Why Most Revenue Leaders Struggle with Their Forecast

To most Revenue Leaders, forecasting every week and month still feels a lot like holding the finger in the wind and hoping for an epiphany. Despite all uncertainties however, these forecasts are getting submitted across the globe in a rigorous rhythm.

As the SaaS industry has been maturing further and investors and boards have started to look closer at the accuracy of such forecasts, business are starting to truly rethink and redesign their strategies behind coming up with such numbers.

And yet again, there is no golden grail.

Look at Many Forecasts, Not One

The example above highlights a few of the key lessons that I have seen working well over the past years when working with Revenue Leaders.

The pitfalls of most companies' executives is that they are blind to the full picture. To add to that, most of the organization's data might not even be connected in one place.

The truth is that simply looking at new revenue getting added to your business is not good enough. Understanding the overall impact (ARR), risk (Churn) and other key indicators is becoming at least as critical as closing new deals itself.

A quote that has stuck with me for some time was a Sales Manager stating that "it is not enough for my team to close these in their current quarter; I need them to start focusing on building up their next quarter too - otherwise we'll fall behind".

This simple yet powerful quote has led to the extension of the key metrics in the spreadsheet here which promote factors such as required pipeline generation and coverage for a future quarter as well.

Ultimately, it comes down to qualifying your forecast by the following criteria (see Martin Reeves, HBR, March 2022 ):

1. Look at many forecasts, not one.
2. Look for underlying variables.
3. Look for convergence and divergence.
4. Look for white space.
5. Look for market trends in competitive advantage.

A final point Reeves mentions is the need to gauge your own org's readiness to execute - in the current as well as the future market conditions. This speaks both to your operational excellence and at the same time to the technological investment you are ready to commit to in order to connect the required data points to get you to your full revenue cockpit view.

Dynamic > Static

As Darwin famously said, the survival of the fittest is not referring to the strongest player in the market but to the player that adapts the fastest.

Likewise, leading Revenue Leaders are looking to rely not simply on static metrics but instead incorporate dynamic variables such as Win Rates or Conversion Rates to base their forecast and pipeline analysis on.

In order to stay ahead, change your Opportunity Probability from the standard percentage to a tailored calculation.        

The CRM wizards amongst you have heard this before. In most platforms out there, an opportunity stage is automatically updating the probability to win the deal. The further along the sales cycle a deal is, the more likely your team is going to close it.

Might there not be the simple challenge of data quality.

Relying on such a standard and basic win rate indication is entailing a lot of risk. Instead, use a custom formula in your CRM to assign a probability score yourselves. Some organizations even rely on their sales teams to update such a "Likelihood to Close/ Renew" percentage on which they base their forecast on (instead of using classical 'Commit' vs 'Best Case' logic).

In the example above, instead of simply relying on your probability percentage being based on the sales team's updates of the opportunity stage, you can develop a score which merges information stored at the individual deal level (e.g. MEDDICC, fit for Ideal Customer Profile) with your own organization's goals (already held roadmap presentation, executives are engaged, change management plan presented to showcase alignment between pre-sales and after-sales teams etc).

Using such tailored scoring mechanisms, you can restructure your forecast rollups view to get to new predictions for your end of quarter or end of year revenue projections - and flag risk across your pipeline proactively.

Forecasting is Multi-Dimensional

Just as your very own organization and product portfolio, forecast has many facets and can become complex very quickly.

Leading SaaS companies out there have already understood that it is becoming the new forecasting requirement to merger your data of the future (anticipated bookings) with your data of the present (monitored consumption) - which can refer to data consumption, license consumption - or in the case of non-SaaS businesses, service agreement consumptions.

I highly recommend you listening into this panel discussion on consumption-based forecasting with Chris Klayko of Databricks & Santosh G. of MongoDB.