Whether you are raising capital or managing an existing business, making accurate financial projections for marketing, sales, and operations is essential. Combining these measurements into a comprehensive model can help you develop an accurate projection of revenue and profitability.
This new era of dot-com companies making news by going out of business highlights that it takes more than marketing to succeed. While it’s hard to tell exactly what causes each failed dot-com to go under, they all have one thing in common — lack of profit. All new ventures start out losing money, but the successful ventures eventually turn a profit. And successful entrepreneurs have a business plan that accurately projects when the company will achieve profitability.
Whether you are raising capital or managing an existing business, making accurate financial projections for marketing, sales, and operations is essential. It’s relatively easy for venture-backed companies to project their early expenses — it’s the amount of cash raised. However, projecting revenues has always proven to be more difficult.
With fewer sources available for a quick venture capital investment, having an accurate projection of revenue, expenses, and profitability has become more critical. As Richard Hoy said in his article “Putting Together a Sound Business Plan,” you should develop your business plan as if you’ll be “running your business for the next 30 years.”
Financial modeling of revenue and expenses is based on knowing the response rates at each point in the process from awareness to shipping products to customers. Modeling a business involves using spreadsheet formulas that calculate projected financials based on the results of activities. The revenue side normally includes advertising and promotions, sales, and other business activities. The expense calculations take into account outside expenses and the cost of staff and overhead.
The accuracy of these projects is based on having a good understanding of the response rates in every aspect of the business. However, many financial projections turn out to be wrong, primarily because the assumptions used in the financial modeling were not accurate.
The response rate for advertising and direct marketing is relatively easy to track. However, once someone clicks on an ad to visit a site, he or she doesn’t always make a purchase on that first visit. This makes it harder to track a web visitor all the way through to a purchase, unless a site uses cookies to track when someone returns to a site.
In addition, many people use the web to gather information, then call an 800 number or visit a store to make the purchase.
It’s not always necessary to track each prospect all the way through the marketing-to-customer process in order determine the response rates for each step in the process. You can frequently rely on the experience of managers who are familiar with the metrics of their portion of the company’s operation to make accurate projections.
For example, call-center managers know the metrics for their operation, such as call volume, length of calls, number of orders, and revenue.
Teams that work trade shows can frequently predict the number of qualified leads that will be generated at shows targeted at their market. And salespeople working in business-to-business frequently know the typical time, from initial contact to contract, for each type of prospect.
While knowing individual response rates for each portion of an enterprise improves the accuracy of projections, there is another important part of modeling.
The overall lead time between awareness-building promotions and resulting revenue is frequently overlooked when constructing financial models. While click-throughs on banner ads occur quickly, results from other media take time. In addition, prospects bookmark sites and save direct mail pieces for later review and possible purchase. The time customers take to investigate and decide on a product — the length of this sales cycle — needs to be included in a model’s calculations.
Click-through rates and other metrics are a way to summarize our understanding of customer behavior as customers investigate, select, and purchase products. Combining these measurements into a comprehensive model can help you develop an accurate projection of revenue and profitability.
