July 14, 2022

Marketplace revamps management of $2M marketing budget with Stratus Data guidance

Lawn Love is an online P2P marketplace that connects home owners to lawn care professionals, hosting hundreds of thousands of jobs across America. Since its inception in 2014, Lawn Love has raised $6.7M from investors like Y Combinator, Alexis Ohanian, and Joe Montana.

Five years in, the company was growing faster than ever, but struggled with a dubious reporting system. Founder Jeremy Yamaguchi began to worry that he was informing decisions with faulty information. He hired Stratus Data, and a few months later, walked away with not only clean, reliable infrastructure, but also an in-house data scientist and a full set of processes to maintain Lawn Love’s new status as a data-competitive company.

 

Challenge

Multimillion-dollar decisions teeter atop slow, fragmented reporting system

By the time Lawn Love hit their five-year mark, their operations had outgrown their reporting system. The company relied on dozens of individual reports, some of which showed conflicting information. The reports were enormous and took up to 10 minutes to load, so that they were frustrating to edit, and even harder to maintain on an ongoing basis. Yamaguchi, who referred to these reports to manage a $2M/year marketing spend, began to feel the issue’s urgency. “We were flying with instruments that may have been wrong in meaningful ways,” he says. “These instruments told us whether we were going to fail on a daily basis, so it was important that they were correct.”

Yamaguchi reached out to a fellow Y-Combinator founder. “I explained my problem and asked him for the best team he knew,” he recounts. “He directed me to Stratus Data.”

“I knew there was a problem, but I wasn’t even sufficiently clear on what the solution was that I could ask for what I needed. Stratus said, “Hey, here’s what you need to solve, and here’s how you can solve it.”
– Jeremy Yamaguchi, Founder & CEO, Lawn Love

Solution

One small, comprehensive fix is the starting point for company-wide renovation

As always, Stratus began with an audit of the company’s data ecosystem to understand how tools and processes might fit. They integrated smoothly with the existing team: “The whole relationship felt like Stratus was an embedded part of Lawn Love,” says Yamaguchi. “They’d work with us to not only answer known questions, but also to anticipate and come up with questions for us that we didn’t think to ask.”

Stratus tackled the reporting system by first targeting one small problem, developing the infrastructure and data logic to solve it comprehensively. Once they established this locus of strength, they built outward, first expanding warehouse infrastructure to full scale, and then designing dashboards to surface actionable insights. In parallel, they mentored Lawn Love’s data analyst and hosted weekly code and work reviews for other employees.

Deliverables over the course of the evolution included: business analysis, dimensional models (for jobs, invoices, customers, and service professionals), self-service Tableau dashboards, coaching, and governance (procedures to ensure accurate data and reports).

 

Results

Stratus collaboration sees great exit for Lawn Love and a new data superstar

By the end of the engagement, Lawn Love was largely self-sufficient: their data analyst had matured into an accomplished analytics engineer, and the entire company, with the guidance of process documentation from Stratus, could independently maintain a data-driven operation. Data pipelines were centrally managed. Reports took mere seconds to load. Team members called the transition “0 to 1” and “night and day,” and leadership slept better knowing their decisions were informed by guaranteed fact.

Lawn Love’s data practice served them well. Ultimately, they were bought by a large company called LawnStarter in an ideal exit. “We wouldn’t have been able to do so,” says Yamaguchi, “without the ability to show our growth and core performance via a robust, highly functioning data operation.” The analytics engineer, who had started with little to no data experience, went on to work at a top analytics company—where she continues to excel today.