Anjeli Jackson, ABM Strategy Manager & Platform Lead at OpenText, shares practical insights on stage-based ABM, AI, intent data, and relevance-driven personalization. She also explores sales and customer success alignment, revenue-focused ABM metrics, and how marketers can build, prove, and scale high-impact ABM programs that drive measurable business growth.
Welcome to the interview series, Anjeli. Could you tell us about yourself and your journey as a marketer?
I started in fashion. That’s usually the part people don’t expect.
I was at Kent State studying fashion, and what I actually loved about it wasn’t the sketching and sewing; it was the problem-solving. Understanding who you’re creating for, what they need, and what makes them choose one thing over another. I transferred to Ohio State for a marketing degree because I realized that instinct applied to a much bigger canvas.
My first jobs were in sales with IBM and GE Healthcare. That turned out to be the most useful thing that ever happened to my marketing career, because I learned early what a bad lead does to a rep’s day. From there I moved into digital and demand generation at Sovos and now OpenText, where I lead ABM strategy and own our 6sense platform across eight product lines.
So the through line is design thinking plus data. I care about how a program feels to the buyer and how it performs in the pipeline report. Those aren’t separate jobs to me.
And I just began my master’s in AI for Business at ASU, because the next version of this work is going to be built on top of models, and I’d rather understand them than just use them.
You led the first stage-based ABM campaign. What made it successful, and what were your biggest takeaways?
Honestly? It worked because it was small.
I built it as a pilot. One motion, a defined account set, and orchestration tied to the buying stage instead of lead status. So an account sitting in Awareness got a completely different experience than an account in Decision. Different message, different channel weight, different ask. Nobody got shoved toward a demo in week one because they downloaded a guide.
The reason that works is in 6sense’s own Buyer Experience Report. Their 2025 research found that 94% of buying groups had already ranked their preferred vendor before first contact, and they bought from that early favorite 77% of the time. The split between independent research and seller engagement moved from 70/30 to 60/40. Buyers are reaching out slightly earlier, but they’ve already decided more. If your program only activates once someone raises their hand, you’re competing for a decision that’s mostly been made.
In just 30 days, our pilot drove 27% marketing-influenced revenue. I ended up presenting the model at the 6sense Breakthrough Conference and discussed it in depth during their Blueprint webinar series.
Three takeaways I’d hand to anyone trying the same thing.
- First, stage-based only counts if the creative is staged too. A lot of teams change the audience and reuse the same asset. That’s not stage-based marketing; that’s retargeting with extra steps. I’ve done it. It doesn’t work.
- Second, sales have to co-own the account list before launch, not receive it after. The minute a rep feels like a list got handed down to them, engagement quietly dies, and you don’t find out for a quarter.
- Third, a pilot beats a platform rollout every time. I could have spent six months trying to launch across all eight product lines at once. Instead, I proved it once, got a number, and leadership actually cared about it, and suddenly the scaling conversation was a very different conversation.
How have AI and intent data changed the way B2B marketers identify, prioritize, and engage high-value accounts?
Intent data is table stakes now. Almost everybody has some version of it, which means the advantage quietly moved from having a signal to knowing what to ignore. We solved a data scarcity problem and immediately created a signal fatigue problem in its place. I say that from experience.
Early on, I chased way too much of it. In any given week, I can see hundreds of accounts researching something we sell into, and most of them are not in the market. Some are competitors, some are analysts, and some are one curious person on a Tuesday afternoon. If you send all of that to sales, you spend down their trust in about a quarter, and trust is the hardest thing in this job to earn back.
Where AI has genuinely changed how I work is prioritization and research. Predictive models are better than I am at spotting which combination of signals actually correlates with a closed deal in our own data. And generative AI is really good at the prep layer, pulling an account brief together so a rep walks into a call already knowing the company reorganized last quarter and just hired a new head of infrastructure. Where I’m more careful is letting AI write the actual outbound. Buyers can tell. The leverage is in deciding who to talk to and what they care about. A human should still own how it sounds.
Building effective personalization strategies is critical for B2B engagement. What approaches have delivered the strongest results?
