Operating Lessons with Mike Botkin (Part 1)
Big Deal Small Business: Operating Lessons with Mike Botkin (Part 1)
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When you’re in the search phase, it’s easy to get enamored with all the ways you plan to improve a target company.
But putting that into practice post-close is a different story. To be an effective searcher, I think it’s crucial to learn about post-close operating before you close on your deal.
Given I have no experience to share on that matter, I’m introducing occasional guest posts to focus on Operating Lessons.
These posts will highlight former searchers who are in the operating stage of the search fund journey, particularly those that recently closed their deals.
Learning about the first 6-12 months of post-acquisition should help the rest of us pre-acquisition searchers diligence our deals.
Today is the first Operating Lessons post, featuring Mike Botkin. It’ll be a two-parter, so please subscribe so you don’t miss part 2 (updated to include link to Part 2) later this week.
I’d love your feedback on the concept after you’ve had a chance to read.
Mike Botkin is the owner-operator of a landscaping business in Florida. He closed on his business in December 2020.
In May, he posted a fascinating Twitter thread discussing his decision to outright drop a large portion of his customers and to raise prices on a second chunk of customers.
He graciously agreed to pull back the curtain and give us the nitty-gritty details of how he figured out the pricing issue and then how he solved it.
Background
Mike’s landscaping business is a classic search fund business. It has recurring lawn care contracts, roughly 14-20 employees and 3-5 trucks depending on the season. They are on track to do around $1.1 million of annual revenue.
One quirk to highlight – Mike’s seller transition period was only two weeks long, unlike many search deals that feature 6-12 month transition periods.
The Seller started in the business decades ago – he eventually became the GM and then took over for the Owner.
In a moment of self-reflection, the Seller described to Mike that he never really stepped up from the GM role into the Owner role. In other words, he continued executing operations, but he didn’t work “on the business” to drive strategy. As a result, there was a ~20 year void of ownership for Mike to fill.
Let’s dive in.
Noticing the Issue
Mike started riding along in trucks shortly after closing. He had visited some properties during the diligence period, but over the first couple weeks post-closing, he visited all customer properties with his Seller.
My assumption was that Mike noticed the pricing issues during these customer visits. In reality, Mike told me he didn’t notice any pricing issues at that time. He had his spreadsheet with him outlining each customer’s pricing & schedule as they visited, but he said he was just drinking from a firehose for the first 30-45 days.
Even if something seemed odd, his instinct was that the Seller must have a good reason for it.
After he got his sea legs, he started keying in on key business metrics, namely margins. He noticed that his margins were tighter than he wanted, and the culprit seemed to be his cost of labor running at 50-55% of revenue.
He didn’t have specific benchmarks for labor costs, but common sense suggested that once he added overhead, fuel, and other costs, a 50-55% labor cost would result in very low profit margins.
He wanted to target 35% as a best case, but no worse than 40%.
Diagnosing the Issue
Identifying the cause of the issue involved trial & error.
Mike’s initial instinct was that the business was undersized, so needed more units to achieve labor cost scale.
To fix that, his initial focus was streamlining and growing sales, with tactics like adding QR codes on all door flyers:
Mike successfully gained customers, but he didn’t see the labor margin issue improve. He was already capacity constrained and had a 3-month backlog of landscaping project work.
His second instinct was that the crews didn’t have enough route density, meaning crews were spending too much time on the road and not enough time generating revenue.
To fix that, he put all the stops on a map and reworked the schedule into more logical routes.
Historically, the Seller had focused on non-optimal routing goals, such as getting the best crews to his friends’ homes. This resulted in crews crisscrossing all over town.
Same as his first solution, the route optimization work did improve operations, but it didn’t significantly change the labor cost issue.
Mike’s third instinct was that his crews were organized inefficiently. The tactic to solve that was reworking the crew mix.
