Janis Zech: Hello and welcome to another episode of the RevOps Lab. I'm super excited about our guest today, Guy Clark. After studying physics at King's College and starting in product management, he found his way into RevOps lately at SaaS companies like ON24 and Cornerstone OnDemand. In his free time, he loves mountain biking and all things cycling. Pretty interestingly, you have a background in physics, you started out in product management. Super curious, like how did you get into RevOps? Yeah, would love to learn more about that.

Guy Clark: It was a full circle really. So I started in construction in a number of roles and then ended up in a sales and marketing department. Having been in planning and more engineering focused roles, it was just an option, you're either redundant or you get this role in sales and marketing helping with bids and helping clean up the database. And that's probably where I got my first passion for understanding how data can help the business. We had a really rusty database that had some very old data in, so it was very much about cleaning that up and then it was ultimately about delivering leads that had come in manually on bits of paper or card or word-of-mouth and distributing them to sales teams so they could work on them and then similarly collecting that data back centrally in a database. It was the first time the salespeople had actually had computers on their desks and we were collecting this data over telephone lines with nine point six kilobits per second modems. So it seemed very clunky but if you fast forward, as you say, I've had product management roles, I've had a variety of other roles throughout my career, predominantly telecoms but also in SaaS, more recently in ON24, fantastic webinar and analytics platform and now in Cornerstone OnDemand in the learning experience and HR systems space, not a huge amount has changed in a lot of ways. The fundamental principles are still the same, you've got to generate leads from somewhere, you've got to capture them somewhere, you've got to deliver them to salespeople, salespeople have to work on them and ultimately you want to close business. So nothing there has changed. What has changed is the plethora of systems out there and the multitude of KPIs that you can study. And I think the challenge we all have is overcoming what could be a data overload and understanding what to measure, what not to measure, what you can change and what you can't change and not just being focused on the dashboards. The quick analogy I'd use is if you're driving a car, you like to have a useful dashboard with your speed and maybe the sat nav telling you you're going in the right direction but you don't want to be looking at that one hundred percent of the time that you're driving the car. You want to be driving the car, you just want to look down at your dashboard to check if you're on track or not and then take the appropriate corrective actions.

Janis Zech: I think that's so fascinating. I mean, sounds like you started out when CRM wasn't even around, right? But still a database and you had to make sure that you track all those data. Now we can track everything and we track everything. So there's a complete overload of data. So what do you do about it, right? Like if you think about your last two roles in software, I assume you have endless amounts of data, whether that's in your CRM or your data warehouse, can actually use and visualize. So how do you create that dashboard that is actually helpful to the revenue teams?

Guy Clark: I think it's going to vary depending on the team, whether it's a sales focus team, the desk based sales team, the marketing team. They've all got different needs. It's very much about horses for courses, what are the key metrics they want to look at, is it MQL creation and then conversion? And keeping it simple, I think you need to have the high level metrics that you want to achieve, they're very obviously in the dashboard, but then with the ability to drill deeper if one of those is not ideal so that you can then begin to understand what might be wrong, why is the win rate a little bit lower, what's the cause of that so you can then go and take the appropriate action. But I would say that the number one is understand what the audience needs, which team that is and what metrics they need. Deliver it in a very simple manner but also have the ability to drill deeper if you need, but keep it simple at the high level. And I think the other thing that's really important is cadence. So how often do you dive deep and you have these real analytical and QBR type sessions, quarterly business reviews, is that quarterly? And what do you do in business time? Is it every day, every week? I think you need to avoid distracting people from doing their day job. So if you're obsessed about certain metrics too much, what is the right cadence to look at them?

Janis Zech: I think that's so fascinating. If you think about the reality, and I think we've all lived through this, you go into these Tableau or Looker dashboards, and you see so much data and you get lost there. Like, how do you create that cadence? And how do you make sure that people actually go back, right? Create that habit of not just looking at the data but using the data to pull out insights that help them be more successful?

Guy Clark: As I say it's very much around understanding where their pain points are and what their processes are. So sales typically might say I'm getting rubbish from marketing in terms of MQLs. At the high level you're going to see this huge volume of MQLs and sales are then going to go the quality is rubbish so you have to have the ability to drill into that, to examine, to inspect those MQLs and decide are they really qualified. One of the things I often observe is the qualification is not as rigorous as sales would ideally want. Ideally they want a perfectly qualified MQL where the customer is pretty much ready to write out the check to say I'm going to buy your service. But the reality is, and this comes back to doing the basics right again, if your underlying data is not great, how do you adequately qualify whether it's systems like LeanData or other processes you have in place to look at that lead that's come in, whatever its source is, what determines that it's qualified? Have you agreed those rules and those criteria with all the stakeholders here so marketing might be happy that it's quite loose and go well, they've kind of got a pulse and they requested a demo so there you go, there's a lead, guys. If your underlying data's right you can do the validation to say well, who is this person, what title have they got assuming you've captured that, what company do they work for, are they in your target market, is it just mickey.mousegoogle.com that's come in as an email address and that's all you've got, maybe that's just a student or somebody else making some inquiry so they can learn about your platform, learn about the industry. A lot of these things come back down to the fundamentals of getting your data and your processes right and only if you've got that and all stakeholders understand that can you properly understand where things might be going wrong if you're not hitting the right metrics.

