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Weighted Pipelines And Forecasts

by David Brock on July 22nd, 2014

I have to admit, I’ve never been able to figure out the value or meaning of the weighted pipeline and forecast.  It’s one of those things that is embedded in every CRM system, it’s one of those things that I see in all sorts of reports, but I have yet to figure out what it means.

I suppose the weighted pipeline is supposed to be some sort of indicator of overall pipeline health, but most processes for assessing probability are hugely flawed.  Take a look at virtually every CRM system and the default methodology for determining probability.  It’s based on where we are in our selling process.  As we move from qualifying to discovering, to proposing, and finally to closing, our probabilities increase.  So while we are measuring progress through the sales cycle (e.g. 25%, 50%, 75%….), we have no indication of the propensity to buy.

Sure, I’ve seen companies with sales processes that include increasing customer commitment as they progress an opportunity through the process, but most of these criteria are either loosely enforced or still don’t measure a customer propensity to buy—-just because a customer has committed to a demo or benchmark, just because they like our proposal, just because they say our proposal meets their requirements, or even if they say we are the preferred vendor, we don’t know the customer will buy or whether they will select our solution.  The numbers become meaningless in terms of a commitment to buy.

Then anyone who remembers freshman statistics knows the sum of probabilities can’t exceed 1 (100%).  That is, if we are competing against two other organizations, they are moving the same deal through their selling process–pretty soon each of the three competitors is in the closing process–each projecting a probability of something like 85% or more.  Somehow, this doesn’t align with my understanding of statistics–if each is projecting 85% probability of winning, the aggregate is 255%????????

So as a pipeline measure, I’ve never really gotten it–what does 45%, 55%, 75% or whatever really mean?  What does the aggregate of these weighted deals really mean?  How does it tell me whether I have a sufficient number of opportunities in the pipeline?  How does it tell me that there is sufficient flow or velocity in the pipeline?  It seems to me, looking at the pipeline based on historical performance of winning/losing and sales cycle time give a much better indicator of pipeline/funnel health.

Then we move to the weighted forecast.  What does it really mean to the business to say, “I have a $1 M deal that I’m projecting at a 75% level, so I’m committing $750K to the forecast.”  If we win the deal, we’ll get $1M, so why are we forecasting $750K?  What meaning does the 75% provide?  When I sit down with people committing deals to the forecast, we review each deal.  We talk about competitive positioning, the attitudes customers have toward our solution and the alternatives, the urgency of the need and business case, and a whole number of other things.  Based on the assessment–deal by deal–we determine whether we are prepared to commit the deal to the forecast–and we commit $1M.

Some people think the weighted forecast gains greater weight in larger organizations.  Frankly, I don’t buy that.  Regardless the size of the organization, the forecast is a roll-up from first line managers.  If you aren’t training and coaching managers, at all levels, in forecasting and pipeline management–then you have bigger problems than forecast accuracy.  So the roll-up should have reasonable accuracy–naturally, each level of management is going to want to have serious discussions about the commitment to forecast.

In some organizations, particularly those with subscription businesses, run rate businesses, or something similar, analytics and trend analysis may provide a more accurate forecast.  The base forecast may be based on these, with the forecast adjusted for major deals.

What do you think of weighted pipeline and forecasts?  Where have you found them to be useful?

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  1. “we have no indication of the propensity to buy.” This is the problem with making decisions from dashboards.

    I recently caught an interview with a professor who teaches risk and probability and how he applied his skills to a life and death health decision; 10-years to live if he did nothing, a transplant that had a 70% success rate that had a 25 to 30% chance of killing him.

    He did not make his decision based on the numbers but on the best outcome. When he looked back he deduced, “that probabilities, while useful, are quite limited in their ability to predict what will happen to any one person.” (or sale)

    He made a strong point that “We’re never 95 percent alive. We either live or die. We experience outcomes. On a population level, I can have 100 people in a room, and some will have something happen to them and some will not. And that’s the hard part because if you happen to be the unlucky one who has that rare event happen to you, you still have the bad thing happen to you in its full awfulness.”

