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"PiinPoint has become an integral part of my role as Retail Analyst at Cushman & Wakefield Waterloo Region. The platform allows me to put together professional looking reports and provide clients with the insights they need to make real estate decisions.

I honestly don’t know how I would do my job effectively without PiinPoint."

Jessica McCabe, M.Ed.
Retail Analyst

Location intelligence gives growing restaurateurs a scorecard and roadmap for success.

Last updated: December 3, 2019


Fresh-casual eateries are one of those everlasting brick and mortar tenants in an age of retail evolution. Alongside concepts like healthcare services, fitness chains, and personal amenities, these eateries continue to experience traditional growth trajectories as consumers consistently go out to eat or order ready-made meals. Many food and beverage chains that originated as hip local restaurants, have succeeded in becoming household names and highly desirable tenants across the US and Canada. Sweetgreen, Wahlburgers, Corelife Eatery, Smoke’s Poutinerie, BurgerFi, Chipotle, Freshii, and Beansprouts, would be some, just to name a few.

Many of these brands are opening up to 40 units each year and occupying real estate at an increasing rate. While not all can pull off a staggering 70+ units per year like Blaze Pizza did in 2019, many manage to do 10, 15, 25 units annually. With industry trends like independent brands taking up high-street retail, consumers demanding local experiences, and whole food options becoming increasingly popular, F&B concepts are proving that they can see spectacular growth and franchising opportunities.

Infill Markets and Uncharted Expansion

As these concepts look to scale, a challenge becomes knowing where to go next. Foodie entrepreneurs who are intimate with their home markets can open up several locations in a given city, and subsequently target other hip, up-and-coming areas. That said, once the low-hanging fruit is picked, then what? Many fresh casual concepts quickly develop a regional or national presence, yet are not fully optimized in each market. Making an uninformed decision on whether a city is a single or multi-unit market can leave money on the table when more than one opportunity exists. Knowing which markets you can infill and leverage your established brand in is key. And determining which geographies make sense to grow into should be based on more than simply franchisee interest.

Traditional Approaches to Growth

Restaurateurs often rely on the local know-how of a franchisee or a broker network to gauge where the best locations are. Without careful decision-making, opportunities can be miscalculated, markets oversaturated, or franchise territories made too big. To help network strategies, location intelligence are applied to optimize for growth and success.

Location intelligence is not new, but what has been a growing concern is the reliability and visibility into the methodologies applied in analytical projects. Modelling requires impartiality and careful scrutiny, as well as transparency and trust by the end user.

Enter PiinPoint.

PiinPoint’s Suitability Study helps fast-growing restaurateurs understand two key things. First, what is it that has made me successful to date? And second, how can I find more opportunities where those success factors exist? 

Our Approach

We take a partnership approach to our Services work to ensure that our scientific framework is artfully combined with the expert knowledge and unique context of the brand. We require engagement from key leadership and stakeholder groups to curiously explore the network and comfortably draw conclusions on what factors most impact success. These factors are then carefully crafted into a site selection scorecard and an easy to use, integrated roadmap in PiinPoint.

This whitepaper expands on how PiinPoint helps growing restaurateurs better understand their network’s performance indicators, and where to find those factors going forward. By looking at a sample concept called, Keto Cafe, we demonstrate the methodology used in our studies and the insights provided to our clients. Please note that this is a hypothetical study: the restaurant concept, real estate selected, sales performance, and suitability profile / scorecard results are all simply for demonstrative purposes.  

About Our Hypothetical Concept, Keto Cafe

keto cafe

Founded in 2012, the Keto Cafe has been quickly growing as the ketogenic diet trend has become increasingly popular throughout North America. It serves fresh ketogenic food that is made on-site, including salads, entrees, and bakery products. It also specializes in ready-made meals, which are great for customers to order online and pickup, or have delivered to their homes. The Keto Cafe has 62 locations across the US, and plans to open an additional 14 franchise units in the coming year.  

Stores are mostly spread throughout major urban markets in the country, including Texas, Arizona, California, Illinois, Georgia, Colorado, Seattle, and Virginia.

Methodology

Sample Size

Location analytics

Any good statistics require a strong sample size. 

