Ann Marie Verhamme: Hello, and welcome to our podcast. My name is Anne Marie Verhamme, and I’m a director at the Alexander Group. Today, we are going to discuss the latest trends in Revenue Ops and how AI will continue to shape and accelerate those capabilities in the tech space. I have the pleasure of introducing my colleague, Mitch Edwards, the principal and the leader of our tech practice to discuss.
Mitch, we recently completed a survey of rev ops leaders and had some compelling insights. Can you share any highlights?
Mitch Edwards: Yeah, absolutely. So our recent research is from about 130 revenue executives really to understand how they’re organizing revenue operations, deploying their investments and really get a lot more insight into how these organizations are structured.
And so what we’ve seen is that a lot of companies are actively making new and bigger investments in Revenue Operations, and we’ll save a little bit for the end, but also quite a lot are investing in artificial intelligence. What we’ve seen is about 61% are either in the process of completing or actively piloting multiple AI use cases.
Ann Marie Verhamme: Now, Mitch, before we jump into some of those details, can I ask you a basic question? What is and isn’t RevOps?
Mitch Edwards: Yeah, and I think this question probably really depends on who you ask, right? Revenue Operations is one of those functions where we see it varying quite a lot organization to organization.
I will say there’s some standard elements that typically end up as part of a revenue operations org. Things like strategy and planning, owning the analytics, managing the data and reporting. Things around training and enablement, territories, quota design and sales compensation. They tend to be more of the mainstays of Revenue Operations, but you can end up seeing a lot of different factors built in, whether it’s more of an emphasis around the sales ops or it’s marketing ops, and we’re digging into the actual side of content and creative services as an example. Pricing could be another one that sometimes ends up in there. So really, it’s a real mix in the clients that we work across, but certainly there are some of those elements that are more foundational that we tend to see quite a bit more too.
Ann Marie Verhamme: So, back to our research, we recently completed the study and four major themes emerged.
One: organizing revenue ops to support specific growth goals and growth plays. So people are having a very targeted intentional approach for how they’re structuring their organization so that it can accomplish and drive that ROI for those specific areas that we’ll talk about in a little bit more detail.
Two: organizations are increasing investment in revenue ops talent. So some specific capabilities that they’re investing in. But overall, we see more companies investing and increasing that investment.
The third piece is that it’s front of mind and important that as we scale these functions that we’re building out and measuring the key activities. So how can we define those metrics and track the success of that organization?
And then the last piece, as Mitch, you alluded to, how to accelerate this function with AI.
So digging into that first theme around organizing RevOps, high-performing organizations achieving commercial excellence or organizing themselves around those growth plays that are aligning to their overall go-to-market strategy.
So that means they’re following the customer journey and they’re executing multiple growth plays that increase their customer lifetime value through expansion and retention. That begins with awareness and customer engagement. Moving on to productivity, ensuring good customer experience. The key is to have a total view of the customer journey and build around that. And an effective Revenue Ops function facilitates that.
Now, the second theme is also interesting. So despite economic uncertainty, we’re still seeing increased investment in Revenue Ops with double the number of organizations increasing their RevOps headcount year over year. Why do you think that is, Mitch?
Mitch Edwards: Yeah, it almost seems counterintuitive, right? Where we’re under this increased cost pressure, we need to drive profitability. But we’re going to go and throw a whole lot more money and investment into the Revenue Operations function. And I think there’s a pretty good reason for that, actually, right? So we know that from a RevOps budget perspective, the people costs, the people that you have kind of running those revenue operations activities account for about 59-60% of the overall budget.
And so the numbers that we’ve seen here is that they’re actually increasing the investment in headcount. And so when we did this research last year, it was really only 29% of companies that were looking to increase that headcount. Whereas the research for 2024 shows that it’s 66%. So a significant increase in those companies that are looking to invest additional resources into Revenue Operations.
And the reason behind this is likely to tie exactly back to that goal around profitability, right? Which is, Revenue Operations as a team unlocks scale for the business. It is a lever that you can then make the sales teams, make the marketing teams, make the service teams much more efficient in how they’re going to market.
I think this investment here is really about building that scale for an organization so that you can drive higher revenue growth, improve your rule of 40 as an example. And so it might seem counterintuitive that you are investing more money here. The expectation is that with that investment, we’re now unlocking a lot more growth for the organization itself.
Along those lines, the third team is really about how do we measure that effectiveness, right? And so metrics and insights that come from those metrics are critical when a leader is making decisions,. What we’ll talk through here is a little bit around the KPIs that are really being looked at in real time tracking and reporting dynamically.
When we look at these, really, we talk about what’s a metric that’s being looked at in real time. So that might show up in what could be a power bi dashboard or a tableau dashboard and some of the most important KPIs that we’re seeing our clients track dynamically really around customer lifetime value, customer attention and existing customer expansion too.
These KPIs, what’s common between those? They really link back to what’s the health of a given customer that we have, which is really going to affect how an organization can expand, grow and retain those customers too. For a Revenue Operations function, greater visibility into those metrics is really critical to have this real time view so that you can inform the decisions that we’re going to make and really be that connective tissue between what we’re learning from my customers and connecting that back to insights for executives as well.
Ann Marie Verhamme: Obviously the elephant in the room, our fourth theme around artificial intelligence. So we see that over half of organizations are either piloting or have already piloted AI in some form. And we expect investment and adoption to change rapidly over the next six to 12 months.