    This is true of everything including sales. As I listened to him, I thought about the same point you made about three competitors in the final phase of a sale all thinking they have an 85% probability because of the stage they’ve reached. In reality, their mathematical probability is 1 in 3. Unless you’re selling an inventory item the buyer divides equally among the three vendors, as this professor said about life, you can’t close 95% of the sale, you either make it or you do not. And determining the real probability requires evaluation skills.

    David, you and I discussed at great length measuring the soft stuff and the challenge of collecting that type of data from salespeople. One metric we don’t speak enough about that fascinates me is how accurately or inaccurately does each salesperson forecast? When a salesperson is consistently off their opportunity assessment by more than 10%, is it because they are fudging numbers, not understanding what is happening in the sale, not realistic, or something else? Coaching salespeople to have a solid understanding of what is happening or not happening in an opportunity is infinitely more important than forecasting and provides the added bonus of more accurate forecasting.

    Isn’t closing sales the ultimate outcome wanted by everyone from the front line to the CEO?

    When your salespeople know where they stand in an opportunity and know what to do next to move the sale forward or disqualify and fish in better waters, they’re win rate improves and they’re forecasting improves too. I think there is too much “weight” on forecasting and not enough emphasis on understanding how and why sales are won and lost. What we can best learn from weighted pipelines, IMO, is where to look for opportunities to improve.

    • Gary, you cover a lot of things in this outstanding comment. I’ll address a few:

      1. For the 3 competitors, each in the same stage of the selling process, each predicting 85% probability of winning–we know that’s statistically impossible. But each don’t necessarily have a 33% chance of winning. One may have 80% and the other 2 10% each. Or one may have 50% another, 30% and another 20%. It just can’t be more than 100%.
      2. More than the statistics problem is the issue of not tying the probability directly to the issues having to do with the propensity to buy and their propensity to buy you. To do this, we have to really assess things from the customer point of view—which we know is difficult because of our tendency to be self centered.
      3. I strongly believe sales people and managers should be measured on forecast accuracy. Forecasting when we get a deal and how big it will be is very important to the organization. We tend to look at our own performance in isolation from everything else, without understanding the impact on those who count on us.

      Thanks for the great discussion!

  2. Dave, you hit the nail on the head. Deals are binary, and if you don’t think about them that way, it’s less likely that you are paying close attention to the actions that make them 100% instead of 0%.

    • Great to hear from you Andy! Great point, we need to focus our energy around winning the deal—not 75% or whatever.

  3. I agree 100% with your points and am as frustrated as you are that the industry refuses to “get it”.

    Here is how I present the same to my students and clients in my workbook:

    A Revenue Forecast asserts that a certain amount of revenue will be earned in a certain period of time with a certain probability that the actual result will be within a certain tolerance of the forecasted result.

    For example: management may forecast there is a 90% chance of actual revenue being more than 10% less than a certain amount.
    % probability of revenue from a source is assigned by management based on their judgment in the face of their collective past experience with similar situations and similar circumstances.

    Some managers set their forecasts to equal the Expected Value (Sum of entries each multiplied by their assigned probability of occurring). There are potential problems with this approach in that:

    It allows fractional results. For example, $100,000 with a 50% probability of occurring would contribute $50,000 to the forecast even though the actual result will either be $0 or $100,000. Actual Results are more likely to binary because the sale either happens or it doesn’t so fractional results do not occur.

    % probabilities assigned often reflect the probability of the revenue ever occurring but do not capture the period in which the revenue will occur. A good approach to forecasting needs to set the probability of revenue in a specific period.

    % probabilities assigned often reflect stage of progression through to a sale but do not represent a real assessment of probability. For example, one might assign a 75% probability to all prospects for which a proposal has been submitted because at this point they are 75% of the way through the sale process but it may be, in reality, that only 50% of all proposals are successful.

    The way for management to prepare a forecast is to think carefully and critically about each possible sale in order to reach an informed judgment as to whether the projected revenue will happen in the period or that it won’t and then, in the face of that thinking, compute a projection that has the target % chance (where target is probability around 90%) of being no worse than a degree of tolerance (say 10%) below the forecast.

    It takes careful, critical and rigorous thinking than most people are willing to invest but when they do, it pays off and they get better at it over time.

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