Quiz Time! Here are two outcomes:
- An opportunity in the sales pipeline was at an 80% probability that it would be won, and it was lost.
- Another opportunity in the sales pipeline (all other aspects including the value, the customer type, the timing, etc., are the same) was only at a 20% probability of winning and was won.
Which outcome is worse?
Most of the people I ask this question to quickly look at me with a quizzical frown and ask, “Is this a trick question?” I respond, “No, no tricks.” They then confidently say the first situation is worse. I understand why they give that answer, and I’d certainly have to agree that when it comes to revenue, profit, commissions and things like that, the first is definitely worse.
The reality is that both are equally bad because both forecasts were equally wrong. If you’re looking to scale growth sustainably, you must have the ability to create a meaningful degree of predictability for that growth; to do that well, your organization’s ability to forecast must be an area of strength.
In addition to the obvious reason that strong forecasting, in and of itself, creates a level of predictability, an individual sales rep’s ability to forecast individual opportunities makes them far more effective at utilizing their time and enhances their effectiveness.
I often compare selling to playing poker. In both situations, you’re forced to utilize imperfect information to make bets (forecasts) about future outcomes within dynamic situations, and you must put your money where your forecast is. In poker, that means staying in the game and putting more money at risk. In sales, it means investing more time and resources.
While we could literally write a book on how to forecast effectively, improving your forecasting ability and improving your tactical sales decision-making doesn’t have to be complex or hard. Implement two simple ideas, regardless of the level of your forecasting proficiency, and you’ll be sure to succeed.
1. Add a Forecast Confidence Rating for Every Opportunity
Brier Score is a metric used to measure the accuracy of predictions. Originally used to quantify (and compare) the accuracy of weather forecasts, it has been used to describe the accuracy of any probabilistic forecasts and to measure the effectiveness of those involved in prediction markets and forecasting competitions.
Here’s a layman’s explanation of how Brier Score works.
It’s easy to say something like, “I think telephones will become extinct.” Actually, if someone were to make that prediction, you’d never be able to prove them wrong, so there would be no means for assessing how good they are at predicting. That prediction lacks two critical elements: 1) the timeline (by what time will this happen) and 2) confidence level/probability. The only way to accurately assess the prediction would be if it were stated as, “I believe there is a 10% chance telephones will become extinct by December 31, 2025.” (For the record, I do not believe that telephones will become extinct by that time.) You see the interpretation of the statement “I believe” changes dramatically when we add probability and with a timeline, we can assess the accuracy of such a prediction.
Let’s apply this to a seller working an opportunity. Here are two scenarios:
- I meet Jennifer who heads the sales team for a company that is in the sweet spot of our Ideal Client Profile (ICP). I learn about her company’s long-term plans, the challenges they have and where their focus is. They’re currently in the midst of rolling out a new ERP system so it will likely be 12-18 months before they will be ready to focus on a go-to-market transformation program.
- Frank submitted a request of information based upon an online search using a directory and review site. His company is in the midst of launching a new campaign and they are looking for expertise to support the design and implementation of that campaign. His company fits our ICP but is between a B- and C- level match. Frank is asking for a proposal. I had a good conversation with Frank and from that conversation, it is clear that they’ve reached out to several potential providers.
Both of these opportunities are qualified and enter the sales pipeline. Jennifer goes into the second stage of the pipeline, while Frank goes to the fourth (of five stages). By traditional sales metrics, Frank’s opportunity is treated as a high probability than Jennifer’s because it is in a later stage. But pause and think about this for a moment. Which opportunity would you have more confidence in winning?
Speaking for myself, I’d have a lot of confidence that, if we manage the process effectively, Jennifer’s company has a high likelihood of becoming a customer. What’s more, because there isn’t any urgency right now, I would also have the opportunity to broaden our engagement beyond just Jennifer, to dig deeper and identify more fully how we could positively impact her company.
What about Frank’s company? Well, I know we’ve got a very good solution there. I’d prefer that he’s looking for something more comprehensive, but it is a service area where we are strong. I also know that competition is more intense there, after all, there are many providers that specialize in specifically what Frank is looking for and some even specialize in Frank’s industry as well. I think it’s worth pursuing Frank’s business (especially when you consider that we don’t have to do that much or spend much time to get a strong proposal to him), but I’m nowhere near as confident that we’ll win that business.
My take (and while these two scenarios are fictional, this is based on real situations in which we regularly find ourselves) is that we’d win Jennifer’s 4 out of 5 times and Frank’s 1 out of 3 times. But, where does that show up in a sales or pipeline review? What’s more, if my confidence level changes, where does that show up?
That’s why we recommend a field called Forecast Confidence. As a general rule, we recommend (and use for ourselves) the following grid for establishing the confidence level for each opportunity (for each level, the first number represents the single probability for each level and the second (in parenthesis) represents the basic probability range:
- 1 - Possible - 5% (1-15%)
- 2 - Hopeful - 33% (16-39%)
- 3 - Probable - 50% (40-60%)
- 4 - Likely - 67% (61-85%)
- 5 - Certain - 95% (86-99%)
On this scale Jennifer would be rated a 4 and Frank would be rated a 2.
This information can also be used as an opportunity progresses through the pipeline. If Jennifer’s opportunity moves from stage two to stage three, but the confidence rating drops to a three, that tells us something important. If an opportunity starts off rated a one or two (which is very common) and as it progresses it doesn’t increase its rating, then we know there’s a red flag that needs to be addressed.
When doing debriefs we can identify where things did and didn’t go as we thought they would. Requiring a sales rep to place a forecast confidence rating automatically puts them in a more proactive position to thinking about how to best allocate their limited resources to win the greatest number and value of opportunities.
2. Add to Forecast to Close By Field
The second element of a prediction/forecast is timing. The specifics for timing are dependent on your overall strategy. At Lift (where we “live” in 90-Day segments) we use 90-days as our timeframe. We work with a company in the MarTech/SaaS space where 30-days is the right timeframe, and in another case, we have a client involved in a far more complex sales environment and the timeframe is 6-months.
This field is a simple yes/no field, answering one question: Do you forecast this opportunity to be completed by (chosen timeframe)?
Going back to our previous scenario, while I rated Jennifer a 4 for confidence, I would clearly not forecast this opportunity to close in the next 90-days. Frank’s opportunity is only a 2 on confidence, but I would forecast it to be completed within the next 90-days. By the way, this also helps with early-stage opportunities that are on a faster-track.
Another benefit of this approach greatly simplifies and improves what is likely the most useless field (historically) in the CRM, “Predicted Close Date.” I can honestly say I’ve never seen a CRM where the predicted close date was anywhere near accurate. When using “Forecast to Close By” you no longer have to get wrapped up in the accuracy of this property, until the deal is forecasted to be completed.
Adding these two simple properties to your CRM will provide a far more accurate and effective means to assess your progress on hitting your targets and will build a skill within your sales team to make better decisions to increase their sales velocity.