Interview with Branden Baldwin, Director of Revenue Operations at Degreed

19 min read


Branden Baldwin’s beginnings in psychology means he offers an interesting and unique view of the sales ops world. I was excited to catch up with him as the now Director of Revenue Operations at Degreed to learn more about his journey into sales ops, the projects he’s currently working on and how his distinctive lens on the sales ops world differs from others’.

Rory Brown (RB): Hi Branden, thank you so much for taking the time to chat with me. What would be good to start with is a quick high-level overview of your career, and more importantly, how you landed up in sales ops.

Branden Baldwin (BB): We’re going to go pretty far back. When I was first going to school, I was studying psychology, split between industrial organisation, business psychology, and then adolescent psychopathology, which is basically youth counselling. That’s what I ended up doing for a while. Got into a youth treatment centre, and it was a very small team.

I got thrown into the operational aspect of it, as I was the only one that really knew how things were functioning at that place. It was my first experience into operations. And that’s when I decided it’s what I want to do long-term.

Then I switched over to the investment world and worked for Fidelity Investments. Whilst there, I was put on a team that had just switched their focus. I once again got sucked into working operations, but this was slightly different, as it was in call centre operations. I was looking at demand and flow and trying to figure all that out, and it wasn’t right. I didn’t like the investment world, and was like, there’s got to be something better. I had some people tell me, “Hey, after being an investment consultant, come over to tech sales and come work with us.”

So I got into tech. If I’m very honest; I hate sales. Absolutely. Personally, I hate sales. I just don’t have that personality, that sometimes I just don’t push back. It’s just who I am in that environment. I was doing that for a little while, worked for a company. I wasn’t a huge fan of it. Then I heard of an opportunity to help build out a sales development department for Degreed. I came over. That quickly changed to being a quota carrier with how small we were. We put the operations on hold.

We went through a board meeting and our CEO at the time was asked for some analysis that we didn’t have. I was going through an MBA at the time, and I got really bored, and that’s my thing; I get bored.

While I was working with the SDR group, I started just dabbling myself into analytics and just giving it to the executives, like, “Hey, this is interesting, look at this. I found that this. This is very interesting.” Through that, they came to me and said, “First of all, can you help us run analytics?” Sales and marketing analytics they told me and they said, “Where do you want to go?” I was like, “I really think sales ops are where I want to go.” I like process. I like analytics. I like helping people. I like being in the group that’s driving the revenue. I did that for a little while and then our sales operations individual left. When they came to me, they asked me why I wanted to go operations to see if I could be a good fit. For me, it went back to the psychology mentality. The reason I love and have been drawn to operations is I love to understand how things function and change management. In the sense of, if you understand the mentality, the structures, and even future direction of a behaviour, there are certain variables that will shift that behaviour very quickly.

When I worked at the youth treatment centre, helping kids that were addicted to drugs, it was the same thing I did for them. It’s actually very similar to what you do in a business sense; helping the business identify the problem. I don’t just go and prescribe any issue myself and say, “Hey, this is what you guys need to do,” but I help them discover themselves, because then we have a better change management. In the sense of, as I was pitching this to them, like “Hey, well, what do you want?” I turned that back on them and they just sucked me back into sales ops, and said you’re sales ops from now on.

For me it was like this ebb and flow of I wanted something where I could still keep within psychology, but still keep in with the business world as well, and working on the back end is the best fit for that in any kind of organisation.

RB: Makes sense. What would be quite good to understand two or three projects that you’ve been working on or have enjoyed, or were difficult, that perhaps you might want to dive into a little bit would be really good.

BB: Right now for us, the one I’m working on most intensively are two bigger projects. One is; there’s always talk of data. Everything’s about data, we need good data. What we’ve focused on a lot was taking data and making it cross-functionally usable.

We are doing a large data project, but not only are we saying let’s service up good data for our sales team, but how do we take that data that we’re collecting here, bring it together with what our product’s doing, with what our client success is doing, what marketing’s doing, and tell an overall story with it. That story can help drive us to bring us on their ops mentality, bring us all into one conversation at one time, that we can make a better experience. We saw historically for us, it was very siloed, and that marketing had their own data. They might share a few of it with sales, but then everyone’s just duplicating the same work and making decisions on their own data, which was contradicting, so we’ve done that.

We ended up helping build a data team for the organisation. We worked cross-functionally, that we now have a data warehouse. We are working on distribution of this data, this insight, and any decision being made by an executive from the sales perspective, we do require them to show what data they’re using to make that decision, to make sure they’re understanding that. We have that buy-in, it’s been a big one for us. It took a lot longer than we were hoping, but the key one.

