Startups Can Forecast Sales Using Sales Reps but Context is Key

A sales and operations professional based in Methuen, Massachusetts, Benjy Orbach has decades of experience optimizing year-over-year sales in the lighting and snack food industries. One of the tools Benjy Orbach has utilized to meet and exceed sales goals is sales forecasts.

Sales forecasting enables companies to effectively allocate capital resources, manage their workforce, and adapt for future growth. Good sales forecasting, however, is only possible if a company has enough sales data to work with. Older companies have this data in their records. Younger companies typically do not have a sufficient sales record to accurately inform their future sales. Therefore, they have to rely on benchmarks such as industry statistics. Another option for startups that want to work from personalized sales forecasts is sales representatives’ individual forecasts.

Sales reps can put together monthly forecasts based on their views of their current pipeline of clients. This data, though, must be evaluated in the context of the stage a client is in regarding the sales funnel and the average time deals take to close. For example, if a sales rep reports five leads but two are in the early stages and the average closing time is 40 days, these cannot be forecast as sales for the next month. Furthermore, opportunities that have persisted for too long without closing, say 70 days when the average closing time is 40 days, should not be considered in the forecast, since they are not likely to close.

Managers preparing sales forecasts have to pay special attention to large individual sales. If a sales rep is working on a deal that is worth $70,000 but the company’s average deal size is $10,000, including the $70,000 sale in the forecast can significantly push a sales forecast upward. If the sale does not close yet the company allocated resources based on the expectation that it would, there’s a problem. In such scenarios, managers should consult candidly with their sales reps to figure out the true likelihood of big deals closing before including them in forecast data.