Inside Datadog’s ABM machine
w. Kevin Driscoll, Global Head of Campaigns & ABM
I honestly can’t believe he shared this with me. But somehow I convinced Kevin Driscoll, Datadog’s Global Head of ABM & Campaigns, to show me exactly how they book 25 meetings/week with the Fortune 500 with one of the most impressive & sophisticated ABM machines I’ve ever seen.
This is ABM done right.
Their universe of accounts is in their CRM, all scored & tiered by ICP-fit.
Every week they prioritize the people most likely to book a meeting at those accounts
Then they run hyper-segmented & relevant campaigns across channels at these contacts based on the specific, unique intent signals they’re showing.
And it is crushing, one PG sprint they did helped an SDR team hit 75% of their monthly meeting quota in a day.
This isn’t a simple strategy, and there is no chance you’ll be able to replicate it (he had 12 data engineers help him build out his signal/scoring infra in Snowflake) but I guarantee there will be one or two golden nuggets you’ll be able to steal and implement in your ABM machine right now.
Let’s get into it.
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Meetings are all that matters
My favorite quote of the whole conversation:
“Yeah. I did Demandbase for a while, and I was like, well, I’m running display and the accounts say we’re warm now. And I was like, none of this shit matters.
When I went to talk to salespeople, they were like, dude, you’ve gotta give me some freaking meetings.
So I just started optimizing everything for meetings. Because if you optimize for pipe, it takes too long. If you can get meetings, they’ll turn into pipe eventually. Sales will figure it out.
The moment everything got easier was when I stopped optimizing for ‘warm accounts’ and started optimizing for meetings. If you can get meetings, pipe takes care of itself.”
Yes, you can find warm accounts (“Disney visited our website”) but if you stop there you immediately lose the trust of the sales team (“OK, who at Disney should I reach out to? What should I say? Why?”). And once you lose it, it’s hard to get it back.
It makes sense why so many ABM teams stop at accounts though, finding the actual people who are most likely to book a meeting, and figuring out what to say to them, at scale, is really hard.
But this is what sets Kevin’s machine apart. I call it his “MLTBM (most likely to book a meeting) list”.
How Datadog builds a weekly ‘MLTBM’ (most likely to book a meeting) list
If you want to give every SDR on your team a list of MLTBM prospects, with concrete signals (reasons for reaching out) and relevant messages for each, you need to focus your machine on people, not accounts.
Kevin does this with signals.
They map signals that actually translate to intent
They track these at scale across their universe of ICP accounts
They bubble up MLTBM contacts showing these signals
And every week they send them to the right reps to work them and push them into contact-based ABM plays
Tracking signals that actually translate to intent
The most important variable in booking meetings is timing. A “mid” message to the right person at the right time beats the perfect, highly researched message to someone who has no need.
They find those prospects with signals. They track anything that might be a proxy for intent (timing being right) for prospects at ICP accounts.
Product usage, de-anonymized GitHub repo usage, open jobs signaling a spike in tech complexity, ad engagement, microsite engagement, etc. They track everything they can across their universe of accounts, and Kevin is constantly testing new signals.
Synthesizing signals into MLTBM lists for sales
But they can’t just send all these leads to sales. So every week Kevin pulls all these contacts into spreadsheets, uses prioritization (signals have different weights depending on corresponding business value and account value), some AI, and frankly, his data engineers, to compress all the complexity into a simple list of 15-20 prospects/rep each with concise ‘reasons to reach out now’.
They then “write” ai driven outbound cadences that take account and behavior context and match to each person to improve ease of use for sdrs.
Actioning on the MLTBM list with ABM plays
He “surround sounds” these contacts with ABM plays.
He pushes all of these contacts into Vector, and then into ad campaigns (personalized to the specific pain point, segment, and persona) across channels. Vector lets them target these specific prospects on channels like Facebook, IG, Reddit, YouTube, etc. It also tracks who is clicking ads (another signal that can move a contact into a MLTBM list for sales outreach).
On LinkedIn they run account-level ads driving to microsites.
And they run direct mail campaigns to these MLBTBM contacts focused on booking meetings.
The power of Niche Signals (Signal Alpha)
I wish I could share all the interesting signals Kevin looks at but A) I can’t and B) even if I could, I’m not sure it would be valuable because Datadog has lots of product usage, GitHub engagement etc that us normal folks at non-PLG companies can only dream about.
One of the most inspiring parts of Kevin’s machine though, that we can all steal, was what Brendan Short calls “Signal Alpha”.
Signal Alpha is the unique advantage you get from niche signals– the one or two unique signals that only you care about because they translate directly to intent for your business alone.
Datadog’s best customers are people with spikes in “tech stack complexity”. When their stack gets complex, they need a tool like Datadog to observe and manage it better. A niche signal for them might be “Hiring a Snowflake Engineer” because that signals that the company is investing in Snowflake, so their tech stack is getting more complex, so they need a tool like Datadog. So you might want to run contact-level ads talking about how Datadog helps companies deal with “Snowflake-induced tech stack complexity” with sales outreach to boot (forgive my complete butchering of these technical terms lol, but you get the point).
Your company might serve companies that need to stay compliant with multiple security frameworks. And because your company is uniquely positioned to serve those customers (because you have auditors in-house so your customers work with just you, instead of one external auditor/framework), you win all those deals. So you track job postings for mentions of multiple frameworks, and then you run ads talking about how you’re uniquely positioned to help them, with sales outreach to boot.
If I worked at Vector I’d look for companies with recent increases in # of ads live across channels. Then I’d run ads + outbound to them talking about how Vector helps you run ads w B2B level targeting on B2C channels w. 50-90% better match rates & targeting.
These are all niche signals. Not valuable to anyone but you, but that means when you run messaging against them, you’ll stand out.
I absolutely loved diving deeper with Kevin, and I hope you found his playbook as valuable as I did.
With love as always ❤️,
Eric
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