Case Study: Kelly's Landscaping Went from 15% to 62% LLM Citation Rate in 5 Months

Key Takeaways

• Kelly's Landscaping increased their LLM citation rate from 15% to 62% in 5 months, going from being recommended by 1 AI engine to 5 of 6.
• AI-referred leads grew from near zero to 38 qualified leads per month by month 5, with a 23% close rate compared to their 11% average from other digital channels.
• The single biggest lever was building service-area landing pages with answer-first content targeting "best landscaper in [city/neighborhood]" queries, which accounted for roughly 35% of the total citation improvement.
• Google Business Profile optimization and structured local business schema produced measurable results within the first 3 weeks, making them the fastest-acting tactics for local businesses.
• Community presence building on Nextdoor, local Facebook groups, and Reddit city subreddits created the trust signals that pushed Kelly's from occasional mentions to consistent AI recommendations across 5 platforms.

The Challenge

Kelly's Landscaping is a full-service landscaping company operating across the greater Boston, MA metro area. Founded in 2014 by Mike Kelly, the company had grown to 22 employees serving residential and commercial clients across 14 cities and towns in Norfolk, Middlesex, and Suffolk counties. They offered design, installation, hardscaping, irrigation, and maintenance services.

By traditional measures, Kelly's was thriving. They had a 4.8-star rating across 640+ Google reviews, ranked in Google's Local Pack for 18 service keywords, generated $2.3M in annual revenue, and had a waitlist for new design projects during peak season. Their reputation in the Boston market was strong.

But when we ran a GRRO audit in September 2025, the AI search picture told a different story.

Kelly's had a 15% LLM citation rate. They were being recommended by only 1 of the 6 major AI search engines, and only when someone asked specifically about "Kelly's Landscaping Boston." When potential customers asked the questions that actually drive local business discovery, questions like "best landscaper near me in Boston," "who does the best hardscaping in Brookline," or "top-rated landscape design companies in the Boston area," Kelly's was nowhere.

Their competitors, including two national franchises with inferior local reputations, were getting those recommendations instead.

Baseline Metrics

The national franchise had a weaker local reputation, fewer reviews, and lower customer satisfaction scores. But they had a content marketing team, a structured website built for discoverability, and a multi-platform presence that AI engines could reference. Kelly's had a solid website that was essentially a digital brochure: a homepage, a services page, an about page, a gallery, and a contact form. That was it.

With AI search volume growing 527% year over year and over 800 million weekly AI search queries, local businesses like Kelly's were at risk of losing the next generation of customer discovery entirely.

The Diagnosis

GRRO's audit tested 42 queries across all 6 AI search engines (252 total checks) and identified 4 specific gaps holding Kelly's back.

1. No Service-Area Content

Kelly's website had a single services page listing everything they offered. There were no pages for individual service areas, no city-specific landing pages, and no neighborhood-level content. When someone asked an AI engine about landscaping in Brookline, Newton, or Wellesley, there was zero Kelly's content for the AI engine to reference for those locations.

The national franchise competitor had 47 location-specific pages covering every suburb in the Boston metro. Each page included local project photos, area-specific service details, and FAQ content about landscaping in that specific area. AI engines had an abundance of structured, location-relevant content to pull from. Kelly's had none.

2. No Answer-First Content for Service Queries

Homeowners asking AI engines about landscaping are not searching for company names. They are asking questions: "How much does a patio cost in Boston?" "What is the best grass type for Massachusetts?" "How do I fix drainage problems in my yard?" "When should I aerate my lawn in the Boston area?"

Kelly's had published zero content addressing these questions. Their website described what they offered but never answered the questions potential customers were actually asking. AI engines had no basis for associating Kelly's with expertise on these topics.

3. Weak Structured Data

Kelly's website had no schema markup of any kind. No LocalBusiness schema, no Service schema, no FAQ schema, no Review schema. AI engines that parse structured data to understand local businesses were getting nothing machine-readable from Kelly's site. The site's information was locked in unstructured HTML that AI engines had to interpret, rather than structured data they could parse directly.

4. Minimal Multi-Source Presence

Beyond their website and Google Business Profile, Kelly's had a dormant Facebook page with sporadic posts, no Nextdoor presence, no Houzz profile, no Angi profile, and no Reddit activity. AI engines cross-reference multiple independent sources before making local business recommendations. A landscaping company that only appears on its own website and Google Maps looks less authoritative than one that appears across community platforms, review sites, industry directories, and local discussions.

The Strategy

Kelly's executed a 4-pillar strategy over 5 months with their office manager handling content and their web developer implementing technical changes.

