and the operation taxed. When the menu is too narrow, sales and add-on opportunities could be missed. Menu engineering through analytics can maximise profits and customer satisfaction.
  • Customer segmentation. Restaurants can segment customers by buying preferences, demographics, and other characteristics to create personalised promotions by segments to increase yield, profitability, and better customer engagement.
  • Staff optimisation. Demand forecasting through analytics can help restaurants optimally staff their stores to meet customers’ demands by day of the week, by hour, etc. It also can be used to measure staff performance and human resource allocations.
  • Operations improvement. Fast-casual restaurants want their food delivered quickly. Good analytics can help improve the speed of service, which improves the customer experience. For example, restaurants can track the speed of services at individual registers and drive-throughs, and make improvements. Analytics also can keep food waste to a minimum by better forecasting product usage and adjusting purchasing and inventory accordingly.
  • Time of day/Day of week analysis. Drilling down into different time segments such as time of day (lunch, dinner, post-lunch, post-dinner) as well as the day of the week can give much deeper and richer insights than using averages of common metrics over a monthly or quarterly time horizon. It allows one to compare lunch sales on Wednesdays versus dinner sales on Fridays by different product categories. This type of time-segment analytics allows restaurants to detect any cannibalization effects of new product introductions or promotions.

 ROI and New Technology

Industry analysts have shown consistently that traditional advanced analytics implementations have over 3:1 ROI. That was with a large upfront investment with the costs and timing of hiring a data science staff.

Now, advanced analytics on a large scale can be done at extremely attractive price points. With no upfront costs and no long-term contracts, analytics-as-a-service provides a much higher ROI in a much shorter timeframe (i.e., the denominator of the cost is much lower, so even a modest business improvement will generate a strong ROI, and a large business improvement will result in previously unheard of ROI).

However, the skill set and knowledge of data science and advanced analytics can still be formidable to most organizations, and most restaurants don’t have data scientists on staff. That is where an analytics-as-a-service model can help restaurants leverage advanced analytics at a low monthly cost with no commitment to expensive hardware, software, and human capital investments.

Big data analytics has changed everything about the restaurant industry. From expanding customer intelligence to improving operational efficiencies, fast-casual restaurants are finally competing with the big brands. With advanced analytics and data science, as well as new technologies such as cloud computing and fast, inexpensive analytics-as-a-service agencies, big data analytics is finally on a level playing field.

This article was written by Rajdeep Dutta, Head of Redwood Knowledge Centre.