Currently Reading

David and Goliath
Wooden: A Coach's Life



Recently Read

Will's books

The Silent Man
5 of 5 stars
Another great John Wells book. I previously compared Alex Berenson and his hero, John Wells, with Vince Flynn and his troubled CIA agent/assassin, Mitch Rapp. Towards the end of Flynn's short life and in his final Rapp books, Flynn got a...
tagged: fiction and troubled-assassin
Getting Started with Hobby Quadcopters and Drones
2 of 5 stars
When I was looking up reviews of drones on the web, I found several mentions of this "book" (a pamphlet,really). It's OK,but all the information can be easily found elsewhere online. The repeated warning about crashing your drone and sta...
tagged: non-fiction
The Martian
5 of 5 stars
Wow. Just . . . wow. This was one of the most entertaining books I have read in a long time. The story is fabulous and the execution wonderful. Basically a diary of an astronaut left behind in an escape from a failed Mars mission (though...
tagged: fiction
The Target
2 of 5 stars
I can't even begin to imagine why this book has gotten good reviews. I have read and enjoyed Baldacci's books before, but this is the first book in the Will Robie series that I've read. Probably the last as well. It's the third one of t...
tagged: fiction and troubled-assassin
David and Goliath: Underdogs, Misfits, and the Art of Battling Giants
3 of 5 stars
I didn't love this book. While I generally like Gladwell's style and analysis, he seems to be running out of interesting observations or topics to cover. There are a few good tidbits and the book is short. If you love Gladwell, it's wor...
tagged: non-fiction
Anthem
4 of 5 stars
I love Ayn Rand's thought-provoking books and stories. I'm fundamentally aligned with her libertarian way of thinking so, for the most part, her stories are just one's that drive home a point that I already agree with or, at least, under...
tagged: fiction
Thinking, Fast and Slow
5 of 5 stars
This is simply a fabulous book about how the mind works and how our behavior is driven by our levels of thought. It's not a terribly difficult book to get through, although it does require a lot of System 2 thinking - Kahneman's term for...
tagged: non-fiction
Killing Jesus: A History
4 of 5 stars
As with the other "killing" books by Bill O'Reilly and Martin Dugard, I really don't like the positioning that the book is based entirely on fact - insinuating the other crappy books I've read are made up. In the documenting of Jesus' li...
tagged: non-fiction
Wooden: A Coach's Life
4 of 5 stars
How can one not like a book about John Wooden? The man is a sports icon. Most of all, of course, he's a teacher, which is exactly what he wanted to be and prided himself on. He based his entire life on teaching basketball fundamentals an...
tagged: non-fiction and sports
Dead Eye
5 of 5 stars
Wow. Just. Wow. This is a great book. In the ex-CIA-troubled-assassin genre, this may be my favorite book ever. Greaney does a fabulous job of balancing action with storyline. Never gets boring, but the reader is overwhelmed by ridiculou...
tagged: fiction and troubled-assassin

goodreads.com

Just Say No To Weighted Average Sales Forecasting

Any reasonable direct selling process involves establishing a specific set of milestones to help track how far along a prospect is on the path to making a purchase.  These usually include one or more of the following steps:

  • Lead found/created
  • Opportunity qualified
  • Prospect visited/contacted
  • Product demonstrated/Eval in the hands of the prospect
  • Follow-up contact
  • Product selected
  • Prospect has requisite financial approvals
  • Paperwork completed
  • Prospect invoiced
  • PO/cash in hand

Of course, these steps are specific to what is being sold and what process is used, but steps similar to these can be readily mapped to most direct sales processes.  In my view, keeping accurate track of where a prospect is using a tracking process like this, or with milestones that better suit your business, is absolutely critical.  Tracking the process with detail is very important for new companies because gathering data about how the product is sold and adopted is critical to future planning and to adjusting the business moving forward.  As your business matures, using such a process helps you characterise your sales efforts further, ultimately giving you a more accurate means for predicting your bookings, revenue and cash flow.

It’s easy, though, for this data to be misused or overused.  I often see sales organizations map these steps into percentages like this, below, where the right column represents the percentage of completion of the sale.

