Over the past few years we’ve figured out by focusing on deal-level risk, we can optimize win rates and deliver highly confident forecasts. The key is to create checks and balances – focusing our management/coaching conversations on the gap between an account exec’s forecast and specific deal risks called out by our forecasting system.
Bad news sooner rather than later – it’s what all managers needs. Unfortunately, the attributes we screen account execs for are often the very ones obscuring the flow of raw information up the management chain.
Here’s a look at our top 3 forecast-killer risks:
1. Timing – deals with “squishy” close dates
Three things that affect our timing confidence at Cloud9 are: deal age, close date changes and sales agenda. Here’s the logic we use: if we’re on a “sweet spot” agenda, and the deal age and stage progression is in line with the average for successful deals, and as long as there have been few changes in projected close date, we don’t worry. If, however, we’re on a less compelling sales agenda (subject to a priority shuffle) or the close date projection is bouncing around and it’s been open too long, we do worry. For example, if a Pipeline Management deal has been open for more than 4 months (double our average for wins), it’s had more than 4 close date pushes and it’s being forecast in the period, the deal is marked ‘High’ risk. Same deal at 1.5x average age and 3-4 close date pushes is marked ‘Moderate’ risk and all deals under 1.5x and 3 close date pushes are marked ‘Low’ risk for this category.
2. Engagement – deals with too little activity
“Touches” are a proxy for engagement. We use a combination of Opportunity record modification history and Tasks/Events frequency to determine engagement risk. Essentially, if it’s early in the cycle, we’re okay with a moderate frequency of communication. But as the deal progresses, we expect more frequent touches (otherwise the deal has probably lost momentum and is at risk of getting pushed). Of course it could also be that everything is on track with the deal but the rep is not recording activity– that’s bad too. Here’s our formula: if a forecast deal in a later sales stage exceeds 7 ‘Days Since Last Touch’, we flag it as ‘High’ risk. We expect less frequent touches on earlier stage deals, which may be forecast for a future period, so they are flagged accordingly.
3. Value Erosion – deals with weak value propositions
We encourage documentation of early stage deals and expect to close/lose from stage 1 more frequently than any other stage. We also expect fluctuation of deal value in stage 1, while the deal is being qualified. That said, as a deal moves out of stage 1, we expect the value to stabilize. Otherwise, we flag it as risky. Our formula: if a forecast deal amount has been reduced by greater than 25% since stage 2, or the discounts/incentives are higher than average relative to size, it’s flagged as high risk. The worry is that we’re not holding our value, which is obviously subject to negotiation pressure and re-prioritization. Other combinations are flagged ‘Moderate’ or ‘Low’.
Result – higher win rates, more accurate sales forecasts
I don’t have to rely exclusively on the upward flow of info from the sales team. By creating these checks and balances using data, we are able to make quick course-corrections and hit the sales targets.
This article is part of the Sales Pipeline Management Mastery series.