Repeat → Predict → Scale
Every revenue leader wants an organization that provides repeatable, predictable, and scalable revenue. What they mostly want is scalability - because that’s what their investors want - “Put in more money, get more revenue.”
But there is a sequence. Scalability is not possible without predictability, and predictability is not possible without repeatability. In the past 18 months, it feels like all of this has been wrecked - nothing is working. But I would argue that the only thing that actually broke was repeatability, which then wrecked prediction and scaling.
Some thoughts and implications:
Most sales leaders will say, “We didn’t change anything, we kept doing the same thing, but it stopped working.” True - what changed was the customer - their buying process, decision criteria, ROI hurdle rate, or de-funding of certain categories. Sales processes did not take this into account. They were based on assumptions about the buying process that were no longer true. And - here is the problem - their sales process did not detect and report that change. Or - and this is an even bigger problem - it did report the change, but leaders ignored it.
We often fool ourselves - we believe we have repeatability when we do not. This is a pattern matching problem - we look for sales pipeline patterns “length is 6 months,” “competitive win rate is 65%,” and “landing ARR is $75K with our XYZ package”. We like these because they are measurable. But we miss the non-trackable factors: “all wins are with early adopter change agents as the champion,” “all wins include our CEO/founder as part of the active selling team,” “all wins are with buyers that looked at us a year ago, went dark - but did put us in the budget, then came back and bought us a year later.” Or, what happened in 2021 - 2022 “all wins are actually 1-year pilots bought by low-level buyers with left-over budget, which they won’t have access to in 2023.”
If we rely on pipeline metrics to show repeatability, we build prediction models based on pipeline data. This works great if the pipeline metrics happen to track closely to the real-world reasons for buying. However, it only provides a correlated prediction model without understanding the underlying causation. When the causes change, the correlation fails, and predictions fall apart.
Going to the next level, scaling is based on predictability - assuming that X investment in the revenue machine will create Y amount of revenue. For example, if 10 AE’s currently produce $8M of new ARR annually, it is assumed 20 AE’s will create $16M (after ramping). This is only a safe assumption if the $800K/AE prediction continues to be true, which is only true if each AE repeatedly closes a $66K deal every month (or whatever generates $800K). This fell apart the moment deals stopped closing every month.
A few recommendations:
Base your sales process on buyer behavior. Map the customer buying process and define buying milestones based on explicit buyer actions. Then build reporting based on those milestones to drive forecasting and to detect anomalies.
Check and re-check buyer adherence to the assumed buying process and behavior milestones across wins, losses, and pushes. Continually look for patterns of change.
Look for non-pipeline-based indicators of repeatability, and build fields in your SFA to capture that. E.g. track the entire selling team and the role/tenure/style of the champion. Who the EB was, and why they wrote the check. The specific use case being addressed. The exact benefit/ROI the business case was predicated on.
Expand repeatability to include all GTM processes - the hiring process, onboarding process, new product release, marketing campaign - and any other relevant process that must become predictable before you can scale.
Be anti-fragile: Build resilience between repeatability, predictability, and scalability by increasing the margin of error. Eg. if convert rate has been 10% from Qualify to closed won, don’t assume you will improve it to 15%, instead assume it might be as bad as 8%, and model predictions from that. For scalability, if 10 reps are closing $8M, assume you will have to hire 13 reps to get 10 acceptably ramped reps who can only deliver $6M.
Hope this is helpful. Please comment or DM with thoughts, questions, or smart remarks 🙂.
Good Selling!
