With so many business and finance leaders facing unprecedented market disruption and the need for wholesale transformation in their businesses, there has never been a more crucial time to get a grip on revenue forecasts.
Strong revenue predictability provides a sound platform for the critical decisions that need to be made in these tumultuous times.
These headlines spell out the obvious: predictability is something many businesses are still not able to find a way to deliver. Although there is no silver bullet in revenue forecasting, my view is that the first place to look at is within the sales function.
Why? Well, I think it’s mostly dysfunctional.
In my experience, the sales profession considers forecasting as nothing more than a necessary evil that has been mandated by finance. I have lost count of the times I have heard, “It’s just a distraction from our real job of selling.”
What’s more, the data doesn’t lie and there are endless metrics that showcase how dysfunctional sales forecasting actually is today:
According to SiriusDecisions, 79% of sales organisations miss their forecasts by more than 10%. CSO Insights reported in their most recent annual survey that 54% of the deals forecast by reps never close.
So as the CFO, statistically you might as well toss a coin if you want a more accurate forecast than the sales team is giving you.
What’s worse is that 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, they often don’t anticipate missing targets or realise too late in the quarter to take action.
So it’s inaccurate and has a huge opportunity cost. This just isn’t sustainable for any business, so what can you do to fix this broken process?
Here are five strategies to use to build a more accurate sales forecast:
1. Ensure sales reps maintain accurate CRM data
Make it mandatory and part of the culture to ensure that sales provides accurate data on deals and opportunities.
There are a number of ways to ensure this happens. You can create dashboards to highlight good and poor data stewardship by team or by rep. You can set up simple flags and alerts to highlight lazy and poor behaviours, e.g., an emailed alert to highlight deals with close dates in the past or deals that have close dates pushed by more than X times in a quarter.
Given the value of accurate data, some organisations have gone as far as linking KPIs and compensation to data hygiene. Ultimately you need to hold sales leaders accountable for data quality.
2. Make sales accountable for forecast accuracy
Link KPIs and compensation to forecast accuracy.
Maybe this won’t be well received in the sales community, but nothing focuses the mind more than putting pay on the line. I have seen this work well when it is introduced alongside other change process in sales forecasting, i.e., when a new tool or process is implemented.
In my experience, I have seen KPIs that are tied to a range of forecast tolerances with the most common being in the range of +/- 5% of the opening forecast.
3. Make the forecasting process work for sales and finance
Keep it simple and don’t overdo the frequency.
Nothing turns salespeople off more than making it time consuming and onerous to forecast. Having to forecast too frequently or making the process overly onerous just means it will not get the sales team’s focus or attention that it really deserves.
What’s more, you will end up taking away critical selling time and the sales team’s ability to deliver a forecast will be hugely diminished.
4. Provide the right tools
Use a common set of tools for pipeline management and forecasting.
Ensure that sales and finance teams use the same platform for pipeline management and forecast processes. Any disconnect here just opens up data challenges and typically wastes endless time discussing the validity of the numbers. When forced to use the finance forecasting tool, it gives sales a reason not to update their data in the CRM system.
Use your CRM as the system of record for pipeline data and avoid spreadsheets at all costs. When you’re juggling multiple spreadsheets and the complexities of a global or matrixed organisation, the errors and inaccuracies pile up. What’s more, building and maintaining these complex spreadsheets is time consuming and often incredibly expensive.
Deploy a pipeline analytics tool that can easily show you what has changed and provide both finance and sales early insight into deal and pipeline risk.
Ensure that the forecasting platform you are using gives both sales and finance what they need. Excel gives finance the flexibility to roll up the numbers in multiple dimensions (e.g., product, region, sales org), but it just isn’t designed to handle the complexity of matrix, overlay and channel sales organisations.
5. Augment the art of forecasting with science
Use data science to score deals by comparing them to deals you have won in the past.
So many sales forecasts are based on the “gut instinct” of the sales team. There will always be some subjectivity in forecasting, but judgments should be based on objective data. Historical trend data and top-down run rate predictions have some value in forecasting, but in today’s rapidly changing markets a bottom-up, deal-by-deal forecast is essential.
The challenge here is that anecdotal details and personal judgments can dilute the accuracy of the numbers as they are rolled up through the sales organisation. Sales managers usually know the right questions to ask, but they often lack the time to inspect every opportunity, so this isn’t the fail-safe that most companies believe it is.
To get around this, I am increasingly seeing companies utilising data science to score deals by comparing them to deals they have won in the past. This provides an objective data-based view of a deal’s likelihood to close and gives a great benchmark with which finance functions can compare the sales leadership numbers.
Although the accuracy of these data science-based algorithms is shown to outperform human judgment, my advice is to use them to augment human forecasting processes, not replace them.
So, my takeaways are that forecasting has become a painful, time-consuming process. Wasted time cripples everyone and company leaders just don’t have the reliable projections they need to build a predictable business.
It’s time for the sales function to stand up and take accountability for its forecasting and become a real business partner to the finance team. For this to happen, the business also needs to deliver the right tools, processes and cadence for forecasting.
Use data and science alongside human insights in every forecasting judgment.
What’s the biggest blocker to accurate forecasting for your company?
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