As a small business startup we spend a great deal of time looking at our data and analyzing that data to help us drive strategy, make adjustments, and better understand our customers. We try hard to not be robotic in our response to numbers, but rather to let them inform our decisions and our direction and we believe that this is how all small businesses should use their own data. Here is the second in what will be a serial discussion of some of the data we look at internally, the numbers we analyze, and our strategic approach with this data.
Like all startups we began life even before we were born. What I mean is, that before crowdSPRING was a business it was an idea, a business on paper only. We worked hard for many months researching the market, writing a detailed business plan, and building a comprehensive financial model. We made projections about our business based on well-informed assumptions: about site traffic, about registered users, about projects posted, about average awards in various design categories, such as the average in logo design and web design. All startups engage in their own version of this exercise. Some might characterize the process as smoke and mirrors, but done well it is a series of well-educated, research-based, detail-justified guesstimates.
These projections are critical to the planning process. Because, in the absence of actual data, and without projections, how can a business budget? How can a business define goals or determine strategy? And how can a business plan hires and personnel decisions? And, perhaps most important to a startup, how can a not-yet-born business justify the funding it is asking of potential investors? In order to raise funds from investors it was necessary to share with them your projections, and justify these with logical, defensible reasoning. This must be done in a methodical manner. In our case we first looked for comparable companies, collected as much historical data as we could, and used these numbers as a starting point to project our own. Second, we applied multiple scenarios to illustrate that the business could be profitable even if we found ourselves facing the worst-case.
The process is fairly straightforward: starting with the data from comparable companies, we built a data base of traffic, registrations, and transaction value from their first 2-3 years in business. Then we discounted those numbers by 50% and plugged them into our own model. This served as our baseline or “most-likely” scenario and the foundation of our planning. Next we created two other scenarios – the “worst-case” and the “best-case.” The worst case was arrived at by discounting our assumptions by another 50%. If we could survive the worst, we knew that we would be OK. We built the best-case by removing the discounting we had applied to the assumptions. In other words we assumed that cS would perform as well as those earlier businesses had. We would generate the same site traffic, we would register the same numbers of users, and we would see transaction prices that matched those that we had seen. When we graphed these scenarios we could see clearly what it would take to achieve profitability no matter which way the winds blew and with rational logic as opposed to blue-sky hopes.
Here’s a graph which illustrates how Best/Worst/Most Likely data would be charted visually:
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