A forecast is a prediction of the future. A weather forecast attempts to predict if you’ll need an umbrella later this week. A professional services forecast attempts to predict the amount of work (and corresponding revenue) the firm will deliver in a future period. The forecast may also predict the specific skills required by the work and the number of hours needed per skill.

The forecast is arguably the most important artifact for firm management to evaluate and scrutinize on a regular basis. This is because the forecast indicates the future supply/demand balance of the firm. Keeping supply and demand reasonably well-balanced is one of the primary responsibilities of firm leaders.

One of the best ways to improve the performance of a services firm is to extend the visibility of the future demand on services. If Consultancy A can only predict its future work for the next 4 weeks and Consultancy B can predict its work for 12 weeks, it is almost certain that Consultancy B is a better business. The further out you can reliably predict the demand for your services, the easier it is to run the business.

In order to maximize the visibility of services demand, the firm needs an accurate forecast. That forecast is comprised of two underlying components – the revenue backlog and the sales pipeline. The revenue backlog is a ledger of all sold work that has yet to be delivered and the sales pipeline is a ledger of all potential work that has yet to be sold. Most forecasts add the backlog for a period of time (i.e. the next quarter) with a portion of the pipeline for that same period.

Since the backlog is comprised of work that has already been sold, it is fairly easy to predict which portion of the backlog will be delivered in a future period of time. If the firm has a good resource optimization and planning function, it can allocate work to individuals and predict future hours and revenue with reasonable accuracy. But the sales pipeline component of the backlog is not as predictable. This is because the deals aren’t closed and key data points (such as probability) tend to change on a daily basis.

Most firms factor only a portion of the sales pipeline into the forecast. For example, a firm may decide to include only the weighted revenue from deals that are >60% likely to close. So, a $100,000 opportunity at 50% wouldn’t count at all in that forecast while a $200,000 opportunity at 60% would add $120,000 toward the forecast (60% of $200,000 = $120,000). This type of approach illuminates how important it is for opportunities in the pipeline to have accurate deal values, probabilities, start dates, and durations.

When a services firm has predictability of future demand, it can proactively adjust its workforce capacity to meet that demand. For example, if a firm with 50 delivery personnel has a goal to deliver 140 billable hours per person per month, that totals 7,000 hours per month across the company. If the firm’s forecast for the next three months calls for 7,000 hours, 9,000 hours, and 10,000 hours, that indicates that the firm will have difficulty delivering roughly 5,000 forecasted hours over those final two months. The only way to deliver those hours will be to hire or require significant overtime from the existing team. Recognizing this future resource crunch, firm management can advise the recruiting team to maximize its efforts to add new delivery personnel.