Beyond the Red Widget: Why Dashboards Miss What Actually Wins Deals (Todd Abbott on The Innovative Revenue Leader)

Announcer

[00:00:00] Welcome to the Innovative Revenue Leader Podcast. I'm your host, Seth Marrs. Join me as we deliver practical insights to help B2B CROs find new and innovative ways to grow in this fast-changing environment. The Innovative Revenue Leader is sponsored by Sandler, empowering sales professionals and leaders to master the craft of selling at all levels.

Seth Marrs

Hello, everyone. Welcome back. This guest is someone I'm really excited to have on the show. He's a veteran B2B technology go-to-market executive, a multiple-time startup CEO, a go-to-market advisor, and really a huge presence in the sales technology space. He's someone I've been fortunate enough to cross paths with on multiple occasions. He has nearly three decades of experience managing enterprise go-to-market organizations and running multi-billion-dollar revenue operations. He led [00:01:00] a $4 billion revenue organization as CRO at Avaya, while leading the operational and cultural integration of the $1 billion Nortel acquisition. He served as CEO of InsightSquared, where he and I worked together, and of Delivr.ai. He co-founded Rev-Lens.ai, which we'll talk about today, and executed major M&A integration strategies across multiple technology companies. Todd, so awesome to have you on.

Todd Abbott

It's great to be here.

Seth Marrs

You and I have crossed paths many times. Even when we're not working together, we always seem to be in the same circles.

Todd Abbott

Always. And listen, you've been a really great advocate in the industry, and I've always enjoyed the time to just pick up the phone and pick your brain a bit. You've been a good sounding board for me, so I do appreciate the relationship we've had over the years.

Seth Marrs

Right back at you, exactly the same. Okay, I'm going to jump in with the question we ask everyone who joins the show. [00:02:00] If you're a CRO right now, what's the most innovative thing in B2B that you're seeing?

Todd Abbott

Well, I think the most innovative thing right now is the ability to merge your data and get a deeper level of analytics that you just haven't had before. When I think about myself jumping in, or any new CRO, what do you do for the first 90 days? You spend a lot of time trying to understand what you just walked into. Where do I win? Why do I win? RevOps can tell you who your top reps are, but do you really know what a winning sales engagement looks like, and how do you replicate that? Do you really know where the friction points are?

I've always been one of these road warrior sales leaders, where you spend a lot of time getting on sales calls, getting in front of customers, talking to your teams in the car, and trying to really understand what's working, what's not, what the competition is saying, and what the value prop is. [00:03:00] The best sales reps and sales leaders, the best marketing leaders, and the best product leaders are the ones who can connect the dots anecdotally. But it's anecdotal, to a great extent, and it's all predicated on how many meetings, sales calls, and engagements you can have. So it's really hard to keep up with that and to see the changes in trends when a competitor starts doing something or the market starts to shift.

What technology has always done is compress time. You have to be able to move faster. All of the components in your tech stack are doing a tremendous job at fit-for-purpose automation in the sales go-to-market. There's been some amazing automation and innovation. What they're not doing is helping you connect the dots over the last 12 months, or the last six months if you've got a smaller transaction and a smaller sales cycle. How do I connect the dots so I don't need to have debates about my anecdotes versus your anecdotes versus what the [00:04:00] CMO thinks?

Todd Abbott

We're finally getting to a point with what I call the three key pillars of any go-to-market: product, marketing, and sales. If we can get those three functions to agree and align on what the friction points are, we can focus our collective efforts there, versus debating what my data says versus yours, or your anecdotes versus mine. Too much of that still goes on today. I think we're on the cusp of solving that problem.

Seth Marrs

If I'm reading the way you're talking about it, data's the answer. Sales, marketing, and product do hate each other at times; they don't get along. But a lot of that is because there's no foundational source of truth for all of them to work from. If I'm reading that right, you're saying solve the foundational source of truth and you solve the problem.

