Same Old Song and Dance

Forecasting sales pipeline accurately in a complex B2B environment is challenging: deciding when to start counting deals as pipeline, selecting the correct deal valuation, determining your weighting methodology, sorting through unclear deal stages, and knowing when you have enough forecasted pipeline to hit your number are typical hurdles sales leaders face. Any one of them can be enough to cause your crystal ball to cloud up.

Sales leaders use many methods to cut through the murkiness. One common way is to ask AEs to provide insight. They rate each of their deals according to established criteria, and these forecast categories act like an additional layer on top of deal stages. An example is any deal that the AE believes has a 90% chance of closing in the current month would have a forecast of Commit. That same deal would likely be in a later deal stage, too. The main benefit of this approach is that it allows sales reps to weigh in on how likely they feel the deal is to close. Two clear drawbacks are that a) it can become a highly subjective exercise, even with defined rules, and b) it’s one more field for your sales reps to fill in and update.

Another way is to apply deal amount weighing by stage. This means setting percentages based on actual or estimated close rates (ex. 10% once a deal is qualified, 25% after completing the first demo, etc.), and applying those percentages to each deal by stage. Although it seems like a straightforward way to decrease the value of early-stage deals, the practice isn’t always understood or supported by AEs. Some prefer to change the deal value itself as it progresses through stages. Set weight percentages can also get complicated if there are drastically different close rates for certain products or business segments that require a variety of weights to be applied.

A Change Would Do You Good

Here’s a practical way to help see the future:

• Look to the past to get more accurate weights: Using a historical measure, such as trailing twelve-month rolling average conversion rates by stage, takes the guesswork out of it. This method relies on actual data, instead of requiring constant input from your reps, or forcing you to choose what would likely end up being arbitrary weights. The best part? Your fortune-telling abilities improve over time: weights will stay up-to-date automatically as you go and better reflect your actual close rates by stage. Bonus points for having your AEs enter a standardized dollar amount for each based on average closed won deal amounts. The removes another layer of subjectivity from the process. Tip: Make sure you have enough data points to calculate a correct average. Too few could cause wild swings in the average.