Most of the time, sales organizations accept their reps’ low win percentages and the linear stages of the sales process. They also keep all of the old process’s stages, even when some of them do not apply to their business or industry. One of the reasons for this is that it allows sales leaders to use a weighted sales pipeline to produce an accurate sales forecast. The weighted pipeline allows sales leaders to use the sales forces’ success rate at each stage of an opportunity to determine whether or not they will win the deal. We'll use simple math here as an example.
The sales force has 10 deals in the negotiation stage with an average deal size of $100,000. This means that there is $1,000,000 worth of deals in negotiation, and the sales force wins 70 percent of deals that reach that stage of the pipeline. The sales leader forecasts $700,000 in future revenue. This seems simple, but it overlooks what is happening on the client side of the negotiation. For example, if a prospective client is still negotiating with three companies at this stage, you have a 33 percent chance of winning that deal, regardless of the historical data and average win rate for the negotiation stage.
The Problem with Weighted Pipelines
To get a firm understanding of the problem with using a weighted pipeline, let’s consider an example. A sales manager using a weighted sales pipeline for opportunity stage forecasting has 10 salespeople on their team. The Pareto Principle would suggest that 80 percent of the sales results will be generated by 20 percent of the sales force. Even if the sales team doesn't match Pareto's theory about the law of the vital few, some sales reps are more effective and produce more revenue.
At the negotiation stage, the three most effective salespeople win at 65 percent, 70 percent, and 75 percent. The other seven salespeople win at rates of 12 percent, 18 percent, 24 percent, 25 percent, 30 percent, 34 percent, and 41 percent. This gives the sales manager a 41 percent win rate based on the weighted sales pipeline. One problem with the weighted pipeline is that it doesn’t take into account which salespeople have those deals. Imagine that all of the deals and sales revenue at the negotiation stage belong to the four best salespeople, who have win rates of 41 percent, 65 percent, 70 percent, and 75 percent.
Using a weighted pipeline can skew your forecast by using an average. Nassim Nicholas Taleb, author of The Black Swan, would ask you if you would walk across a river with an average depth of four feet. Averages are not always as useful as we believe them to be.
Let’s return to our example. None of the six sales reps with the lowest win rates have deals in the negotiation opportunity stage. The four reps with active deals have an average win rate of 62.75 percent, so that is what the sales manager should use for the forecast. This is an improvement over the inaccurate 41 percent win rate. It is also more accurate because it removes the sales reps who have no deals in that stage. That is an improvement, but there is an even better way to create an accurate sales forecast.
The Case for Individually Weighted Pipelines
Because there is variability in the effectiveness of individual sales reps, a better sales forecasting method would not only look at the sales data in the period but also at the individual level. There are several reasons to create a percentage rate for each salesperson at each opportunity stage.
The first reason to use an individual pipeline is that it prevents estimates from being too optimistic or too conservative. If two of the three deals in the negotiation stage belong to the sales reps with a 75 percent win rate, using a 41 percent win rate underestimates the forecast. If two of the three deals belong to the salespeople with 18 percent and 24 percent win rates, your forecast is likely inflated.
With a spreadsheet and a year's worth of deals in your CRM (customer relationship management software), you can calculate each salesperson's win rate at every stage of the pipeline. By looking at everyone’s effectiveness rate at each stage, you can improve your sales forecast. If you use a weighted pipeline, you are better off looking at individuals.
That a salesperson has a deal in a certain stage doesn't suggest they have the average chance of the entire sales force's percentage of winning that deal. The sales professionals with the highest effectiveness rates have a better chance of winning their deals than those who are not nearly as effective. By looking at everyone, you can forecast the deals likely to cross the finish line, while omitting the deals where the chance of a signed contract is unlikely.
Dealing with the Variability of Sales Effectiveness
One challenge of growing net new revenue is the variability of results you find in every sales force. If the only reason you would create individual pipelines is so you can have greater certainty with your pipeline and your ability to forecast, that is enough. But there are other reasons to do the sales math.
Your top performers are better at selling than the salespeople who haven't yet mastered the sales conversation. The salesperson who has a low win rate or a tough time moving opportunities forward can learn from salespeople with a more effective conversation at those stages. Doing the math at the individual level will provide you with data that illuminates the stage of the conversation where each person struggles. (This is likely to be discovery, especially if you still use the outdated legacy approach that prevents a second meeting.)
If this sounds like a lot of work, consider the alternative. It’s worse to accept the variability of sales effectiveness and not hitting your targets. By pinpointing the conversations each salesperson struggles with, you can train, develop, and coach them in the exact conversations that can improve their sales performance. You want to see each person on your team improve their win rates over the previous year. That individual level of development depends on corresponding individual data.
You can improve your forecasts by using individually weighted pipelines instead of the default win percentage across the stages of an opportunity. Use simple math to identify sales reps with higher win rates to create a more accurate sales forecast. The benefit of creating a percentage rate for each salesperson at each opportunity stage is you can level out the variability in the effectiveness of individual sales reps. Finally, the potential to use the data to improve sales performance by training and developing the exact conversations will help you improve your sales results. Additionally, integrating pipeline production forecasting can further enhance the accuracy of your projections, making your sales strategy more robust and predictable.
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