Todd Abbott

100%. And the beauty now, with GenAI and these agents, is that you can create an agent for you as a [00:05:00] product manager that's different from the one for a CMO, a demand gen leader, or a sales leader. But if you're all getting the same view and the same version of the data from the same dataset, now you've got a fighting chance. It's amazing to me, to this day, how much friction there still is between those three groups in the monthly business reviews and quarterly business reviews, and the CEO is left to try to figure out where the truth is in all this.

Seth Marrs

Yep. As we dive into this, what's really cool about your experience is that you've run companies that have been trying to do this, succeeding in some areas, and in some areas, I would argue, you were too early for it to be done. So I'm going to jump into that. You've been in technology for a long time. Can you talk through your perspective on the evolution of sales technology...

Todd Abbott

Sure.

Seth Marrs

...and the [00:06:00] change that's happening with AI in that context? This isn't the first time a change has happened. We moved from on-prem to SaaS. You've seen it, going back to Avaya. How does this fit in that context? Are we getting too hyperbolic with it? Are we not hyperbolic enough? You've seen all of this play out over the years from the lens of a sales leader, right in the thick of it, working at some of the companies with the most advanced technology they could use with their sales teams.

Todd Abbott

Yeah. I'm going to date myself a little bit, but I think the first big technological innovation was CRM. The issue we had before CRM was that when you lost a rep, all their data went with them, and you had to go rebuild the territory. In a sales cycle of six months, you lost the whole notebook. Every rep had their own way to [00:07:00] capture their data. So the biggest change with CRM was a cultural one: basically telling reps, "Hey, no, that data's not yours. That data is the company's."

Seth Marrs

I remember having those conversations. "That's my customer."

Todd Abbott

Go on.

Seth Marrs

It ain't your customer. You're helping with that customer.

Todd Abbott

Bingo. And you'd always give a bye to your best reps, who would give you the most pushback, because you wanted their results, but you also wanted to capture their data. Then when we moved to automatic data capture, to get the rep out of having to put the data into CRM, wow, that was a huge advancement, and it took a lot of the friction out of the system. So those two were really big.

Then there's Salesloft and Outreach and what they did for sequencing. In one sense, what they did was amazing. Unfortunately, now nobody will look at emails, [00:08:00] you can't get through spam, and you can't get people on the phone. It was so effective, but it became so ubiquitous for everybody that now we've got the next challenge.

Todd Abbott

I think the next big challenge is that each of these platforms in your tech stack is now, in a lot of cases, being disrupted by GenAI and agents. Agents can replace an awful lot. If I were still running InsightSquared, I'd be running for the door, because I can create an agent that does the dashboarding InsightSquared did. Not hard. And there are a lot of components in your tech stack in a similar risk position.

But think about what they're all doing now: Salesforce, Gong, the sales engagement platforms, and Clari all want to be the platform that has all your data. It's a classic move of a legacy provider: if I own the data. Everybody in tech understands that it's the [00:09:00] data that's going to be king, but the data still exists in so many different places.

What we're now seeing is the MCP protocol from Anthropic. Everyone is implementing MCP to enable GenAI to go get data from any one of those component platforms. But it's still a deal analysis. If I want to review this deal, I can go get the data from each of the platforms and analyze this deal. What I can't do is take all of my engagement data, consolidate it, and throw an agent at it to extract the insights I don't know exist. That's the beauty of GenAI.

Todd Abbott

I learned this at InsightSquared, when we went from a data warehouse implementation, gen one of InsightSquared, and we bought Olono. Olono was the data capture, and we had our own LLM. This was before GenAI. Very expensive. We were creating the learning models, the training models. [00:10:00] But the beauty of it, and it opened my eyes, was being able to say, "Hey, listen, I don't know what I'm looking for." When you create a data warehouse, you have to know what you want to report on and what the dashboard should look like. If you want to change that view, you have to get the architects to change the structure of the data warehouse so the reporting works every week. That's why, in the old world, it would take IT two to three weeks to make a change to get the view you want.

Seth Marrs

Because it was the data, not the view. I couldn't structure the data the way I needed to make the view.

