Episode 37: How to Manage Search, Sentiment, and Scale for Restaurant Brands and Franchises

Key Takeaways

• The customer discovery journey for restaurants has shifted heavily toward third-party platforms, delivery aggregators, and brand apps
• Nonbranded and near-me searches still present a major opportunity, especially with AI search expanding query interpretation
• Local landing pages need location-specific menus, FAQs, unique descriptions, and frictionless calls to action
• How brands respond to reviews directly influences what LLMs say about them.
• A/B testing local SEO changes on a regional subset before scaling nationwide is a proven strategy for large multi-location brands

Managing search visibility, online reputation, and operational consistency across hundreds or thousands of restaurant locations requires a fundamentally different approach than local SEO for a single storefront. As customer discovery shifts across platforms and AI reshapes how people find where to eat, large restaurant brands face a unique set of challenges at scale.

In this episode of the Local Marketing Beat podcast, host Christian Hustle sits down with Ari Nahmani, CEO and Founder at Kahena, to explore how enterprise restaurant brands can navigate the evolving discovery journey, optimize local pages for conversion, leverage review sentiment for AI search, and A/B test local SEO changes at scale.

Timestamps

00:00 Introduction to the Local Marketing Beat and guest Ari Nahmani

04:19 How the customer discovery journey has changed for restaurant brands

07:12 Branded vs. non-branded search and the near-me opportunity

09:39 Why near-me and long-tail local queries still matter in AI search

12:54 Non-branded local landing pages and AB testing at scale

15:44 Local pages V2: What best-in-class store pages look like

19:22 CRO and conversion optimization for location pages

21:30 Reviews, sentiment, and the diversity update

25:06 How review responses feed AI search and LLM grounding

28:14 What breaks first at scale: data, tech, or reviews

The Discovery Journey Has Moved Off Your Website

"The customer journey, at least the introduction to that brand or that restaurant, is happening on third-party platforms now. Whether that's delivery aggregators, DoorDash and Uber Eats and GrubHub, or review aggregators like Yelp and Open Table. The experience is happening elsewhere." — Ari Nahmani

Ari explains that for large restaurant brands, the days of customers discovering a new restaurant primarily through the brand's own website are waning.

Today, the first touchpoint is more likely to happen on a delivery app, a review site, or through a Google Maps listing. This means brands need to think about optimizing their presence across every surface where potential customers might encounter them — not just their homepage or menu pages.

At the same time, repeat customers are increasingly loyal through the brand's own app, receiving push notifications and coupons that bypass search entirely. When listings management teams see brand search declining year over year, Ari notes it may not mean the brand is losing popularity — it could simply mean that the interaction has moved to a direct-to-app experience.

For marketers, this shift means that location data management becomes the foundation: making sure every third-party platform, every aggregator, and every map listing reflects accurate, up-to-date information for every location. The challenge is not just being findable — it is being findable everywhere, consistently.

Nonbranded and Near-Me Searches Are Still a Major Opportunity

"Because of AI search, I think it adds context around the user's location... The search engines, the LLMs, they have so much more rich data about the users, and I think that is why near me matters." — Ari Nahmani

While the total volume of search-to-website clicks may be shrinking, Ari argues that there is actually more opportunity in nonbranded search than before — particularly thanks to AI search.

Users are searching longer, more specific queries, and AI-powered search engines are processing those with richer contextual understanding. A query like "best reviewed burger place open at midnight near me" is exactly the type of long-tail search that AI can match to the right locations, provided the data is there.

Ari still recommends nonbranded keyword-driven landing pages that sit hierarchically beneath store location pages. For example, if a restaurant offers breakfast at certain locations, a dedicated breakfast landing page for each relevant city can capture that specific demand. His team has seen these pages continue to work when they provide genuine value — showing which restaurants match the criteria, with correct menus and hours — rather than being generic clones.

The key is combining semantic relevance with structured data so that both traditional search engines and LLMs can match these pages to user intent.

Local Pages Need a V2 Upgrade

"This is the first experience that customers have from search. These are not just directions pages." — Ari Nahmani

Ari and Christian agree that the era of basic local landing pages — an address, phone number, and a couple of photos — is over.

