CFA Guidebook Series: Sponsored by PiinPoint

CFA Guidebook Series

LEVERAGING DATA & ANALYTICS TO SCALE YOUR FRANCHISE Sponsored by:

Published by the Canadian Franchise Association

CFA Board of Directors BOARD CHAIR David Druker*, The UPS Store PRESIDENT & CEO Sherry McNeil*, Canadian Franchise Association 1ST VICE CHAIR Ryan Picklyk, A&W Food Services of Canada Inc.

ABOUT THE CANADIAN FRANCHISE ASSOCIATION (CFA) The Canadian Franchise Association (CFA) helps everyday Canadians realize the dream of building their own business through the power of franchising. The CFA advocates on issues that impact this dream on behalf of more than 700 corporate members and over 40,000 franchisees from many of Canada’s bestknown and emerging franchise brands. Beyond its role as the voice of the franchise industry, The CFA strengthens and develops franchising by delivering best-practice education and creating rewarding connections between Canadians and the opportunities in franchising. Canadian franchises contribute over $100 billion per year to the Canadian economy and create jobs for more than two million Canadians. Learn more at www.cfa.ca or www.FranchiseCanada.online.

CANADIAN FRANCHISE ASSOCIATION 5399 Eglinton Avenue West, Suite 116 Toronto, Ontario M9C 5K6 Ph: 416-695-2896 or 800-665-4232 F: 416-695-1950 W: www.cfa.ca / E: info@cfa.ca © 2022, Canadian Franchise Association (CFA).

All rights reserved. Contents of this publication may not be reproduced either wholly or in part, without the consent of the CFA. Legal Disclaimer: The opinions or viewpoints expressed herein do not necessarily reflect those of the Canadian Franchise Association (CFA). Where materials and content were prepared by persons and/ or entities other than the CFA, the said other persons and/or entities are solely responsible for their content.

The information provided herein is intended only as general information that may or may not reflect the most current developments. The mention of particular companies or individuals does not represent an endorsement by the CFA. Information on legal matters should not be construed as legal advice. Although professionals may prepare these materials or be quoted in them, this information should not be used as a substitute for professional services. If legal or other professional advice is required, the services of a professional should be sought.

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TREASURER Lyn Little, BDO Canada LLP SECRETARY & GENERAL COUNSEL Larry Weinberg*, Cassels Brock & Blackwell LLP PAST CHAIR Gerry Docherty*, Good Earth Coffeehouse CHAIR, LEGAL & LEGISLATIVE COMMITTEE Darrell Jarvis*, Fasken CHAIR, FRANCHISE SUPPORT SERVICES Kirk Allen*, Reshift Media Inc. DIRECTORS Steve Collette, 3rd Degree Training Chuck Farrell, Pizza Pizza John Gilson, COBS Bread Andrew Hrywnak, Print Three Franchising Corporation Rimma S. Jaciw, CFE, WSI Digital Joel Levesque, McDonald’s Restaurants of Canada Ken Otto, Redberry Restaurants Gary Prenevost, FranNet John Prittie, TWO MEN AND A TRUCK Stephen Schober, Metal Supermarkets Family of Companies Frank Stanschus, Little Kickers Thomas Wong, Chatime Todd Wylie, Master Mechanic *Executive Committee member

CFA Guidebook Series: Leveraging Data & Analytics to Scale Your Franchise

TABLE OF CONTENTS Leveraging Data & Analytics to Scale Your Franchise ������������������������������������������������� 6 Leveraging Data for Site Selection ����������������������������������������������������������������������������������������7 Finding New Markets for Growth ����������������������������������������������������������������������������������������� 10 Optimize Your Franchise Network for Profitability �������������������������������������������������������13 Fast Facts & Keyword Definitions ������������������������������������������������������������������������������������������17

The CFA wishes to acknowledge and thank these National Sponsors for their support throughout the year. Find out more about these companies at www.cfa.ca/sponsorship.