Todd Abbott

Bingo. And I think that's the beauty of LLMs: I don't need structured data. The way I like to say it is, it's just a big flat file. Now I can have an LLM and say, "Hey, go review this. These are the deals I've won; these are the deals I've lost. Give me the recurring themes. Connect the dots for me." It's the anecdotal thing I used to do in my first 90 days making sales calls, trying to hear what came up four, five, six times. So [00:11:00] now you can point a GenAI LLM at it and say, "Go find the trends. What happens over this six-month sales cycle when I win versus what happens when I lose?"

Todd Abbott

We're doing an engagement right now with a company where we found that in a large portion of their deals, the rep began to descope, to reduce the configuration to fit a budget. And what we learned was that every time they did it, they lost. Their win rate was zero.

Seth Marrs

Really?

Todd Abbott

Zero. So now what do you do with that? Is that a qualification issue? Do I need a different set of tactics? I certainly need a heads-up on which deals in the forecast I've descoped. So there are multiple actions you can take, but nobody had connected those dots, because it plays out over a series of deals, in this particular case over nine months, because that's roughly the sales cycle.

Todd Abbott

When I think [00:12:00] about how far back you go, it really is predicated on how long your sales cycle is. If you've got a six-month sales cycle, you probably want to go back 18 months. Go back any further and it was a different world. For a 30-day or 45-day sales cycle, a year or nine months is probably more than enough. The data becomes less relevant when you go back too far.

But on consolidating all this data, I just don't think any of these platforms are the winners. They don't have the big compute platforms; it's going to be way too expensive. And creating a data warehouse, doing an extract from all these platforms into a standalone dataset, a data lake, for a GenAI LLM to point to, is not that hard. It's not that expensive.

Seth Marrs

Is it even needed anymore with MCP? You talked about the data warehouse. Or is it a different type of data storage structure you need?

Todd Abbott

No, it's different. You have to extract the data. MCP, as I said, is for when you want to go look [00:13:00] at a rep's deals: going and getting this little snippet of data, and this little snippet, and this little snippet, and doing analytics. I would say keep doing that with all the components of your tech stack today. It's a great implementation. But what if you want to look at it holistically? I don't know what I'm looking for; I want to understand the underlying correlations between winning and losing, by industry, size, and actions.

Todd Abbott

The analogy I always like to use is this. You know who your best reps are. If you ask them, "Hey, what do you do? How are you winning?" they look at you like you're speaking Greek. "What do you mean? It's intuitive. I don't even know how to answer that question." So you don't even need to ask them. I'm just going to take all your data and connect the dots on those top 10% of reps. What's the consistency? What we find most often, and we found this back at Olono, is that the best reps know when to unhook from a deal way early.

Seth Marrs

Yeah. Get out. It's not going to happen.

Todd Abbott

The vast majority hang [00:14:00] on, on a hope and a prayer. That's in the funnel, inflating the funnel, and in some cases inflating the forecast. The example I gave you, where you don't win a descoped deal, is a whole other set of analytics that an MCP implementation won't give you.

Todd Abbott

So I really don't think any one of your existing platforms is going to win as the data lake to point your LLM to. I think it'll be much less expensive for you to extract your data into an S3 bucket, or whatever you want to use for a data lake. There are lots of different options there, and it's not that expensive.

I think the bigger debate will be: do I point the generic GenAI platforms at my data lake, or do I bring that in-house? Because I don't want to train the general AI models, since this becomes proprietary information on where I'm winning. If I [00:15:00] know where I win and how, and I double down on that, and I can take my conversion rates up 10 or 20%, or 5 or 10%, I don't want that exposed.

So I think the next big thrust is going to be this. I'm certainly running a data lake myself, in my own infrastructure, or in a clean room run by a service provider. It doesn't really matter, as long as it's just yours. But where does the LLM sit? Are you going to trust Anthropic or ChatGPT when they say, "No, we've got an enterprise configuration. It's not going to go into our training model"? Do you trust them or don't you? I think the larger the organization, the more the answer is going to be no: "I'm not going to trust them." The smaller the organization, the more you'll say, "Yeah, wing it. Go ahead. No worries." I don't know where that demarcation line is quite yet, but I do think there are ultimately going to be go-to-market enterprise LLMs that sit either in a clean room or within [00:16:00] your four walls, behind your firewall.

