Originally circulated through The Franchise Voice (Winter 2021) hosted by the Canadian Franchise Association
The future of retail continues to come forward at a quickening pace. As consumer trends have been accelerated by recent events in 2020, the demand for an increasingly unique, localized, and personal retail experience grows. To build organizational resilience and maintain relevance in this retail climate, more and more brands are embracing data, analytics, and technology to support their evolving strategy. When it comes to marketing, data and analytics can both help franchisors dig into insights on their customers and their ever-evolving needs and desires, which in turn can fuel larger organizational strategies for evolution.
Perhaps one of the most illuminating aspects of big data for marketing is its interaction with location. While not all marketing data points have a location associated with them (and for good reason too!), there are a growing number of data points that have a spatial component like an x,y coordinate, FSA, or postal code. These data points can be used respectfully and safely by the franchisor to maximize their ability to tailor their retail branding and experience by geography to create a spectacular customer experience and set franchisees up for success.
This article outlines three approaches of how retail brands are leveraging big-data to innovate with a location-based marketing strategy.
For franchise brands to evolve their data and analytical strategies, the most difficult yet rewarding practice is in having programs and processes in place that allow them to collect, organize, and manage internally-created data. Whether it’s benchmarking store performance on a regular cadence, assessing sales and transaction data, tracking loyalty data or membership, or otherwise; this is living, breathing data that can be directly applied to the business.
This can be extremely difficult to do. Many franchise brands operate across numerous territories and support dozens, if not hundreds of operators. This requires strong systems for reporting and accountability, even in “simpler times” before the COVID19 pandemic. Historically, customer data has been collected via receipt surveys, in-store intercept studies, loyalty programs, point systems, or rewards cards; eCommerce delivery addresses or distribution locations; or by transaction data that includes a location element. Luckily, with an increasing demand for retail brands to develop a hybrid network that includes both a brickand-mortar and eCommerce network, there are a growing number of digital tools that make collecting this data and managing it inhouse more accessible.
Managing in-house data empowers franchise brands to learn about their customers and how behaviours change by location. Understanding what products they like, what price they buy at, their lifestyles and behaviour, if they purchased online or offline, how far they travelled to visit a store location; all offer a treasure-trove of information for marketing and beyond. These data insights can be applied through brand messaging, ads, promotional material, pricing strategy, where to pursue new franchise locations, even things like merchandising and store format or planning at the individual store level, or across segments of stores that have similar shopper traits.
Having a strong grasp on your brand’s data and an organized process for using it to your advantage lays a foundation for any data-driven organization. It is the most difficult, yet rewarding approach to support the network’s overall data strategy. Beyond the data a franchise brand can collect on its network and activities, two other datasets have become standard tools for the datadriven organization: mobile location data and geosocial data.
Understanding observed movement patterns
Mobile location data has a dual purpose in that it can augment the data collected in-house on franchise locations, as well as offer insights into competing brands or areas of interest. It comes from aggregated and anonymized GPS data on consumer movement patterns, which allows organizations to discover activity trends around a given location. These trends include observed visits to a site, the distance travelled to get there by visitors, the demographic characteristics of those visitors, the frequency of visits over time, related brands where they may spend time, as well as the estimated volumes of vehicular or pedestrian traffic.
When analyzed across an entire location network, these data points can help to inform large-scale and foundational marketing initiatives. For example, learning who the customers are, as well as the size of the market they currently capture. Because the data is constantly updating, it can be measured across different points in time. For many, the impacts that COVID-19 has had on customer capture rate and the changing nature of their trade area has been top of mind. For example, mobile location data shows a significant decline in movement patterns from 2019 to 2020 with some high street retail areas seeing over 54 per centi decline in traffic.
Digging into individual sites, mobile location data can shed light on potential marketing strategies specific to the store itself. This allows the franchise brand to develop unique and customized approaches to marketing depending on each specific market. For example, by understanding what other stores visitors go to, marketing teams can pursue new partnerships and co-marketing campaigns with neighbouring tenants or organizations who share the same customer. By understanding the traffic patterns on nearby streets, marketers can optimize their out-of-home advertising by knowing where the highest volumes of traffic are for ad placement. By looking at observations of visitors over time, marketers can evaluate the efficacy of online campaigns aimed at driving traffic in store.
All of these insights can be analyzed on the franchise brands’ network, as well as on competitor locations or general places of interest. Mobile location data gives marketers a pulse on activity by location. This has never been more important.
Getting local market knowledge on community behaviour
Geosocial data, pioneered by Spatial.ai, offers the inside scoop to franchise brands on the ‘vibes’ of a community, for one-off locations, but also at scale. Rather than relying solely on survey-based segmentation or demographic data, geosocial data illustrates consumer behaviours by organizing billions of public, geotagged social media posts into different customer segments. These segments add another layer of insight into the characteristics of the dominant customer in a market, informing marketers on the best ways to target their audience and where to prioritize their activities.
Content created by consumers and citizens alike, which has a locational aspect (geotag), and is posted on platforms like Instagram, Twitter, or Facebook, is used to tune into the sentiments, opinions, highlights, and experiences of people by location. These segments reveal personal, and up-to-date customer attributes that can be used for more tailored marketing and drive franchise success at the dissemination area (DA) level. Its ever-changing nature allows marketers to tap into the conversations happening at a local scale in the markets they operate in. This helps franchise brands understand the characteristics and behaviours of a community, and in turn, ensure that the service and products they offer align with the desires and needs of the customer.
Location-based marketing has a big role to play in the retail “new normal.” As each consumer expects an increasingly personal and convenient experience from the brands they trust and love, that path to maintaining or achieving loyalty can feel unclear. Leveraging data as a marketer helps to reduce the uncertainties presented in a rapidly changing environment, and equips the entire franchise organization with information to make better decisions.
ABOUT THE AUTHOR
Sarah Steiner is the Chief Product Officer at PiinPoint, who support retail franchisors in optimizing their location strategy. To learn more, visit piinpoint.com.
As identified by PiinPoint using Mobile Location data on a sample of high-street retail communities in Canada.