LAW FIRMS:

SHOWCASED FRANCHISES

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Leveraging Data & Analytics to Scale Your Franchise

LEVERAGING DATA & ANALYTICS TO SCALE YOUR FRANCHISE

If there’s one thing we know in retail, it’s that consumer demand is always changing. While this constant and accelerating evolution can feel overwhelming, there are moves you can make to confidently plan for success, regardless of the maturity of your network, even amid uncertainty.

Many retail franchising brands are planning to expand their physical networks, just as many are planning on rationalizing and optimizing their existing networks in the face of rising digital e-commerce demand and rising operational costs. Now more than ever, developing a data-driven expansion/optimization strategy, and then executing that plan with speed and precision, is critical for gaining or maintaining a competitive advantage for access to the most desirable markets and real estate listings.

The three most common questions that retail and franchise organizations ask are: 1. W here do I put my next location?

2. W here are the ideal markets to grow my network over the next three years?

3. With a mature store network, how do I optimize the network for overall profitability?

In this guidebook, we outline our thoughts on how franchisor organizations can leverage data and market insights to address these important and ongoing questions.

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CFA Guidebook Series: Leveraging Data & Analytics to Scale Your Franchise

Leveraging Data for Site Selection

LEVERAGING DATA FOR SITE SELECTION

One of the more frequent questions we hear from clients is “where do I put my next location to ensure success?” Choosing new locations is one of the most important decisions you can make for your company. Whether you’re a real estate professional for a large franchisor, or the CEO of a growing small business, this decision will have long-term effects on the health of your brand.

The process of selecting a new location has a particular workflow. These days, it involves far more than just finding available listings and choosing based on gut feel or past experience. It’s critical that you have a thorough understanding of the consumers who live and shop there, the flow of people and traffic at various times of day, the competitive landscape, co-tenancy, and a range of other considerations. Best practices would suggest that you need to begin by identifying the right Sponsored by:

market and trade area, and then select the right site within that market using fundamental data points about the trade area that location would serve. Therefore, choose the right market, and then choose the right site.

Choose the right market From marketing to product development, real estate to sales, the entire company needs to answer a few not-so-simple questions: 1. W hat demographics, preferences, and behaviours define your target customer?

2. W hat anchors and complementary brands exist in the trade area that will drive traffic to your business?

3. W hat are the vehicle and pedestrian traffic counts in the area, and do the high-volume times align with your hours?

4. Is this area growing or declining?

5. W hat is the competition like in the area?

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Leveraging Data for Site Selection

(Fig. 1. Demographics, traffic counts, mobile location data, and points of interest all provide an excellent bird’seye view into the profile of people and businesses in an area; Geosocial Segmentation; Piinpoint.com; 22 Aug 2022; https://help.piinpoint.com/en/articles/3840181-geosocial-segmentation-explore-the-personality-of-aneighbourhood.)

Demographics and customer segmentations systems are the best way to identify and locate your target market. By combining demographics such as median income, education level, and population in a set age range, as well as preferences and behaviours through commercial or home-grown segmentation systems, real estate decision-makers are equipped with important demand signals that can make or break the launch of a new location. Customer segmentation data contains rich variation in customer preferences, spending behaviour, and potential loyalty to your brand to get a better idea of what a suitable area might look like.

Anchors can be any high-traffic location that serves as a focal point within a neighbourhood. Perhaps it’s a shopping centre, grocery store, or even a sports facility. For instance, universities are often major community anchors for low-cost restaurants because the university students’ annual restaurant food expenditure matches their product offerings.

Traffic data will tell you how visible your potential storefront will be based on time of day and day of the week, and even how much

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organic traffic it might be able to generate.

The days of sitting in a parking lot and watching cars pass by are long gone. Leveraging traffic models built using mobile location data gives businesses reliable and up-to-date traffic information. This data can even identify the difference in traffic for the right and left sides of the road.

Another consideration is accessibility.

You’ll want to know if a potential location is only accessible by a left-hand turn or if there’s a road median blocking traffic in and out of the parking lot. Make sure you have access to photos from the street view of a potential location to determine how accessible it is for your customers. While Google Street View will give an initial insight into a location, there’s nothing like visiting it in person to fully gauge the location’s accessibility.