Seth Marrs

Yeah. You're already starting to see it happen. Salesforce announced COA. You're going to see the same thing happen with SAP and with Oracle. They're starting to pull all of that into a clean room, into your own space. Absolutely, because that is your competitive advantage.

Todd Abbott

That's right.

Seth Marrs

Is it fair to say that it used to be, "I need to be the single source of truth, I need everything in front of me, and that's where I'm going to work"? Now it's a layer back: "I need to have the context, and I need the intelligence to help me make sense of the data." That's what software providers are providing as value, compared to the single pane of glass they would talk about three years ago.

Todd Abbott

That's right. That's 100% right. Over my 30 years, I've been part of large companies that were disrupted, and it is really hard to move from a legacy model to a disruptive [00:17:00] model. We had that, in a very small case, at InsightSquared. As I said, going from a data warehouse to a data lake was a rip and replace, and a rip and replace for a SaaS vendor is death. You can't do it. We would've been better off starting a new company versus buying Olono.

Seth Marrs

Really?

Todd Abbott

Yeah. Literally, we were in debates with customers: "Hey, I've got so much more compute and insight to provide you." And they were like, "Yeah, that's great, but where's the red widget on that dashboard?" I'm like, "A red widget on that dashboard? Do you understand that a red widget works really well when it's a data warehouse? On a data lake, do you really need that red widget? Because I can give you 20 other widgets that are much more powerful."

We spent way too much time doing that. I realized that as much as I thought the value prop was so compelling that customers would jump and run to make this transition, it was too hard.

Seth Marrs

Let's talk about [00:18:00] that a little bit, because that was around 2021, and you just said it. You did the acquisition of Olono into InsightSquared, so you had an LLM. It wasn't the type of LLM we're talking about today in terms of power and capabilities, but the vision was all there. Today everyone's talking down dashboarding versus it being the central source. So why? Is it as simple as we just didn't have powerful enough tools to do what we wanted? And what stopped you when those tools started to become available? Was it, like you said, that the architectural side was just too much, and if you had taken the concept of InsightSquared, started a new company, and pivoted them over, it would've been better?

Todd Abbott

I think it would've been. [00:19:00] It was too much of a paradigm shift for my existing customers. Too many of my existing customers were really just interested in the pretty charts to bring to the boardroom, or the pretty charts to explain their story to the private equity investors or to the CEO. They used it much more as a bridge. And I'm not trying to talk down any of my customers, because a lot of them were highly successful.

But I had a vision for where this was going, what I like to refer to as the science of sales. I had to find the science-of-sales leaders who had a constant thirst for more analytics and more data, who didn't want to live on their anecdotes, on their gut. Those are the ones I had the most success with in painting a vision, and they would let go of the widget in a flash. But too many of my customers, to my great surprise, [00:20:00] were just so wedded to the red widget. And what I mean by red widget is the format of the dashboard that my CEO, my CFO, and my board wanted: "I don't want to go back and explain to them that I'm going to give them a different version of the dashboard." Because some of the dashboards we had at InsightSquared, you just couldn't do on a data lake implementation. It's expensive, and it's just such a waste of time.

So I underestimated how much they were using it for storytelling versus really using it to understand what they needed to do to optimize their go-to-market. That was the big shift. A dashboard is a rearview mirror look. A predictive engine is about what's going on now, and what that means for my conversion rate, my close rate, or my forecast over the next 60 to 90 days, and how I optimize that to get better results. I had too many customers who were very comfortable with the rearview mirror view.