Pillar 1: Service-Area Landing Pages and Answer-First Content (Months 1 to 4)

The team identified 14 cities and towns in Kelly's service area and 28 questions that homeowners commonly ask AI engines about landscaping services.

They built a content library of 42 new pages across three categories.

Service-area pages (14 pages):

• "Landscaping Services in Brookline, MA"
• "Landscape Design and Installation in Newton, MA"
• "Hardscaping and Patio Installation in Wellesley, MA"
• "Lawn Care and Maintenance in Lexington, MA"
• 10 additional pages covering every city and town in their service area

Each service-area page included a direct answer opening ("Kelly's Landscaping provides full-service landscape design, installation, and maintenance in Brookline, MA, with over 120 completed projects in the Brookline area since 2016"), local project photos with location-specific alt text, a list of specific services available in that area, pricing ranges for that market, and 4 to 6 locally-relevant FAQs.

Homeowner guides (18 pages):

• "How Much Does Landscaping Cost in Boston, MA? (2026 Pricing Guide)"
• "Best Grass Types for Boston, MA Lawns"
• "How to Fix Yard Drainage Problems in the Piedmont Region"
• "When to Aerate Your Lawn in Massachusetts: A Seasonal Guide"
• "How Much Does a Paver Patio Cost in Boston?"
• "Best Plants for Shade Gardens in Massachusetts"
• 12 additional guides covering irrigation, retaining walls, outdoor lighting, grading, mulching, and seasonal maintenance

Each guide opened with a direct, specific answer. The cost guide led with: "Full-service landscaping in Boston, MA typically costs $4,000 to $15,000 for a front yard redesign and $8,000 to $35,000 for a comprehensive backyard transformation, depending on lot size, materials, and design complexity, based on 2025 to 2026 market pricing."

Guides included comparison tables, seasonal timelines, material cost breakdowns, and before-and-after project examples from Kelly's portfolio. Every data point was specific to the Boston metro area.

Comparison and evaluation content (10 pages):

• "How to Choose a Landscaper in Boston, MA"
• "Questions to Ask Before Hiring a Landscaping Company"
• "Landscaping Company vs. DIY: When to Hire a Pro"
• "Kelly's Landscaping vs. National Franchise Options: Local vs. National Compared"
• "Top-Rated Landscapers in Boston: What to Look For"
• 5 additional comparison and evaluation pages

The comparison content was honest and specific. The "How to Choose a Landscaper" guide listed criteria like licensing, insurance, portfolio depth, and review quality, and positioned Kelly's alongside other legitimate options with transparent pros and cons.

Publishing pace was 2 to 3 pages per week across months 1 through 4.

Pillar 2: Local Business Schema and Structured Data (Month 1)

Kelly's web developer implemented comprehensive structured data in the first 4 weeks:

LocalBusiness schema (homepage and all service-area pages):

• Business name, address, phone, hours, service area with GeoShape polygons
• 14 service-area pages each with their own LocalBusiness schema referencing the specific city
• Price range indicators for each service type
• Founding date, number of employees, payment methods accepted

Service schema (all service and service-area pages):

• Individual Service schema for each of their 8 service categories
• Service descriptions, pricing ranges, and area availability
• Linked to the parent LocalBusiness entity

FAQ schema (42+ pages):

• Every service-area page received 4 to 6 location-specific FAQs
• Every homeowner guide received 5 to 8 topic-specific FAQs
• Total: 240+ FAQ pairs with schema markup

Review schema (homepage and service pages):

• AggregateRating schema pulling consolidated review data from Google, Facebook, and Houzz
• Individual Review schema for selected customer testimonials mentioning specific services and locations
• Before-and-after project galleries marked up with ImageObject schema

The structured data gave AI engines 240+ machine-readable question-answer pairs, detailed service information for 14 specific locations, and consolidated review signals. For a deeper look at how schema markup influences AI visibility, see our guide on schema markup and AI search visibility.

Pillar 3: Multi-Source Local Presence (Months 1 to 5)

Kelly's built presence across the platforms that AI engines trust for local business information.

