Skip to content

Win/Loss Analysis–Are You Learning As Much As You Should?

by David Brock on January 6th, 2022

I’m always stunned by how little win/loss analysis we do. Of course, when we win or lose a deal, there is some reason code–usually some drop down in CRM that gives us a handful of choices about why we won or why we lost.

Sales people usually don’t put much thought into it, wins are generally the result of the “fantastic job they did selling,” losses are either price or product deficiencies–often both.

The win/loss reasons don’t give us much insight into what really is happening, or what we might do about it.

Sometimes, usually on the “mega-deals,” we might go through some sort of nominal review, particularly if we lost, but too often, the underlying objective is not to learn and grow, but rather to assign blame. Even when well conducted, these reviews are increasingly rare. Sometimes we might send the customer a survey, but we rarely get much meaningful, if they bother to respond.

Our win rates are critical to understanding and driving performance. But too often, we accept these win rates as “laws of nature,” doing little to change them, rather adjusting the volumes of work we demand to hit our goals.

We must learn from these results, we must leverage these learnings to change, improve, and grow.

Where do we start?

First with the data–it’s sitting in our CRM systems, yet most of the time we leverage it poorly. Every year, we analyze data from 100’s of thousands of deals that have been won or lost, there’s a huge amount of insight we can get from a simple data analysis. Here are some thoughts:

  1. Are we assessing our win rates correctly? Often, I see organizations assess their win rates against every deal sales people work on. Win rates can only be calculated from qualified deals. But often, I see organizations penalizing sales people in win rate calculations by including prospected deals that have been abandoned or deals the sales person has disqualified. Most of these deals aren’t real and shouldn’t be a part of our win rate calculations. The win rate can only be calculated against deals the sales person has certified as real opportunities in which the customer will do something.
  2. What are the patterns we see in where we win, why we win, or how we win? They enable us to understand systemic issues the entire organization faces, not the performance of select sales people. Here are things we should be looking at:
    1. What is the average deal size for deals we win? What about deals we lose? Recently, I worked with several organizations in which the executives proclaimed, “We’re great at the big deals, we go after them and win them!” But the data presented something completely different. In each of those cases, the average loss was more than twice the value of the average win. It turned out the sales team was being outsold on the largest deals. We tend to remember and celebrate the big deals. High 5’s, “attagirl’s,” even bonuses for those big wins. Likewise, we want to put those losses behind us, we want to forget about them. We develop a collective memory of the big wins, but those memories are so often not supported by the data.
    2. Likewise, how much time do we invest in winning deals? How much time do we invest in losing deals? Recently, with a client, we discovered the average sales cycle to lose a deal was more than two times the average sales cycle to win a deal. As we dug deep, we started to see a number of things. Sales people chasing after the wrong deals or deals that were never real, working them for a long time driven by wishful thinking or desperation. Sales people chasing deals they never should have been pursuing, wasting huge amounts of time. There are all sorts of reasons, but we consistently see sales people investing more time in losing deals than in winning. We need to understand what is happening.
    3. Are there certain patterns in the types of deals we win or the types of deals we lose? For example, are there certain categories of customers–do we win/lose in certain markets, or with certain customer sizes, or with certain customer “behavioral types?” Is it with certain categories of problems? Is it in certain product categories? In every analysis we do, we start seeing some of the same patterns–across all the sales organization. Understanding these are important and give great insight into where we are most successful and where we are least. We can start leveraging this to more effectively direct our future sales efforts to those segments where we are most successful. This data is, also, helpful in systematically assessing why we win and why we lose in those segments.
    4. We should also assess patterns of where in the process we win or lose. These point to areas of systemic weakness in the ability of the organization to develop and execute strong deal strategies.
    5. What are the patterns over time? Have they changed or do we see consistent patterns year after year? These can help us better understand shifts in customer sentiment and competitive actions.
  3. The data can give us a lot of insight. Patterns in the data will help us focus on certain areas to do deeper dives into understanding why we win and why we lose. We have to dive deeply into analyzing specific deals. Do we have competitive weaknesses? Do we have product/solution weaknesses? Do we have strong deal execution strategies? Do we create meaningful value with our customers? It’s important to recognize we are trying to understand systemic issues in our ability to compete–not the individual strengths and weaknesses of sales people. Where do we win and why? How do we do more of that? Where do we lose and why? What do we need to change to reduce the losses?
  4. Then there is individual performance–it’s the responsibility of front line sales managers to understand these, coaching each person on their teams to improve performance. Things like: Is the win rate for an individual significantly lower than the norm for the organization? What causes this? Are they chasing the right opportunities? Are they chasing real opportunities? Are they developing and executing strong, customer focused deal strategies? Are they creating value in every interaction with the customer? Do they have the skills and knowledge to execute and win? Do they have the support they need to develop and execute winning strategies? Front line managers must take the time to understand wins and losses in their teams. They have to work with each individual, and provide information to the rest of the organization helping better understand why and how we win and why and how we lose.
  5. Likewise, we can learn a lot from those individuals that have significantly higher win rates than the rest of the organization. We need to understand things like: What are they doing differently? How do they work and engage customers, can we learn from them and start coaching/training the rest of the organization to do more of what our most successful people do.
  6. Everyone in the organization has a role in understanding sales performance and how we win/lose. Sales ops may take the lead in doing the data analysis to understand the systemic issues and patterns that cause us to win or lose. Each sales manager needs to look at the patterns within their teams and assess the performance of each individual. Each individual must assess their own performance, honestly, to see how they can improve their win rates.

Good win/loss analysis doesn’t take a huge amount of time. We have lots of data, sadly too few organizations leverage it as effectively as they should.

Too often, we accept what we get and what we don’t get. We don’t look at and understand why we win, why we lose, and what it takes to improve and grow. Rather than going after more, what if we won more of what we pursue? And when we win more of what we pursue, then we can, in a meaningful way, go after and win more.

Book CoverFor a free peek at Sales Manager Survival Guide, click the picture or link.  You’ll get the Table of Contents, Foreword, and 2 free Chapters.  Free Sample
Be Sociable, Share!
Please follow and like us:
No comments yet

Leave a Reply

Note: XHTML is allowed. Your email address will never be published.

Subscribe to this comment feed via RSS