Powering ABX with Account Prioritization: Lessons from Deep Instinct

We have the privilege of working with some of the most cutting-edge and accomplished GTM leaders in SaaS. These leaders and their teams at fast-growing companies like DeepInstinct, Esper, Hiya, Carrot Fertility, iSpot, and more have helped shape the Relevvo product. Here’s the next post in our series where we share some of these gathered insights with you.

Today we’re thrilled to welcome Theresa Woodiel and Bryant Pulecio from DeepInstinct. Theresa Woodiel is the Director of ABM and Integrated Marketing at DeepInstinct. Theresa has been practicing account-based marketing since before ABM existed as a term. Theresa has more than twenty years of experience as a marketing leader who has led efforts to accelerate growth through ABM and develop innovative digital ways to reach and educate, and enhance the brand value.

Bryant Pulecio is the Director of Web and Digital Strategy at DeepInstinct. #WebTech, #MarTech, #ABM, #AdTech, #WebOps, and #MarketingOps best describes Bryant’s last 15 years of experience. As the Director of Web and Digital Strategy, he oversees the website, marketing technology stack, and digital experience of prospects as they flow the buyer’s journey.

In our chat, we cover the new world of doing more than less in ABX, the importance of getting account selection right, taking a data-driven approach to account prioritization, selecting the right signals for account prioritization, and how Relevvo is helping the team drive business impact at Deep Instinct.

Aashish Dhamdhere (AD): Bryant and Theresa, Thanks for joining us today! Let me start by asking about the new world of ABX, which is all about doing more with less. As it turns out, this is something that I’ve seen you do since we started working together a year back. Can you share your guiding principles for this approach, especially as it relates to the importance of account selection?

Theresa Woodiel (TW): If you go back 15 years, you had this idea that if you kept feeding the top of the funnel, you would get results at the bottom of the funnel. We know now that this is not true. That top-of-the-funnel demand doesn’t always translate into closed-won business. The big a-ha here is that account selection at the top of the funnel is absolutely critical in driving business results. 

Now, with account selection, it used to be that Account Executives would just select their accounts and then let everyone know. But, of course, just because you have a “gut feeling” doesn’t mean that the account is in a buying motion. The second big a-ha here is that account selection needs to be done based on a data-driven process.

Corporate Executive Board, back in 2007, shared that something like 60% of the Buying Journey is already done by the time that the prospect connects with Sales. You just can’t afford to ignore the first two-thirds of the buyers’ journey. What we’re doing at Deep Instinct is paying close attention to the right prospect signals to pick the right accounts. We don’t stop there, of course. We then work to get the right message in front of the prospects at the right time.

AD: I know you’ve both selected the signals that you use for account prioritization very carefully. Can you share more about that approach?

Bryant Pulecio (BP): Yeah, I want to start by strongly agreeing with what Theresa shared earlier. I’ve been in teams before where there has been a big disconnect between marketing and sales goals and what actually drives revenue for the company. Even today, there are still a lot of marketing organizations that focus on, to Theresa’s point, putting more emphasis on the top of the funnel with the end-all-be-all goal of more MQLs. 

The challenge with basing your goals only on MQLs or even SALs is that with typical B2B sales cycles being in the 6-months+ range, you’re not going to be able to track the impact on revenue. What you have to do is, as Theresa shared, track the digital body language of the prospect. You use these signals to guide your ABX and outbound investments.

This is something that Relevvo is helping us with! You’re helping us track signals in a completely new way. We can now see patterns and develop approaches to target accounts way upstream. This not only helps us understand which accounts are moving in the right direction but why they are going in that direction as well. 

AD: Love this! So to recap, you really have to get your prospect early in the buying journey to influence the buying process. And you need to tie the signals that you’re using to target prospects to the same ones that are driving revenue for you today. How are you selecting and scoring these signals?

TW: Bryant and I talk about this all the time. Our key shared observation is that lead scoring the way that it’s traditionally done isn’t accurate at all. You can’t just arbitrarily assign points to things, and there really needs to be an industry-wide evolution of this. So, what do you do instead? 

The best path forward is through correlations between signals and accounts. It has to be the combination of the pre-intent digital body language signals and activity. Because the activity that you do to engage that account is going to drive an outcome that then drives a business result. That’s the approach that we’re taking with your help, and it’s allowing us to be a lot crisper and more accurate in how we prioritize accounts, time, resources, and people. 