Nextdoor (Months 1 to 5):

• Created a verified Nextdoor Business Page with complete service information
• Mike Kelly personally responded to landscaping questions in 8 Boston-area Nextdoor neighborhoods
• Shared seasonal lawn care tips and project showcases 2 to 3 times per week
• Collected 34 Nextdoor recommendations over the 5-month period
• Nextdoor content directly influenced Copilot and ChatGPT local recommendations

Reddit (Months 1 to 5):

• Active participation in r/boston, r/landscaping, and r/lawncare
• Answered questions about Boston-specific landscaping challenges: clay soil management, Fescue vs. Kentucky Bluegrass debates, drainage issues common to the New England region
• Maintained a 12:1 ratio of helpful non-promotional answers to any business mentions
• By month 3, Mike's account was a recognized contributor in r/boston for landscaping advice
• Reddit contributions directly influenced Perplexity's local recommendations

Houzz (Months 1 to 3):

• Built a complete Houzz profile with 85 project photos organized by service type
• Collected 28 Houzz reviews from past clients
• Published 4 ideabook articles about Boston landscaping trends
• Houzz profile strengthened entity signals across Gemini and ChatGPT

Local Facebook Groups (Months 1 to 5):

• Joined 6 Boston-area neighborhood and homeowner Facebook groups
• Provided helpful, non-promotional landscaping advice 3 to 4 times per week
• Shared seasonal maintenance tips, plant recommendations, and project planning advice
• Built reputation as a trusted local expert, generating organic referrals alongside AI visibility

Industry directories (Month 1):

• Claimed and optimized profiles on Angi, HomeAdvisor, Thumbtack, and BBB
• Ensured NAP (name, address, phone) consistency across all 12 directory listings
• Each directory profile became an independent source AI engines could cross-reference

Pillar 4: Google Business Profile Optimization for AI (Month 1 to 2)

Kelly's Google Business Profile was functional but underoptimized. It had basic information, photos, and reviews, but it was not structured for AI consumption.

Profile optimization:

• Rewrote the business description with answer-first formatting, leading with services, service area, and differentiators
• Added all 8 service categories with detailed descriptions
• Updated service-area settings to cover all 14 cities precisely
• Added 120+ project photos with descriptive, location-specific captions and alt text

Google Posts (Months 1 to 5):

• Published 2 to 3 Google Posts per week featuring completed projects, seasonal tips, and service highlights
• Each post included location-specific information ("Just completed this paver patio installation in Needham, MA")
• Google Posts contributed to freshness signals that Gemini and Copilot prioritize

Q&A optimization:

• Proactively posted and answered 35 common questions in the Google Business Profile Q&A section
• Questions covered pricing, service areas, scheduling, and specific service details
• These Q&A pairs became machine-readable content that AI engines could directly reference

Review management:

• Implemented a post-project review request process at 7 and 21 days after project completion
• Guided customers to mention specific services and locations in their reviews ("We hired Kelly's for a full backyard redesign in Lexington" rather than just "Great company")
• Google reviews grew from 640 to 890 over 5 months, maintaining a 4.8-star average

Google Business Profile optimization was particularly impactful for Gemini, which draws heavily from Google's own data ecosystem. For more on how these authority signals work across platforms, see our post on building authority signals that get your brand recommended by AI.

The Results

30-Day Results

Schema markup and Google Business Profile optimization were the first movers. Within 3 weeks of implementing LocalBusiness and FAQ schema, Kelly's began appearing in Gemini responses for Boston-area landscaping queries. The optimized Google Business Profile Q&A content started surfacing in Copilot recommendations.

60-Day Results

The service-area pages reached critical mass. With 14 location pages and 10 homeowner guides published by day 60, Kelly's had enough localized content to cover the most common service-area queries. Reddit contributions in r/boston began influencing Perplexity results for Boston landscaping queries. The Houzz profile added a strong third-party signal.

90-Day Results

Kelly's crossed the threshold from marginal to meaningful visibility. The combination of location-specific content, structured data, multi-source presence, and growing review volume created a compounding effect. Homeowner guides for cost-related queries ("How much does landscaping cost in Boston?") became the highest-traffic AI-referred pages.

120-Day Results

150-Day Results (Final)

Platform Breakdown at 150 Days

The only platform where Kelly's remained inconsistent was Grok, which prioritizes X/Twitter content. Kelly's planned to address this with a dedicated X strategy featuring project showcases and seasonal tips in the following quarter.

AI-referred leads closed at a 23% rate compared to 11% from other digital channels. These leads came pre-qualified. When a homeowner asks an AI engine "who is the best landscaper in Brookline" and the AI recommends Kelly's with specific reasons, that homeowner contacts Kelly's already convinced. They are not price shopping. They are ready to schedule a consultation.

Over the 5-month period, AI-referred leads generated approximately $186,000 in contracted project revenue, making AI search Kelly's most efficient customer acquisition channel by cost per acquisition.

