The Landscaping Owner Who Found His Best Repeat Customers Came From 3 Zip Codes

A 15-crew landscaping operation in the Midwest was planning to hire a full-time outbound sales rep to cover its 40-mile service radius. The owner assumed his customer base was evenly distributed across that footprint because his trucks were always moving across it.

He pushed the hire off by a week to answer one question first. If he could see where his highest-value repeat customers actually lived, could he cover them with fewer crews and tighter routing? He pulled three years of invoices into Sully and asked it to group the top 20% of customers by zip code. 62% of his repeat-service revenue came from three adjacent zip codes. The hire got canceled. The routing got rebuilt. His gross margin climbed 7 points in one season.

The map he had never drawn

Landscaping and lawn care businesses scale by density, not reach. Every extra mile between stops is payroll, fuel, and lost billable hours. The owner knew this in the abstract. He had never looked at it in the data because his CRM showed a customer list, not a density map.

When Sully returned the grouping, three zip codes accounted for $1.4M of his $2.3M in recurring revenue. The other 22 zip codes in his service area accounted for the rest, spread thin.

Text Sully: "Group my recurring-service customers by zip code. Rank by total revenue over the last 36 months."

Peterman Brothers in Indianapolis grew from a three-person operation to $150M+ by being ruthless about density. In multiple podcast conversations on Owned and Operated and The Home Service Expert, Chad Peterman has repeated the same principle: "We only expand into a zip code when we can support a crew there five days a week."

The "one neighborhood" that was really four streets

Inside his top-performing zip code, the owner went one layer deeper. He asked Sully to cluster customers by street. 71% of the revenue in that zip came from four streets, all within a half-mile radius of a country club.

The math on that was absurd. A single crew could service 18 customers on those four streets in a morning. Fuel and drive time between stops was effectively zero. His gross margin on that cluster was 53%, versus a blended 34% across the rest of the book.

Text Sully: "Inside zip 46250, group customers by street. Show total annual revenue and number of jobs per street."

That's a profile almost every landscaping shop has, and almost none can see in their CRM without the raw address data getting unified.

Where the churn was hiding

He then asked the obvious follow-up: which zip codes had the worst retention. The answer explained why his second sales rep from last year had underperformed.

Three of his outlying zip codes had a 58% one-and-done rate. Customers bought a one-time cleanup or install, then disappeared. His three dense zip codes had a 79% three-year retention rate.

The Jobber Home Service Economic Report 2024 frames the same pattern at industry scale: recurring-service customers are worth 3-5x a one-time job over customer lifetime, and the shops that hit those multiples are the ones clustered geographically.

Text Sully: "For each zip code, show the percentage of customers with only one invoice in the last 24 months."

The referral pattern that named itself

One more cut of the data surprised him. Of all new customers acquired through word-of-mouth in the last 18 months, 83% came from the three dense zip codes. Customers clustered near each other refer each other. Customers 30 miles out on the edge of his service area don't have neighbors who use him.

That meant every dollar of service revenue in those three zip codes was also generating demand for more service revenue in those three zip codes. A compounding effect his CRM could not show until the addresses were unified.

Text Sully: "For customers who gave us a referral, group by zip code. Show total referrals per zip."

The seasonal upsell he had been giving away

Before he shifted strategy, he ran one more query. For the top three zip codes, he asked Sully to show him how many customers had ever received a quote for a seasonal add-on: spring cleanup, fall leaf removal, fertilizer program, or holiday lights.

Only 41% had ever been offered any of the four. Of those offered, 68% had accepted at least one. He had been running four upsell products through a customer list where more than half of his best customers had never been told the products existed.

At his average add-on ticket of $620 and the observed 68% acceptance rate, that was roughly $380K of annualized upsell revenue sitting inside his three dense zip codes, unasked-for.

Text Sully: "For recurring mow customers in my top three zip codes, show which ones have never been offered a seasonal cleanup, fertilizer program, or holiday lights quote."

Chad Peterman has made the same point about Peterman Brothers' growth on Owned and Operated: "The quote you never offer is a quote you'll never close. Most shops think they're selling. They're only selling the thing the customer asked for."

What he did in the next 60 days

The outbound sales hire was canceled. He instead spent that budget on a neighborhood-level marketing push inside his three dense zips. Door hangers, Nextdoor placements, and a referral incentive tuned for the same geography.

He also dropped eight customers at the far edge of his service area who were contributing less than $400/month each and costing a full hour of drive time per visit. Those eight slots filled in inside the dense cluster within 30 days at 2.4x the revenue per stop.

His routing software, tuned against the new zip-code map, cut average drive time per crew by 22 minutes per day. Across 15 crews over a 220-day season, that was roughly 1,200 hours of reclaimed billable time.

The price differences his spreadsheet had blurred

One last finding reshaped his pricing. When Sully grouped average ticket by zip code, the three dense zip codes averaged $720 per visit. The edge zips averaged $540. He had been running a flat price book across his whole service area.

The customers in the dense zips had larger properties, more complex hedging and irrigation, and higher willingness to pay. They had been subsidizing the thin, cheap, one-and-done work at the edge of his footprint without anyone in the shop noticing.

He rebuilt his price book with three tiers based on density and average property value per zip. Revenue per stop in the dense zips climbed another 11% in 90 days without churn, because the tier increase was still below what the customers were paying other local vendors for equivalent work.

Text Sully: "Show me average ticket, average property size (from public records), and average job duration grouped by zip code."

The quote he pinned to the office wall

John Wilson of Wilson Companies, a $40M+ Akron operator, said it on The Home Service Expert podcast: "You can't out-hustle bad routing. Density beats volume every time."

The landscaping owner had been solving a volume problem. Sully showed him it was a density problem. Totally different fix.

What this means for your shop

If you run a landscaping, lawn care, or grounds maintenance business between $1M and $10M, your top three or four zip codes are almost certainly carrying 50-65% of your repeat revenue. You have not drawn that map because your CRM organizes customers by name, not by where they cluster.

Pull that map once and three decisions get obvious. Which zip codes justify a crew. Which ones should be priced up to compensate for the drive. Which ones should be dropped entirely. Most landscaping shops can lift margin 5-10 points inside one season just by tightening routes around their real density.

Sully can pull that zip and street breakdown in a single conversation against your existing invoice data. See AI customer reactivation for contractors for how to work the dense list harder, AI agents for landscaping businesses for the full agent stack, and home service KPIs: the complete metrics playbook for the metrics frame.

Sources

• Jobber Home Service Economic Report 2024
• Pete & Gabi 2026 AI reactivation research
• Owned and Operated podcast
• The Home Service Expert podcast
• LocaliQ 2025 home services benchmarks
• BrightLocal 2025 Local Consumer Review Survey