For local marketing in the AI era, local businesses face an inherent challenge in digital marketing. The machine learning (ML) AI models favored by large ad platforms like Google, Meta, and TikTok prefer, and optimize for, broad audiences and large spend — tactics and scale unreachable for local businesses. Some franchise groups address this by centralizing contributed marketing funds and running large, broad campaigns for their franchisees.

However, this centralized model not only diminishes localization, but also introduces unavoidable spend imbalances when the AI, in a relentless pursuit of optimization, prioritizes some franchisees (zees) over others. For local marketing in the AI era, the only solution to avoid all of these issues and to treat franchisees fairly is, ironically, human collaboration!

The scale challenge in digital marketing

Meta, Google, TikTok, and all of the modern ad platforms, utilize advanced machine learning (ML) and artificial intelligence (AI) to analyze vast amounts of data. These algorithms assess user behavior, preferences, and engagement across the platforms to predict which ads are most likely to lead to desired outcomes, such as clicks, conversions, or sales.

By continually learning from interactions, the systems get better at targeting ads to the right users at the right time. Each marketing program learns from these rules, and each ad account accumulates overall learnings to improve performance in each platform. The more programs you run within an account and the more money you spend on an individual marketing program, the more the AI learns about you, your goals, your products/services, and your customers to increase performance.

This has accelerated performance increases in digital marketing significantly and has been instrumental in making the ad platforms some of the most valuable businesses in the world today. However, as AI gradually takes over marketing in search of increased performance, a big issue has surfaced that disproportionately impacts location-based businesses: how machine learning models (AI) and broad targeting strategies are designed to benefit and learn from a high level of spending, training the algorithms at Meta, Google, Tiktok, etc. to favor those who can afford expensive, broad campaigns.

This system inherently favors larger entities with significant budgets, leaving local businesses and small-scale franchisees struggling to compete due to their inability to spend sufficiently to train these models effectively on their own.

Localization and spending control: Lost in broad campaigns

The most experienced marketing teams at franchise groups are keenly aware of these issues. Some franchise groups address this by centralizing franchisee-contributed marketing funds (MDF) and running large, broad campaigns for all of their locations. However, there are important side effects to this approach.

First, when franchisors take over location marketing efforts, the specificity and unique appeal of local businesses often gets diluted in the pursuit of broad targeting. They often take out what makes the local business special!

This loss of localization not only reduces the relevance of marketing messages for local audiences but also undermines the unique strengths and community ties that local businesses typically leverage for success.

Further compounding the challenge is the issue of unbalanced spending within franchise systems. When marketing campaigns are centrally executed, resources are often disproportionately allocated, favoring some locations over others. It’s impossible to avoid this when instructing the AI to maximize group performance, it’s just how the AI works.

This imbalance creates a system where not all franchisees benefit equally from the collective marketing efforts, with some always inadvertently subsidizing the reach and visibility of their counterparts. Often, ironically, the strongest performers are subsidized by the rest of the network, hurting the performance of their smallest peers the most!

A call for strategic local empowerment through collective scale

The pathway to overcoming the challenges of scale and localization in digital marketing necessitates a unified, network-based approach. By harnessing the network effects of a franchise working together, where all locations contribute to and benefit from a collective marketing effort, local businesses within a franchise system can achieve the scale required to effectively train machine learning models without these centralized side effects. This collaborative strategy not only amplifies the impact of each campaign but also levels the playing field, allowing locations to compete more effectively in the emerging AI-driven marketing world.

Crucially, this collective approach does not mean sacrificing the autonomy of individual locations in managing their spend and tailoring their messages. Instead, it offers a dual advantage: the power of scale to influence algorithmic learning and targeting on major platforms, combined with the ability to maintain the unique character and relevance of local marketing efforts. By pooling resources and insights, franchises can optimize the effectiveness of their digital marketing, ensuring that the models learn from a diverse set of data reflective of the entire network’s activities.

Local marketing in the AI era

Empowering franchisees to partake in this network effect requires franchisors to facilitate access to technologies and platforms that enable shared learning without compromising individual control. This strategic local empowerment through collective scale ensures that every location can harness the benefits of advanced digital marketing technologies, making their marketing efforts more competitive and impactful.

By embracing this network-driven model, franchises set the stage for a more equitable, effective, and localized marketing ecosystem, where the sum of individual efforts leads to greater overall success for the entire system.

Learn more about how Evocalize empowers franchises with local marketing in the AI era.