Lead found/created 5%
Opportunity qualified 15%
Prospect visited/contacted 20%
Product demonstrated/Eval in the hands of the prospect 40%
Follow-up contact 60%
Product selected 70%
Prospect has requisite financial approvals 80%
Paperwork completed 90%
Prospect invoiced 95%
PO/cash in hand 100%

On a superficial level, there’s nothing wrong with this.  It simplifies where the company is in the process of closing a sale by mapping the sales progress to a single number that everyone can understand.  “We’re 80% along the way to closing a deal with customer”  is much easier to understand than, “customer X has his internal financial approvals so we should close soon.”

The problem is that sometimes this simple number morphs into something that it wasn’t intended to be – the probability that the deal will close.  80% along the path to closing is different than having an 80% chance of closing.  Even worse, the percentage of completion of the sales process is often used to mathematically calculate the likely booking amount for a particular deal.  Say, for example, a prospect has selected your product as the one he/she wants and is getting ready to invest $50K.  With the mapping above, you might say that the prospect is 70% along the path to closing.  Through the magic of weighted average forecasting you would take the percentage of the sales stage, multiply it by the $50K the prospect is willing to spend and come up with a $35K forecast for that prospect ($50K X 70%).

When stated this way, it sounds absurd that anyone would do this, but it’s done all the time.  I frequently sit in board meetings where the Sales VP presents a list of potential customers, their sales stage percentage (from a table similar to that above), the projected bookings from a sale to a particular prospect and a forecast that is the result of multiplying the sales stage percentage by the projected bookings.  These numbers are them summed to come up with the quarterly forecast.

Among the myriad of problems that this process presents is that sales just don’t work this way.  They are far more binary-like events than the stages of the sales process would indicate.  Even at the 80% level, there is fallout.  One deal falling out at the 80% level can invalidate the entire forecast, depending on its size.  Just as likely, a deal at 20% can come in quickly, similarly invalidating the forecast.  Since forecast accuracy is critical, especially in small companies, using a weighted average forecasting methodology is fundamentally flawed.

There are simply too many factors involved to accurately boil down sales forecasting into simple equations.  A good, experienced sales person has a gut feel for where a prospect is and the likelihood that he/she will make a buying decision in a given period of time.  While a sales stage percentage is a reasonable benchmark for where a prospect stands and is an absolutely critical tool for junior sales people, it is not nearly accurate enough to base the progress of a company on. 

Sales people need to be close to their prospects, knowing who the key decision-makers are with a thorough understanding of the purchasing process in the account.  Once they have this, they will be able to estimate what deals will come in for how much during any given period with far more accuracy than a simple weighted averaging forecasting tool does.  As always, good management and loads of wisdom trump virtually any tool that can be created.

  • Willchris bauman

    Great advice.  I was always questioning the model but didn’t know exactly how to put my finger on it.  Even though I have a strong mathmatical background, I look at most things in Sales as 50/50.  Yes or No.  Of course, the sales process tries to increase the likely hood of the sale, but until it is closed, it is still 50/50.

  • Willchris bauman

    Great advice.  I was always questioning the model but didn’t know exactly how to put my finger on it.  Even though I have a strong mathmatical background, I look at most things in Sales as 50/50.  Yes or No.  Of course, the sales process tries to increase the likely hood of the sale, but until it is closed, it is still 50/50.

  • John McCarty

    “As always, good management and loads of wisdom trump virtually any tool that can be created.” I agree with this statement, but as a CFO who has also run a sales organization, I am very much aware that Good Management and Wisdom are often outcomes received, in part, through using tools appropriate to a given task.  Anyone that puts blind trust in any one, single tool is looking for trouble.  And if a weighted average sales forecast is your ONLY tool for making revenue estimates, then you are in trouble.  Taking information from multiple sources, including weighted average sales forecasts, inventory/delivery schedules combined  with regular conversations with sales reps and management, is the best way to go.    If you only have a hammer, everything looks like a nail…..