Second one for us right now, which is a shift from others, is, with our growth, we took a very broad channel partner approach. In the sense that we were doing any type of partnership we could to get business to please our board. What happened is our head of channel left and it left a mess that no one understood what was going on. We had no visibility, no tracking, no process. I was actually pulled into that one to reevaluate the entire process of how we’re interacting with our partners and make our partners successful.

Treating them as if they’re – which they should be – treating them as our own sales organisation. Actually having that happen so they can do better for us, so training, resources, systems, we’re working with all that. Then next we’ll be going to a route starting back, of reevaluations of our process, of our analytics, everything, a refresh.

RB: Awesome. Well, I think number one is a good one to cover because, as you probably guessed, it’s something that everyone struggles with. We’ve got lots of data, but how do we organise it and keep it true. I think this idea of making it cross-functionally usable is really neat. Perhaps you could just maybe start from the top and talk about what was life like before you took on this project? How did you establish that it was something that you wanted to tackle? That might be a good place to start.

BB: Being how small we were two years ago, everyone wore multiple hats. Within those hats, certain things were predominant like with operations, we built operational enablement, and they liked enablement more so they put more emphasis on that. When it came to marketing, even our chief marketing officer; he was really good digitally but not necessarily great with ABM or events, and what we saw was everyone had their specialty. As we were trying to run very quickly, no one questioned if we should be doing more outside of what they were doing.

This project actually started three years ago when I was working in analytics, is I was trying to understand what was going on. A basic quarterly report would take days, and I would do marketing and sales to start out. I’d have to plan on three or four days just to go through all the marketing, on the manual cleanup of what’s going on. Then I’d do sales and realised that they were basically trying to tell the same story but with different data. The first thing we looked at was, how many different tools that we’re using to gather data and report out of.

What we found was very basic. Almost every organisation had a tool they used solely themselves, and didn’t share that data with anybody else. When they did reporting, the only time that reporting was shared was in the presentation. What happened is, as we took to those meetings, we’d hear our marketing team make a decision on an event, whereas our sales team would be making decisions that did not support that, and actually went a different direction. I just sat back and said, “Okay, why are they not talking? We have the data in this system,” and I was the only one that realised I had it in my hand and there needs to be a system. I could say, “Okay, well, we have this data in our marketing system and our sales system, if we brought it together, we could tell each of them exactly where they should be spending their time.” We could go deeper, which was what I thought were basics, but actually weren’t basics for those that don’t have an analytics background. For them, they didn’t understand that we should actually be communicating and sharing that data. Step one is we laid out all the different systems, all the different data we’re collecting. From marketing, their marketing automation tool. For us at Degreed, we use Salesforce for our sales team. Then we actually use a different client success tool for our client success team.

Among those, I also looked at all the times that we were trying to share data, or we had a data point that was similar, that was entered in a different way. Step two was probably what took the longest, is we said, okay, this wants to work cross-functionally, we need to make sure that every data point matches and where it does not match, we need to figure out how we can make it match. That became the most difficult, because when marketing has a definition for something, and then sales has a definition, and client success and products and our executive team, we found that almost every group had a different definition for every term.

We worked with a team, put a committee together, worked with the team to create a data dictionary. Data dictionary was phase one and our data team took that and ran with it, and spearheaded for us while we were doing nothing. But that was instrumental in us being able to get a cross-functional data set up. We have to speak the same language and that was the tough part, because you have to get on the call with everybody that has this different definition and say, “Okay, what’s your definition? What if we said this?” Then of course, you can debate it back and forth. You finally find common ground between every group and you say, great.

Now we have a definition, how does that data get in there? That was our second part of, “Okay, Salesforce is our central location. I don’t want anyone manually entering anything else, so integrations need to be set up. If it’s not, get us in touch with the product team at the other company so we can get that set up.” That was phase two. Probably took about six months of working with some of these other providers to say, “Okay, this is the data we have, how can we get into your system? If not, do we need to do an evaluation of another tool?” Because if we can’t do that, we’re always going to have data integrity issue. To the point where we’re on a call where we’re talking about a renewal date. Somebody made an interpretation without telling us in the historic way, and it was three years off on a renewal date. We’re now making decisions that we then pull out of. We’re like, “That’s completely off. You guys are making decisions on data that’s not correct.” Once we brought that together, the biggest issue that comes across is consumption. Yes, we have data that talks, that we have that alignment, but how is it received? How’s it consumed?

That’s the other difficult part, because now that we have so much data it becomes so much noise that it can be basically debilitating. They just get frozen with how much data they have. We have to be very careful on how we’re distributing the larger amount of data, at what point, to what individual. Then we have to actually spend more time in educating and helping explain what that data means, since a lot of them may not be used to it. Someone in marketing may not be used to client data. That when we helped them explain it like, “Oh yes, we could do more marketing for our clients to make them successful, increase renewals, increase upsells , those types of things.” That we had to basically coach everyone initially on; here’s what this means, this is the direction it can go. Then as they start to develop themselves, they get that empowerment of, “Great, I’m going to go run with some other data,” and we slowly open up the gate of what data they can consume.

The second part that became difficult for us, and we had to do more of an entire organisation project with this, is with that consumption. We couldn’t base it just off of like, “Okay, we’re suited to Salesforce. Salesforce is our go to or we can’t do it out of our marketing system.” Because not everyone had access to it, so we’d send out a link, it’d be like, “Great. I need a license.” I’m not going to pay for that license just to be a report.” We had this issue and they’re like, “Oh, send us the spreadsheet,” that spreadsheet would take time to generate, getting the screenshots, and they’d want to click into it. We actually decided, as an organisation, to move forward for the first time for us in a valuable business analytics tool. Once we got everyone on board, that’s where that consumption actually matters for us.

That’s the other great part of that project is, I wish all this would be done in one or two months, but we’re now basically two years down the road at this point of when we started this conversation. Then we do the business analytics tool, which took another six months. We are now at the point over the span of two-and-a-half years almost, where we are at the first consumption and true consumption of cross-functional data. That was the thing is when I first talked to somebody that did this, they’re like, “Yes, I just need to dump the data in, pull it out, call it good.”

Once you dive into a multi-year project if we’re going to do it right, and that ‘right’ is the key word, because you could easily just dump and go. Six months from now, if you do not have a data dictionary, if you do not have everything unified, it becomes obsolete. All the work you did when you have different data with different definitions, that people can’t actually use and then it truly is a debilitating data problem, that you have more than you had before.

RB: Nice. That’s really interesting. Working back, you talked about there’s loads of data, it can become too much. How do you work out which data points you want to focus on, or capture or fix? What drives that decision, what drives the data that you collect would be good to know as well.

BB: I’ll share some downfalls first. When we first started, it was, “Let’s get anything we can to try to identify what’s broken. Let’s just run everything, find what’s broken, call it good.” We made a shift; there are three progressions. First step was let’s get everything. Step two was what story do we want to tell? What do we want to accomplish? What are we trying to look at? What are we trying to do and do we align with that? What we were trying to work was still pretty broad, so it became very important for our groups in our organisation to have OKRs. If we had objectives that we were working towards, four or five specific things we’re driving to, that’d help us identify, these are the objectives the company’s trying to address, each organisation’s trying to address certain things. Then from the analytics perspective, we can go and say, “Hey, these few data points will help you tell the story of how we’re all reaching the same objective.” That was very fine-tuned. We learned from this, “Let’s throw a net out there.” If we can tell the story of these specific things we want to address, that that opens it up to say, “We’re not quite hitting this objective. Why not?”

Then we can dive a little deeper that we don’t spend too much time in the wheel. The best part with that objective key result model is, when you’re the executive, you can look high level. If you want to dive in, you can, but as you get lower in the organisation, we can get very granular in the data you’re seeing, to help you be able to drive that top number. It helped us build a more streamlined level of function analytics process that based on level; they can see certain data as quicker, actionable. Not just, “Hey, here’s everything, go digest it yourself.”

Which was part of our old model of, “Here, CEO, here’s everything, go make a decision, but it can take you 40 hours to even review what we just sent you.” Objectives aligning with objectives, you can still identify other things going on, but within those objectives, that’ll give you the priorities with the data and with the actual process changes.

RB: How do you get the knack for which level of people in the business need what depth of information, and how much free rein do you give them to go and explore?

BB: Yes, this is a fun one. First of all, communication is key. You have to communicate with these individuals. Then for me, it comes back to psychology. You need to understand the behaviour and the personality of those that you’re working with. Not every executive can be the same. You do have some that when you look at who they are as an individual, you’re like, “yes, they can handle more data. They’re adept to be able to consume something like that and make a decision.” Others, their personality is very quick, “I can’t spend more time. I get irritated, I get frustrated.”

As you understand the thinking of these individuals, as you spend more time working with them, talking to them, even ask them like, “What would be your ideal state?” you can learn very quickly of that personality traits. That’s where we had to come back in and say, “Okay, with these three executives, let’s open up the gates,” and that makes it more difficult, because it’s not just cut and dry of like everyone gets the same access. But it’s, we really want to empower these people with what they can do best, and then these ones we want to restrict for what they’re going to do best.

