Local Marketing Is Broken: How CMOs Can Fix Disconnected Strategies for Multi-Location Brands

| Editorial Team On 9 Jul, 2026

Local marketing is broken at scale: What the data reveals and what CMOs must do now

Multi-location brands are drowning in disconnected AI tools and fragmented marketing stacks. Only 1 in 4 location marketers can prove their local marketing drives sales — and the problem is getting worse.

Local marketing has never been more important or more difficult to execute at scale. For multi-location brands managing dozens or hundreds of storefronts, the promise of AI-powered marketing has collided with a painful reality: layered tooling, inconsistent data and an almost complete inability to demonstrate return on investment. A new framework from Uberall suggests the solution is not more AI experimentation but smarter orchestration of the tools already in play.

The scale problem hiding in plain sight

An Uberall survey found that only around 1 in 4 location marketers can demonstrate the impact of their local marketing on actual sales. The same research revealed that 61% of CMOs and VPs at multi-location brands describe their current workload — tracking AI visibility, managing listings, monitoring reviews and posting local content — as "complex" or "very complex."

The consequences of a fragmented local marketing stack are not abstract. Brands operating without proper infrastructure face a cascade of visibility failures:

Business listings managed inconsistently across platforms
Reviews left unanswered or sporadically addressed
Local pages disconnected from social and inventory systems
Outdated or generic content that weakens local search relevance
Deprioritised website performance that creates friction for users and AI crawlers

These are not minor inconveniences. In a search environment increasingly shaped by AI-generated answers and zero-click results, the stakes for accurate and consistent location data have never been higher. Adobe data cited in the article reports a 254% increase in revenue per visit for retail brands discovered through AI search — a figure that is drawing intense boardroom attention to local search performance.

The irony is that many brands chasing AI-driven local marketing gains are actually moving further from them. 89% of technology leaders say their tech investments have not fully delivered on their promise, and integration complexity is the top reason why. Understanding how the core components of a modern digital marketing strategy fit together is increasingly essential before layering AI tools on top of a stack that was never properly integrated in the first place.

Why fragmentation compounds over time

Each new tool added to a local marketing stack without a clear integration strategy creates compounding risk. Data inconsistencies multiply across directories. Ownership of AI outputs becomes diffuse. And the gap between what a brand believes its local presence looks like and what customers and AI crawlers actually encounter widens with every passing quarter.

The brands most exposed to this risk are not those ignoring AI — they are those adopting it enthusiastically without the governance infrastructure to support it.

A new kind of CMO for a new kind of challenge

The central argument for fixing local marketing at scale is that it requires a leadership shift as much as a technology one. This is where the concept of the "Chief Marketing Orchestrator" becomes critical — a reframing of the traditional Chief Marketing Officer role built around governing AI output rather than simply adopting AI tools.

The distinction matters. As innovation strategist Shawn Kanungo puts it: "The companies I am watching win are not the ones optimising the ROI of existing workflows. They are the ones using agents to do things that were previously impossible at any price."

That principle sits at the heart of what Uberall calls the orchestration model.

What AI orchestration looks like in practice

The platform's agentic AI tool, UB-I, is designed to handle the volume and velocity of local operations before a marketing team even starts their workday. On a typical day that means:

Drafting review responses prioritised by urgency and brand guidelines
Correcting name and address formatting across directories
Generating missing business descriptions from existing location data

The human team logs in to approve decisions. They do not log in to discover what is broken.

This division of labour reflects a broader governance philosophy. A streamlined stack with an AI orchestration layer creates clear ownership: the platform executes and analyses, the CMO sets strategy, and the team handles human approvals and guardrails. Without that structure, every marketer and every leader is urged to own AI — which in practice means no one owns the outcome.

Building the governance layer your stack is missing

For CMOs navigating this transition, the orchestration model only functions if the underlying strategy is coherent. Developing a robust and well-structured digital marketing strategy before deploying AI agents ensures those agents are executing against clear commercial objectives rather than simply generating activity. Governance without strategy is as problematic as strategy without governance.

From AI experiments to location performance optimisation

Introducing the LPO framework

The article introduces a revenue-first framework called Location Performance Optimisation (LPO), presented at brightonSEO in October 2025. LPO connects a brand's digital presence to commercial outcomes through four pillars:

Visibility ensures every location appears accurately across all relevant discovery surfaces including Google, Apple, Bing, Yelp and industry directories.

Reputation reinforces trust through ratings, consistent review responses and customer resolution.

Engagement signals fresh business activity to high-intent customers through local posts, photos and offers.

Conversion removes friction by enabling clear actions such as bookings, directions and click-to-call functions.

Each pillar is only as strong as its connection to the others. A location with strong visibility but poor reputation management, for example, is actively losing conversions at the moment of highest intent.

The shift from vibe to value

The LPO framework is positioned as an antidote to what EY has described as the current marketing moment: the shift from "vibe to value." The vibe phase — characterised by AI experimentation, pilot programmes and escalating compute costs — has left many multi-location brands with impressive-sounding initiatives and no attributable revenue to show for them.

The brands winning in local search right now are those that have moved from exploration to operation, using AI to do things that were genuinely impossible before rather than replicating existing workflows at marginally greater efficiency.

Connecting location data to commercial outcomes

For multi-location brands, the LPO framework only delivers measurable results when it is built on a foundation of coherent, well-maintained digital infrastructure. Reviewing the essential elements of a successful marketing plan provides a useful baseline for ensuring that local marketing activity is connected to broader commercial objectives rather than operating in isolation.

According to Google's Search Central documentation, structured and accurate business data remains one of the most significant factors in local search discoverability — reinforcing why clean, orchestrated location data is not a technical nicety but a commercial priority.

As AI search continues to reshape how consumers discover local businesses, the gap between brands with clean, orchestrated data and those without it is likely to widen further. The multi-location brands that establish clear AI governance now and connect their location data to measurable commercial outcomes will be better positioned when boards demand accountability for AI investment.

How to act on this now

Audit your current local marketing stack for redundancy and integration gaps before adding new AI tools. The problem is usually not a lack of tools but a lack of coherence between them.
Assign clear ownership of AI discoverability across your locations. Without a designated Chief Marketing Orchestrator or equivalent role, accountability is diffuse and outcomes suffer.
Use the LPO framework as a diagnostic tool. If visibility, reputation, engagement and conversion are not connected to each other and to revenue data, your local marketing infrastructure has gaps that no single AI tool will close on its own.