- June 7, 2022
- Intent Data
Big Claims vs. Big Levers: Illusions of Sophistication Can’t Beat Real Purchase Intent
In everyday life, when we make a substantial purchase like a car, we get to “look under the hood.” But in much of martech, and it seems to me especially in the B2B intent data category, that’s often not the case. And that’s a problem for you as the potential buyer.
In everyday life, when we make a substantial purchase like a car, we get to “look under the hood.” But in much of martech, and it seems to me especially in the B2B intent data category, that’s often not the case. And that’s a problem for you as the potential buyer.Just the other day, I saw a public admission from a vendor’s sales team on LinkedIn, saying: “You don’t have to guess with us anymore!” That jolted me. I really wonder: “Is anyone out there paying attention to how this organization just publicly admitted that before some new change, their product actually left you guessing?!” Or are buyers so worn down by these ongoing illusions of sophistication that they fail to recognize real product limitations?
Here’s the reality I see: Since so much of what’s positioned as intent data is gathered from many far-flung, arms-length sources (and then further manipulated by opaque algorithms), you’re not able to evaluate it very transparently. Neither you, nor the supplier, can clearly connect what’s sold as a “signal” back to exactly what content supposedly triggered it. Simply put, there’s no way to double-check that what you’re buying is real purchase intent – or even that it’s a business-relevant behavioral signal at all. This blog is partially about being wary of enticing buzzwords (particularly the specific case of intent data), and partially about how we ourselves have been working to focus our teams better on what matters most to the business and our clients right now in everything we do.
All intent data is not the same
Obviously, I’m biased here because of the unique model TechTarget offers, so definitely don’t passively take my word on this. But let’s get it out on the table. I’m not saying you should work with just one intent data source and that we’re the only game in town. A recent communication I wrote struck a nerve with some because it exposed real questions you need to think about. I’m always trolled about this stuff, so let’s get those pundit retorts out of the way right here: their unsupported claims about TechTarget having limited data, the nonsensical canard about “walled gardens,” etc. Everyone can write whatever they want. When they do, the “illusions” they present simply become that much more visible. I’m writing this piece, and you the customer can know what to do if you’re buying data from other providers. You should ask your suppliers to show you exactly what triggered a given signal that they’re claiming is business relevant. From what they’re able to show you, you can then decide for yourself how confident you should be about its “fidelity” – meaning, how directionally helpful you think it really is.
My contention is that since so much of what’s being positioned as “intent” isn’t something you can investigate to this degree, you really have no way of knowing how tactically useful – how actionable – it is for your go-to-market teams. And if you can’t be sure of what’s really in there, as a sales guy, I’d be hesitant to commit my resources to pursuing it without much better assurances.
We’re one of the very few providers that can show you exactly how our data is created. We can show exactly who took what actions, what the content is actually about and what else each of those people cares about. We can do this because we’re the publisher of the content they’re consuming and because we have opt-in permission. As I think is true in a variety of categories, the reality with intent data is that better quality comes from business models specifically designed to create it.
Our formula for better data is very simple: It takes great content to attract the right audiences. The right audiences rely on the content sources that best serve their buying research needs. They exchange tracking permission for access to this information, which allows us to share their profiles and behavior with you. It’s conceptually very simple, and super powerful, but very hard to imitate.
Data is one thing, but systems are another thing altogether
So that’s some straight talk about intent as a standalone addition to the stack. But I also want to go a level beyond the idea of your being really rigorous about the data sources you add. I want to extend this idea to touch on some observations I’ve made (especially recently) about adding “big promises” into your complex RevTech stack. (By “big promises,” I mean complicated solutions that profess to “do it all in one platform”).
Like many of you, I’m an avid follower of Forrester analyst writings on topics that relate to GTM improvement and RevTech. The recent four-blog series from Terry Flaherty (and especially this one) goes into great detail about some chronic shortcomings of classic marketing lead management approaches. With reference to this conversation, the thing I’m making sure that every member of our GTM teams takes away from Terry’s blog is that, while leads and account engagement are both important, what my salespeople really need is insight into buying groups. Where this comes home to roost (with respect to the sophisticated new technology that many of them might want to add) is that unless you can show me precisely how it will get us a major step closer to more buying group penetration, I’m very reluctant to add more tech right now.
Do we really need another system? Will that system get us where we need to go?