If we did not know the executives, or even the users, if we don’t know them, and we’re just making assumptions, that’s where we have that loss. Even a couple of minutes long phone call, hopping on a call like this, we can very quickly see their type of personality. They’re going to be able to spend some time in it. They won’t spend all day in it, and we can then open up accordingly. Now the thing that we do have to identify and adjust is watching usage and time spent.

If we hear they are spending a lot of time, then we have to restrict or spend more time on, “Hey, why you’re spending too much time here because you’re wasting your time, wasting the organisation’s time. How can we streamline that for you?” Maybe shift them to a different bucket, depending on what we feel after they’ve gone into it and started having some of that exposure.

RB: Brilliant. Going to data capture next, which when it comes to commercial people and sales people and whatever else is quite challenging. Let’s say you’ve chosen data points you want. You know the story you want Jill to tell and to whom you want Jill to give it to. What are some techniques for going somewhere to ensuring that that data is captured with some form of integrity, given who’s entering it?

BB: This one’s had a lot of internal debate for us. In the sense of, operation, with the direction everything’s going I’m like, “Let’s automate everything, anything we can automate, let’s automate automatically.” I was surprised when we had some leadership pushback and saying, “No, we want accountability.” From everything I’d been reading, everything I’d been seeing, it was you need to go automation, you need to go to AI otherwise you are not going to succeed. But accountability is more important than automation. That was eye-opening to me. Hearing them pitch their cases to, “Well, what if we had a rep still be accountable to those data points?”

We had the conversation of, “Okay, but to keep that data clean and usable, who is the end accountability?” Is it just the rep, is it the manager? Is it myself and operations? We had to create an accountability map of, “Okay, well, if this data is bad, sales manager- that’s your responsibility.” The best part is, you then talk about who gets to clean it up, because if we say, “Hey, sales manager, you’re accountable, but sales ops will clean it up,” they’re not going to care. When we say, “Okay, if your rep does not put in this data properly, you get to personally clean it up so we can use it,” they’ll make sure they do it right every time.

They don’t want to spend that time. They don’t want to waste that time. That accountability mapping was what made that necessary for us. Now we’re still not perfect. As we continue to expand that scope of what do we automate and sales operations will own, or what do we keep for accountability purposes, and what the sales managers and sales reps own, as we’re cleaning that up we’re getting the better data. Where it becomes more difficult is you want to create efficiency in the process for reps, but at the same time, you don’t want to create something so easy for them to do that you have a large chance of bad data.

Which is what happened to us in the early stages of anyone could create anything, anyone could create an account, add a bunch of contacts, anything they wanted. It just made a mess of our system, and that process lost our credibility, even when we cleaned up the system. As we continued to clean it up, for those reps that had been here a long time, they lost credibility. Then as we restricted to say, only certain people can create certain things for that purpose. We’re going to be able to audit who put it in, where it came from, fixing the issues, so you can actually act on this.

Now we have to change the mentality. If they say, “Hey, you’re no longer efficient. Why do I have to wait versus when I could create it automatically?” Then telling that story of, “Well, this is what the data integrity looks like and what you can do with it if it’s put in properly,” is what’s shifting that conversation for us. To not only have them put in better data, but for them to respect that we’re putting in data to make sure that what they have can be acted upon, and not just this game of everything you give me is negative and horrible, and I’m never going to use any of it.

A full circle. We had to look back to them, not just the sales ops mindset, but from everyone’s mindset, and then have everyone on board if we were going to do it properly.

RB: Yes, that’s really good. Thank you. The last question on that is about  making sales people accountable, which is one thing, but how you’ve managed to give value back to them to also perhaps encourage them capturing data as well. Have you seen any successes there or anything you’ve tried?

BB: Yes. A few things that we looked at, so this is where we then brought in some AI from some of the tools to say, “okay, take some of that data, turn it into something actionable for them.” Our sales reps are responsible for renewals. We don’t have that up to our clients, this is where our sales reps are. When we had the data, when renewals were coming up, I just proposed the question, how do you know they’re performing? Are you going to go for an upsell? How do we feel about renewing, what’s the plan? Is the usage high or low?

They would have to go ask a few people, come back and be like, “Yes. Usage is low. We probably need to nurture them a little more.” We started identifying, saying, “Okay, how long does it find for you to get an answer to that, what’s the time frame, one, two days, an hour, 20 minutes?” We said, okay, well, with this project, what if we pulled that in automatically and you could see it. They’re like, “Oh, I’d love it.” We just gave them little what if scenarios, “Oh, I’d love it. I’d love it. I’d love it.” We got them excited for some of these key things that we found were pain points.