Is the location in an area that’s growing or in urban decline? An indicator of future opportunity is the status of new developments in the area, which indicate up-and-coming neighbourhoods for modern buildings and an increased population.

Investing in locations early will establish your brand as a key player in the neigh-

CFA Guidebook Series: Leveraging Data & Analytics to Scale Your Franchise

Leveraging Data for Site Selection bourhood before competition becomes fierce. Another source of information is understanding the population shifts from one municipality to another. Statistics Canada and other databases will have this information available to show how the population has changed over a five-year period and what areas have seen the greatest increase.

The density of competitors around a new location can be both a win or a loss. Regional and national brands in the area may be well known, but don’t underestimate your momand-pop shop competitors. Local businesses often have a deep connection with the community that will be difficult to replicate as a newcomer.

Choose the right site Now that you’ve found the right market and determined that there’s significant demand for your product or service, you need to answer three more critical questions: 1. How do I determine if a particular site will be successful?

2. W here are the available sites/listings in the target area?

3. How do I prioritize listings to increase the potential performance of the new site?

What real estate is available and how can I make sure it meets my criteria for success?

Real estate departments need to guide their broker networks to identify the properties based on the physical attributes you know are successful. Simple scorecards or templates provided to agents ahead of time go a long way in reducing the time spent on analysis after they’re submitted.

Success scorecards for listings are a great way to prioritize the available site inventory to improve long-term success.

Efficiency and accuracy are everything, and having a consistent “scoring” mechanism for listing submissions by brokers or agents is important to reduce the time to leasing negotiations. It’s just as important to keep the scoring mechanisms fresh over time, as data refreshes and things change in the market, such as a competitor opening in your market or population growth due to immigration, for example. As location intelligence technology evolves, real estate departments are moving to automated scoring and sales forecasting systems to prioritize listings as they come in. Automation technology ensures that your broker network sticks to your “success” profile and isn’t wasting time by chasing bad sites.

The store format and physical attributes that are correlated with existing high-performing stores can be derived from historical data. This data can help you and your brokers optimize your real estate listings.

For example, certain retailers would strongly consider a mall location simply because placement within a shopping centre will drive traffic to their site.

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Finding New Markets for Growth

FINDING NEW MARKETS FOR GROWTH

As outlined in the previous “Leveraging Data for Site Selection” article, finding the “ideal” market fit for your concept, value proposition, and product offering is the first step in an ongoing battle for real estate and store operations development executives.

Historically, supporting these decisions has been the domain of geographic information systems (GIS) analytics teams armed with reams of internal and external data.

Advancements over the last 10 years in big data, machine learning, and artificial intelligence have helped in making location intelligence more accessible!

Predicting successful new markets The trick to finding the right “white space” market is to figure out which factors have the most influence on a market’s potential value. Site characteristics and operational management issues will have a significant influence on store performance, but only after a specific piece of dirt is converted to a

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(Fig. 2. A 5-, 10-, 15-, 20-, and 25-minute drive time heat map in St. Catharines, ON; Build a Trade Area; Piinpoint. com; 22 Aug 2022; https://help.piinpoint.com/en/ articles/422809-build-a-trade-area.)

location. One has to identify whether the fundamentals of the market can support a solid baseline of revenues prior to the real estate approval process.

If you can identify these key market factors, you can build a scoring system using your existing network’s high-performing markets as the baseline data. Brokers appreciate it when you can narrow down their search and allow them to focus on what they do best: finding the right listings!

CFA Guidebook Series: Leveraging Data & Analytics to Scale Your Franchise

Finding New Markets for Growth Building the scorecard There are many ways to build a scoring system. The best practice these days is to use data and predictive modelling methods to remove the “broker or experience bias” from the system and allow the data to drive the outcome. There are three major steps in doing this: 1. Identify top- and bottom-performing stores through annual sales metrics; 2. Run a statistical analysis among trade area attributes (demographics, competitive density, etc.) against top- and bottom-performing stores and identify those attributes that differentiate them the most; 3. Build the analysis into an automatic scoring system—a scorecard.