In order to conduct a successful suitability study, PiinPoint requires a strong network sample size. The more data points to assess, the better. Without a strong sample set, it’s hard to confidently determine whether dynamics that are occurring across a store network are significant. Keto Cafe, with its network of 62 stores, works given that it has a very strong set of customer data as well, which adds granularity to the assessment. To learn more about the impact of sample size on creating a profile, see the Customer Profile versus Location Profile section below.

Performance Data

Top Performing Keto Cafe Locations

Performance Data

Bottom Performing Keto Cafe Locations

To understand what’s going on across the network, stores are assembled into groups to determine commonalities based on performance. For this study on the Keto Cafe with 62 locations, we’ve taken two groups with the top fifteen performing sites versus the bottom fifteen performing sites.

For the Keto Cafe, we used a simple performance metric of the previous year’s sales.

Additional years’ historical sales can be applied, or other metrics can be compared for the performance grouping. Some examples of this have included operational factors, no. of customers, market capture, and so on.

Normalizing the Data
PiinPoint creates opportunities to leverage the client’s expertise throughout the Suitability Study to ensure that they trust the methodology being used. The brands’ expert knowledge is considered at this point to normalize performance where necessary. To the restaurateur, those IDs are not random stores, they represent real-life scenarios that are either desirable to replicate, or not. Some sites may have experienced an abnormal year of sales due to external factors like road construction, renovations etc., all of which need to be considered.

Creating a Profile
After creating performance groups for the Keto Cafe, we begin developing the Customer Profile. At the heart of the Keto Cafe’s Customer Profile is an in-depth understanding of their trade area. Understanding the trade area helps concepts like the Keto Cafe to understand:

  • how far their customers travel to visit a cafe,
  • the optimal distances between locations, and 
  • the appropriate franchise territory size. 

The trade area in a Suitability Study can be determined in a few different ways:

  1. Customer Location Data (e.g. loyalty data, in-store surveys, delivery data, etc.)
  2. Mobile Location Data (e.g. GPS data)
  3. Company Mandates (e.g. legally obligated to use 5-mile ring for franchise territories)
  4. Expert Knowledge (e.g. our customers live within a 5-minute walktime) 

Ideally, the trade area is created with customer data. This makes the difference between PiinPoint crafting a Location Profile and a Customer Profile.

Location Profile on 1 Mile Ring

Location Profile on 1 Mile Ring

Customer Profile on Customer Pockets

Mapping Keto Cafe Customers in PiinPoint

Customer Profiles versus Location Profiles
A location profile describes what is happening around a location, by a given trade area distance. Location profiles assumes that generally anyone within that distance to the store could be a customer.

A customer profile is more in-depth. It describes what is happening where customers exist around a location. Rather than assuming all people living within 1 mile of the store are customers, it studies the areas where there are pockets of customers.  

The Keto Cafe has a best-in-class customer rewards program, which collects zip code data from customers. They also conduct bi-weekly, in store surveys to gather zip code data. What is most compelling is that the data they can collect from their delivery program includes delivery addresses.

All of this was observed by PiinPoint to determine that the trade area size for a Keto Cafe is a 7-minute drivetime.

Statistical Modelling
With performance groups and trade areas identified, PiinPoint conducts the suitability analysis. We use a statistical model to identify what factors are statistically significant for the Keto Cafe’s performance groups, drawing from a sample of both high and low performers, and across a variety of different datasets.

PiinPoint’s standard datasets include demographics, competition, anchors, and existing locations. Additional inputs can be added. For brands like the Keto Cafe, who wanted to better understand what kind of online conversations impacted their success, could also include geosocial segmentation data in their analysis. Mobile location data could have been used to understand the impact of traffic.

Modelling Demographics
PiinPoint measures the impact of thousands of demographic variables to understand their relationship to the Keto Cafe’s performance groups. We only consider variables which are statistically significant. With these insights, Keto Cafe better understands what it takes for a high-performing market to succeed. To illustrate some of the dynamics at play, PiinPoint provides a summary on each of the major census topics. Let’s take a look at the results:

Census Breakdowns

Population: Higher median age (with more 40 to 50 year olds), more married or common-law relationships, more couples with grown children, less divorced or separated individuals, smaller families or couple units, with higher population density.

Education: More trades educated or college educated. Typically educated in Arts and Humanities, social sciences, or science related fields, or trained in manufacturing, warehousing, and utilities. 