Seth Marrs

But [00:21:00] that was the world we were living in back then. The way you were talking about it, your ability to visualize that was the story. The way I interpret what you're saying is, "I need a system to help me find unknown unknowns. I don't know the question to ask. I don't know what I'm going to get as the answer, so I need a system to tell me." And that's one of the cool things you can do with AI now: "Hey, what the hell's going on?" Let it run, and then companies like yours can go in and figure out how to make that better. As a user, I don't have to have the answers to all of the questions.

But back then, the only value in analytics was having the answer to all the questions. If I was an enterprise, I had to architect that all the way through my tech stack, and once I did, I was bound to it unless something crazy happened. It's crazy, but it was about the pitch deck to the [00:22:00] board, "Don't mess with it," rather than, "How do I use this to dynamically figure out how to do better for my business?" Listening to you say it, it seems nuts now that we operated that way, but that was the truth then.

Todd Abbott

I still think there are a lot of sales and marketing executives running their businesses based on how they've run them for the last 15 or 20 years. I saw a similar version of this at Delivr.ai, where we were providing person-level intent. About a year to 18 months in, we concluded not to sell to the enterprise, because what I found was that a lot of CMOs' biggest measurement was leads. How many leads did I put into the system? That was their vanity metric for claiming success. [00:23:00] So despite me telling them 1,000 times, intent is not a lead. Intent is something you need to nurture.

If you're a science-of-marketing type and you realize, "Wow, you mean I can take my digital advertising and my campaign spend and narrow it to who is actually demonstrating search and buying behavior, to get them qualified as a lead?" it was a home run. But too many were vanity-driven, and they said, "Well, I just want to give it to BDRs." No. The customer's not ready to be contacted, and the BDRs will tell them, "Hey, I see you're searching," and that's the death knell.

Todd Abbott

So we wound up spending so much time consulting on what to do with this data, and it's very similar to my experience at InsightSquared. That guy who wanted that red widget, I'd say, "Hey, did you know you win at about [00:24:00] 10 to 11 meetings in the sales cycle?" Because every sales cycle is a series of meeting conversions: get them to see value, want to have the next meeting, and bring in somebody else. The buyer group expands.

I remember this one customer distinctly. I said, "Listen, your big inflection point is between meeting four and five. If you can get to meeting five, your win rate goes to X. If the CXO gets engaged by meeting six, your win rate goes to X." And then we would show them deals in the funnel where you're at meeting six, you've only got two people in the buying group, and the CXO is not there. This deal has every variable that says it's a hope and a prayer. And they would say, "No, that's Johnny. Johnny's really good. He'll get that deal across." Sixty days later, I'd come back around and say, "Okay, what happened?" I had their data. Johnny didn't execute; the deal didn't convert.

Todd Abbott

They're all learning opportunities, but you need somebody who is more science of [00:25:00] sales or science of marketing, who takes these insights and says, "What am I going to do about it to help my team execute better?" I've just been really surprised how hard it is to find the science-of-marketing, science-of-sales type that this resonates with, who goes, "Absolutely, let's go."

When I joined InsightSquared, it was because of the acquisition of Olono. I was like, "Oh my God, I'm so tired of beating up reps to give me data so I could have RevOps (sales ops, at the time) do analysis," and they were always dealing with stale, incomplete data. No matter how much pressure I put on the reps, I could move the needle like this much. So when I saw that, I was like, "Oh my God, this can solve the biggest pain point, and now I want to see what kind of analytics I can get on it." I didn't understand how much of a minority a science-of-sales type like me was.

Seth Marrs

You are. And it's really behaviors. [00:26:00] There are weird things that happen in a go-to-market motion. If you're told your job this year, your MBO, your success, is this many leads, you stop caring that those don't turn into revenue, even though the company is eventually going to let you go if you can't generate enough of them. And as a seller, you care about your quota however you get it. You're not necessarily focused on the right way to get it that generates incremental revenue. So it's not surprising. But it is, because you'd think in a revenue engine that those teams would be myopically focused on how to grow. And you're there telling them how to grow, and they're like, "Well, I just want a lead," or, "I just need to do this."

