Outside Insights | February 2016 | By Guest Author

Embrace Your Big Data, Part II

Four steps to growing your business through information management.
Top QSR chains leverage big data to strategically build new business.

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Note: This is the second article in a two-part series about why and how to boost your bottom line by embracing restaurant information management. Read the first article here.

To catch you up, here’s a summary of the first article in this series: Multiunit operators should embrace their big data because data offers better information. Better information brings better decisions. Better decisions, in turn, result in better profits.

Sounds easy, right? One industry expert says that the process, while not simple, can be laid out, like a road map. And he encourages viewing that map as a journey rather than simply a destination.

“Look at mastering your restaurant information management as a learning curve, like any other learning curve,” says Dave Bennett, CEO of Mirus Restaurant Solutions. “There are four distinct stages along this particular curve, with four distinct stages of restaurant information management capabilities.”

Good news: Most operators today aren’t behind compared to their fellow foodservice competitors, Bennett says. Plenty of large, multiunit chains are low on the learning curve—from 30-unit chains up to 1,500-plus-unit ones. But, he cautions, that’s changing, and “playing offense is better than playing defense.”

Step 1: Consolidating data

“Look past the salespeople hawking off-the-shelf products [and] for a partner who will listen to your needs and tailor a solution that meets those needs.”

Companies who are in the first step of the restaurant information management have moved beyond the typical chain’s rudimentary spreadsheets and various siloed transactional systems. These operators are consolidating all their data sets in elementary, in-house systems. Whereas their data previously were divided into various transactional systems—point of sale, labor scheduling, loyalty program, etc.—consolidating all those data sources allows these operators to begin looking at relationships between and among data sets. They have moved beyond simply generating data and toward analyzing their data.

At this stage of the learning curve, Bennett says, companies can answer the “what” questions: What were yesterday’s sales? What were last month’s losses? About 10–15 percent of chain operations are in step 1, he estimates.

Stage 2: Reviewing data against benchmarks

Operators who are at step 2 have progressed to evaluating their data against predefined benchmarks or yardsticks. MBA types call this “managing by objective.” Most chain operators are at this stage, Bennett says.

These operators can answer more complex questions, such as: Did yesterday help or hurt us against budget? Is our failure to meet budget consistent across all stores or markets? Operators’ decision-making capability is still basic, but it is improving, Bennett says.

“Let’s say store sales were higher than expected. Stage 2 operators can identify what happened because they are now evaluating sales against a yardstick,” Bennett says. “But they can’t yet explain what else happened, or why. When sales went up, did labor, food, or other costs also go up disproportionately to erase that margin? We don’t yet know if we in fact made more money.”

Stage 3: Understanding your data

Operators at this step are beginning to answer “why” questions, Bennett says. These operators know that when sales went up, labor went up disproportionately or product outages kept those sales from going up as much as they could have.

“These operators are looking at 2–3 orders of magnitude more data than companies that are still looking at spreadsheets. That returns value because when you know more, you can do more,” Bennett says.

Stage 4: Better managing your business.

Chain operators who are at step 4 of the restaurant information management learning curve are actively making business decisions based on what their data is telling them, Bennett says. They have progressed to answering questions about causality (‘When I do X, then I have 80 percent confidence that Y will happen.‘)

“These companies analyze a tremendous amount of data, looking for the needles in haystacks,” Bennett says, adding that this is where the real business value is revealed.

“Mining the lessons to be learned from your chain’s big data will increase your company’s performance,” Bennett says. “Moving from step 2 to 3 can mean a 200-basis point improvement in your bottom line; moving from step 3 to 4 can add another 200–400 basis points.”

He notes that few chain operators have reached step 4. Over the next 10–15 years, the ones that have will have industry-leading performance, he says.

If this particular learning curve seems too steep to tackle, Bennett stresses operators do not have to make this journey alone. Help is available.

“Look past the salespeople hawking off-the-shelf products [and] for a partner who will listen to your needs and tailor a solution that meets those needs,” he says. “That’s how you’ll conquer the big data learning curve, and translate the business benefits to your company’s bottom line.”

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