Companies miss quarterly numbers. Deals slipped, conversion rates sank, or there just wasn’t enough volume.
This happens despite sales teams spending lots of time on the forecasting process. According to SiriusDecisions, reps spend on average 2.5 hours per week and managers 1.5 hours on forecasting. Yet, nearly 80% of sales organizations don’t forecast within 10% of where they eventually land. They often don’t anticipate missing targets or realize too late in the quarter to take action.
Luckily, there’s plenty CFOs can do to build a more accurate sales forecast. Here’s what I tell our portfolio companies.
On the first day of the new quarter, the leadership team gets in a room. Often they do some “Monday Morning quarterbacking” — analyzing what could have been done differently to have won missed deals.
While everyone agrees to make changes next quarter, most often that doesn’t happen, and sales organizations continue doing the same things as before.
The first step is acknowledging that without a change in action, results won’t change. Investing time and effort into the forecasting approach and process will result in improved visibility into where you’ll actually end the quarter, and give you earlier knowledge that you’re going to miss your numbers so that you still have time to do something about it.
Every company occupies a point on a scale of forecast maturity. Whether you’re just starting to prepare forecasts or creating multiple predictions for each quarter, you’ll want to identify where you are before setting forecast accuracy goals.
There are four stages in forecast-process maturity:
Stage 0: Basic Forecast — This is where everyone starts. The process begins with a list of opportunities categorized as either closed (an order has been accepted and processed), commit (based on available information, the opportunity will close within forecast period for the estimated amount), or upside (the opportunity could close under ideal circumstances). This is out-of-the-box Salesforce functionality.
Stage 1: Single-Threaded — Most scaling companies are here. Typically CRM-driven data, a forecast is based on an opportunity’s size, the stage it’s at in the sales process, and its likelihood. These are probabilities that have been layered into Salesforce-based sales cycles. This is where sales leaders will add the most value by overriding and rolling up reps’ forecasts.
Stage 2: Multi-Threaded — Moving from a single forecast to multiple forecasts is where your finance team can make the biggest difference. This approach uses multiple ways to forecast the number and triangulates to the most likely range of results. Most companies will have added complexity by having multiple routes to market.
Stage 3: Automated Insights — By adding automation to the process, you can remove the “human factor” that so often leads to forecast inaccuracy. Adding data science also allows you to generate insights from your own data. This means you can get to the answers faster and have more time to react.
The sooner you can invest the time in a more robust forecasting process, the sooner you will have visibility into potential pitfalls. Sales will always make its call on the number, but finance teams should play an active role in applying a more analytical approach to the process with “Capacity-Driven” and “Pipeline-Driven” models.
These models offer a great way to generate additional insight on what your sales will be and complements the sales model in forecasting discussions. Here’s what you need to know about them:
Sales-Driven: Sales leaders use CRM pipeline opportunities, values, and likelihood to close to determine ranges of sales outcomes with their management adjustments. Using your CRM pipeline opportunity values, stages, and probabilities, calculate what your weighted forecast looks like. Separately, look closely into your top deals by segment to get a better handle on how those deals will likely play out.
Capacity-Driven: Use quota-carrying headcount and attainment rates to derive sales capacity in a period. Creating a capacity model gives you visibility into whether you even have the bandwidth to make your number. It’s common for companies to fall behind on hiring. Without sufficient capacity, your chance of being able to achieve your revenue goals is severely hindered.
Pipeline-Driven: Use opening pipeline and historical conversion rates to determine a range of sales outcomes. Creating a pipeline-driven model allows you to use historical close rates of opening pipelines to predict how much you will close in any given quarter. Get down to a degree of granularity that allows you to see how different segments and regions perform. Conversion rates for an opening pipeline (that is, the value of a pipeline at a pre-set date at the beginning of a forecast period, typically a few days to two weeks after the start of a quarter) can easily be converted to another sales metric, “pipeline coverage.”
Get all of these ratios and conversion rates on the same page and study them over time. It’s rare to see a company drastically change its conversion rates in a short period of time. Past performance is the best predictor of future events. Once you have your ranges, line them up and find where they overlap. That overlap will be your forecast range based on all the data at your disposal. (See example below; click on the chart to make it larger.)
Finally, don’t forget to trend your forecast accuracy over time. It’s a great way to feed your insights back into your process to make it more accurate over time. This process also sets you up for automation in the future. (See example below; click on the chart to make it larger.)
A forecast, by definition, predicts what will happen in the future. Being able to forecast more accurately and with a longer runway is the key to being able to take action in time to positively affect any forecast shortfalls.