Commercial skills for customer success, forecasting

In the science (and art) of forecasting, CS can lag sales badly. CSMs are particularly prone to forecasting based on recent customer sentiment which is not only a narrow view of the renewal but misses the point of CS. This article introduces a forecasting methodology that can help individual CSMs to improve their commercial acumen and leaders to improve forecasting accuracy too.

COMMERCIALMETHODOLOGYSCIENCE

5/17/20244 min read

Forecasting is an often neglected art

This is the first in what will be a series of posts about a topic dear to my heart – the commercial impetus of customer success and what we can learn from our colleagues in sales.

I know what you’re thinking - with a shared commitment to ICP, authentic solutions and serving the customer’s best interests - CS and sales are practically indistinguishable - but we are even closer still. We both need to plan for the future, we both need to forecast accurately.

In CS (as in sales) an accurate forecast is a good indicator of a good CSM. Quality forecasting demonstrates understanding, empathy and control. Surprises are bad – even the good ones can scare management (precisely because they demonstrate a lack of understanding, empathy and control).

One of the joys of learning lessons from sales is that (unlike CS, apparently) their discipline is the oldest of all and there is an endless sea of well-documented research and support for the aspiring rep. These resources should be pounced upon by every CSM, after all we’re all selling something and some of these resources (listed at the end) are brilliant.

Strategies from sales, for customer success

Moreover, in the case of forecasting at least, CS - particularly in startup and scaling businesses – lags sales badly. CSMs are particularly prone to availability bias, forecasting based on recent customer sentiment which is not only a narrow view of the renewal / deal but misses the point of our blossoming discipline. If you are a CSM not currently using a standardised forecasting methodology, adopt one of these to really stand out and if you are a leader with big gaps in forecasting accuracy then establishing a framework will make an impact quickly.

The most well known method is BANT which stands for Budget, Authority, Need and Time. Are you speaking to the correct stakeholder and do they have a reason to renew / sign on X date for Y price. Simple. A little too simple perhaps.

Don’t get me wrong, BANT is a lot better than going with your gut and is perfect for high frequency scenarios where relationships are short. But when you’re forecasting the results of a year's (or more) worth of product interaction and relationship building, you can do better.

There are several more detailed methodologies that have biases towards practical requirements or emotional pain points and they too have funky acronyms like SPICED, MEDDIC and MEDDPICC. But I’m going to recommend a personal evolution of a framework introduced to me at Pure360. It has been adapted to place greater focus on stakeholder management and of course, demonstrable success.

Particularly for high touch CSMs this framework serves two equally useful functions, improves forecast accuracy and improves renewal / close rates by encouraging a pre-mortem (uncover where it could go wrong before it does).

A couple of notes on using this framework:

  • I have adapted the way I train this out over time and found it's important to stress that we're scoring the forecast, not the deal. The deal has a value and in the case of renewal, a fixed date, and the CSM provides a %likelihood of renewal / close. The framework is a measure of how surprised they could be by emerging facts and events under each heading (from; 0 - I know very little and could easily be surprised to; 10 – I see all and would fall off my chair in shock, before getting up and falling off my chair again / literally unforeseeable).

  • The example questions are illustrative and very far from exhaustive. You should continually ask different variants of them as an individual CSM, leader and as a team. They do two things: provide an example of what a trustworthy forecast looks like and; get CSMs thinking like the MD of their portfolio (not like a CSM trying to pass an arbitrary test).

  • As with all central processes continual training and use is obligatory but - like all good processes - this one serves its users. This framework is a genuine benefit to the daily work of a results-oriented CSM so ongoing focus will continue to deliver returns and should not be a chore.

  • If you aggregate the score for an overall forecast weighting, ensure you can see the make-up somewhere.

  • Most businesses will do well to add specific questions that are relevant to their industry, USP or market to ensure that introducing such a framework can hit the ground running.

More effective forecasting is more effective CS

Introducing a framework like BOSSCRIPTS will immediately improve commercial acumen across the team and when used correctly will improve forecasting accuracy too. A word of warning to leaders challenged with inaccurate forecasts, any framework can be undermined by reacting in a manner than disincentives honesty, your team will quickly learn what they think you want to hear. Remember that forecasted churn is an opportunity to earn a renewal, especially where early and precise. If you discourage honesty with punitive actions (even unmanaged stress) then expect to have the same rose-tinted glasses you’re used to, only this time in 4k resolution.

Having said this, changing culture and introducing complex processes can take time, particularly for larger teams. There are brilliant methods for calibrating estimates but you are unlikely to be furnished with them consistently. Fortunately where forecasting is concerned, inaccuracy is often a pattern. Over time it is possible to reliably identify the degree to which certain colleagues over / under estimate renewals and deals – check out Kluster (below) which has mastered recognising these patterns (among others).

Finally, forecasting is a predictive model and as you might imagine, various ML / AI solutions will readily make such problems a thing of the past. However, as is always the case with data challenges, what goes in, comes out. Some businesses may well have an abundance of well-structured, clean data and where this happens, kudos – I know it wasn’t by chance! Most businesses that haven’t been through a process of deliberately building this resource will have apple- and pear-shaped metrics in orange-shaped data baskets. In my experience they probably have the odd Findus crispy pancake in there too! If you don’t have the data to train a model just yet then BOSSCRIPTS is ready to improve your forecasting in the meantime and what’s more, your CSMs will know why.

References and further reading: