Andrew Peterson: Welcome to Win-Loss Week. I'm Andrew Peterson. I'm joined by Spencer Dent. We are the co-founders and co-CEO's of Clozd. And I'm excited to talk with you, Spencer today about where we've been in terms of our journey with win-loss analysis, and most importantly, where we're going. And so to kick this off, I want to go back in time and talk with you about your early experiences with win-loss analysis. And you graduated in 2010 from Duke Business School and you went to work in management consulting at Bain & Company. And management consulting is notorious for being a grind, for being challenging. Just tell us a little bit about what that was like.

Spencer Dent: Yeah, it was like three or four years that felt like 10 years in terms of the number of hours you worked. You worked a lot. But you learn a lot. You work on really hard problems. You get a lot of exposure to different industries, and it's a great way to get broad exposure, learn very quickly and figure out what you really like and where you want to go.

And what part of the Bain practice did you work in? What types of projects were you doing for your clients?

I was naturally drawn to the commercial excellence practice, which is really everything around sales, marketing, go-to-market efficiency. How do you help big organizations get the most out of their investments and their revenue teams?

So these big corporations are hiring Bain to come in and help them essentially sell more, grow revenue. And how much do these engagements cost?

They're more now. But back then it was like a half a million bucks a month.

A month? So we're going to pay a half a million bucks for this team of Bain consultants to come in, sit in our offices and coach us through some of our biggest challenges in terms of growing revenue. And as you think back on those years that you were in management consulting, you had some of your earliest encounters with win-loss analysis during that time. Is there any experience that comes to mind?

Yeah, I mean, the whole thing's about efficiency, right? How do you get more efficiency? One of my favorite client's I worked on was a big commercial construction company. This group sells everything from bridges and rail, bridges, railroads, skyscrapers, et cetera. And one of the projects that I was working on with them was helping them figure out how could they improve their win rate on RFP's? How could they bring better resources to sales engagements, and to the bid process to make sure that they won as much as possible? Because these deals take multiple years to chase, millions of dollars invested, and they have to win, otherwise it's just kills their margin. So one of the ones that sticks out the most to me was the Daytona 500 Speedway in Florida. Huge NASCAR site.

I just actually saw it in person for the first time, and the scale enormous.

It's crazy. It's crazy.

And what was this project?

So they were bidding on a remodel of it. So this is like 10 plus years ago, and it was a half a billion dollar bit, couple of years chasing it. And it's not like they had scrubs on the deal. They had the very, very best people in the organization chasing this deal. And-

Did they win?

They were pretty confident they were going to win, right? And then they ended up losing the deal and it was shocking to the organization. And I remember sitting there thinking, wow, that has to hurt so bad and be so frustrating. And then to really not know why it happened, to not be able to get that kind of feedback loop of why did they make this decision? What were we missing? What do we have to do better so that we can win next time? And they actually had us help do the win-loss interview. And it was super insightful, because we learned that there were things very early on in the process that were real concerns that were never really addressed, that led to the loss. And it was eye-opening to that organization. But that stuck with me, and it stuck with me till today, of how impactful in understanding one deal can be to your whole organization.

So you decide to leave consulting and take on an operational role at a very promising, still somewhat young software company, $70 million in revenue. Eventually they sold to SAP five or six years later for $8 billion. And your job was going to be to support the growth of the sales team globally to expand to new geographies, to grow from maybe dozens of salespeople to hundreds or maybe thousands of salespeople worldwide. So a lot of challenges. What experiences did you have at Qualtrics as you helped the sales team navigate this growth that relate to win-loss analysis?

One of the things that sticks out, and it's different than the Daytona example, because that's like a specific deal, was when you're responsible for helping drive sales productivity and your job is to figure out how do you get the most out of everybody and all the resources, you have to think about it at a different plane. And so, one of my jobs was to help the international offices. And as anybody who's ever opened up new international offices, has gone into new geographies, you get different levels of productivity out of those different groups. So I remember specifically our EMEA team was not performing at the level we wanted them to perform at. They were not hitting their targets. And there was a clear distinction between what we were seeing out of North America and what we were getting out of EMEA.

And I remember being in Dublin Ireland and sitting down with the sales leadership there and asking them, "What do you guys think is the difference? Why are we struggling to get the same amount of quota attainment? Why are we struggling to hit our revenue targets, the same way... Because proven we can do it here.". And the answer was, "Well, it's just different here.". And to me, that is a really, really, really bad answer.