  • John McCarty

    “As always, good management and loads of wisdom trump virtually any tool that can be created.”
    I agree with this statement, but as a CFO who has also run a sales organization, I am very much aware that Good Management and Wisdom are often outcomes received, in part, through using tools appropriate to a given task.  Anyone that puts blind trust in any one, single tool is looking for trouble.  And if a weighted average sales forecast is your ONLY tool for making revenue estimates, then you are in trouble.  Taking information from multiple sources, including weighted average sales forecasts, inventory/delivery schedules combined  with regular conversations with sales reps and management, is the best way to go.    If you only have a hammer, everything looks like a nail…..

  • Jim Pollock

    Will, Applying percentages and equations to sales forecasting is trying to force fit a science on something that is at best an art and one with incosistant training and capability of the artists. The size of the deal is important.  The stage of the deal is important.  But multiplying the two makes no sense at all.  However, there are some mathmatic things and simple “equations” that can be applied to add some usefulness to the din. Every organization I have been in from Hewlett-Packard to several startups have tried to apply a method for acquiring forecasts.  The one that I like the best is where we’ve taken the anticipated order size (ie 50K) and multiply it by 2 percentages that the sales dude is left to determine:  percent chance that ANY order will be placed by this customer and the percent chance that a placed order will be ours. The first (chance of placement) takes into account the sales person’s knowledge of the customer, buying habits, stage of the order, and overall gut feel.  This number replaces the straight percentage based on stage that you’ve discussed, which I agree that by itself is virtually worthless limited.  This number is an attempt to quantify all the sales person’s knowledge, not just the over simplistic stage which reflects NONE of this good stuff. The 2nd percentage (chance that if placed, you get it instead of competition) reflects the competitive nature of the deal.  If you’re head to head on two deals and give each 50%, it says and reflects that you are likely to get one, but probably not both. By multiplying these two percentages by the deal size, you end up with a weighted order size that directly reflects the gut feels of the sales person.  Again, on a sales person by person basis, this forcast is virtually guaranteed to be wrong.  (100K order, 90% sure it will place, 70% chance I’ll get it gives you a forecast of 63K which can’t happen since ultimately it is either 100K or zip. But, if I am working 10 such deals, statistically it becomes more reliable, and if I’m one of 100 reps, the 1000 deals being worked starts to provide a number that you might be able to plan against. Ultimately, any forecdast that is mathematically derived is only useablle within the context of the rules you’ve used.  Multiplying by the stage of the order IS inherantly flawed since it assumes all deals will result in orders to your company, its just a matter of time.  If you have ten 100K deals at the 10% mark, this model says you have high confidence that you are going to book 100K.   The final factor to throw in is the estimated month of closing.  With 20, 50 or 100 sales people, you end up with a set of numbers that is low maintenance for the sales person to maintain, and one that IF INTERPRETED REASONABLY gives you some basis for cash flow and production planning. At a couple of my companies, depending on what information we need we look at this forecasts on a quarterly basis, and we play games like taking 50% of the current month, adding 50% of the previous month and 10% of the following month to reflect the high tendency for orders to slip and smaller but still possible tendency for orders to fall early. Tools.  Nothing but tools.  But, they can be useful.

  • Jim Pollock

    Will,

    Applying percentages and equations to sales forecasting is trying to force fit a science on something that is at best an art and one with incosistant training and capability of the artists.

    The size of the deal is important.  The stage of the deal is important.  But multiplying the two makes no sense at all.  However, there are some mathmatic things and simple “equations” that can be applied to add some usefulness to the din.

    Every organization I have been in from Hewlett-Packard to several startups have tried to apply a method for acquiring forecasts.  The one that I like the best is where we’ve taken the anticipated order size (ie 50K) and multiply it by 2 percentages that the sales dude is left to determine:  percent chance that ANY order will be placed by this customer and the percent chance that a placed order will be ours.

    The first (chance of placement) takes into account the sales person’s knowledge of the customer, buying habits, stage of the order, and overall gut feel.  This number replaces the straight percentage based on stage that you’ve discussed, which I agree that by itself is virtually worthless limited.  This number is an attempt to quantify all the sales person’s knowledge, not just the over simplistic stage which reflects NONE of this good stuff.

    The 2nd percentage (chance that if placed, you get it instead of competition) reflects the competitive nature of the deal.  If you’re head to head on two deals and give each 50%, it says and reflects that you are likely to get one, but probably not both.

