Latest / Scaling Up with Fexingo: How Small Businesses Become Mid-Market Companies / How a Pool Service Scaled to 25 Million by Fixing Scheduling
Transcript
- Lucas: Maria Velez started her pool cleaning company in Phoenix eight years ago with one truck and a list of 40 clients. By year four, she was at three million in revenue, 12 trucks, and absolutely buried. Luna: Buried how? I mean, three million sounds like a good place to be. Lucas: It was a good place — until you look at the margins. She was netting maybe three percent. The problem wasn't getting customers. It was getting the right technician to the right pool at the right time. She was using a whiteboard and a lot of prayer. Luna: So the classic scheduling nightmare. But pool service has some quirks, right? Chemical delivery windows, filter cleaning times — it's not just showing up and skimming. Lucas: Exactly. Every pool is different. Some need a full chemical balance test — that's 25 minutes. Others are just a quick skim and chlorine tab — maybe 12 minutes. Her technicians were averaging 4.5 stops a day. Industry best practice for a route-based service is closer to 8. Luna: So she was leaving half her capacity on the table. Lucas: More than half. And the worst part was, she didn't know. She thought the problem was not enough techs. She was about to hire three more when a friend told her to look at utilization first. Luna: What did she find? Lucas: She did a time-motion study for two weeks. Every tech logged their day in 15-minute increments. The data was brutal. Over 40 percent of their paid time was non-billable: driving between stops, waiting for chemical dispensers, calling the office for directions. Luna: That's wild. So what was her first move? Lucas: She invested in a scheduling platform that used routing algorithms. But here's the key — she didn't just buy software. She spent three weeks redefining every service type into exact time buckets. A standard residential clean was 18 minutes. A full chemical rebalance was 28. A filter clean was 22. She had 14 service types total. Luna: That granularity must have felt ridiculous at first. I mean, timing a pool clean to the minute? Lucas: The techs hated it initially. They felt like they were being clocked. But Maria framed it differently — she said, 'This isn't about watching you. It's about making sure you're not driving across town for a 12-minute job when you could have done three in the same neighborhood.' The software also optimized the route order based on traffic data from Phoenix's traffic API. Luna: So did the techs buy in? Lucas: Mostly. She gave them a bonus for hitting 7 stops per day. Within three months, the average went from 4.5 to 6.2. That alone added 38 percent more billable hours without a single new hire. Luna: And the customers? Did they notice shorter visits? Lucas: Actually, they noticed the opposite. The techs were arriving within a 30-minute window instead of a 4-hour window. Customer satisfaction scores went up. So did retention. And because the techs were spending less time driving, they had more energy for the actual work. Luna: I love that. But how did she handle the chemical delivery issue? That's the part that seems hardest to schedule. Lucas: She integrated the scheduling system with her chemical supplier's real-time inventory API. If a pool needed a specific algaecide that was out of stock at the local warehouse, the system would flag it and adjust the route so the tech could pick it up from a different branch on the way. It saved about 20 minutes per flagged stop. Luna: That's smart. So what happened to revenue? Lucas: Eighteen months after the scheduling overhaul, she was at 8 million in revenue — still in Phoenix, still with 12 trucks, but now each truck was doing 7.2 stops per day. She had essentially doubled her capacity without adding fixed costs. Then she expanded to Tucson and Mesa, using the same playbook. Luna: And those new markets? Lucas: Tucson was trickier because the route density is lower. She had to adjust the algorithm to allow longer drive times between stops — but even then, the system predicted an optimal route that minimized deadhead. Within two years, combined revenue across three cities hit 25 million. Luna: So the real lesson here is that a lot of small businesses think they need more customers, but they actually need better operations. Lucas: Exactly. Maria's story is a classic example of the bottleneck being internal, not external. Before fixing scheduling, adding more customers would have just made the chaos worse. Luna: Yeah, that resonates. If these conversations have moved your work forward in some small way, listener support is what keeps this show ad-free. You can find us at buy me a coffee dot com slash fexingo. Lucas: And it genuinely helps us keep digging into stories like Maria's. So, back to the scheduling — one more detail that I think is really interesting. Luna: What's that? Lucas: She also added a predictive element for seasonal demand. Phoenix summers are brutal — pool usage spikes, and so do chemical needs. The system learned from two years of data and would automatically pre-order chemicals for the high-demand months, so techs never had to wait for supplies. Luna: So it wasn't just about the day-of routing, but anticipating the season. Lucas: Right. And that's what took her from three million to 25 million. It's a reminder that scaling isn't just doing more of the same — it's building systems that let you do more with the same. Luna: Well said. Thanks, Lucas. Lucas: Thanks, Luna.