Step 1: Identify top- and bottomperforming stores through annual sales metrics The key to coming up with a benchmarking scorecard at scale is to understand where the top and bottom performers can be found in your store network. It’s critical to identify these two groups, such as the top 20 per cent and bottom 20 per cent on store annual sales, or you can normalize it and use sales per square foot. Either way, you need a consistent way to identify top performers.

Step 2: Run a statistical analysis among trade area attributes against top and bottom performers The concept of a “retail trade area” has been around for a long time. By broad definition, a retail trade area is the geographic area that a retail store draws customers from.

Defining your trade area provides you with a geographic zone within which you can profile all the variables that define the variation and potential for demand in the area around any store. The assumption is that this area covers most of your potential customers, competitors, and retail anchors that will be major contributors or detractors to driving market potential.

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Traditionally, there are three methods you can use to define a trade area: rings, polygons, or drive/walk times. These methods are purely geographically-based distance measures. However, advanced retailers are collecting postal codes at the POS, while others have established loyalty or rewards programs that integrate with their POS system, providing a wide range of insights about customers’ buying habits, in addition to where they live. At a minimum, customers’ postal codes provide the most accuracy about the size and dimension of your trade areas. With spending data by customers, as well, you have the “gold standard” for trade area construction. Now you can not only understand the size of the trade area but also your spending share of that trade area!

In our experience, the factors that influence the potential of a new market (or a trade area) fall into one of five areas: 1. Demographic and socioeconomic profiles (census variables like population density, median income, household size, ethnicity, etc.); 2. Daytime population; 3. Vehicular and pedestrian traffic; 4. Urban-rural profile of the area; 5. Consumer/customer preferences and behaviours; 6. Proximity to your existing locations (cannibalization effects); and, 7. Competition and other related retail profiles (called “points of interest”).

Predicting the performance of potential sites using modern machine learning and statistical methodology depends on the availability of historical data at the dissemination area (e.g. Canada) or block group (e.g.

United States of America) that covers these five “buckets” of variables.

Statistical modelling techniques (multiple correlations or regression analysis) can estimate the impact of thousands of variables to derive the significant relationships among high-/low-performing markets.

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Finding New Markets for Growth Step 3: Build the analysis into a scorecard The statistical relationships derived in step 2 are then compiled into a prediction algorithm that can be applied to potential sites (e.g. intersections, dissemination areas, census blocks, or trade areas) to provide a ranking and allow real estate professionals to prioritize markets for further investigation, and provide their brokers with guidance on finding the right “dirt.” This will show which prospective markets are over- and underperforming, and identify the markets with the highest potential based on the surrounding localized market’s characteristics.

Example scorecard: In this scorecard, the trade area is a seven-minute drive time around a point on a map (e.g. intersection) and receives a score

of 79 out of 100, meaning it has a medium-tohigh probability of “looking like” a high-performing market. You can see the factors that have varying levels of influence on the overall score outlined in the table. The factor scores and weights are derived from the analysis in Step 2.

Statistical models aren’t a universal solution; they’re representations of reality based on historical data. Accuracy varies in direct proportion to the amount of data you have.

However, the models can provide a first look at all markets and provide the franchisor with a tool to improve the efficiency of, and remove decision bias from, their market selection process. They enhance the real estate approvals protocols by ruling out evident underperformers from the system and allow decision makers to focus on those with the best chance of success.

(Fig. 3. Sample scorecard from hypothetical case study; Giving Restauranteurs a Road Map for Market Growth; PiinPoint.com, 22 Aug. 2022, www.piinpoint.com/keto-cafe-case-study-download.)

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CFA Guidebook Series: Leveraging Data & Analytics to Scale Your Franchise

Optimize Your Franchise Network for Profitability

OPTIMIZE YOUR FRANCHISE NETWORK FOR PROFITABILITY The path to success isn’t as straightforward as it used to be. The pandemic has impacted consumer choices and behaviour, requiring the physical store network to pivot. Retailers’ brick-and-mortar growth and performance formulas must also be altered to appease franchisees and investors alike.

