Article

How Data Analytics Promises to Change Independent Restaurant Operations

"Big data" is embedded in business vernacular today, and most people have an intuitive idea of what it means. Let's get one thing straight. Human beings have been processing big data since the dawn of man for survival - the environment, sources of food, and subtle social cues. With modern computing power, we are able to shift some of that role from our grey matter to data processing platforms. It is shifting the accuracy and speed with which we make business decisions and predictions. If you don't think it has anything to do with your single-unit or emerging growth restaurant concept, read on.

Lisa Arthur, the author of Big Data Marketing, defines big data as: "A collection of data from traditional and digital sources, inside and outside your company, that represents a source of ongoing discovery and analysis."

Unless you are living off-the-grid somewhere in the desert, you are likely sending massive amounts of data into the "cloud" every day. You already know that your mobile devices are tracking your daily travel patterns each day, and can then tell you how long it will take you to get to work or the gym. So, how might this technology boost the profitability and success of your existing or startup restaurant?

It is difficult to discuss this topic without using superlatives. This massive volume of data can be stored and accessed only thanks to the tremendous gains and the reduction in the cost of computing speed, storage and access to data via the cloud, and huge Internet bandwidth.

These technological advancements allow data analysts and data scientists to see patterns in unimaginably large data sets. This has helped advance medicine, product design, and scientific discovery. Data collected from the cloud, based on the words we use, and where we go, can predict - with uncanny accuracy - what we will do next.

For many companies, this is a boon to marketing. Social media sites, such as Facebook, are collecting information on each of their more than one billion users, and determining their demographics, interests, and even psychological profiles for highly customized advertising. How does a company that provides "free" access to their tools gain so much value on the stock market? They have data.

Big business has been invested in data analytics - including "predictive" analytics - for several years to understand and predict customer preferences and gain deep and detailed financial, marketing and operations insights to drive business strategy. These businesses include, of course, restaurant chains that are collecting transactional data from hundreds, if not thousands, of units.

It begs us to wonder if our startup restaurants could harness some of this computing power, instead of us merely feeding our data to large companies for their use. If only we might be able to determine with a high degree of accuracy how much inventory to purchase this week, or what your guest traffic might be on a particular day part sometime next month.

In fact, Software as a Service (SaaS) companies have not ignored the independent restaurateur or those planning on opening a restaurant, and are developing tools with which we can interpret and react to the data we collect and produce, efficiently and effectively. In the near future, it is foreseeable that data analytics will be the way all independent operators will identify guest patterns and preferences and evaluate internal inefficiencies.

The increased affordability and availability of these tools means that now, even small, independent restaurants and startup operators can benefit from the same predictive analytics and business insights previously reserved for large chains and tech giants like Google or Amazon. In this article, we will look at what exists, what to look for on the horizon, and how it could provide a competitive advantage to operators who adopt it.

Data vs. Usable Data

Many independent restaurants compile tremendous amounts of data, even if it is not always immediately - if ever - usable in time to drive decisions.

In most restaurants, usable data is deeply interconnected, and using it to make decisions is almost always the correct path. Every entry into a POS system, whether it is a physical order, mobile order, interaction using a loyalty app, social media review, or any other customer journey, touchpoint provides a restaurant with data.

This gives insights into everything from customer preferences, to server strengths and weaknesses, to staff scheduling issues. It can help ensure appropriate amounts of ingredients are ordered, cutting down on waste and spend, and can even flag suspicious employee behavior.

This data can then be used to make those small, incremental changes which add up over time, and large-scale innovations informed by inarguable fact, rather than hunches and gut instinct.

If your restaurant is using technology for credit card processing, point-of sale, loyalty programs and participating with social media platforms, then you have a lot of potential data with which to work. The rate at which you use condiments is data. The number of times employees come in late is data. The number of comped drinks you give because of service errors is data.

Usable data, on the other hand, is any information that provides actionable information to help identify trends, reduce costs, gain customer insight and provide a better experience.

