Why MULOs Need a Localized Unstructured Citation Strategy
Citation management for multi-location businesses has long been a structured affair, consisting of claiming listings for each location, ensuring NAP data is up to date and consistent across those listings, and repeating for every location in the portfolio. These types of structured citations still matter for local SEO, but with the advent of AI-powered search, unstructured citations have become more important than ever for MULO brands.
What Are Unstructured Citations?
Unstructured citations are the brand mentions that live outside directories and listing platforms. Think: a local news feature on a newly opened location, a neighborhood subreddit thread recommending your service, a regional magazine’s roundup of the best spots in town, or a blogger’s city guide.
Unlike structured citations, there’s no standardized format for these mentions, no submission form, and no guaranteed placement. These are largely earned mentions, scattered across the open web, and they’ve quickly become one of the most significant factors in how AI-powered search surfaces and describes businesses.
Why MULOs Should Pay Attention
For a single-location business, unstructured citation gaps are a small-scale local problem. For multi-location brands, they’re a large-scale, portfolio-wide one.
When someone performs a local query on Google that triggers AI Overviews or AI Mode results, or asks a standalone AI platform like ChatGPT or Gemini for local business recommendations, the AI evaluates the nearest relevant locations on their own terms, drawing on whatever third-party coverage exists for that specific market.
This means a flagship location in a major metro with years of press mentions and community coverage may perform exceptionally well in AI-generated results, but a newer location in a secondary market may be nearly invisible or described in neutral, generic terms that don’t reflect any of the brand’s actual differentiators.
This is the challenge that MULOs face with unstructured citations. Coverage is almost always uneven across a portfolio of locations, and that unevenness directly translates into inconsistent AI visibility and inconsistent brand narratives, or AI sentiment, at the local level.
AI models don’t just determine whether a business appears in an AI-generated response to a local query. The language they use actively persuades potential customers, and the language AI uses to describe a particular location is frequently drawn from the third-party content that exists about that location.
If the local coverage for a given unit emphasizes speed and friendliness, AI-generated search results are likely to reflect that. If there’s no meaningful local coverage at all, the AI may either skip the location entirely or fall back on thin, generic descriptions that won’t win customers.
Building Unstructured Citations at Scale Without Losing Local Authenticity
This is where multi-location brands face an important strategic decision. The instinct in a MULO organization is to centralize and standardize citation building, but it’s harder to do this for unstructured citations. They’re inherently local, and the sources that carry the most weight with AI, sources such as regional news outlets and neighborhood publications, often require local networking to earn mentions.
What MULOs can do is build localized outreach infrastructure. That means equipping individual location managers or regional teams with the tools and relationships to pursue local coverage, while providing centralized support in the form of things like press release templates, pre-approved story angles, and a roster of local PR contacts in each market.
Start With a Location-by-Location Audit
Before creating a localized unstructured citation building strategy, multi-location brands need to understand exactly where the gaps are across their portfolio. That requires knowing exactly what AI is currently saying about each location, and more importantly, what sources it’s drawing from.
Tools like Local Falcon’s AI visibility reports allow you to see which locations are appearing in AI-generated recommendations, which ones are being overlooked, and critically, what citations are shaping the narratives that AI is generating for both the brand and its competitors. That last piece is especially valuable for MULOs.
When you can see that a competitor’s downtown location is consistently surfaced in AI results because of recurring local news coverage, or that your own outlying location is underperforming sentiment wise because its only unstructured citations are a handful of outdated blog posts, you have a clear brief for where to focus your local PR energy.
Running this analysis across every location in the portfolio, and benchmarking AI search performance against local competitors market by market, gives multi-location brands the kind of granular visibility gap data that’s nearly impossible to surface any other way. It also makes the business case internally for investing in localized unstructured citation building, which can be a hard sell in organizations that have historically measured citation performance through structured listing audits alone.
The Bottom Line for Multi-Location Brands
AI-generated local search results are directly influencing customer decisions at the market level, making it crucial for multi-location organizations to start paying more attention to their unstructured citations.
Strong unstructured citations are not only determining whether you show up in AI-generated results at all, but also whether what AI says about you is actually compelling enough to drive a customer through the door.
For MULOs managing dozens or hundreds of locations, figuring out how to build local unstructured citation equity at scale, location by location, market by market, is now key to winning business through AI-powered local search experiences.