As mature retail and franchise organizations look to the future, unit growth is only one of several strategies to reach profitability goals. Those organizations who seek profit per unit growth need to look at “optimizing” the existing network to improve profitability, as well. Real estate teams are charged with building strategic plans for closures, relocations, consolidations, and renovations to support the brand. Many are also refreshing the brand to increase profitability of poor performers and/or replace them with alternatives. As such, real estate planners are leveraging geospatial machine learning tools to help simulate how changing the network strategy will impact long-term organizational value.

Network optimization: Moving toward more profitable outcomes The denotation of the term network optimization suggests moving toward the one true outcome from which you can’t improve any further, ”all else held equal”. This is a scientific definition but is difficult to apply in business decision-making, as we know that world events, consumer behaviour, and competitor actions are continually evolving. That is, it’s a moving target!

As such, we think of store network optimization as a process by which multiple changes to an existing network of stores have a predicted net positive impact on organizational profitability with a margin of error, subject to market constraints, such as changing consumer behaviour, market saturation, store unit performance, and predictable competitor impacts. Hence, the process of “optimizing” is incremental by executing manageable ongoing adjustments toward higher performance.

(Fig. 4. Customized and interactive sales forecasting models can help you quickly query a candidate location and predict mature store sales; Sales Forecasting; Piinpoint.com; 22 Aug 2022; https://help.piinpoint. com/en/articles/2020940-how-will-this-site-do-in-sales.)

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Optimize Your Franchise Network for Profitability The tools required A new combination of data, machine learning methods, and interactive scenario planning can inform retailers seeking optimized performance across an evolving omnichannel network. The tools in this endeavour include, but are not limited to: • Sales prediction models (forecasting), • Cannibalization (sales transfer) models, • Simulation engine for multiple scenario analysis.

Sales forecasting models have been used for decades, and in retail, a well-understood field of practice is evaluating store success and replicating it in a geospatial platform.

A sales forecasting model consists of three components: • Mature store/unit sales prediction model, • Time-to-maturity estimation using historical patterns, • A forecast model based on historical growth beyond maturity.

The main purpose of these models taken together is to predict the potential sales at a new location from day zero to maturity, at maturity, and from maturity onwards. In smaller organizations where unit growth is the goal and there is lots of white space, sales predictions are all that’s needed to grow profitably.

In mature networks looking for infill, or other changes like replacing or closing poor performers, the sales forecasting capability is not enough - enter the cannibalization model.

Cannibalization (sales transfer) models are built on historical patterns of sales changes to existing stores when a new store opens up in the same trade area. The sales prediction and the cannibalization prediction work together to establish the “net sales impact” of adding a new store. In mature, larger networks, overpopulating territories is a real threat to franchisee value and the organization as a whole.

(Fig. 5a. Sample network optimization opportunity prediction; Network Analysis; piinpoint.com; 22 Aug 2022.)

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CFA Guidebook Series: Leveraging Data & Analytics to Scale Your Franchise

Optimize Your Franchise Network for Profitability

(Fig. 5b. The Network Optimization exercise is formatted into layers that allows for visualization at the local, regional, provincial, or national level. In Fig. 5b, the Greater Toronto Area is depicted and the green dots represent opportunities to expand or relocate; for example, the Scarborough area; Piinpoint.com; 22 Aug 2022.)

Both model types (sales and cannibalization models) are used in a simulation engine to provide insight into the net revenue or profit impacts of several real estate events.

Such events include opening or closing a location, relocations (essentially a closing and an opening together), renovations (downsizing or upsizing floor space), or consolidations (essentially closing locations in a market of several).

Real estate events that drive improved network productivity Chances are you’ve encountered all of these decisions at one time or another. It’s likely your internal real estate teams, working together with the finance department, come up with calculations for a business case. These days, the game is getting more sophisticated, and the use of the tools outlined above is fast becoming best practice. They are data-driven and scalable to allow you to estimate the net revenue and/or profitability impact of several different real estate decisions (perhaps multiple at a time or across time) that you would consider on your three-to-five-year road map.

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1. Store openings: When evaluating expansion opportunities, you want to predict sales with precision and confidence by measuring incremental and mature store sales, as well as the potential cannibalization to your existing network. Retailers also can’t forget to consider how their market share changes with new store openings.