Previously, there were some crews with all $16/hour guys and others with all $12/hour guys. He reworked each 3-man crew to have a $12/hour, $14/hour, and $16/hour person. (Numbers are just examples, not actuals).
He introduced titles of Supervisor, Tech, and Labor to help codify this. The crews already knew this hierarchy informally given pay rate correlates to longevity & seniority with the firm – he just gave it a label.
This ensured that every crew had an experienced person onboard, and there weren’t any crews with higher paid guys doing work that an entry level crewmember could handle.
Again, like the previous tactics, this helped on the margin but didn’t alleviate the issue.
Mike had exhausted his ideas on managing costs lower. This left only one potential culprit: pricing.
As mentioned above, the Seller was only around for 2 weeks post-closing. After the seller had left, Mike was on his own and identified a strong employee to elevate into a managerial role.
To confirm that pricing was the issue, Mike started visiting properties with his new manager. To avoid leading the witness, he’d ask the new manager “how much do you think we charge for this house and the neighboring house?”
After the manager responded, Mike would show him the actual pricing sheet, which would often be off significantly. Two neighboring houses with similar yards might have an 80%+ pricing difference.
In hindsight, diagnosing the issue seems obvious, but in practice it was not.
Measuring the Issue
It’s one thing to know you have a pricing problem. It’s another thing to figure out how much of a pricing problem, and with which customers.
The core driver of labor cost is time. Mike needed to know much time his crews were spending with each customer.
Mike had already introduced scheduling sheets after closing (which the Seller did not have), so it was a simple addition of two columns – what time did they park the truck, and what time was their key back in the ignition.
In practice, convincing the crews to take this task seriously was harder. The first week resulted in timesheets showing crews left one site at 9:25 and arrived at the next one at 9:25, with no driving time.
There was suspicion amongst the crewmembers that Mike was micromanaging them by monitoring their every movement.
This is where the prior decision to create pay tiers on every crew paid dividends.
Mike collected his Supervisors and walked them through the math, which went something like this:
You guys know Steve’s house, right? He pays us $100 / month.
Jose, your crew has 3 guys at an average of $20 / hour, right?
If it takes you an hour to do Steve’s house, that’s $60 per visit, right?
We do Steve’s place twice a month, so that means we spend $120 on labor for Steve, right?
In other words, we’re losing $20 / month on Steve before even accounting for fuel and other costs.
But we can only figure this out if we know how long it takes your crew to complete each and every house.
Mike laid that out on a whiteboard for his Supervisors, and they got it. They then took the torch to get their crews onboard.
By Week 3, Mike was getting clean data. By Week 5, he started seeing patterns.
Analyzing the Data
No fancy tools or methods here. Mike inputted the timesheets into Excel then took averages of how much time his crews spent at a given customer. He collected data over ~60 days, which provided ~7-8 site visits per customer.
That time data gave him average # of crew hours for each customer visit. Multiply by his average hourly crew rate to get to labor cost for each customer visit. Divide that by each customer’s pricing per visit and you have labor cost as a % of revenue for each customer.
This analysis yielded the shocking finding – he was losing money on labor alone at roughly a 1/3rd of his customers.
Another 1/3rd of his customers were only generating 0-10% margins. But this analysis only included labor costs, so these customers were right around breakeven at best once you incorporate other costs.
Said differently, his top 1/3rd of customers were delivering more than 100% of his profits given they had to offset losses at his bottom 2/3rd of customers.
Conclusion of Part 1
Figuring out the pricing issue was easier said than done. Hopefully this post provided detailed insight into how Mike identified the issue, diagnosed the issue, measured the issue, and then analyzed the data.
Later this week we’ll cover how Mike solved the issue. In the meantime:
Subscribe here so you don’t miss Part 2 (updated to include link to Part 2).
Give Mike a follow on Twitter, where he posts peeks into his life operating in landscaping.
As always, I’m open to feedback on this post or ideas for future posts. Hit reply to this email or find me on Twitter.
Thanks,
Guesswork Investing