Setting aside the technology for a moment, successful operators have always been data analysts, of sorts. Consider the following.

A restaurant owner observes a church two blocks from his restaurant. He prints a flyer with a discount coupon to promote Sunday brunch, which the church agrees to distribute to congregants. He counts the number of coupons that are redeemed to determine the rate of return. He learns that the response is much higher in the fall and spring months than in the winter and summer months. He observes that the church congregants tend to order the peach pie for desert. He processes these various "data" to determine how much peach pie should be available for Sunday brunch.

Indeed, observant restaurateurs have a sense of which days of the week guest traffic is brisk or slow. They have a sense of which menu items are popular and which are not. They have a sense of whether a new billboard advertisement on the freeway has increased business.

In the recent past, one could run a small business by paying close attention to guests, financial statements, and gut instinct. Growing a business by the proverbial seat of one's pants has never been easy, however, and in the best case, this kind of old-school data analytics is time-consuming and imprecise.

What menu items should you promote on Tuesdays in April? How many staff do you need next week based on the weather forecast? What is the best price point for the oysters you offer as a special a few times a year?

These are the questions most independent restaurateurs navigate through with a mix of trial, error and intuition. And while this intuition is frequently based on the invaluable experience that has helped a restaurant grow and survive, it may no longer be enough.

Old Guard vs. New Guard

David Mogavero is the founder of Avero, a restaurant management software company, and a pioneering advocate for the use of technology in the restaurant business. In his book The Underground Culinary Tour, he coins the term old guard- restaurateur, who operates by gut and instinct. He argues that, while it has worked for years, this approach results in doing things the way they always have been done.

The risk is that he or she will continue to offer the same menu long after the items are new, interesting or impressive, and will be left behind as "new guard" operators innovate based on data-driven insights that simply would never have occurred to them until long after they could have capitalized on this information.

Mogavero suggests that old-guard operators merely use their point of sales devices as nothing more than a tool for computation and running credit cards, not as the critical collection point for day-to-day data to be analyzed and acted upon to improve the bottom line.

Some operators are using technology to compile data. This is a step in the right direction, but it requires a great deal of time and energy to discover useful patterns. True data analytics is an "emphasis on actionable data rather than presentation of data," says Jorn Wossner, vice-president of brand and communications for CAKE, a SaaS and hardware company serving the restaurant industry with several thousand users.

Says Wossner, the vision of an analytics-driven restaurant is to allow the operator to implement changes (e.g. a specials campaign or a price change of an item) in real time, rather than simply presenting an operator with data.

The Future is Now

For independent restaurateurs, Wossner's vision is neither "blue sky" nor theory. And here's a case in point.

Lutz Finger, a data expert in residence at Cornell University's S.C. Johnson School of Business, worked with a team of graduate students to help a small, family-owned restaurant on the Jersey Shore harness its point-of-sale data to make more informed decisions about inventory management.

Circus Drive-In is an iconic concept launched in 1954 and based, in part, on fresh made-to-order burgers, without using a frozen product. (Unfortunately, the well-known restaurant was reportedly closed in 2017 after the sale of the business's property to new owners.)

Because of the long-established restaurant's proximity to the shore, weather often influenced guest traffic. The owners certainly understood that poor weather could hinder sales and fair weather could boost it; however, not with great precision.

In addition, while most people who live at or frequent the Jersey Shore are aware of seasonal weather patterns in a broad sense, to predict the weather week-to-week with any consistency would require deeper analysis into data garnered from archived weather history.

Lutz and his team analyzed historical sales against weather data to predict guest traffic and sales with the objective of creating a predictive model of weekly purchasing needs. Using this model, the amount of waste was reduced to 700 patties per season, and prevented 3,100 hamburgers from being discarded. That previously wasted inventory amounted to a lot of hard-earned cash in the trash.

This underscores a major challenge and opportunity for independents and startups. If restaurateurs want to increase their customer base and boost repeat business, they need to pinpoint opportunities to increase revenue per order, drive business when it's slow, and predict when activity will be unusually heavy.

