I was recently tasked by a PE firm to evaluate a portfolio company’s operating model and build an upgraded model for a portfolio company that has had flat revenues and EBITDA from the three-year period 2020-2023. The company is a commercial contracting company, and I was provided the following documents:

• Monthly Financial Package
• Shared Vision Statement Prepared by CEO, CFO & President
• Contract Bid Worksheet and Contract WIP Schedule.

The formal task was to build an operating model for 2024-2027, including an income statement, balance sheet and CF statement for all years. An acquisition strategy was to be included as was exit calculations in 2027 (multiple of invested capital, IRR and profit dollars).

I was given three days to complete the project and used the entire time.  Below is a synopsis of my findings and recommendations:

Analysis

Gross Profit:
The organization had two primary business lines, Services and Contracts. Services was primarily residential customers which was approximately 60% of the revenue but was dramatically more profitable than Contracts. Gross profit margins averaged just over 40% for the organization, whereas best practice organization should exceed 60%. I modeled out improving margins up to 54% in 2027 as I believed it would take time to get to the 60% best practice margins.

SG&A:
The elephant in the room – Contracts had a whopping 44% SG&A which resulted in a negative EBITDA in 2023 for the product line. Best practices are between 10-15%, and this product line needed to either improve this or shut down. I did not have Contract SG&A breakout data for 2020, 2021 & 2022, but assumed it was consistent, just not broken out. Again, a negative EBITDA contribution on 40% of the revenue – this issue needed to be addressed years ago. This abnormally high SG&A amounts need dramatic improvement immediately or the product line needs to be shut down. I modeled Contracts SG&A improving to 20% in 2024 and 10% going forward.

Revenue:
I had good call volume metrics for Services with fallout rates and average revenue per job. We had 9% fallout from calls which was unexplained and, of the booked jobs, 14% went unsold. I thought we can do a better job with the 14% unsold jobs and modeled the conversation rate from 86 to 92%. I also recommended increasing lead generation and provided recommendations for doing so. Each 1% increase in conversation rate resulted in an additional $152k in gross profit. Average revenue per ticket was $1200 and I believed, based on research, that we can bump this to over $1400.

I was hesitant to do much with the Contract revenue and modeled a conservative 10% increase annually. 

Acquisitions:
I modeled one acquisition per year for Services with a 6X multiple assumption. My acquisition strategy recommended finding horizontal acquisitions with a 45% or lower gross profit to keep the acquisition cost low. Then we work to bring the gross profit to our 60% target. I did not model any other efficiencies to hedge my conservative bet but know that overhead would normally be reduced at the acquired organization and be another pickup.

I recommended considering some vertical product lines but did not model them. Included in the analysis is a heatmap showing the average age of houses per state (see below). The portfolio company’s primary state has a median house age of 41-50 years. I believe there is a lot of opportunity to offer services that need upgrading based on household age. Our technicians in the field know a lot about what our customers need that we do not offer and may be best equipped to identify services to add or a vertical acquisition. Assessing the fallout from the call center may help us better understand what we are not offering that our customer needs.

Some final recommendations: I also recommended treating each location as a specific profit center with gross profit targets. Perhaps the ultimate answer here is to have each location head responsible for their own P&L, with compensation incentives built in to align interests.

Adding KPI’s for more than just the call center could benefit the organization. My SaaS and financial services background has proven to me that the right metrics help monitor performance and allow for data-driven decisions to improve efficiency and profitability. For example, for this company I am interested in the incremental revenue that a technician can bring in from the field- outside of the initial engagement. Perhaps we can strategize and train our technicians to perform a 10-point customer checkup for free at the site – just to keep customers aware of any potential issues (like a Jiffy Lube 10-point car inspection). Can we measure response time, customer satisfaction or cost per lead? Answers to these questions can drive strategic and tactical decisions to increase revenue, customer satisfaction and profitability.

Valuation:
The updated model resulted in a 254% return in estimated value in 2027 over the 2023 results and an 8.42X multiple on invested capital. I believe this result is from a model with very conservative assumptions and believe this at this time that this can be the floor for returns. I did not have the opportunity to speak with management, so adding a disclaimer on the current assumptions until proven incorrect. Even if incorrect, we continue to re-engineer the process until we get the right formula. As I usually discover – opportunity for scalable growth exists if you execute and grow with a disciplined approach, keep everybody on the same page and constantly re-evaluate who you are and how you do it. Competitive markets and organizations are constantly changing, and we need to stay dynamic to meet these changing needs