So million dollar question for you, Mitch. If an organization is trying to figure out where do I go first, what’s the first step in our journey to an AI-enabled Revenue Operations function? What does that look like?
Mitch Edwards: Yeah, maybe to answer the question with the question back is where’s the priority, right? And so again, I think you hit on the growth plays in our opening, which is everyone is talking about artificial intelligence at the moment. Everyone wants to make that investment, but we’d push our clients to say, well, should you be making that investment? And is the AI project, the most critical one for your commercial organization right now? So where we push our clients to think about this is really linking back to the idea of growth pays.
What are those initiatives that are important to the organization? For one company, it might be driving higher rep productivity. For another really might be around resolving customer satisfaction as an example. And so certainly there’s some AI-based initiatives that you could undertake to help address some of those, but there might be bigger elements around how we re-segment our customers, or do we need a different type of role or a different profile in our sales team as an example.
So certainly think about AI investments in context of all the other initiatives that you might take on. So that’s really how we want you to think about making those investments.
Ann Marie Verhamme: Yeah, and it seems like that’s an important caveat because AI isn’t going to solve something that’s broken right now.
So we heard from a revenue leader recently that automating a broken process results in a broken automated process. You alluded to wrap productivity as a potential challenge. You might look into solving it by how you segment your customers first and then apply AI to get a bigger lift.
Mitch Edwards: Yeah, that’s exactly it.
I mean, especially with the broken process you talked about, maybe your first initiative is around let’s build our data lakes or let’s solve some of those challenges with the broken process before we go down the path of AI. I guess with that said, people probably want to know what they can do with AI.
I’d say there’s more straightforward, lower risk things that a company can take on. Something like adopting Microsoft Copilot or getting that subscription to OpenAI’s chat GPT, or one of the other offerings that has that large language model. Certainly something you can implement now that can start working towards productivity uplifts where a rep or a marketer can now do something that used to take them a little bit more time, a little bit quicker, and maybe a little bit more tailored to who they’re reaching out to as an example.
But if we dig into RevOps specifically, let’s talk about two areas where there are real applications for artificial intelligence for those teams today. So the first one is really around the sales forecasting process, generally a challenge for a lot of the organizations we work with. And so traditionally, this process would really be around we’re running some predictive models on historical sales data. It might be pricing data, pipeline data, as well as some assumptions that we have about the market. That insight can feed into the CRO and be used to make a lot of decisions, whether that’s headcount planning, territories, quotas and so on.
The advantage of those more basic predictive models is that they’re very easy to understand, but they’re not necessarily as flexible or accurate as they might be with artificial intelligence. That’s one area where AI can be applied to really help CROs and revenue operations leaders. And so the approach here would be, let’s look at how we can improve our top-down planning by incorporating much more and newer kinds of real time information that we might not have been using in the past.
So this could be things around industry trends, customer trends, it might be taking NPS scores and customer surveys as an example. It could also incorporate market sentiment. So you might go through references that your company has, messages that are out there publicly on LinkedIn or news articles and so on.
What you could also fold in are things around churn information or things that come from your telemetry data for how your customers are using your product. And essentially you can load all these in to a machine learning-based algorithm to really help improve the accuracy of those forecasts day to day, rather than those simpler models, which again, the benefit is simplicity, ease of understanding, but don’t necessarily capture or unlock all this information that exists out there that you could be using as part of that process.
The second area we’ll talk about where AI can be used is around segmentation. And so every company has some kind of a segmentation strategy. Most either look at splitting their customers around some basic firmographics.
Typically, either revenue size of those companies or number of employees, but very few companies go down the path of looking at what you’re really interested in, which is the opportunity or how much we can sell to a given customer. And the challenge with that is, as a person, as a seller, it’s very difficult to look at this huge list of things we know about that account and really derive what’s the opportunity from that.
That’s another application of where machine learning can really help drive a lot more insight into those customers. One example of that is around cluster analysis. Where again, we can load in a lot more factors or information about a given client. It might be how they’ve spent with us, the products they bought, who they’ve engaged with.
So once we have these clusters, then we can look at for each of those cohorts. What really correlates with maximum spend? We might see that one company isn’t buying a certain product that most of the other companies are that creates an opportunity for our teams to then go in and focus on that additional product.
Ultimately, by going down this path of using artificial intelligence for segmentation, we’re now able to unlock richer insights that we may not have seen without more algorithmic-based predictive approaches that we’ve used in the past.
Ann Marie Verhamme: Thanks for the insights, Mitch. So to summarize a few things that you should be considering for your Revenue Ops org.
Organize your Revenue Ops function to drive those most impactful growth plays that are aligned to your go-to-market strategy.
Invest in the tools and the talent necessary to enable that function. So you want to have the right people, the right tech stack in place that allows you to execute those growth plays.
You want to ensure that you’ve got the right KPIs and data insights so that you’re making decisions to drive that go-to-market strategy, but that you’re also using that data to assess the effectiveness and to course correct what your revenue outsource is doing.
And then the final piece is obviously AI. Leverage AI to accelerate that profitable growth. Invest in AI that are aligned to those use cases.
Mitch Edwards: All right. So if you’ve made it this far with us, we appreciate it. Hopefully you picked up a few tips that you can take back to your organization.
If you are looking for more insights on Revenue Operations, benchmarks for your teams, information about our events or our community, feel free to head to our website www.alexandergroup.com and you can find a lot more information and get in touch with us from there.
Thank you so much and we look forward to sharing more in the future.