They need to identify if they're overstaffing and thus overspending on labor, or understaffing and therefore negatively impacting customer service. The answers to these critical questions are often buried away and inaccessible in point-of-sale systems and spreadsheets.

You Are Not Necessarily 'Behind the Curve'

If any of the foregoing makes you believe you are "behind the curve" in your adoption and use of technology, consider the restaurant industry is typically a technology laggard in comparison to other business sectors.

And yet it is among the last major industries in the world to adopt data analysis as a critical part of doing business. Perhaps, in part, this is because of the large number of independent businesses that comprise the industry. According to information published by the National Restaurant Association, restaurant industry sales were close to $800 billion last year, with 70% categorized as single-unit concepts.

Usable data, on the other hand, is any information that provides actionable information to help identify trends, reduce costs, gain customer insight and provide a better experience.

Wossner notes "the vast majority of CAKE users are single up to five restaurants, and we do not target medium to large chains with our point-of-sale products." He adds, "From studies and data analyzed, we see that the majority of our operators do not take full advantage of the analytical platform provided," he noted, adding "The reason for this, we think, is that operators are busy with their day to day operations and simply do not have time or the state of mind to sort through analytics at the end of their busy, busy day."

Seth Temko, vice-president of product marketing with POS system company Xenial, Inc., also appreciates the mindset of the startup and indie restaurateur. "Given the restaurant business's low-profit margins, particularly among independent operators, technology is often disregarded as a passing trend by restaurateurs who, understandably, are more concerned with getting through tonight's service than tools that can help them identify a change which will boost tonight's takings by one-half percent," says Temko.

"What they fail to see, however, is that the number of potential one-half-percent boosts that predictive analytics can win you, multiplied night after night after night, will add up to huge increases in revenue over a year," he continues.

The benefits detailed above are those which big data and predictive analytics can bring in the present. In the not-too-distant future, as more investment is made in these areas, costs will plummet. Says Temko, "Although the restaurant industry has generally been a laggard in data computing technologies, there are now available, affordable and comprehensive tools that any restaurant can use to better understand how to improve their business, and those tools will continue to get better and cheaper in the near future ".

Nick Low is senior solution manager at Oracle Hospitality, best known to operators for its Micros POS systems. Oracle's 2014 acquisition of Micros was a bellwether of the emergence of analytics in the restaurant and retail sectors.

Oracle has a long and rich history in analytics and business intelligence. For Micros, joining forces with Oracle appears to be an ideal marriage. Micros launched a cloud-based POS system in 2007. At first, the key selling proposition for cloud-based architecture was remote access to the system.

Micros understood operator reservation to cloud-based systems. Low notes, "We designed the system from the get-go with operator wariness of the Internet in mind", including seamless operation when Internet access is interrupted. "If the net comes down, we can't afford the kitchen printer to go down," he adds, regarding what's happening outside; such as [the Internet provider] cut the cable line for all the stores on the block."

Cloud-based architecture now allows SaaS providers, including Oracle Hospitality, to assist operators in analyzing their data. "There's a ton of value in data generated by businesses via the cloud," says Low. "We're bringing it down to the independent operator." According to Low, "the company employs a data science team that will work with customers to analyze transactional data to create predictive models to assist with budgeting and forecasting."

From Low's perspective, independents need data analytics tools as much as, if not more than, the large chains "which often have more room for error," he says. "The independent operation might be run by one man or woman. [It is critical] for him or her to have ready information to help make payroll."

"With the right tools behind the operator, determining the menu mix, forecasting sales, knowing your customers' demographics with accuracy has great appeal." While most operators will learn which unsuccessful menu items should be eliminated over time, the advantage of these tools is to help him or her make those decisions more quickly.

What Matters

As noted in the above example of Circus Drive-In, predictive analytics moves further into the realm of probabilities, exploring both large numbers of records in a data source, and the relationships between seemingly disconnected data sources that could provide rich insights. Many POS companies offer data reports that can be customized and updated in real-time in the cloud, and staff can pull reports from virtually anywhere at any time using their guest waitlist management product.

