How To: Create a Complete Forecast Including Sales and Backlog

Last week’s post focused on a technique to forecast revenue from work in a services business where the contracts had already been signed (i.e., “sold work”). That forecast (hopefully!) doesn’t tell the complete picture because it doesn’t account for work that will be sold and delivered in the future. Luckily, with the framework from last week in place, it’s easy to incorporate these additional details. Let’s dive in…

Three categories of forecasting

It’s useful to think about revenue forecasts in three categories:

1. Backlog: The amount of revenue you would recognize by delivering only the work to which your current clients are contractually committed, as covered last week.

2. Pipeline: This is work that has not been sold yet, but you have some “line of sight” to finding and closing. Of course, different opportunities in your pipeline may have different levels of certainty, so each contribution to the pipeline should be weighted to reflect the probability of closing. Also, there is usually some future pipeline you expect to develop from sales activities but are not far enough along (yet) to connect to a specific customer.

3. Soft Commit: This type of forecast is from work that sits in between the first two categories. It is work that you don’t yet have a signed contract for, but it is virtually guaranteed. You may have a verbal commitment from a customer, a contract may be in the process of being signed, etc.

With these categories in mind, let’s revisit our forecast from last week.

Forecasting all categories of future revenue

Adding a column to the table from last week’s forecast table allows each row of the forecast to have a “forecast type”. As a result, the same customer may appear multiple times:

In this example, we have Acme Corporation with backlog through August and a soft commitment for work extending through the end of the year. This fictional scenario is pretty common, where an existing customer has work that is “in flight” and knows that an extension is needed and has already verbally agreed to it. Similarly, we have Initech with soft commitment overlapping with backlog. This could be a case where we know the team needs to grow to accommodate the work that has already been sold and needs to continue longer to do knowledge transfer or ramp down.

Conversely, we have Globex Corp, which has pipeline appearing beginning in June. This fictional scenario could be a case where a new opportunity has developed unrelated to the existing work appearing in the backlog. The example above could be a similar amount of work as the existing project but weighted at 50% because of uncertainty about whether the opportunity will be won.

Finally, we have some new companies appearing with only pipeline numbers. InGen and Stark Industries weren’t in the forecast example last week because they aren’t current clients. They are new prospects with varying degrees of certainty around whether the work will be won. And, as the forecast gets close to the end of the year, we have a placeholder labeled “Customer TBD” for one or more new customers who we expect to contribute to the pipeline.

Analyzing forecasts by category

This decomposition of the forecast by category shows how much revenue is coming from each category in total by month:

Reviewing forecast to actuals every month will reveal important sources of variance:

• Backlog that wasn’t recognized for some reason. As discussed last week, this could be from a variety of causes. Assuming the work is continuing with the client, it can usually be easily corrected in future months’ forecasts.

• Soft commit forecasts that didn’t convert to backlog as expected. This can be particularly worrisome for work you thought was all but guaranteed hasn’t been signed and usually warrants investigation.

• Pipeline that didn’t convert to backlog as expected. Of course, not every individual opportunity in a sales pipeline will convert, but in the aggregate, it is helpful to be able to accurately forecast the sales pipeline’s contribution to revenue. It can be helpful to examine variance from both individual opportunities and in the aggregate to make the forecasting process more predictive.

Visualizing the forecast by category as a stacked bar chart is easy from the pivot table view and can also be helpful:

This visualization highlights where and when the composition of a forecast gets more (or less) risky.

Assessing the generation and probability of pipeline

One of the biggest challenges to this type of forecasting is accurately forecasting how much new pipeline can be generated and the probability of an opportunity to close.

Without a lot of data from historical opportunities that have been closed (either won or lost), this process can be more art than science. Initially, monthly reviews of pipeline forecasts and variance are the best way to get more accurate. Focusing on minimizing “downside” variance - being surprised about a lack of pipeline or lost opportunities - is the most beneficial place to start.

Over time, a more data-driven approach can be adopted as a business gets more predictable. Metrics can help estimate pipeline value by looking at things like:

• win rate percentage

• lead-to-opportunity conversion rate

• average initial opportunity value

• average duration from opportunity identification to win/loss

Just start

This forecasting method may feel uncomfortable initially, especially in organizations that have been successful through “just figuring it out”.

As businesses scale, this kind of financial discipline helps solve (or prevent) the challenges that come with bigger teams, bigger revenue numbers, and more people involved in the sales and delivery processes.

The best way to overcome the discomfort of an unfamiliar process is to just start. Don’t let perfection be the enemy of good.

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✌🏼 That’s it for this week. Are there topics you’d be interested in seeing covered in this newsletter? Drop a comment below and let me know.