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Unlike Labor and Supplies, Restaurants Are Not Short on Data. Let’s Learn How to Use It.
  • Corporations and individuals create an estimated 2.5 quintillion bytes of data daily.
  • Restaurant finance teams spend 80% of their time collecting data and 20% analyzing it. The industry needs to flip that equation.
  • Poor data collection prevents accurate benchmarking. Good data collection is complete, accurate, timely, consistent and comparable.

In a Bain & Company longitudinal study (1993-2017), 80% of top executives ranked benchmarking as one of the most desirable management tools. Yet less than half were satisfied with their ability to use their existing tools.

Interestingly, the study revealed that as data becomes more available, executives are less satisfied with their company’s overall benchmarking usage.

Why? The old term “garbage in, garbage out.”

The number of bytes in the digital universe is 40 times larger than the number of stars in the observable universe, and we create 2.5 quintillion more bytes daily.

Thanks to this overwhelming flow of data, the disparate systems that manage it, irregular reporting methods and the extreme lag times inherent in franchise systems, we estimate that the average restaurant finance team spends 80% of its time collecting data and 20% analyzing it.

That’s a great deal of time spent sifting through the garbage.

Benchmarking is hard to do. It’s even harder to do right.

Our goal for our restaurant clients is to flip this equation. And that means creating a data collection system that generates actionable insights.

Through our new metiRi platform, used in more than 30,000 franchise locations globally, we’ve gained valuable insights and established more than a few best practices.

We start with how, when and where operators obtain data. Knowing the data origin is key to avoiding an unwanted payday of data junk. When reviewing data, we recommend assessing the following factors.

Completeness

Benchmarking information is not always held to the same standard as financial reporting. Safeguards confirm that all transactions are reported and occurred. Financial teams should ask themselves, “Do we have all the data required to assess performance accurately? Is any data duplicated?” The latter is particularly common with store transfers.

Accuracy

When working with an extensive franchise system, the sophistication of methods may vary significantly. Large franchisees may use the latest technology, while others rely on manual entry, causing inaccurate reporting. On the other hand, reports may tell you “what you want to hear.” Whatever data is collected should have an equal validation process.

Cutoff

Franchisees often dictate fiscal calendars, which may not align with the brand standard, resulting in a different number of weeks in a reporting period. Teams should ask, “Were the transactions entered for the correct accounting period?”

Classification

Franchisees operate as independent businesses and classify accounts and financial statements to fit their needs. For example, some franchisees may consider utilities a controllable cost, while others might not. However, these individual franchisee decisions complicate meaningful comparisons between entities. Teams should assess how to standardize unit-level financial reporting.

Time factors

Slightly different than cutoffs, time factors can also undermine data quality. For example, franchisees may report financials on different frequencies — for example, some monthly and others quarterly. Franchises may also report using different lags (e.g., 45 days after a period ends vs. 30). Teams should assess how they standardize and validate collected data.

Comparability of entities

By definition, benchmarking requires comparing similar data, which requires careful identification of relevant peer groups. Franchise operators and their franchisees can compare geographic regions or designated market areas, size (based on capacity or sales) and other attributes, including urban/suburban, shopping center/free-standing and no drive-thru/drive-thru. Teams should assess which comparisons are valuable to the franchisee and the operator and include those details in standard reports.

Create actionable recommendations by overcoming inherent limitations of data collection.

The bottom line: If your data collection is wrong, so are any conclusions you may draw from it. We have developed a set of best practices to support our franchise system clients.

Benchmark your franchise brand with confidence.

At RMS, we understand the complexities you face managing multiple franchisees, each with an individual approach to collecting data and with varying degrees of accuracy.

To create change and profit, benchmarks must be credible and actionable. Real-life scenarios and best practices inform our platform, and our systems meet franchisees where they are. As a result, metiRi unlocks the truth in your data, helping you distill it into growth-supporting insights that you can use right away. If you want to eliminate the garbage and benchmark in ways that support brand success, request your personalized metiRi demo today.

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