As an owner, you need to be involved in the process. Before restaurants can determine which data will help them drive sales, they must first establish their pain points, advises Roman Stanek, CEO at GOOD DATA. If you don't know what you are trying to find, you'll never see it.

Keep your goals simple, at least initially, so that you don't suffer from "paralysis by analysis".

It's essential however, to make time to answer questions like: "how will we measure success, effectiveness and value, and more importantly, what will we do with the information when we find it?"

Independent restaurant owners typically start by using data to validate a return on their efforts, for instance, to determine if their latest promotion brought in new customers. From there, they may look at the redemption curve to understand the timing of advertisements being responded to, and the tail expectations from ongoing sales.

Stanek recommends identifying which problems you are trying to solve, and then choose one or two of these issues to focus on. By homing in on a couple of issues at a time, restaurateurs can concentrate only on the data that is relative to that problem, and stream-line their data analytics process one step at a time. For example, if a restaurant wants to determine how to boost sales of its worst-performing item, it should evaluate the fundamental differences between its top and bottom performers.

In fact, this is part of the process of menu engineering, and can be analyzed with a spreadsheet and sales and profitability reports. Perhaps, ideally, this information might be accessible in real time without you having to plug in the data.

In the meantime, you might want to read, "Menu Engineering Basics: How to Make Your Menu Your Top Salesperson", which provides an explanation of the process in simple terms.

Another factor that operators would like to control and reduce are guest wait times, which are notoriously difficult to predict, and are a major customer loser - a guest left waiting for far longer than they were told may not only be likely to leave, but is much less likely to return in the future, or to recommend the restaurant to others.

Staff in charge of seating guests will often get wait times wrong, through no fault of their own; guests may choose to linger at a table long after they've finished their meal, and if the restaurant is busy enough for guests to have to wait for a table, it's not the time to be looking at historical wait-time data and calculating an estimate one-self. There's an old saying that two people don't have that much to talk to each other about, four have more, and six will stay at a table forever. Perhaps the data might confirm that old saw.

By feeding historic data through its algorithm, CAKE Guest Manager marketers claim it will automatically suggest a wait time which they say has proven to be up to 40% more accurate, and helps front-of-house staff estimate more accurate wait times for customers, ensuring a higher retention of guests.

This is one example of the kinds of information that can be mined from these systems offered by Oracle and Xenial, and others as well. Even then, however, it's often difficult to predict human behavior with so many factors involved such as are the kids along, are old friends getting together, is there romance going on. It's a little easier if it's a quick hamburger at the local chain.

A Growing Array of Tools

There is a growing array of analytics tools available to operators. In addition to the POS manufacturers, many new data sources are taking information from the hospitality industry to new levels of data. Many of the POS companies are now partnering with "data" providers to integrate data with real-time usage. For example, CHD in Chicago is now offering extensive information that can give current owners or those hunting for new startup locations packages of demographic and competitive information based on their extensive databases of every foodservice location around the world.

Upserve enables operators to create "heat maps" of customers on a zip code basis to create, for example, an instant visualization of where advertising and marketing is most effective in attracting guests. Email opt-in programs such as MailChimp and Constant Contact can reveal receipt, open and click rates by customers in your database. Loyalty technologies such as Moving Targets, Punch and Fishbowl help to identify specific customers who join a program, whereas social reputation management platforms compile various online review sites into a dashboard, giving overall ratings compared to your competitors, allowing operators to quickly interact across social platforms from a single interface.

A discussion of how to integrate all the available tools in your business is beyond the scope of this article, but educational resources abound. Google provides free web analytics tools that can help restaurateurs understand what their customers are searching for online. YouTube videos can help newbies get started. Regional and national trade shows are also a good way to meet with representatives of these companies and learn what they offer. However, be prepared to face competing claims and literally hundreds of different ways to approach the problem of data engineering. At the recent NRA Convention in Chicago there were over 100 POS suppliers. Make the marketing representatives discuss their systems' features and benefits in terms of how it will help you operate your restaurant more efficiently and profitably.

