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How AI & Location Insight Can Solve the Franchise Analytics Problem

Franchises are equipped with a unique position in the local business landscape. Individually operated, they serve an immediate geographic area and often function as a small, local business. However, each unit is still supported by a central, corporate presence that can provide curated resources that traditional SMB’s don’t have access to. This positions franchises with a rare opportunity to leverage technological advances to facilitate their sales cycles, and franchisors should be eager to implement such innovations.

At its core, the relationship between the corporate franchisor and the local franchisee should function as a partnership, exchanging ideas, resources, and perhaps most importantly, data. Although underutilized, big data decision-making is especially valuable for these types of businesses. Franchisees may need to articulate how essential this data is (and the resources required to capture, analyze and action it) to them in order to reach their full potential in revenue, sustainability and growth to the franchise, in general.

Many already have. In a recent survey, more than half of participating franchisees believe that analytics are critical to their success, with 73% believing it aids in customer acquisition. It’s no wonder that analytic tools and resources continue to be a top ask of franchisees. On the aggregate level, locations complete thousands of transactions every day, enabling them to serve as vital insight farms for their corporate headquarters. Giving each location more context around where its consumers’ buying patterns fall in relation to its peers can inform marketing strategy, staffing, inventory and reordering, and product offerings.

It also facilitates the performance of analyses to account for regional preferences, and allows units to plan accordingly. In one instance, a hardware store was able to identify a missed opportunity for many locations within a specific radius to a large body of water. By stocking marina equipment, these locations were able to convert more in-store customers and encourage repeat business. This was a win-win for both the individual locations and the overall health of the franchise.

As with all things in the franchise world, this exchange of insights is not a one-way street. The corporate franchisor can monitor top sellers, marketing campaigns, in-store conversion rates and much more. They can then use these insights to guide a winning formula that can be replicated across subsets of, or all their locations. The aggregated data across all of its units can inform various growth and expansion strategies, and allow them to provide more resources to keep individual units happy and profitable.

Oftentimes, current franchisees fear the opening of new locations anywhere near theirs. But if corporate can run an analysis on the location of existing customers in relation to the distance between their homes and the location they currently patronize and compare it to the proposed new location, you can estimate the extent to which the new store will cannibalize current customers from the pre-existing one, and make decisions based on that. They can use this to ease fears, and fuel decisions to allocate additional resources or training to compensate for the deficit. As is usually the case with data-driven approaches, knowledge is power.

It’s no wonder that franchisees are increasingly eager to implement marketing automation software that provides reporting and analytics tools. However, the familiar barriers to entry continue to obstruct adoption of such technologies. Unsurprisingly, BIA Kelsey has found that the top three reasons cited as obstacles are: reliability, ease of use, and cost. Few individual units can afford expensive enterprise-level platforms, let alone a dedicated, technical expert to create, maintain and run analyses on the program.

This impasse can be bridged with new innovations and intelligent technologies that seamlessly collect, analyze and action data in the background, without requiring maintenance or a technical expert to wield the software. Machine learning and artificial intelligence is already being developed to provide a more personalized approach, beyond rules-based logics. Journey Orchestration Engines are only one example of what will soon be possible for customizing the individual purchasing journeys tailored for each consumer.

A unified AI-driven CRM can harness customer info to segment and automatically send the right communications at the optimal time, in order to accelerate sales cycles. It can also track all interactions to identify trends in buying decisions, and help franchises create data-driven strategies around regional and seasonal patterns. Such an intelligent solution will empower franchises, arming them with the ultimate competitive edge: accessible and actioned data, so they can go back to what they do best, focusing on the day-to-day of their business.

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