I think that dovetails into some of this. You got the engine with InsightSquared, and then [00:27:00] it seems like you got an even bigger explosion of broader data around the buyer with Delivr.ai. And that led you to go, "You know what? No one's figured this out. I've seen both sides of this." So tell me how Rev-Lens.ai fits into that.

Todd Abbott

Rev-Lens.ai is a services company, because, in essence, you can do everything we're talking about. You can stand up a data lake and get all the data from all your components. You can point GenAI at it. You can go create the agents. But there's a tremendous learning curve. So what we've concluded, me and a number of sales leaders and some RevOps leaders who are really deep technically and have done this, is that we want to help companies, the science-of-sales and science-of-marketing types who see what this technology can do and have a thirst to get at those [00:28:00] insights. We want to accelerate your learning curve.

So we come in with a tool set and with learning on how to consolidate all the data. What's your LLM of choice? There's the discussion of whether you're comfortable with a public frontier model, or whether you want to move to a private LLM. As I said, I think that will be predicated on size to a great extent, but I think it'll become more standard over time.

But it doesn't matter. We're not wedded to anything. I'm not trying to displace anything in your tech stack, because that tends to be a, "Wait a minute, I don't want to change the workflow or what my team is working in." Let me help you stand up the data lake. We come in as a team of consultants. We can do it for you, or we can help you do it with your team. We can bring in a tool set of agents for the product teams, for the [00:29:00] marketing team, and for the sales team, to give you a rapid head start on the types of things, and the interface, that you want to give your sales leaders or your product leaders. Because when we talk about a product team, depending on the size of the company, you have four or five different product lines, and they want to look at things from their own vantage point.

So we help them with the deployment and the picking of the LLM. Most everybody has an enterprise LLM model. Then we help you set up the agents, and we consult with you to evolve those agents to get you the insights you want. It can be a two-month engagement or a three-month engagement. We can continue to stay with you to help you through your learning curve on developing the agents, because once you've got the infrastructure set up, it's about continuing to modify and evolve the agents. Seth, I don't know what you've seen, but what I've seen is that most people don't really [00:30:00] understand how to query an LLM effectively.

Seth Marrs

No, it's way harder than it sounds.

Todd Abbott

Wow, right? So we'll come in with a tool set for each of those three disciplines, with different agent configurations, and take them and evolve them for your business. You get a really fast start, and we'll teach your team. If this sits in RevOps or marketing ops, depending on how your team is structured, we'll help them get acclimated to using this technology, which we stand up within the customer's four walls, behind the firewall, so to speak. It's your data. It's your infrastructure. We're really a team of consultants who have done it before, and as we do more of them, we're going to be able to help you accelerate. What might take you six months to figure out, we can get stood up and running in five weeks.

Seth Marrs

Got it. I want to double-click on something, because I think a lot of people, when they think about how to engage with an agent, stop at "I prompt it." [00:31:00] You've got your Claude warriors who'll build their project in Claude. You're talking about a tool set that takes advantage of way more than even the best possible prompting.

Todd Abbott

That's right. Think of an agent in this context as a UI that's doing the LLM prompting behind it. We've got all of the LLM prompting done, and it will help you evolve that prompting through a UI. And it's oriented. So we'll give you a quick head start as a product team: we know these are the 10 things you're going to look for. As a sales rep or sales manager, here are the things you're going to look for.

I really think what we're deploying is leadership-level, and it will evolve down to the first-line level. We can create agents for a first-line manager to prompt and give reports out to a rep. But I'm not trying to create a rep-based tool, [00:32:00] because at this point our focus is: get me, the customer, the insights at an aggregate level, and then start to take it down through the hierarchy. If I've got five first-line managers, I want them to understand the big picture, but I also want them to understand what's going on in their team. Do I need to focus my time and attention on coaching?

If you think about it, the way I think about conversational intelligence: God, it was amazing. I could get all this data, and everybody got all excited about it. And then three or four months later, after you've listened to so many calls, you just stop listening to them.

Seth Marrs

You need a better way to extract that.