The problem with multiple tools and too much information is that it leads to confusion. While each tool will allow for some analysis and reporting, the challenge is in pulling diffuse data sets into reporting and analytical tools, relating them to one another and identifying areas of correlation, without needing a background in programming or a degree in statistics. Much of the data won't import and "crunch" other data. Unfortunately, this leaves the restaurateur having to cross-reference one spreadsheet from one tool to another. That's in the process of changing with new offerings from POS makers and other data providers.

The key is using powerful technologies to pull data from many sources into one view. For example, Xenial, which offers an analytics platform that unifies information from physical orders, online orders, email, social media, online reviews, loyalty apps, and more. This is the wave of the future. The question becomes will you need to purchase new POS units, or will your current service organization be able to offer add-ons to their current offerings. It's a competitive field and no manufacturer wants to be left behind. A hidden key to all of this comparative information resides with the credit card companies and what they are willing, and able, to release with the coming privacy laws. They are the ones with real economic data. When you add the important factor of PCI compliance and the many steps it takes to gain access to credit card information, hard data can be difficult to obtain. We'll have to see how that shakes out.

Why this Article?

"That's all nice," you think, as you look across your dining room grounded firmly in the here and now, rather than the cyber-restaurant of the future. Data scientists understand the independent restaurant sector is a tough nut to crack.

Wossner acknowledges that companies which provide data analytics "face the challenge in getting operators to adopt the appropriate processes. Therefore, we think that a combination of education and awareness, as well as making data/analytics simple and actionable, is needed to help the average restaurant operator overcome this hurdle."

Large national chains are already using teams of data analysts to predict inventory and labor scheduling. And the competitive nature of the restaurant business in which you fight for every point of margin is not likely to change.

Upshot: Whether we like it or not, big data is here to stay, and the story of how you came to adopt data and technology might be the story of how you will survive.

What about instinct and judgment? Sure, these will always be a factor in restaurant success. Even the most ardent data geeks understand quantitative data analytics needs to be combined with a qualitative understanding of guests and emerging trends. In this regard, the independent operator, who is often much closer to his or her guests than chain concept decision makers, has a keen competitive advantage. With the adoption of data tools, the independent operator could gain even more ground on the chains.

LISTEN TO THE HEART OF YOUR BUSINESS

If you are a startup in the market for a new POS, you need to shop wisely. The POS is the operational heart of your business. Spend time making sure it offers the information growth you'll need long term. Once put in place, it is difficult to change systems as more and more sales and other data is compiled. Also realize that one of the most important aspects of any POS is the service organization that comes with your purchase. As technology changes and upgrades happen (and they always do), it's the service behind the POS that makes or breaks the system.

There are several actions that current operators can take to start the process of using their data more aggressively. Current operators know their POS is a key to running both the front and the back of the house. They also count on the service they get from their local or online POS provider. One important way that those with current POS systems can do is to take the time to really understand the reports which are already built into their systems.

Just about every POS has more ways and reports to review data that aren't used in the day-to-day operations of a busy restaurant than are used. One report that's easy to take advantage of is very simple: Weather. Almost every modern POS system has an input for what the weather was today: sunny, cloudy, rain, etc. In order to compare sales data, the shift manager has to make sure that they are consistent in logging the correct weather situation. The result can provide insights that can help you understand why one day was slow and another was not.

POS computer developers and programmers write code. Depending on the system, there can be, literally, a hundred or more useful reports on the back-office POS computer, or in the cloud, that may be helpful. That said, many operators are not aware of them, or haven't taken the time to review how they might help them run their restaurants more effectively.

It is well worth your day off to spend some time seeing what you already have on your system. That can be followed up with a call to your service organization for help and to see what may be coming. Before you start chasing after the next cool technology, make sure you are using your system to its full potential.