Janis Zech: So what I've often seen is that the qualification and stage exit criteria are there but they're actually not lived by the team, right? So how do you help them live it day to day?

Guy Clark: I think that's hard because they have different motivations to perhaps the business objectives. So you might specify for example the desk based lead gen team have to BANT to qualify, so that's budget, authority, need, timing on that opportunity for it to be qualified and handed off to sales. Now a sales person may be prepared to accept something that's substandard in terms of that because they would rather have something rather than nothing. So they would rather have a lead that's poorly qualified than no lead. I think that's very difficult to police unless you've got very binary criteria and they're evident on the opportunity itself. Things like that you'll never get around and maybe it's okay. Maybe it's absolutely okay for a salesperson to get a slightly poorly qualified lead because maybe they're in a better position to turn it into a qualified lead, particularly if you're not necessarily immediately selling to a customer who's decided I need X, here are the criteria for X, there's my shopping list, I want that. If you're in the space and we are certainly at Cornerstone selling a vision where you're trying to encourage companies that if they look after their employees and train their employees well, they improve retention, the skill set of those employees improves and they become more productive and better employees. Not every chief HR officer is sat there thinking that is their number one mission. So a lot of times in qualification, is it possible to get that full qualification in terms of need and whose responsibility is that? Sometimes sales are ambiguous because sales maybe should do that because they're better qualified than maybe your desk based sales or even the marketing team generating that opportunity in the first place, that lead rather. So I think the lines are blurred and I don't think you'll ever get it one hundred percent and you have to accept that certain things are grey, but if you're measuring the overall journey in the metrics, ultimately whatever that journey is and whatever those dropout rates are between say marketing with their MQLs, desk based lead gen people or BDRs who are evaluating those, determining whether they can qualify them further before handing them off to sales. As long as you can follow those steps and look at the fallout and you're happy with the ratios then I think it's fine that you'll never have an absolute theoretical best practice.

Philipp Stelzer: The way I understand you right now is essentially, so it's fine to keep things quite simple because, like, through simplicity, it actually becomes, like, tangible, easy to understand. People can sort of, like, grasp what the KPI is about. And that's already a really good baseline basically to have just to keep things running. And then every now and then, like, there's sort of, like, a need to do a deep dive, and then you can do that, and you can either do that basically through, like, a QBR, for example, or you do it more, like, on an ad hoc basis. That's sort of like how I understand you. Is that a fair assessment?

Guy Clark: That would be my recommendation. I think there's a temptation to try and look at every one of these detailed KPIs that might lead you to better understanding some of the higher level ones to look at that too often. And I think if you look at that too often you're into analysis paralysis. You're also in a position where you don't have enough time to actually effect a change. So you might look at one of those KPIs and say that's suboptimal, we need better qualification, right, okay, what processes do we need to put in place to make sure we've got better qualified leads? If that takes several weeks, several months, there is no point in continually diving into the detail of that every day or every week. You need to give it time to change and then similarly if your sales cycle, so I've worked in everything from consumer where the sales cycle could be minutes on a phone call, the first time you've contacted a consumer is you've called them and they've either bought or they haven't bought, they've told you where to go, whatever, through to big enterprise deals of software that might take six, twelve, eighteen months from that first touch point through marketing or sales through to the sale. So you've got to have a cadence of inspection of some of those metrics that's appropriate to some of those sales cycles and changes in internal behavior. If you want to get an improvement in the average deal size for example, what does that take, if you're looking at that and the trend's down and you think well maybe that's a macroeconomic condition or maybe that's a condition of sales trying to get a quick sell and not selling the complex combination of products or whatever it might be. If you want to change that, if you want your average deal size to go up, what do you need to change in terms of internal processes, behaviors, product, pricing, packaging to make a change? So if you're drilling too much detail too often you're never going to see that change and you might be spending too long looking at the data. So it's about appropriate inspection and appropriate cadence so that you have time to understand what you need to do to change what you don't like or do more of what you do like.

