Food & Beverage Tomorrow: Expanding restaurant chains are tapping powerful analytics to avoid cannibalizing sales

With restaurant chains’ sales growing despite numerous economic, operational and real estate challenges, many are in expansion mode, both by entering new regions and infilling in markets where they already have a presence.

Infilling poses a challenge for real estate decision-makers, however, as brands run the risk of opening new stores that take sales away, or cannibalize in industry lingo, from existing locations. In addition, they need to assess the impact when competitors open near existing stores or when they make changes to e-commerce strategies.

Limits of predictive real estate models

Until recently, the restaurant industry relied on predictive real estate models, which forecast sales for a potential site using customer attributes like demographics and consumer behavior patterns, as well as marketplace attributes like competition, shopping center types and synergies. Decision-makers then compare sales forecasts for each potential site to home in on the one likely to perform best.

However, predictive models are unable to measure the impact of a new location opening (or closing) near an existing one. Decision-makers essentially make well-informed educated guesses based on past experiences when infilling, waiting on actual results to determine the extent of cannibalization.

Enter Cannibalytics

To aid retailers struggling to assess the risk of cannibalization, CBRE’s Retail Analytics statisticians and analysts developed Cannibalytics, a proprietary approach that combines 5-10 years of a brand’s historic monthly sales data with a host of market metrics like site type (mall, shopping center), competitors, distance and sales channel (brick-and-mortar, online) to quantify the potential impact of opening or closing a new location near another one.

Cannibalytics can transform retail site selection by shifting the focus from the performance of a single store to a brand’s overall presence and success in a market, including:

• Quantifying changes in sales at existing locations after a store opens or closes;
• Identifying if and by how much sales will bounce back;
• Tracking how existing customers change their spending after a new location opens; and
• Revealing other trends that a new location may have on existing locations.

A case study: the quick-service burger chain

A national quick-service burger chain has four strong-performing locations in a region, grossing a combined $20 million in annual sales. They’re seeking to open another store, and its real estate team has proposed two sites with equivalent lease terms, using predictive real estate modeling to establish that Site A could gross $4.5 million in sales annually and Site B could gross $5 million. Based on predictive analytics, Site B appears to be the better option. But the decision-makers want to understand the risk the potential sites pose to sales at their existing locations.

Cannibalytics analyzes how sales historically have been impacted from new sites opening and estimates the sales each potential site could take away from existing locations. In this case, Cannibalytics determines that 6% of existing customers are within the trade area of Site A, and the new store could cannibalize 5%, or $1 million, from the existing locations. Meanwhile, 18% of existing customers are in Site B’s trade area, with the potential to cannibalize 20%, or $5 million, of sales from the current stores.

When accounting for cannibalization, the burger chain would gross a combined $23.5 million in sales annually with Site A and $20 million with Site B. Put differently, Site A adds $3.5 million in revenue, while Site B effectively generates no new revenue despite adding another location. Site A wins out and the brand avoids the misstep of developing an unprofitable location.

Cannibalytics can also determine that a new site won’t cannibalize existing sales, an insight that is especially useful for franchise arrangements where potential and existing franchisees need to understand the impact of openings and closures on their sales.

Other uses for Cannibalytics

• Store closures: Restaurant decision-makers can use Cannibalytics to determine the impact of a store closure on neighboring locations. Using the burger chain example above, if one of the four existing locations were to close, Cannibalytics can forecast if and by how much the remaining stores can recapture the sales lost from the closed location.
• Bounce-back rates: Cannibalytics assesses bounce-back rates when new locations open, enabling retailers to see if and by how much existing stores can recover lost sales over time. Whereas a new store could cannibalize 10% of sales upon opening, for example, over time that number could fall as market dynamics like population growth evolve.
• Competitors’ activity: Cannibalytics can estimate the impact on sales when competitors open nearby, providing decision-makers valuable insights to gauge and respond to potential new entrants. For instance, if a competitor of the quick-service burger chain is also considering Sites A or B, Cannibalytics can measure the impact on existing sales if the competitor opened at either of the sites.
• E-commerce: Cannibalytics assesses if a new brick-and-mortar location cannibalizes a chain’s existing e-commerce sales or helps increase brand awareness to boost e-commerce sales. For example, the burger chain can use historical sales patterns to measure both sites’ trade areas to see if in-store sales at the new location will erode or enhance the brand’s e-commerce sales from apps and delivery services. If a first-time diner in a new trade area enjoys the food and experience, they will subsequently generate repeat e-commerce business—a relationship sparked by the brand’s brick-and-mortar presence.