Todd Abbott

100%. I think Gong is making some really good progress on getting the data out on a deal. But on the broad go-to-market, they're not set up to do that, and it doesn't have all the data. Now it's setting up MCPs to get more data from the other [00:33:00] technologies. The question is, are you going to bet on them and what they're going to charge you for that LLM compute, or do you need to stand it up in-house?

Last point. I just think this really unleashes you to have so much more flexibility. I'm not telling anybody to get rid of Gong or get rid of Salesforce. Those companies are going to be around for a long time. But you should have greater control over where you're going to take your business architecturally from an AI and LLM perspective to get these insights. And the companies that do it the fastest, the ones that get those insights and the continuous improvement to out-execute the competition, will win.

Seth Marrs

And those companies are going to have to enable that too. They have to be more flexible in doing that, for sure. It's a great point. Okay, let's talk a little bit about you. You've been a CEO at multiple companies, and you've been involved in tech for a long time. As a kid, were there any signs [00:34:00] that this would be the journey you went on? Did you know it from the beginning? If I were to ask what you wanted to be when you grew up, what would it have been?

Todd Abbott

It's a great question, and I won't spend a whole lot of time on this, but I came from a modest background. Both of my parents were school teachers. I had an uncle who was a salesman, and I thought he was just the coolest, and he had money, and I'm like, "Hmm, I want to make money." I went to Northeastern and got a co-op job at IBM, and that just opened my eyes to a world I had no idea existed. I'm dating myself, but this was when IBM was a kickass company, and the whole concept of dark suits and starched shirts was like, wow. I felt like I'd landed on a different planet. Then when I went to Cisco, that was all about speed and execution, and I learned so much.

But what I learned as I went through that process, if I would tell [00:35:00] anybody anything, is what I wish somebody had told me: "Don't be in a rush to keep getting to the next job." I always look at a professional career as the opposite of a sports athlete's. An athlete makes all their money based on their DNA in the early part. Executives, business people, make all their money in the latter part. So capture as much as you can early in your career, and grow into what your capabilities are. Go into it with a level of intellectual curiosity. I was humble and intellectually curious about how [stuff] worked, how the sales process worked, and what our solution really was and what it wasn't. I didn't necessarily think, "Wow, I can be a CEO," but I just started to really enjoy building teams and hiring great people. We had a bunch of no-[nonsense] guys who were just execution-oriented. It wasn't for everybody. But I learned early on how I [00:36:00] could motivate and be part of a team, and the team just kept getting bigger and bigger.

Todd Abbott

But I never lost sight of the fact that it's always the rep. No matter how many layers were between me and the rep, the rep is king. I always wanted to know what was going on in the rep's head, what was being filtered down and filtered back up. When I was out on the road, and now it's virtual meetings, it was about connecting with the reps to understand what's going on: how my message and our vision were translating, and what the friction points in their job were. I never lost that touch, that focus. And over time I just learned how to scale it, up to this level.

Seth Marrs

That's crazy. It's always those threads. I think about it very similarly. [inaudible], and I had an uncle as well, and for me it wasn't IBM, it was GE. [00:37:00] You have these points along the way that really make you. But your point about the rep: if you ever want to know what's going on, that's the place to go. It's the only place you can go to really find out what's going on and actually have a person who'll tell you.

Todd Abbott

Great point. The hard part was getting them to feel comfortable. I used to always tell them, "Hey, listen, give it to me straight. You're not going to get fired for telling me what the reality is. Just give it to me straight." It was hard to get them to open up, but once they did... We have a lot in common. GE was an execution machine back then.

Seth Marrs

Yeah.

Todd Abbott

To me, Cisco was an execution machine.

Seth Marrs

Phenomenal.

Todd Abbott

So between IBM and Cisco, that was the foundation for me to have the career I've had.

Seth Marrs

I can see it in working with you, and you can see it in the foundations of everything you do. You bring it to every job you've done, and even to the conversations we've [00:38:00] had. It's been phenomenal to get to talk to you. Thank you for taking the time.

Todd Abbott

Thank you. I appreciate it. As always, good to talk to you.

Announcer

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