About Our Hypothetical Concept, 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 201 locations across the US and Canada, and plans to open an additional 20 franchise units in the coming year. Stores are mostly spread throughout urban markets in both countires, including in the US; Texas, Arizona, California, Illinois, Georgia, Colorado, Seattle, and Virginia, and in Canada; the Greater Toronto area, Vancouver, Calgary, Edmonton, Montreal, and Ottawa.
Methodology
Sample Size
Any good statistics require a strong sample size.
In order to develop a Regular Marketmatch roadmap, 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 has a network of 201 stores, lending a significant amount of historical data points to work with.
*PiinPoint Marketmatch can be tailored to also support smaller chains. To learn more, regardless of your network size, contact us sales@piinpoint.com.
Performance Data
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 201 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 development of a Marketmatch solution 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.
Store closures due to COVID-19 can also be considered here. In some cases, performance data from pre-COVID versus post-COVID may be more or less applicable.
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 areas used in Marketmatch can be determined in a few different ways:
- Customer Location Data (e.g. loyalty data, in-store surveys, delivery data, etc.)
- Mobile Location Data (e.g. GPS data)
- Company Mandates (e.g. legally obligated to use 5-mile ring for franchise territories)
- 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 versus a Customer Profile.
Location Profiles versus Customer Profiles
A location profile describes what is happening around a location, by a given trade area distance. Location profiles assume that generally anyone within that distance to the store could be a customer.
Image 1. The ring is used to understand customer characteristics.
A customer profile is more in-depth than a location profile. 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.
Image 2. The pockets are used to delineate customer characteristics.
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 Marketmatch 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, such as segmentation data. For example, 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.
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
Every concept has an idea of what other brands, organizations, or places fit within its market. While Marketmatch can consider 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 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, for example, drive-time capture rate between markets.
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 to, or further from. Here is a correlative assessment for top competition, anchors, and Keto Cafe locations:
Scorecard Compilation
A struggle with having crafted narratives and summaries like those shown in Demographic or Retail landscape 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 Marketmatch 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.
Marketmatch Scorecard
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% |
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% |
Variable Name
|
Association |
Range |
Weight |
| Keto Cafe Canni |
Negative |
Away 2 miles |
N/A |