They’re nothing huge. Really, there were nothing that’s like, “Oh, this is absolutely amazing.” It was basic foundational stuff for them to be successful, that they felt they didn’t have. Once we gave them the foundational aspects to what they didn’t think they had, and as we started to produce them, that’s what got them excited for some of these larger projects of saying, “Okay, we can go even crazier with all this data and you can use it.” Then they already had that buy-in of like, “Oh yes, you’ve already showed us some of the basic foundational things that we just would hope that would happen, but never did in any of our prior organisations,” and brought that together.

Now they’re bought in. Once they saw those few basic ones, that we tell them, “here’s the end goal, but we need you to do all these different steps.” Because we gave them some of those little nuggets of data, they understand that, “well, if that’s what that little step did, if I do these, then I can get something bigger and better.” They’re now bought in. At the same time, we did find out it did take a few power users of the data to drive it. Once we found a few people, and I feel it does seem like there’s at least one or two data-driven individuals, that if you just tell them the data project you’re doing, that you want to improve it, they’ll be bought in right away and say, “Great, let’s do this.” Then they can drive it also from their perspective.

So taking those, giving even the non-power users some of that data, but then really diving in deeper with the power users, letting them share their experiences, their success, and it has to be a lot on the success metrics of, “Look, they used this, and this is what happened,” to help people say, “If it’s going to take five minutes, but it’s going to get me a deal that’s $100,000, then yes, I’ll take the five minutes to get $100,000.” That’s where we have to tell a story and we can’t just say, just do it, but that why in operations is so important. That you have to lay out exactly why you’re doing it, show the numbers of why you’re doing it, and it goes back to that psychology mindset in my mind of let them think they’re the ones driving it.

If you’re saying, “Well, what else would you use?” and coach them on that path, then they say, “I would love if we could do this data with this data, with this data.” You’re like, “Yes, we’ve already documented that map, that we’re working on it, but oh, that’s a great idea.” Like, yes, let’s drive it and get them involved. As they get more involved, they are just powerhouses and helping drive that project for us.

RB: Nice. Yes. I like that a lot. It’s really good. For a project like this, you’ve already said it’s taken a long time, which is nice for someone to admit. How do you look at, or define the success of a project like this? What does that look like?

Branden: Yes, so for us initially, and this is where once again, we thought it would be quick, but as we quickly dove into what needed to change, it opened up a large scale of issues. I’d say, personally, I felt for the first year of doing it, that we were making no progress. Even though we were making progress, it felt like there was no progress compared to a project of, “When we look from A to B, we can see all these metrics that have improved.” When you have to revamp the entire way you’re doing things before you’re measuring it, it was a matter of looking at the progression of did we take steps every day? What was the progression step this week? What did we accomplish this week?

Even though the metrics weren’t showing the actual success, we could show that we were moving in a direction. For us, that’s where it became very important that we had an envision in mind of where we want to be, because as we looked at that progression, we could say, “Yes, we did a lot of stuff this week, but it didn’t drive towards that end goal.” It was wasted time, but everything we could stop and reevaluate and say, “Okay, the work we did this week is aligning to the end goal. This is how we’re this step closer.”

Then once we got to the basic structure and start playing with data and the distribution consumption, that’s where we start looking at the metrics of, before pre-consumption, what was the benchmark? Now with consumption, what are we able to accomplish? This is where you have to work in the mindset of revenue operations. Even though we don’t have revenue operations, you have to think in that mindset, because when you’re talking success for a sales rep, you give them data, they act upon it and sell a deal. Well, does that data help drive a customer that’s now going to retain longer than they did before, or have higher usage than they did before?

You have to look at those metrics from a cross-functional behaviour perspective. Did one thing from one group impact something down the road that we did not account for? That’s why understanding before, the different baseline metrics and whether a sales ops needs to go to the other groups and say, “Hey, I need metrics on some of these different factors of satisfaction scores, retention rates conversion rates, anything it is.” Then keeping that as a bench line and then saying, “Great, these reps had different data. How did that relate to any adjustment? We’re now tying that in.” We can now say, “This has increased one tick, two ticks,” whatever it is, on the impact to later stages.

The second part is when you work for that model, some of it you will not know for months upon end. For us, we have a very long data cycle, anywhere from nine months to a year, depending on the size of the customer. When we’re looking at metrics from a year ago to now, does that make an impact, so we do have a lag, and that’s what we have to tell everyone. Like, “Okay, we’re working for next year. We’ve already marked where we were this year, to be prepared for that number next year.”

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