    By multiplying these two percentages by the deal size, you end up with a weighted order size that directly reflects the gut feels of the sales person.  Again, on a sales person by person basis, this forcast is virtually guaranteed to be wrong.  (100K order, 90% sure it will place, 70% chance I’ll get it gives you a forecast of 63K which can’t happen since ultimately it is either 100K or zip.

    But, if I am working 10 such deals, statistically it becomes more reliable, and if I’m one of 100 reps, the 1000 deals being worked starts to provide a number that you might be able to plan against.

    Ultimately, any forecdast that is mathematically derived is only useablle within the context of the rules you’ve used.  Multiplying by the stage of the order IS inherantly flawed since it assumes all deals will result in orders to your company, its just a matter of time.  If you have ten 100K deals at the 10% mark, this model says you have high confidence that you are going to book 100K.  

    The final factor to throw in is the estimated month of closing.  With 20, 50 or 100 sales people, you end up with a set of numbers that is low maintenance for the sales person to maintain, and one that IF INTERPRETED REASONABLY gives you some basis for cash flow and production planning.

    At a couple of my companies, depending on what information we need we look at this forecasts on a quarterly basis, and we play games like taking 50% of the current month, adding 50% of the previous month and 10% of the following month to reflect the high tendency for orders to slip and smaller but still possible tendency for orders to fall early.

    Tools.  Nothing but tools.  But, they can be useful.

  • Dave Jilk

    So I agree with your points in one specific context – relatively early stage companies with “lumpy” sales (i.e., individual sales are large and/or a significant percentage of each period’s total sales).  If your pipeline has 20 prospects in it, and you’ve made 15 sales so far, then this approach is indeed absurd. However, if individual sales are relatively small, then a mathematical forecasting process MUST take place, because there is no way to be that tuned-in with the individual customers.  And in a larger company, there is no way to get a decent forecast from the larger organization by hoping that individual managers are good at it – but on the other hand larger companies can gather good statistics over time and have enough prospects in the pipeline that the statistics can work. There are three things that need to happen to make “expected value” forecasts work: 1. The “stages” need to be objectively defined, rather than subjectively assessed by the salesperson. 2. The “percentages” need to be based on HISTORICAL ACTUAL results rather than subjectively estimated probabilities. 3. N (the number of individual prospects in the pipeline, and the number of historical datapoints) needs to be large relative to the amount of any one sale.

  • Dave Jilk

    So I agree with your points in one specific context – relatively early stage companies with “lumpy” sales (i.e., individual sales are large and/or a significant percentage of each period’s total sales).  If your pipeline has 20 prospects in it, and you’ve made 15 sales so far, then this approach is indeed absurd.

    However, if individual sales are relatively small, then a mathematical forecasting process MUST take place, because there is no way to be that tuned-in with the individual customers.  And in a larger company, there is no way to get a decent forecast from the larger organization by hoping that individual managers are good at it – but on the other hand larger companies can gather good statistics over time and have enough prospects in the pipeline that the statistics can work.

    There are three things that need to happen to make “expected value” forecasts work:

    1. The “stages” need to be objectively defined, rather than subjectively assessed by the salesperson.
    2. The “percentages” need to be based on HISTORICAL ACTUAL results rather than subjectively estimated probabilities.
    3. N (the number of individual prospects in the pipeline, and the number of historical datapoints) needs to be large relative to the amount of any one sale.

