Project at a Glance:
I was engaged on a contract basis to design an interactive financial modeling tool, which predicted the profitability and sustainability of zootly, an application for moving truck companies. The model took three weeks to research and four weeks to build, at which point it was presented to potential investors, along with a pitch deck I created. Zootly secured an infusion of $22 million and continues to thrive today. Shortly thereafter, I was offered a position as head of Research & Development. Tools used included Excel, US Census data, industry contacts, calculator, and whiteboards. Skills used included research, comparative analysis, budgeting, org charting, determining seasonality, technical writing and formula writing.
After contracts in Analytics and as an Operations Specialist at One Kings Lane, I was brought on at Zootly, a startup application company owned by Totally Edge, a small holding company in Greenpoint, Brooklyn. I was tasked with designing a business model for the app that could be presented to venture capitalist firms as the company was seeking funding for launch. Although I was adept with using Excel, it was a project for which I felt almost entirely unqualified to attempt. Nonetheless, because it was an interesting challenge and because the stakeholders assured me that they wanted my help, I pressed ahead nonetheless.
Designing an interactive tool for modeling Zootly’s financial outcomes under a variety of conditions turned out to be one of the most fascinating and rewarding projects of my non-linear career.
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The first task was to determine the most basic building block of how Zootly would earn revenue. The purpose of Zootly is to allow users to book a moving truck in the same way that a customer would book an Uber ride. It’s a great model and is beginning to be copied all over the country. It’s great is because the moving industry writ large is based on a highly antiquated model in which there are minimum hours charged by a moving truck and truck crew. What this means is that, after fulfilling one or possibly even two moves in a day, the truck and the crew wind up sitting idle on a parking lot. Not only do those idle hours represent lost money for the owner, it actually costs money to store and maintain a truck, especially in a market like New York where parking costs are astronomical.
Zootly offered those owners a way to leverage those idle hours and gave users a way to make smaller, quicker moves, such as moving two office desks and a couch across town, which would only take an hour. This was the fundamental profit unit for the company – what does Zootly make per hour per active truck? Solving that problem was actually quite difficult but luckily many of the people on Edge’s staff had extensive experience in the moving industry and were able to point me to industry resources that informed the model.
Other research involved trying to understand the moving market as a whole. By comparing industry statistics and US Census data, I was able to establish a stable corollary between metropolitan populations and the number of moving trucks within each market. This was vital to figuring out the maximum possible profit by capturing 100% of the moving hours in ever city and then extrapolating what our possible upside would be for seizing 0.5% or 1% in certain cities across the United States. Another piece of the research involved determining the exact shape of moving industry seasonality and feeding that data into the monthly and annual shape of income throughout the model.
As Zootly was in Beta at the time that I was designing the financial model, I was also able to incorporate a large amount of proven, on-the-ground data coming in from thousands of already fulfilled moving jobs.
Once I had an understanding of the basic space, my goal was not to create a static presentation of expected revenue and expenditures, but to create a dynamic tool that could be used by principals to guide the business in reality, as well as to give investors and understanding of how the company should behave under a variety of conditions. By employing conservative and defensible logic, I wanted to illustrate the perimeters within which the company could remain sustainable. A good model should show an investor or stakeholder what would need to happen to make multiples beyond their wildest expectations as well as the minimum threshold for the company to break even if the environment proves more challenging than expected.
I created a set of interconnected Excel sheets. These included Revenue, Expenditures, Annual Cashflow, Job Type Calculator, as there were several varieties of Zootly jobs), Rollout Calculator, to illustrate the effects of different strategies of rolling out from city to city, Trucks Calculator, to show how each enrolled moving truck in each city could be expected to perform, a Monthly Cashflow, to show the balance at the end of every month in operation, and Income Statement, a Valuation, and an extensive set of explanations of all of the logic used within the model.
Every page that I designed had variable fields. These could be number of trucks purchased by Zootly or the salary paid to the CTO. Every page took the variables entered by the user and fed them into the assumptions of other pages. For example, if the total annual cost of personnel was $5.5m in the second year, it would be expected that $458,000 would be spent each month. But seasonality of each month’s expected showing affected the cash flow from month-to-month in specific ways. Changing a single variable adjusted the conditions throughout the model.
Cribbing lessons I learned at OKL and Gilt Groupe, I also included little key performance indicators throughout the model. These were not necessarily variables that could effect the rest of the business plan but that highlighted important aspects of the business, such as how much money the company would expect to spend on advertising to obtain each job in the first year.
The biggest challenge in designing a modeling tool of this complexity was that this was my first try. I didn’t have an MBA or any formal education in how economic engines run sustainably. I had to start with such embarrassingly basic assumptions like, “If I sell W number of things at X number of dollars, I’ll make Y amount of profit minus Z amount of expense.” It was also rough trying to figure out things that any first year MBA would know, such as what the purpose of EBITDA is or how to calculate amortization or what sorts of things need to be depreciated.
One of the most mysterious aspects of the project was determining the valuation of the startup. After significant experience in this space, this remains frustratingly mysterious to me. Even when I talk to really smart business people about how to fairly determine the value or multiple for a company, they tell me, “Well, it’s more of an art than a science.” That occurs to me as being a wildly unsatisfactory answer, especially when investors are placing tens or even hundreds of millions of dollars on the line. When placing a value on a company with easy comparisons, like a brick and mortar clothing retailer or a grocery store, determining a value seems relatively straightforward. But when determining the value of a company that is unique or offering a new type of service, arriving at a fair valuation seems akin to the sort of black magic math that drives much of marketing and advertisement.
Another challenge was realizing when to stop. I quickly understood that every aspect of the model could be broken down further into their own pages of calculators, which would make the predictions more and more accurate. But as this was not a full operation budget, the purpose of the model was to illustrate a defensible plan for a potential company. Once that argument had been successfully made, it was time to wind down the project.
When Zootly was funded, one of the most satisfying moments was when I got to speak to one of the analysts from the investment firm. He told me that the model that I had composed was the reason they sat down for a meeting in the first place and ultimately supplied the capital. That was eye opening for me. Many of the people on their team were Ivy League MBAs and to see how interested they were in the model was super exciting. In part, I think they appreciated that, because I was so new to analysis, I wasn’t smart enough to obfuscate the logic operating behind the numbers. I just let the common sense algebra speak for itself and could therefore answer any question they had regarding how I arrived at my conclusions. And when they asked me a confusing question or used a term I had never heard of before, I just straight up told them that I didn’t know. To me, it had felt like doing Sudoku or a crossword puzzle.
Zootly ultimately ended up securing $22 million in two funding rounds. The most thrilling aspect of the project was seeing an idea go from a back-of-the-napkin sketch modeled by someone who had never done something like it before, to a fully staffed office in a fancy building on the west side of Manhattan. Since then, I have designed a number of other business models, many of which have secured funding.
Important note: I'm unable to share the Excel sheets of the zootly financial model as this is heavily proprietary information protected by a non-disclosure agreement. However, I can share the document that explains the logic and algebra behind the model and illustrates the process by which I arrived at my conclusions. This document was provided to potential investors in conjunction with the model in order to defend our variables, assumptions, and ultimately, our valuation.