Why?

Because it doesn't explain anything. It's completely like, well, it's just different. Like throw your hands in the air. I can't do anything about it.

It makes me think of the sales rep, who when they lose a deal and they try to disposition the outcome with a loss reason to fill in the CRM, and they pick, competitor.

Yeah.

It's like, well, what does that mean?

What does that even mean?

Of course we lost to a competitor, but why? So you say, okay, of course, it's different here. This is a EMEA. We grew the business in North America. But why? What's different?

Yeah. And I need to understand.

And what should we do about it?

If I want you to get as productive or more productive, I need to understand the root causes of why. And we saw this not just geographically, you saw it by product line and you saw it by sales teams. And so at that point, what became really apparent, I hadn't been involved in the direct evaluation of a win-loss vendor, but separate from that, we had realized as a feedback company that we did not know why we won and lost, and that we needed to do some form of win-loss analysis. And when I realized that we actually had been doing win-loss analysis, and I had no clue of what was going on, and that any of that feedback would be useful for me to understand, it was super frustrating. Because it's like, wait, we might already be sitting on the answer, but it's sitting on somebody's desktop in rev ops or in product marketing, and I have no access to it.

Just because it was siloed.

It was totally siloed, totally siloed. And guess what? Come to find out they were only covering a couple of a EMEA deals, so we wouldn't have gotten the answer anyways. But knowing... That was another aha moment of, if you actually were closing the loop on every single opportunity that was going through pipeline, you're willing to invest the time to chase the deal. Why aren't you willing to figure out why you won or didn't win it? And if you were to do that, you'd be able to step back and say, "Wow, here's why we're winning and here's why we're losing.". This is the difference in EMEA performance, versus the North America performance. This is a difference in product X versus Y.

So then in 2017, you decide to leave Qualtrics, because of these experiences you've had showing you the value and importance of win-loss analysis, but also the challenge that it can be for a company to do it well, and you found Clozd. We head to the basement at your house, tripping over the kids' toys to the basement bedroom, where we get this business off the ground, win some early clients. And to me, it felt like the lessons came fast and furious at that stage. Every day we were learning something new about different challenges different companies had, different priorities, different go-to-market strategies or revenue models, and trying to accommodate for those. But I think looking back, there's some overarching lessons or themes of challenges that companies face when trying to implement win-loss analysis. As you look back, what are some of those early lessons that you took away from delivering from our very first clients?

One, I would say is it's a very manual intensive process, especially if you try to do it on your own. I remember one client, what today would probably take us a week to do, it took them a year to do their win-loss program. And the whole reason why was, they didn't have in their CRM the contact names of the buyers. So the chief of-

What type of company was this?

It's a security software company. The chief of staff is our customer.

Probably used Salesforce.

They used Salesforce. This is sponsored by the CEO. They want this information. But guess what? If you don't actually have the contact information to the people, you can't reach out to them. Well, he had to manually-

Why didn't they have it? Sales reps just weren't putting it in?

Sales reps weren't required to put it in. And so just think about it this way, I now manually, our customer now manually had to go out to all of these sales reps and ask them for contact information. Well, guess what? Sales reps don't really want to give you that. So that just choked the program and slowed it down a ton.

Another thing that I found early on was, companies always felt like they wanted more. They felt like they had to throttle their program. Because the process is so manual, it is costly, it was costly. They wanted more feedback, they wanted more data.

Coverage of more deals.

Yeah. So I might have 10,000 deals go through my pipeline, but I'm actually only going to try to get feedback from 500 of them. And so, that also was frustrating to customers and frankly frustrating to us is, we wanted to help our customers have a more clear, holistic view of what was happening in their business. And they were just having to kind of narrow down very tightly into pockets of the business.

What stopped them from doing more?

The biggest thing was it was cost prohibitive, right? It was like, oh man, we can only do interviews with Clozd. Now, there's ways that that's been addressed, but it's for sure that was something that was frustrating to customers.

So to recap, it can be challenging to get the data you need, particularly contact information to facilitate the feedback in the first place. Then it can be very expensive to collect the feedback. And so a lot of these early clients just weren't doing enough data collection to feel perhaps confident in the answers they're getting, or maybe ignoring certain pockets or segments of their business, because they just didn't have the coverage.

Exactly.

Anything else?