Industry: More people employed in Finance and Insurance, Real Estate, or professional, scientific, and management industries, fewer people employed in Agriculture, Forestry, Mining or Fishing.

Transportation: Commute outside of city of residence, with longer commute times (that will not carpool), with more late leaving times, less likely to take public transit, or else only take public transit.

Ethnicity Descriptors: Strong European (western and northern) background representation, and Hispanic origins (Brazil, Chile, Peru), less third generation immigrants and more non-immigrants, more US citizens with place of birth in Americas or Europe, fewer visible minorities (especially West Asian, Arab, Chinese)

Economics: High rate of employment, middle to upper class household earnings, between $70,000 to $140,000. Mid to High home values and higher value renter market. 

Household Characteristics: Smaller homes (fewer bedrooms), more condominiums, less suitable homes with more repair needs, smaller household size, fewer rented than owned dwellings, more and older household maintainers, fewer apartments, and fewer attached dwellings. 

‍‍Demographic Insights

In summary, PiinPoint determined that the demographics for top performing locations could be described as follows. This profile is shortly compiled into a scorecard.

Older, middle-aged couples or individuals with older children, smaller households living in affluent areas with highly-valued homes, employed in white collar or highly-specialized blue collar occupations, and employed in professional services or finance.


‍‍Modelling Influencers

‍‍Modelling Influencers
Every concept has an idea of what other brands, organizations, or places fit within its market. While the Suitability Study considers assumptions that the Keto Cafe came to PiinPoint with, like being near Trader Joe’s and Orangetheory Fitness, PiinPoint takes a scientific approach to determining what type of retail landscape they truly thrive in and how to consider that in their decision making.

Modelling Competition
If a brand has competitors, they also need to be considered as new markets are explored. For the Keto Cafe, it was important to see how the presence of trendy, fresh-casual brands like Tropical Smoothie Cafe, Corelife Eatery, Tender Greens, sweetgreens, Freshii, etc. had on their market success. Though close proximity to such competing concepts intuitively seems important to avoid, in some cases the presence of similar brands in a market can be a strong indicator that the clientele that exist. If the market is undersaturated, competition becomes less relevant in decision making.

Modelling Cannibalization
Finally, proximity to one’s own stores is vital in planning markets and optimizing a network. For the Keto Cafe, in some markets there are multiple locations, in others, they are single site markets. It’s important to know how the dynamics of brand awareness and oversaturation come into play. The Keto Cafe had a company mandate to protect their franchisees, meaning each store was always at least 2 miles away from each other. In other cases, understanding proximity of one’s own stores can have a major impact on market success.

POI Insights
PiinPoint assessed a variety of competitors, anchors, and co-tenants for the Keto Cafe. It was identified that some concepts or categories are important to be closer, or further from. All these details are shortly compiled into the scorecard.


Scorecard Compilation

A struggle with having crafted narratives and summaries like those shown in the above profiles is that they can be difficult to directly translate into a strategy. How do you apply demographic segmentation, customer behaviour, and important influencers to the map at scale? 

PiinPoint pulls the most significant criteria from the Suitability Study into a scorecard for success. This scorecard can be directly translated into a real estate strategy in PiinPoint. For the Keto Cafe, we broke it down into two components: Demographics and POI Influencers. 

Each of the following variables are the key ingredients to measure suitability of a location:

Variable Name
Association Range Weight
Age 45+ Positive 200 - 300 people 12%
2 person households Positive 400 - 500 households 18%
Households with retirement income Positive 150 - 250 households 10%
Population below the poverty level Negative 30 - 100 households 7%
Population in Educational services, and health care and social assistance industry Negative 500 - 600 people 8%
Household Income $60,000 - $100,000 Positive 30% - 50% 15%
Population Density Positive 2,000 and 6,000 ppl/square mile 10%

Points of Interest Influencers (anchors, competition, cannibalization):

Variable Name
Association Range Weight
Orangetheory Fitness Positive Within 3 miles 8%
Whole Foods Market Positive Within 1.5 miles 7%
Starbucks Positive Within 1 miles 5%

Cannibalization Impact

Variable Name
Association Range Weight
Keto Cafe Canni Negative Away 2 miles N/A

Summary of Results: Location Intelligence Roadmap in PiinPoint

The scorecard allows PiinPoint to produce an integrated set of Suitability Layers for the Keto Cafe in the PiinPoint app. Keto Cafe can use these layers in whatever geographies they explore, be it brand new markets or infill opportunities.

