Quickly rising labor costs are a major concern for quick-service operators these days as initiatives to accelerate minimum wage increases dominate the news. Since 2014, many states and municipalities have announced hikes, and New York City has even targeted the fast-food industry for mandatory increases.

Quick-serve companies are now re-evaluating pricing strategies, with brands like Starbucks, Chipotle, and Panera Bread already increasing prices this year.

Historically, sustained periods of economic growth come with outsized effects on labor-intensive industries like retail, restaurants, and hospitality. This time around, by employing advanced analytics—so-called “big data”—quick-serve operators have a unique ability to adjust pricing (relative to market opportunity) proactively, automatically, and on an everyday basis.

Even before the potential big data opportunity in the limited-service industry, getting pricing right required an in-depth, planned strategy aimed at finding the right price for both companies and consumers.

Analytics light the way

With so many businesses facing labor cost challenges, it’s an ideal time to engage in pricing strategy and analysis, but the decision to change prices is never an easy one—and shouldn’t be. Choices need to be based on careful analysis of robust and relevant data.

The “big data opportunity” involves a massive sampling of shoppers’ habits and behaviors to determine how consumers will react to variations in the price and combination of products.

Thoughtful, simple pricing also usually yields significantly higher margin profit.

To illustrate, let’s look at one quick-serve executive’s past experience. Chas Hermann is presently acting as interim CMO at Papa Murphy’s restaurants. Prior, he led operations at a major theme park, where he discovered most visitors looked at the cost of camera film and soft drinks as their references for determining value. The common availability of these products allowed customers to frame value across all other products in the park. “Naturally,” Hermann says, “we anchored those prices closer to what a guest might see in other retailers and attractions.”

Basing pricing strategy on instinct and intuition is a long shot; analysis of big data predicts future outcomes far more accurately. Moreover, the influx of data available at our fingertips means it’s easier to build healthy, market-determined pricing strategies than firms might realize.

Finding the best approach

So how can leaders create effective price strategies for their brands?

A cost-based approach ties pricing strategy to items, but because customers don't know the cost of ingredients, such complex and confusing pricing models force them to consider personal costs over the value of what companies are offering.

Anchoring a strategy to category pricing makes more sense in many cases, as customer focus then shifts from price to flavor (and overall enjoyment of the meal).

Hermann believes market-based pricing is a better plan, as this method involves looking at direct and indirect competitors in a category and aligning a brand’s position within the market’s price range. Thoughtful, simple pricing also usually yields significantly higher margin profit.

If you’re a price leader, your brand should command the highest price; if your brand is built on having the best prices, they should reflect that position.

Putting it into action

Guiding pricing strategies against a backdrop of escalating minimum wage hikes isn’t simple, but here are six actionable steps quick-service operators can take to develop customer-focused pricing strategies that build brands, satisfy customers, and deliver value to shareholders:

  1. Identify how you bring your products to market. Most quick-serve brands use in-store menus to communicate gross product prices before any discounts or promotions.

  2. Determine whether to communicate price by item or by category. Because products are usually presented via menuboards by product category, more brands are moving toward category pricing to simplify presentation and guide customers toward the emotional benefits of flavor and quality.

  3. Measure each product’s velocity to find the best performer. A category’s “key product” anchors price to that of market competitors. Once key products for each category are identified, all others within it should be set either at the same price or in relation to the key product’s pricing. Apply the same rationale to sizing, add-ons, and other aspects to optimize price logic across the entire menu.

  4. Prepare for system communication. Execution across a system usually includes training and education about a store or franchise, identifying new pricing, and making POP updates for all marketing assets to communicate the changes.

  5. Develop a proactive price strategy. Partner with a data science adviser and create an algorithm informed with competitive and current market trends for more accurate results. The idea is to glean invaluable information on an ongoing basis, not just once.

  6. Decide if your brand is ready for dynamic pricing. More adventurous quick-service companies can consider dynamic pricing, making it easier to capture the most price-sensitive consumers—especially as customers order more food online for home delivery or pickup. Adjusting premiums and discounts for online shoppers according to known spending patterns (tracked via customer IDs, loyalty programs, coupon codes, or credit card transaction data) makes it easier. With this information, firms can tap into each customer’s perceived value of the product, capturing money still on the table with high value perception segments and achieving new revenues with customers on the other end of the perceived value spectrum.

Without harnessing the power of big data, quick-service businesses could struggle as minimum wages increase across the board. Incorporating analytics into pricing strategy can be the key that opens the door to sustained growth and success.

John Kelly leads the predictive analytics practice at Berkeley Research Group, which works with marketing, sales, and operations leadership across a range of industries to leverage the power of econometrics and data science. This work results in evidence-driven management, delivering dramatic growth and performance improvement. Some of the specific ways it helps clients include dynamic pricing optimization, loyalty program design, and predicting consumer behavior.
Back of House, Customer Experience, Ordering, Outside Insights, Restaurant Operations, Story