2. Store relocations: Sometimes a neighbourhood might be just right for your concept, but you can miss out on fully capturing the market due to poor real estate conditions, such as when access to the centre is awkward, you’re on the wrong side of the street, or updates to the unit are cost prohibitive. Ensure you can distinguish whether your unexpected low performance is due to the geography versus the real estate, and/or operational issues. While it’s easier said than done (and dependent on listing availability) if your store is already in a high potential market, try maximizing your revenue opportunity by moving it to an optimal location nearby.

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Optimize Your Franchise Network for Profitability 3. Store closures: As spending migrates online and accessing goods and services evolves, it might not make sense to have a physical presence in the same way you do now. If your business model is favourable for e-commerce and products can be delivered through alternate forms of sale, downsizing real estate may make good sense. In uncertain economic times, capital efficiency is your organization’s guiding principle. Retailers are choosing to close underperforming stores. While these closures may feel like the obvious, clear-cut thing to do, there are best practices to follow when downsizing: 1) Identify the optimal locations to exit based on their future potential. Closing stores should be low performing because they struggle operationally, in addition to being located in markets where there is low potential for success.

2) Make sure to understand your sales transfer due to a store closure and where those sales will go: your sister stores or your competition.

5. Store consolidations: Brands that eventually right-size their network often do so as a result of growth that happened too quickly. That, or the market has changed, and the stores required to service a trade area are suddenly redundant based on updates to transit access, population change, or economic health. Consolidations can make sense when you have multiple stores near one another and now none are hitting their targets due to the changing market attributes. Consolidation decisions can be simulated by estimating the “sales transfer or recapture” of the closing store by the one(s) still open.

The bottom line is that having a full suite of prediction tools can help real estate planners plot strategy with a data-driven approach to complex network changes rather than relying solely on historical intuition.

4. Store renovations: How are you redefining the purpose of your physical footprint to better match expectations consumers have for your store space? Your real estate must become increasingly digital, safe and health-conscious, purposeful, and convenient. As you strategically plan for potential changes to store format, adding or removing selling area, increasing accessibility, offering curbside pickup or delivery options, or better meeting physical distancing protocol, leverage these tools to help you to predict and demonstrate how changing the specs at a site can impact your network.

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CFA Guidebook Series: Leveraging Data & Analytics to Scale Your Franchise

Fast Facts & Keyword Definitions

FAST FACTS ● PiinPoint was founded by Jim Robeson and Adam Saunders in 2013, after meeting at the University of Waterloo.

The company was accepted into the Y Combinator startup accelerator program, where the company grew and evolved.

● PiinPoint is located in KitchenerWaterloo, Ontario. ● PiinPoint acquires data from data providers such as ChainXY, Spatial.ai, NEAR, Statistics Canada, U.S. Census Bureau, and Synergos Technologies.

KEYWORD DEFINITIONS ● Site selection: Site selection is the practice of new facility location. Site selection involves measuring the requirements of potential locations. ● Demographics: Demographics are statistical data relating to the population and particular groups within it. ● Geosocial data: Location-based social media data is known as geosocial data.

On social media platforms, people create media material that is linked to particular places. Latitude and longitude coordinates are frequently used as the location component. ● Traffic data/traffic counts: Traffic counts are counts of vehicular and pedestrian traffic, which is conducted along a particular road, path, or intersection. ● GIS (Geographic Information System): A geographic information system is a type of database that combines software tools for managing, analyzing, and displaying geographic data with the database itself.

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● Location intelligence: Geospatial data visualization and analysis enable location intelligence. Analysis of spatial data improves comprehension, insight, forecasting, and decision-making. ● Daytime population: Daytime population is a term used to describe the total population of a place, usually a city or an urban region, during regular business hours. This includes both local inhabitants and commuters from places outside the city or urban area. ● Cannibalization: In marketing strategy, cannibalization is a reduction in sales volume, sales revenue, or market share of one location when the same company introduces a new location within a competitive distance of an existing location. ● Network optimization: In order to monitor, manage, and enhance network performance, a variety of technologies, tactics, and best practices are together referred to as “network optimization.”

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