61% of CMOs Say Local Marketing Is Too Complex: Here’s the Fix

This post was sponsored by Uberall. The opinions expressed in this article are the sponsor’s own.

Who should own AI search visibility across all our locations?

Should I add more AI tools to manage local listings and reviews, or is that making it worse?

When 99% of senior marketers say they want an AI orchestration layer, the question is who leads it.

The ideal multi-location marketing world is one where agentic AI fixes duplicate listings, responds to customer reviews, analyzes sentiment, and spots optimization opportunities before the marketer can say “GBP.”

However, what multi-location brand CMOs actually have, in today’s far less ideal world, is layers of disjointed AI and marketing tooling creating an unclean and unclear infrastructure.

This lack of infrastructure makes it nearly impossible to track overall ROI.

An Uberall survey last year revealed that only around 1 in 4 location marketers can show the impact of their location marketing on sales; I’ll bet that with varying levels of AI tool adoption since that survey, this issue hasn’t improved — if anything, it’s been exacerbated by it.

The AI understands what needs prioritizing and resolves it in the background while teams focus on their marketing for multiple locations. It squashes impatience or uncertainty surrounding ROI reporting because its model is built on delivering and visualizing real-time attributable location performance: bookings, table reservations, foot traffic. The clean and clear data that stakeholders wait for.

The results of ill-equipped and layered martech tooling are bleak for local visibility:

• Business listings are managed ad hoc per platform, creating inconsistencies with critical data
• Reviews are left unanswered or sporadically answered, breaking down customer trust and engagement
• Local pages are disconnected from social and inventory systems
• Content is outdated or generic, weakening relevance to local search intent
• Website performance is deprioritized, causing friction for users, search engines, and AI crawlers

Today’s real ideal world is about bringing some sense back to the location marketing stack. It will deliver a combination of that sought-after AI orchestration layer, omnichannel search visibility across locations, and the even more sought-after ROI numbers. It’s the Chief Marketing Orchestrator who will lead it.

Step 1. Decide Who Your Chief Marketing Orchestrator Will Be

Value won’t come from simply plugging data into an LLM. 89% of leaders said their tech investments haven’t fully delivered, with integration complexity the top reason.

Instead, it comes from plugging all your multi-location marketing data into an orchestration layer that implements the nonnegotiable context engineering tasks, making sure every location’s data and signals are structured for any search system customers are using to discover local businesses.

Someone needs to do this, and that person becomes your Chief Marketing Orchestrator (CMO). And, luckily, it’s a new evolution of a Chief Marketing Officer.

The Key Responsibilities of a CMO

The Chief Marketing Orchestrator (CMO) must decide which tasks require human sign-off. Where are the trade-offs? Who owns AI discoverability at a brand and location level? Where can they relieve their team from operational workload and reallocate them to tasks that influence revenue — turning sentiment analysis into actionable reports for operations, or producing content that drives local engagement? It’s not just a technology story but also a leadership story.

Any CMO who is truly passionate about what they do for their multi-location brand doesn’t want to blindly outsource every single task to an AI agent. They want to trust the performance numbers and location marketing initiatives they’re reporting back to stakeholders. And they most likely want to feel in control of compute costs.

At a time when every marketer and every leader is urged to own AI, this often means no one owns the outcome. A streamlined stack with an AI orchestration layer changes that, in that the platform owns the execution and analysis, the CMO owns the overarching strategy, and their team owns the human approvals and guardrails.

This is the principle Uberall’s agentic AI, UB-I, is built on: The marketer remains in control — governing the AI’s output, not just guiding or prompting it.

A CMO investing in the right people to govern agentic AI is a CMO focused on output, not adoption.

Try doing this manually across 50 locations:

1. Open each location’s profile across GBP, Apple, Bing, and relevant directories. Check for formatting inconsistencies, missing attributes, and incorrect hours.
2. Draft a review response for every pending review — starting with the negative ones — matching your brand’s tone and guidelines.
3. Audit each location for missing business descriptions and generate copy that reflects the right local keywords and service context.

That’s the daily baseline. At scale, it’s unsustainable — which is exactly the workload UB-I handles before the team logs in.

UB-I handles the volume and velocity of local operations that no human team can sustainably match at scale, while flagging anything that requires human judgment before acting. On any given day, that means:

1. Drafting AI-generated replies for all pending reviews, according to strict brand guidelines, prioritizing negative reviews first.
2. Correcting name and address formatting to each directory’s requirements, preventing sync failures, and suppressed visibility.
3. Generating missing business descriptions, attributes, and special hours from location data

The team logs in to approve, not to discover what’s broken. Each of these is context engineering in practice — making location data usable for both human and AI-powered search, at a scale no team can manage manually.