Every scored intersection, along major arterial road networks and within a population center, is ranked with a Suitability Score. This methodology ensures that scores provided represent legitimate markets for consideration where there are presumed retail and commercial real estate availability, i.e. not at a rural crossroads, within a suburb, National Park, etc. 

These integrated Layers give Keto Cafe a strategic roadmap for assessing new markets going forward, helping to answer the questions of:

  • What areas across the US represent the best opportunities for me?
  • Where within X geography is most suitable?
  • How many opportunities are there for me in each market?
  • And many more…

Let’s take a look at some case studies.

Portland, Oregon.
This is already a 3-store market. The left-side Suitability Layer shows what the market looks like assuming there were no Keto Cafe locations there. The right-side Layer shows the same market but considering suitability given the existing store network. Even with three stores in this market, Keto Cafe can identify that there are additional opportunities to grow outside of the downtown core, that have excellent suitability, including Beaverton, Hillsboro, Tigard, and Oregon City:

Portland Suitability Stats
Total Population: 641,494
Highest Suitability: 78.03
Lowest Suitability: 15.37

Portland, Oregon - Without Considering existing Keto Cafe locations

Portland, Oregon - Considering impact of existing Keto Cafe locations

Austin, Texas. 
Austin already has two locations. If we look at the Austin Suitability Layer, not considering the existing network, we see that Store 1 is situated in a relatively well-suited area for Keto Cafe, with a suitability in the 60th percentile.

Store 2 on the other hand is in a less suitable area in Austin, with only 27% Suitability. Store 2 happens to be one of Keto Cafe’s lower performing locations. As they begin to compare the poorer performing location to the surrounding market, they start to discover that the characteristics of this location may be contributing to the lower performance:

Austin Suitability Stats
Total Population: 938,200
Highest Suitability: 72.2
Lowest Suitability: 8.83

Austin, Texas - Without Considering existing Keto Cafe locations

Austin, Texas - Considering impact of existing Keto Cafe locations

Austin, Texas - Store 1

Austin, Texas - Store 2

Seattle, Washington. 
Seattle is a brand-new market that was identified as having high suitability for Keto Cafe. There are no existing stores here. This map identifies top opportunities existing in Bellevue, Kirkland, Magnolia, West Seattle, Redmond, and Queen Anne.

Seattle Suitability Stats
Total Population: 707,255
Highest Suitability: 74.78
Lowest Suitability: 7.93

Denver, Colorado. 
Denver also has some locations already. The stores in the downtown core are mid-performing, but given they are some of the newest additions to the network 

This time, Keto Cafe is assessing additional opportunities for their Denver market. The top opportunities are within Cherry Creek, City Park, Lakewood, and surrounding communities Grant Ranch, and Arvada / Westminster.

Denver Suitability Stats
Total Population: 688,643
Highest Suitability: 84.07
Lowest Suitability: 10.97

Additional opportunities were identified in net new markets including Boulder, Cleveland, Cincinnati, Chicago, Atlanta, and the greater Los Angeles area.

Key Applications of Suitability

By conducting the Suitability Study, we’ve shed light on what’s happening across the Keto Cafe’s network, and outlined a roadmap for success as they grow. The Suitability Study has helped to provide insight into decisions regarding real estate, franchising, marketing, and operations, including;

Marketing: Knowledge on the Customer
Data has been pulled to identify customer groups that are most impactful to the Keto Cafe’s success. This can be used in marketing and advertisements that target the ideal customer.

Franchise Operations: Gaps in Performance
For those markets that are highly suitable, but host poor performing locations, that’s a great indicator for the restaurateur to take a closer look at the store in more detail. It could be there are operational issues that need to be addressed. On the flip side, those sites that are poor-performing, AND located within a less suitable area, represent opportunities to optimize the network; possibly relocating to more favourable conditions, negotiating reduced rents, or converting to a distribution-type store format.

Real Estate: Identifying Top Markets for Expansion
Growth no longer becomes a guessing game, there is a game plan on the best markets for which to expand, and why.