Philipp Stelzer: I really like that and I think it makes a lot of sense to me. I think the only challenge that I think then would still not go away is that you still have to collect all the data, right? So you don't want to end up in that position where, now you have a question and you wanna do the deep dive, but then you lack the data to actually do it and you realize, oh, damn. Like, actually, we need, like, those three other reference points or, whatever, like, data points to actually come to, like, a legitimate conclusion. And then you can only start collecting the data from that point where you realize it, and then, you know, it takes another six months. So, like, is it fair to say that you're more like a proponent of, like, keep it simple, but collect all the data that you can collect?

Guy Clark: Definitely, yeah. Collect all you can but without putting a burden on the teams doing the job. So if you want extra data points to be collected, can you do that on their behalf? How do you engineer the processes such that if it's something like you want to understand data by industry or segment, you can obviously make sure your underlying data against the companies, you have a field that has the employee count, you have a field that has the industry. If there are other data points you wish to be captured, try not to put that burden on sales or other people in the process if it's not also helpful to them. So don't say I want you to capture these two hundred extra data points so that we can better understand what's happening in lead gen through to qualification of opportunities through to actually closing opportunities, try and make it a natural part of their process that aids them as well. And if you can automate that in whatever way then that's absolutely as it should be.

Janis Zech: Yeah. I think there's different types of data, right? Like data you can automate and just enrich and clean automatically. And then there's data where you need the input of sales folks. I think especially around qualification frameworks like MEDIC or SPICE or BANT, like, that's often the case and often fairly challenging to achieve. But if you have an overload of data or fields that are required, and nobody understands why they should actually do that and it slows them down, you basically put everybody on a piece of glue and slow them down collectively, and that can be extremely costly. I think that's similar to the cadence topic you refer to, right? Where if you have a cadence that maybe should be monthly, but it's run weekly, and the entire leadership team is doing the pipeline reviews and forecast calls weekly, but the sales cycle doesn't allow for it, it actually creates a lot of overhead. So I'm curious, you know, about your experience there, right? Like, I mean, you alluded to it a bit, but I'd be curious to learn a bit more.

Guy Clark: Yeah, I think I don't know what the real source of it is, I've seen it in a number of companies where that cadence, in my opinion, is too frequent. The analogy I'd use is if you've got somebody digging a hole, maybe they've got to dig a hole every hour, and over the day they've got to dig eight holes, but maybe you've said well they need to increase productivity, we really need nine holes a day or ten holes or why can't they do twelve? And you then say right, we need to do some reporting, we need to understand why this hole digging is not quick enough. So the temptation of management in whatever it is, whether it's forecasting or other processes, I've seen it so often in different companies, is to inspect too often and to say, right, okay Mr. Hole Digger, five minutes into their first hole, it's like, how's this going? How much soil have you thrown out of the hole so far and what were the challenges? How did you cope with those stones and what about the sand and what about the clay in there and how's it all going, can you describe that for me and what do you think is going to help improve the process? And they tell them and maybe the conclusion is I just need a bigger shovel or I need a mechanical excavator. Five minutes later you're in there again asking them what they're doing and how it's going instead of stepping back and going right, okay, let's give them some help, let's change how they're doing their job, let's give them a mechanical excavator, let's give them a bigger shovel and then let's step back and see how that goes and not inspect in five minutes because we're then taking time away so that poor individual digging a hole, instead of spending sixty minutes digging a hole flat, they spend five minutes digging, then five minutes having a conversation and then five minutes digging, so actually their productivity halved. That's a pretty extreme example, but I've definitely seen examples in business where people are spending fifty percent of their time reporting and getting those reports produced and being able to articulate them on fire drills in the morning or whatever it might be rather than getting on solving the challenges that will move the business forward. So okay, if management want to inspect something every five minutes, fine, let them inspect it every five minutes. So back to the hole digging scenario, you'd put sensors on the shovel, you'd put sensors on the individual, you'd put a depth gauge showing them how far they've dug and then they can look at a dashboard and say right, okay, how many holes has this person dug, how deep are they, how many shovel strokes do they use, etcetera, etcetera, but you're not touching the digger, they can get on with their job. So that's how I think most reporting should be, it should be unintrusive wherever possible for the people doing their jobs. You make it, and if they are putting in data, it directly benefits them if they're putting data into a CRM. So they get some benefit further down the line as well.

Philipp Stelzer: What do you think would the prerequisites be just from a cultural perspective? Because it strikes me sort of like as a cultural and behavioral topic, right? Like you need to believe that the people who are digging the hole, like just to stay with that analogy, actually understand what their job is, like how deep the hole needs to be, how wide, right?