  • http://www.2-speed.com/ Will

    Cool.  Some interesting stuff here.  I agree with the thoughts about how the number of deals (note: not the size of the deals) effects the binary nature of the process by virtue of the large numbers, themselves.  One deal falling out among hundreds or thousands has a statistically much smaller effect on the forecast (assuming it’s not a huge deal $-wise) than one deal among a handful.  I also agree that using multiple “tools” rather than a singular one substantially increases the likelihood that the forecast will be more accurate – depending on the tools, of course. Ultimately, though, any reasonable tools or models are only predictive in the sense that they map forecast to bookings based on knowing the type of customer that you have already sold to with a saleforce that is seasoned enough to know how to translate their body language into some form of number-morphing formula.  There is no provision for a new type of customer, a larger (or smaller) deal size than is normal for your company nor a change in the experience level of your sales team. In my experience, any such tools that have been employed have been repeatedly beaten by a process I have used – it’s called: ask the Sales VP.  As the end of each quarter grew near, I asked the head of sales to tell me what’s going to come in.  His/her response was based on his knowledge of how each individual sales person (or territory or region or division) worked; the type of customers that were involved in the biggest deals in the pipeline; the level of support those prospects had; how high up in the organization that contacts had been made, and so forth.  Even then, many of deals that made the final list were because: “My gut tells me that one will come in.”  He/she took into account a myriad of factors, some of them only valid for that particular time and place.  It’s difficult for models to adapt like this.  Perhaps there is some form of calculus that I have not discovered though.  In the end, I always had comfort that the smaller the difference between the bottoms-up forecast that came from the salespeople and the Sales VP’s “gut feel,” the more likely it was we would hit a particular number.  The larger the difference indicated problems that need to be immediately ferreted out.   For this method to be successful, of course, one has to have a sales manager that has the experience to make the call.  I have always believed that the level and experience of this person is one of the key factors in the success or failure of an organization. As mentioned elsewhere in these comments, this final Sales VP cut could be considered just another tool in you quiver.  The wisdom and experience of the head of sales was the secret sauce.  It’s certainly not the only data or process you can work with, but I’ve found it the most accurate.

  • http://www.2-speed.com Will

    Cool.  Some interesting stuff here.  I agree with the thoughts about how the number of deals (note: not the size of the deals) effects the binary nature of the process by virtue of the large numbers, themselves.  One deal falling out among hundreds or thousands has a statistically much smaller effect on the forecast (assuming it’s not a huge deal $-wise) than one deal among a handful.  I also agree that using multiple “tools” rather than a singular one substantially increases the likelihood that the forecast will be more accurate – depending on the tools, of course.

    Ultimately, though, any reasonable tools or models are only predictive in the sense that they map forecast to bookings based on knowing the type of customer that you have already sold to with a saleforce that is seasoned enough to know how to translate their body language into some form of number-morphing formula.  There is no provision for a new type of customer, a larger (or smaller) deal size than is normal for your company nor a change in the experience level of your sales team.

    In my experience, any such tools that have been employed have been repeatedly beaten by a process I have used – it’s called: ask the Sales VP.  As the end of each quarter grew near, I asked the head of sales to tell me what’s going to come in.  His/her response was based on his knowledge of how each individual sales person (or territory or region or division) worked; the type of customers that were involved in the biggest deals in the pipeline; the level of support those prospects had; how high up in the organization that contacts had been made, and so forth.  Even then, many of deals that made the final list were because: “My gut tells me that one will come in.”  He/she took into account a myriad of factors, some of them only valid for that particular time and place.  It’s difficult for models to adapt like this.  Perhaps there is some form of calculus that I have not discovered though.  In the end, I always had comfort that the smaller the difference between the bottoms-up forecast that came from the salespeople and the Sales VP’s “gut feel,” the more likely it was we would hit a particular number.  The larger the difference indicated problems that need to be immediately ferreted out.  

    For this method to be successful, of course, one has to have a sales manager that has the experience to make the call.  I have always believed that the level and experience of this person is one of the key factors in the success or failure of an organization.

    As mentioned elsewhere in these comments, this final Sales VP cut could be considered just another tool in you quiver.  The wisdom and experience of the head of sales was the secret sauce.  It’s certainly not the only data or process you can work with, but I’ve found it the most accurate.

  • Pingback: 2-Speed » Communicating with Your Board: Sales Numbers

  • Pingback: Are You Making These 3 Mistakes With Your Sales Manager Training? at Sales Manager Training

  • http://www.dogaltasmarket.com do?al ta?

    office chairssocial media and having a well thought out social media/blog commenting action plan are critical to getting

  • Anonymous

    using such a process helps you characterise your sales efforts further, ultimately giving you a more accurate means for predicting your bookings, revenue and cash flow.
    mantolama izolasyon

  • Anonymous

    But, if I am working 10 such deals, statistically it becomes more
    reliable, and if I’m one of 100 reps, the 1000 deals being worked starts
    to provide a number that you might be able to plan against.
    aluminyum