As globally recognized innovation strategist Shawn Kanungo puts it: “The companies I am watching win are not the ones optimizing the ROI of existing workflows. They are the ones using agents to do things that were previously impossible at any price.” The efficient orchestration of local marketing tasks across multiple locations has always been impossible at scale — and this orchestration layer is exactly what 99% of senior marketers say would be “valuable” or “very valuable,” according to an Uberall survey.

The true value here in implementing an AI orchestration layer to manage omnichannel presence isn’t to optimize the efficiency of existing local marketing workflows — it’s in enabling what was impossible for marketers to achieve at scale in an eight-hour workday. The workload that 61% of CMOs and VPs at multi-location brands currently describe as “complex” or very “complex” — tracking AI visibility, managing location data and listings, monitoring and responding to reviews, and posting local content on social media.

Step 2. Pivot From Finding New AI To Restoring Search Visibility

As I see it, the solution CMOs will want to implement is to stamp out the ROI-burdening exploratory agentic AI projects and focus on operating with it. Because the prize that comes from operating with it well is attractive for multi-location brands, who need to work quickly to restore declining traffic amid zero-click searches.

Reports indicate that revenue is increasing for brands as customers discover them via AI search — Adobe reports a 254% increase in revenue per visit for the retail segment. It’s no wonder stakeholders are more interested in SEO and GEO performance than ever before.

Let’s imagine a multi-location brand as a building with 200 rooms, each hosting its own party. The furniture hasn’t changed, the walls haven’t changed, the infrastructure hasn’t changed — but there’s a new entrance to the building, one that seems to be a shortcut for guests intentionally looking for you. The other entrances are still in use too. You want to maximize access through every single one so more people find the right room, have a good time, and come back for the next one. You don’t hire someone to manually bring guests to each entrance. You invest in technology to put up signals that do the work for you, so your team can focus on the experience inside the rooms.

Context engineering is what builds those signals. It’s when AI can orchestrate how brands make their digital footprint machine-readable, consistently accurate, technically discoverable across multiple surfaces, contextually relevant, and socially validated — without individuals needing to unpeel layers of tech stack insights.

Implement The 4 Pillars Of Location Performance Optimization (LPO)

If visibility on any search or marketing channel improves, every other location performance pillar improves: engagement, reputation, and conversion. These are the four pillars of Location Performance Optimization (LPO), a revenue-first framework I spoke about at brightonSEO in October 2025. LPO connects a brand’s digital presence to commercial outcomes by activating location data and signals across these performance pillars:

• Visibility: Every location is accurately represented across all relevant discovery surfaces (website, Google, Apple, Yelp, Bing, industry directories).
• Reputation: Trust is reinforced through ratings, regular reviews, and customer resolution.
• Engagement: Local content — posts, photos, offers — signals fresh business activity and relevance for high-intent customers.
• Conversion: Customers can take clear action — bookings, directions, and click-to-calls.

An AI agent that implements these LPO measures to attract more customers, reach new audiences, and influence revenue isn’t exploration. It’s a hard-ROI workflow that pays for the program; they’re the crucial layer that restores and increases search visibility, customer acquisition, retention.

So, when the board asks about AI ROI and local marketing performance, this new CMO doesn’t just demonstrate AI adoption; they justify AI investment to continue to fund their operations. The gap between the brands measuring real ROI and the companies pretending to — or being preoccupied by their complex local marketing stacks is wider than ever.

How To Shift From AI Experiments To ROI-Driven Operations

EY described the moment we’re in well: moving from vibe to value. The “vibe” phase was every company exploring AI — experimenting, piloting, racking up compute costs, layering up their tech stack — and either still being in that phase or having concluded it with the frustration of not knowing how to progress to real, quantifiable returns.

Marketing leaders at multi-location brands, like the Chief Marketing Orchestrator, must adopt and govern agentic-AI-powered stacks that are less exploratory and more ROI-driven. These are stacks that are sensible, streamlined, and enable teams to do things that just weren’t possible before, like logging in to approve fixes, not to discover or prioritize what’s broken. And that approval might not happen before a marketer can say “GBP,” but it’s the orchestration layer — the added AI — senior marketers and leaders are looking for.

Find out how to use Uberall’s UB-I agent for multi-location marketing for your operations

Image Credits

Featured Image: Image by Uberall Brand Studio. Used with permission.