Guy Clark: Absolutely, yeah, yeah. So I think there's quite often a lack of trust or a lack of confidence. So maybe that stems from expecting perfection. So I think if you have a sales force or any other team they're not all going to be clones of you. So you might be the world's greatest salesperson and then you've moved up through the ranks or you might be the world's greatest engineer and you've moved up through the ranks. And maybe your expectation is everybody is going to do things as well as you. Some of them are going to do them rather better and some of them are just going to do them differently. But ultimately you need to trust them, particularly if you want to scale the business. You can't be micromanaging everything they're doing. So for salespeople you can't be involved in every deal. You might want to be involved in the bigger deals because you think oh this is a big fish, we don't want to let it go. But actually you need to make sure that your sales team can do that on their own. So you need to learn to let go, it's hard for all of us, it's like letting go of a child to go to school on their own for the first time. You need to let your sales people, your engineers or your product demo people, you need to trust them to do their jobs and you need to not micromanage that. Yes inspect it and see where do people need help to be better, but have the trust and empower them to do their jobs.

Janis Zech: Yeah. I absolutely love this theme of measuring everything, but not interrupting the day to day workflow, and then feeding it back into the sales force as a value, not as a thing that they have to take care of on top of their already very busy days, right? I think that's really powerful. And something that unfortunately often isn't the case. I mean, I'm curious, like, you know, I think these interrogation, you know, pipeline reviews, forecast meetings, I think we've all been there and seen that, right? Like it's painful and it's culturally difficult from my point of view. But it feels a bit like, yes, we can measure everything, but then there's still key information missing, right? Buyers don't see themselves being qualified into BANT and MEDIC and SPICED. Right? So somehow, like, do you think that, you know, the data quality on a deal level is often, or the insights on a deal level, often problematic. So yes, you can level it up and you have leaderboards and you have aggregated dashboards. But then once you basically inspect, there's a bit missing and that's leading to the interrogation, or do you think it's other core reasons? I'm trying to understand why is it so often that these sessions feel more like interrogation rather than jointly winning together.

Guy Clark: Again, I think it depends on how well you're doing against your forecast currently. I think a lot of this inspection can come from pressure. So if you're not hitting their numbers, if you've got a target of ten million and your forecast is saying it's going to be seven or eight or something below that or you're saying it's going to be twelve but nobody has confidence in the twelve, they really think it's only six or whatever, it comes from that pressure. Everybody feels it, whether it's the board, down through the execs and then into the sales team. Depending on that level of pressure then people become less patient to let natural processes take their course. There's a tendency to want to intervene, to say alright, this number is not good enough so I need to step in and find out why it's not good enough, and it becomes an interrogation rather than stepping back and maybe trusting the system to provide data. There's a lot of systems out there being developed on the forecasting side that will look at so many of the metrics, so many other features of a deal progression beyond the basics in terms of what stage is it at, what's your average win rate, they're looking at as Weflow does and other providers, you're looking at how much activity is there on that deal, how many emails are going to and fro, how many appointments and meetings have they got with the client, as well as the duration in stages and other factors. And that builds up a more detailed picture that you can learn from, machine learning and AI can begin to build a better picture of whether that deal is likely to win. I think these senior management who are rightly under pressure and rightly feel that pressure and feel the need to want to inspect, I think over time need to trust the systems, that the systems are telling them this is your forecast, step back, it's either not good enough or it's good enough, you now need to worry about how you change it. But don't interrogate your people unnecessarily and certainly not too regularly such that they're actually not doing the work to get better, you need to step back and think about how you make things better and then help the teams do that.

Janis Zech: Look, I think this is a fantastic closing remark. Before we let you go, just a final closing question we ask every guest. So I mean, you've been around for a while, right? And I'm curious, you know, what would you tell yourself if you were to start new into RevOps? What was your biggest learning or what are the things you would tell yourself?

Guy Clark: It's amazing how much you're going to see change but at the same time how little. Because the processes, particularly when we look at sales, are very much the same, there are different tools there to help people do their jobs, there are different tools there to extract information and analyse it, but the underlying process of having to communicate with human beings, negotiate, create a need maybe, negotiate a deal, close a deal and drive a business forward. Very little has changed there. That's still the same, you're still dealing with human beings. So that hasn't changed but we almost have too many tools, too much information. I think I would just tell myself to be prepared to be overloaded and have to work out a way to simplify because I think the thing that hasn't happened is whilst all of humans through their creativity and entrepreneurship have developed a plethora of new ways of doing things and new tools, we haven't developed more up here in terms of we don't have more capacity, more storage capacity, we don't have necessarily any more ability to do more in a day apart from where we've got productivity enhancing tools but particularly here. We can't think about a million things at once, we can maybe only handle a small handful. So it's about simplification, how do you cut out the noise and concentrate and prioritize the things that are most important, whether it's in your life or in your business.

Janis Zech: Love it. I think that's a fantastic closing remark. Guy, it was a pleasure talking to you. Thank you so much for this wonderful session. And yeah, look forward to talking soon.

Guy Clark: Thank you so much. Yeah, likewise. Thanks. See you soon. Thank you. Bye. Bye.