As you know, every time you add a major piece of tech into your stack (assuming you already have the extra staff bandwidth to install and use it in the first place!), the budget’s gone and the implementation takes both time and focus away from other things. There’s just a lot of sunk cost before you’ve got anything significant to show in terms of business improvement. To be specific, right now I am being very clear with my peers and team on the point that, from where I sit, tech that can’t clearly and transparently find me more opportunities or help me create and accelerate more opportunities has got to be of lower priority. And I feel so strongly about this that I’ve made sure my CMO is totally on board with respect to our shared plans for RevTech stack additions. It starts with agreement on desired outputs and outcomes, but now it also includes staffing allocations required for implementation, maintenance, usage and so on.
Here’s where the data pieces of my thinking and the systems-tech considerations come together. Right now, we’ve got a pretty good handle on how much activity we need from each of our GTM teams across our shared value chain and processes. We’ve got plenty of automation-oriented tech in place and an operating model strategy for smart ways to scale. If we decide we need to do more, we don’t look immediately to add more tech to do it. Instead, we’ve grown more focused on getting better results out of our core activities, both topline and bottom. We’re pursuing this strategy by raising quality in the core. Specifically, we’re looking to be more and more relevant in all our interactions. We’ve realized that this requires a greater level of precision – first in how we understand the customers and their needs and then in how we execute against it.
Executionally, to find and secure more opportunities, we need to go deeper than “the account,” and we need to go broader than “the MQL.” This realization is driving much greater interest in the insights we can glean from data sources, the messaging/content we build out from there and both the formats, the channels and the teams we action this through. These are relatively basic, but very high-leverage concepts. Whereas we obviously have a huge need for the most precise data possible, our systems needs are actually quite straightforward: we need to execute the right way based on what we can see occurring in a buyer’s journey and within each individual player’s behavior and preference. As long as we have the data, we can do the right execution with the tools we have.
What’s really the right action, which is the right tool for the job at hand?
Interestingly, as we migrate beyond the limits of accounts and MQLs (concepts that I may be as guilty as the next guy of drilling into my marketing colleagues’ focus), what I’m finding is that our teams are starting to align much more organically to each other around opportunities and buying groups. In this process, I’m also observing a growing understanding of differences between macro-levers (like audience-based targeting, awareness-driven consideration and thought leadership) and micro levers (like outreach prioritization, SDR-executed multi-threading, and one-to-one messaging). Our teams are getting good at recognizing where and how in the process a particular capability can add meaningful value to the whole. They’ve grown a lot wiser about reconciling must-haves and nice-to-haves on their own. For example, they get where AI or advanced automation will deliver big efficiencies (i.e. where a lot of routine work is creating administrative overhead) vs. where the actual business benefits of a “hot” idea remain relatively modest.
Building on this, there’s a growing awareness of who (which team) and which tactics (i.e. marketing vs. sales) are most powerful for which objectives. For example, our marketing team is great at crafting messaging. We definitely need to expand on how we leverage those skills – not by buying tools that focus on increasing top-of-funnel activity volumes, but instead, by connecting them in better to where we have the resource to execute real personalization, i.e., into our sales development and field sales motions.
To get to the one-to-one personalization we seek in sales interactions, and to better leverage messaging into this process, Marketing needs to be fueled by the same level of precision that salespeople themselves require if they are to take more precise action with complete confidence. We can’t get to where we want to be if we leave a large gap between how Marketing delivers and what Sales requires to prioritize its own next best actions. MQLs must be tied to buying groups (that means included into a better opp package!), MQAs are meaningless (the accounts are probably already in our ICP!) without clarity on the buying group players and their needs are precisely relevant to the active, visible buyer’s journey at hand.
The net-net that I’m seeing right now is that the past 24 months or so have really helped educate us as an organization. We’ve become much more focused and pragmatic. As a result, our own RevTech requests have been evolving significantly. Our people have become much better at connecting what we need as a more unified GTM team to what they can see when they really look under the hood of any given solution. We’re much less attracted by flashy functionality that might benefit one sub-group but offers little real impact on what the whole needs right now to better meet its shared objectives. I’m proud of all of us for our growth as businesspeople these past two years. I know the company is stronger for it and I think it puts us in a position to provide even better advice and better service to our clients as well.
Steve Niemiec is TechTarget’s CRO. He can be reached here.
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Actionable intent data signals, intent data, real intent data, revenue technology