What Ari calls "V2" local pages need to function as the digital front door of each location. That means featuring the correct menu for that specific location (especially important for franchise models where not every location carries the same items), unique descriptive text mentioning local landmarks or colloquial neighborhood names, and FAQs that address what real customers actually search for.

Critically, Ari stresses that the top of the page should feature a frictionless experience: a prominent call to action for online ordering or driving directions, since that is what most visitors are actually looking for.

If the brand does not offer native ordering, links to all delivery aggregators should be front and center. The richer content — the story, reviews, local details — can live further down the page for those who want it. This approach aligns the page with the customer journey that brought someone there in the first place, which Ari sees as a missed opportunity for many large brands.

A/B Testing Local SEO Changes Before Rolling Out at Scale

"Don't do it at scale right away. Don't just flip a switch. That could be really risky. Start small and then scale up if it works." — Ari Nahmani

One of Kahena's biggest strategic unlocks, according to Ari, has been applying A/B testing principles to local SEO. When working with brands that have hundreds or thousands of locations, any change to a Google Business Profile or a store page template carries significant risk at scale. A single bug replicated across thousands of locations could mean substantial revenue drops. Instead, Ari's team selects a specific region or subset of stores, implements the change there, and monitors results before deciding whether to roll it out nationwide.

He shared a concrete example: changing the primary Google Business Profile category to "fast food" for a QSR brand. They tested it on a subset, saw strong results, expanded gradually, and it became one of their biggest wins. The beauty of testing at the location level, Ari notes, is that it provides a natural control group — same time period, same seasonality, same holidays — just different regions. Marketers working with analytics and reporting tools can track these changes at a granular level and make data-driven decisions about when to scale.

Reviews Are Now Fuel for AI Search

"All of that user-generated content, including your response, gets ingested and trained upon to the LLM and to the AI search. Not just for the great reason of making sure that people who are scrolling see that you care, but also for the LLMs to train on the fact that you consistently say we pride ourselves in ABC." — Ari Nahmani

Ari emphasizes that review management has taken on a new dimension in the age of AI search. Reviews have always mattered for local rankings and consumer trust, but now the words used in reviews — and in brand responses to those reviews — directly influence how LLMs describe and recommend a brand. If a restaurant consistently responds to reviews by mentioning its unique selling points (the best char-grilled burgers, late-night hours, family-friendly atmosphere), those phrases become part of the training data that AI models use when answering questions like "tell me more about this brand" or "compare these two restaurants."

Ari also highlights the impact of Google's diversity update in late 2024 and early 2025, which shifted the Local Pack to favor local heroes with high review recency and velocity. Large chains face a structural challenge here: loyal customers rarely leave reviews for a brand they already know, so the reviews that do come in skew negative. To combat this, Ari recommends post-purchase email prompts, and making it as easy as possible for satisfied customers to leave a review. For brands managing sentiment analysis at scale, this is about increasing the volume of positive signals to counterbalance the natural negativity bias.

What Breaks First at Scale: Data, Tech, or Reviews?

"Because of platforms and tooling, data accuracy is not the challenge. It's more about tech and reviews and being careful about making changes because the impact is great — which is great for opportunity, but the risk is also great." — Ari Nahmani

When Christian asks what tends to break first for brands operating at scale, Ari's answer is nuanced. He notes that location data management platforms like Uberall have largely solved the data accuracy problem that plagued multi-location brands a decade ago. NAP (name, address, phone number) propagation across third-party listing sites is now handled systematically, and Google has improved at resolving data conflicts. The real challenges at scale are technical — any update to a store page template that introduces a bug affects every location — and reputational, since managing review volume and quality across hundreds of locations requires dedicated resources and clear processes.

Ari's takeaway for enterprise brands: treat every change as high-stakes. The upside of getting it right at scale is enormous, but so is the downside of getting it wrong. Combining a robust local SEO strategy with careful testing, strong review management, and a commitment to AI visibility is the playbook for restaurant brands that want to win at scale.