A third thing that I think happened a lot, and is a real watch out I would say to anybody that's doing this for the first time is, the hoarding of the information. Win-loss is the most cross-functional program that you can run at a company, I would argue. And in the early days, we had at times customers who this was their project, this was their thing, and they wanted to be in charge of it, and they wanted to be the ones that represented it back to the organization, and they wanted to control the messaging. And because of that, they didn't leverage things like technology to share the feedback more broadly.

So it's a lot like your experience at Qualtrics, where you're in sales strategy and you need this data. Product team needs it to inform roadmap and product strategy decisions. The marketing team needs it for pricing strategy, for messaging and positioning, for competitive intelligence. So you've got all these different teams with different needs, but they all need win-loss analysis.

And the interesting part about that is, what I think a lot of our clients realize once they start collecting the data is there's applications of win-loss that you didn't think of upfront. The people who set up the Qualtrics win-loss program probably didn't think about how important it would be for me to know the difference in EMEA and North America. And we see this all the time. It's always so interesting to me when companies... I'll give you the opposite of the hoarding examples. One of our early customers, they're still a customer today. We signed them up in the basement. They've been a customer now for a long time. They share this feedback broadly. They don't withhold it. They leverage the closed platform as much as possible. And it's been crazy over the years to see the number of people that are just accessing the data. And we have people that leave that organization that reach out to us and say, I can't do my job without Clozd. And we've never even talked to them.

And it's because they use the win-loss feedback to help them in their job, whether that's as a sales leader, or as a marketer, or as a product manager or whatever it happens to be. So there's lots of applications that's super useful to the people who think about those problems every single day. And so when you hoard it, you leave a ton of value on the table for your organization, and it stems from trying to control it. But the reality is, if you have the right culture where you want to learn and you share it broadly, you will get way more value as an organization.

So we've just talked through all of these challenges that our early clients face and these lessons that we learned early on about running a win-loss program. And frankly, if I'm a newbie to win-loss analysis, I'm pretty intimidated right now. This sounds really hard. It's probably worth it, but it sounds really, really hard. So are circumstances the same today as they were six or seven years ago, or have things changed?

Yeah, the good news is, we've invested seven years and millions of dollars into solving this problem. So the process is actually automated at this point. You can cover and collect way more feedback than ever. You can share it really easily. One of the things that I'm most excited about is what we just launched. Ask Clozd is game changing. Historically, you go get all these interviews and you have to read through them all and parse through them, and look at the different quotes and try to figure out what the big themes are, and the technology can do some of that for you. But now Generative AI is going to allow you to get the answers to your questions immediately.

And for the listeners who maybe missed the launch of Ask Clozd last week, tell us what that is.

It's basically similar to like an OpenAI ChatGPT function. You can just go into your data set and start asking questions of the data. So you can ask questions like, "What do people like about my product? What don't they like about my product? How are people reacting to our pricing model? What should we put in our sales training?". What's been interesting is, as we've played around with this in our own Clozd for Clozd, the interviews we do ourselves, it's amazing how accurate it is, and how something that we used to have to spend time digesting the feedback and understanding it, we can get the answer right then, and get moving. So that is a cool early innovation around the generative AI technology that our clients are going to be able to take advantage of.

What's awesome is where else can you go with that? You start thinking, where else can we go with it? Like persona-based alerts. Hey, here's a theme that's emerging around your product. Here's a theme that's emerging around your sales experience. And not be dependent upon humans reading information, connecting the dots and coming to conclusions, letting the AI alert people of what they need to take away and act on quickly.

Love it.

So it's pretty sweet.

So to recap, in this day and age, thanks to Clozd, my program can be completely automated, no manual effort for me to keep my program running. I can scale my data collection with a variety of feedback methods. AI may influence that in the near future too, to broaden those opportunities. I can easily share the feedback across my organization with the closed platform, and now individuals across the organization can ask targeted probing questions about why the business is winning and losing, whatever else they're trying to find in the data set using Ask Clozd.

Which is awesome, right? Those things all tie together to give you so much value. You can do it in an automated way at scale, share it broadly and answer your questions in a very personalized way. Thinking back seven years ago to where we started, and being able to do that today, that's shocking to me to see how far this space has come.

Totally. It's so exciting to see how far we've come in the last six or seven years, and really excited to think about where we're going in the near future. So it's been a great conversation. Thanks, Spencer. We appreciate all of you for joining and participating in Win-Loss Week. We hope you enjoy the rest of the sessions.

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