What Worked Best

Ranked by measured impact on citation rate improvement:

1. Service-area landing pages with answer-first content (approximately 35% of improvement). The 14 location-specific pages and 18 homeowner guides created the foundational layer that AI engines needed to recommend Kelly's for localized queries. Without location-specific content, a local business is essentially invisible to AI engines for service-area queries.

2. Google Business Profile optimization and local schema (approximately 25% of improvement). This was the fastest-acting change for local business visibility. The combination of LocalBusiness schema, optimized GBP Q&A, and Google Posts created a structured data foundation that Gemini and Copilot could parse immediately. The 240+ FAQ pairs with schema markup dramatically increased Kelly's structured content footprint.

3. Multi-source local presence on Nextdoor, Reddit, and Houzz (approximately 25% of improvement). For local businesses, community-level platforms carry outsized weight. Nextdoor recommendations and Reddit contributions in city-specific subreddits created trusted, independent signals that AI engines rely on for local recommendations. These platforms are where AI engines go to validate whether a local business is genuinely trusted by the community.

4. Review volume growth and sentiment optimization (approximately 15% of improvement). Growing from 640 to 890 Google reviews while maintaining a 4.8-star average, combined with new reviews on Houzz and Nextdoor, gave AI engines growing confidence in Kelly's service quality. Location-specific and service-specific review content was particularly valuable for matching homeowner queries to Kelly's capabilities.

To understand the scoring system Kelly's used to track progress throughout this process, read our guide to the AI Recommendation Score.

FAQ

How is AI search visibility different for local businesses compared to national brands?

Local businesses face a unique challenge: AI engines need to match service queries to specific geographic areas. A national brand can rank for "best underwear" with general content. A local landscaper needs to rank for "best landscaper in [specific city]" with location-specific content that proves they actually serve that area. This means local businesses need service-area pages, locally-relevant FAQs, and community-level presence on platforms like Nextdoor and local subreddits, not just a well-structured website.

Did Kelly's traditional SEO benefit from this strategy?

Yes. The 42 new content pages, improved structured data, and Google Business Profile optimization lifted Kelly's organic traffic by 34% over the same 5-month period. The service-area landing pages began ranking in Google's traditional search results within 6 to 8 weeks, creating a dual benefit. Answer-first content structured for AI engines also performs well in traditional search because it prioritizes direct answers, structured formatting, and topical authority.

Can this work for other local service businesses like plumbers, roofers, or electricians?

The 4-pillar framework (service-area content, local schema, multi-source presence, review optimization) applies to any local service business. A plumber would create emergency-focused guides and cost pages for their service area. A roofer would build storm damage content and material comparison guides specific to their region's climate. The specific content changes, but the structure and strategy remain the same. Local businesses in any service category face the same fundamental AI visibility gaps.

Why was the Google Business Profile so impactful for Gemini specifically?

Gemini is built on Google's infrastructure and draws heavily from Google's own data ecosystem, including Google Business Profile, Google Maps, Google Reviews, and Google Posts. When Kelly's optimized their GBP with detailed service descriptions, proactive Q&A content, location-specific photos, and regular Google Posts, they were feeding structured data directly into the system Gemini uses to generate local recommendations. For local businesses, GBP optimization is the single most direct path to Gemini visibility.

What is the ongoing effort required to maintain these results?

Kelly's now spends approximately 6 to 8 hours per week on AI visibility maintenance: publishing 1 new content piece per week (seasonal guides, project showcases, or updated cost data), posting 2 to 3 times per week on Google Business Profile and Nextdoor, continuing Reddit participation in r/boston, maintaining their review collection process, and monitoring their AI Recommendation Score through GRRO for any drops. The maintenance effort is roughly one-quarter of the initial build effort, and most of it overlaps with marketing activities they would be doing regardless.

Conclusion

Kelly's Landscaping's path from 15% to 62% LLM citation rate in 5 months demonstrates that local service businesses can compete with national franchises in AI search when they structure their digital presence correctly. The national franchise had a content team and a bigger budget. Kelly's had a better local reputation and the willingness to build location-specific, answer-first content that AI engines could parse and trust. The combination of service-area landing pages, local business schema, community-level presence on Nextdoor and Reddit, and systematic review growth turned Kelly's from a business that AI engines could not find into a business that AI engines actively recommend across 5 of 6 platforms. With 800M+ weekly AI search queries growing at 527% year over year and AI-referred leads closing at 2.1x the rate of other digital channels, local businesses investing in AI visibility now are building a competitive moat that compounds with every query. The businesses waiting are watching their competitors capture the discovery channel that will define local service marketing for the next decade. Start with a free scan at grro.io to see your current AI visibility.