BP: To add to that, as it applies to ABM, you stop looking at MQLs and start looking at MQAs. So it’s natural to start thinking about account scoring versus the lead scoring model. This is a very important point because you want to aggregate the signals that you’re seeing all back to the accounts. However, when you start crafting your account scoring model, you want to take care not to get lost in the wrong details. You could start to lose people in discussions like, “well they’ve been on our website in the last 30 days so shouldn’t that be 5 points vs 2?” or something like that.

But the point isn’t whether it should be 5 points or 2 points, but more importantly that you establish a bar, based on say 5 or more visits in the last 30 days. Calculating engagement of just website activity is easy, but creating a composite score of vastly dissimilar data, becomes the challenge. This is why we simplify to 5+ website visits in the last 30 days, yes or no, so we can then compare against other attributes like 3+ engaged people on the account, 3+ instances of high keyword intent in the last 30 days, G2 activity in the last 90 days, etc. 

This allows for simple scoring that sales can buy into, and avoid the days of old when marketing would throw a bunch of “MQLs” over the fence and sales would complain about wanting more leads. This older mindset creates a lot of friction because marketing isn’t listening to sales and is defining its own measure of success that doesn’t align with revenue. 

But now, one of the really exciting things about Deep Instinct is that we have a richness of data to be better aligned. Using the data from Relevvo, we know which accounts we should be going after for both sales and marketing and we are able to work better together. 

AD: Super actionable input, thank you! I’d like to now talk about your account scoring model because it’s the most sophisticated one I’ve ever seen. I’d love to get a sense of how you think about it and then what it’s taken for you to pipe all of it together. Kudos to you both by the way because I have a sense of the hard work that it’s taken to get here.

BP: Yeah, well, I guess, you know, that’s, the dynamic duo in action here! Theresa’s experience and learnings over time combined with the tech stack we have now really made it all come to life. There was a lot of trial and error involved but we used this capability within DemandBase that not everyone necessarily leverages. It’s the data stream capability for building custom connections to model data outside of the platform. That was something that we had to speak with them about to set up but we ultimately had it piped into BigQuery. 

Then, in order to simplify the back end, I created a series of account lists in DemandBase that pretty much defined the ones and zeros weighting exercise. You were either on an account list or you weren’t. I think we have like 14 or 15 different ones but it helped simplify it so now I just have to run a BigQuery SQL query to return the list of accounts in each of those DemandBase account lists. Then we do the pivot table to elevate the accounts that show up on the most lists. The more lists they show up on, in this set of 15 or so, the higher the score. Then, piping that information into Relevvo further gives us another level of intelligence. 

TW: Totally, if I may just build on that, I’ve never met anyone like Bryant before. Just the intersection of skills that he has is amazing and has been extremely influential in us figuring this out. So that’s one piece of it and the other thing, coming to his last point, at the end of the day what we’re looking for is which accounts are the super high intent, which ones are the mediums, and which ones are the low ones. We are trying to get a more nuanced view of intent data and the prioritization of our resources. 

AD: Super sophisticated and I really appreciate the breakdown. Hopefully, it gives the other ABX and RevOps folks out there a template that they can also follow. Last question. Super curious to hear how Relevvo has helped you in this journey.

TW: We have high pipeline and revenue targets, and we can’t just rely on intent or think about all intent as being equal and prioritize accounts that way. We have to differentiate. That’s why Relevvo has been so helpful because it allows us to remove bias and guessing from the equation. 

We’ve removed the normalization of data that a business intelligence group would normally have to do because Bryant has done it. We’ve set these thresholds working with you guys to have a simple account score. It helps us to understand the technological crossing of the chasm. Who are the innovators and early adopters and how do we foster a relationship of education as it relates to our messaging and positioning in the market.

BP: Relevvo’s technology has been especially helpful with that last point there, about who the early adopters or innovators are. Whenever you’re trying to go in and replace another technology, especially with IT, it’s never an easy sell. People typically try to avoid the whole rip-and-replace thing unless the pain is extremely high or there is someone there that wants to update to the latest version of that technology. Evidence of high pain is especially difficult to find until you start talking to someone on the phone but knowing if someone is open to change or looking to innovate is a little easier using the technology available. So that’s what you’ve really been able to help us find. We can now locate those early adopters and innovators by combining all of these signals of implied intent. Sorting that compiled account list based on behavioral attributes, tech stack, and other signals that you provide to help us accurately apply scores. We then pipe those accounts out to the SDRs and go from there.

AD: This has all been really fascinating and I think we should probably have another call on this. This will be our first multi-part blog post. Let’s schedule some time to dive into these points in more detail. This would be massively helpful for anyone in an ABX position or otherwise. Thank you for your time, this has been really fun!