I’d push back on the word a little. The goal isn’t personalization; it’s relevance. We treat those like synonyms, and they really aren’t. Personalization is a first name in a subject line. Relevance is showing up with the thing someone is actually trying to solve this quarter. One is a merge field. The other is a strategy.
What has consistently worked for me is segmenting by problem and buying stage instead of firmographics alone. Two enterprise manufacturers of the same size can be in completely different locations. One is trying to consolidate systems after an acquisition. The other is staring down a compliance deadline—same profile on paper, completely different conversation.
Tactically, that looks like message variants mapped to stage, paid audiences that shift as an account progresses, and nurture logic that never drops a 101 explainer on someone already in evaluation.
The other half of this is knowing where the ceiling is. There’s a point where personalization stops feeling thoughtful and starts feeling like surveillance. Referencing a company’s stated public priorities is smart. Referencing something you only know because you were watching them is unsettling, and I’ve been on the receiving end of that as a buyer. You notice it instantly, and you don’t give that sender a second read.
“ABM’s honest claim was never more deals. It was better deals, closing faster, with more of the committee already convinced.”
What strategies help ensure marketing, sales, and customer success teams stay aligned around ABM goals?
Alignment fails on definitions way more often than it fails on effort. Everyone is usually trying. They’re just working from different definitions of the same words.
So start there. Agree on what an account tier actually means and what happens at each one. Not a doc nobody opens. A real working agreement: here’s the list, here’s who owns follow-up, here’s the response window, and here’s what we do when an account goes quiet.
Then build the list together. Let sales challenge accounts and add their own. Some of those additions won’t be data-supported, and I’ve made my peace with that, because the credibility you buy by including them is worth more than the precision you give up.
Meet on a rhythm, and make the meeting about accounts rather than activities. No seller in history has been moved by an impression count. They want to know which of their accounts got hot this week and what changed.
Customer success is the piece most programs skip, and I think that’s a mistake. Your best expansion signal usually lives inside an account you already have, and CS knows things about that account no intent platform will ever surface. Pulling them into account planning changed how our whitespace motions performed.
The unglamorous version of all this is that alignment is mostly a shared definition and a standing meeting. Everything else is decoration.
Beyond leads and engagement, which metrics best demonstrate the impact of ABM initiatives?
Leads are the metric everyone reports, and nobody believes, including the person reporting them. Here’s what I actually manage to.
Buying stage progression. Are accounts moving toward a decision, and how fast? It’s the closest thing ABM has to a real leading indicator.
Account penetration. How many people inside the buying group are engaged, not how many contacts are sitting in the database? Enterprise deals get decided by committee, and the committee keeps growing. Gartner puts the average buying group at six to ten people for complex solutions. Forrester’s 2024 State of Business Buying Report goes higher, around 13, with 89% of decisions crossing more than one department. So if you’re engaged with one person, you don’t have a deal. You have one opinion. One champion and nobody else is a hobby, not a deal.
Engagement lift against a holdout. Comparing engaged accounts to unengaged ones is flattering and close to meaningless. Comparing against a holdout is uncomfortable, and that’s exactly why it’s useful.
Pipeline velocity, win rate, and deal size on targeted accounts. ABM’s honest claim was never more deals. It was better deals, closing faster, with more of the committee already convinced.
And marketing-influenced revenue, but with a definition you can defend. I’d much rather walk into a QBR with a smaller number that survives questioning than a big one that falls apart the second someone asks how I calculated it.
One more that gets ignored constantly: cost per engaged account. Budget conversations get so much easier when you can talk about efficiency instead of output.
What advice would you give marketers looking to build and scale successful ABM programs?
Don’t start with the platform. And don’t start with AI either. Yes, I hear the irony, given that running a platform is most of my job and I’m about to spend two years studying the other thing.
Both are amplifiers. If your account data is messy and things are tense between you and sales, a platform will amplify both, and AI will amplify them faster and more convincingly. Get your account list, data hygiene, and one genuinely supportive sales leader in place first.
Then start with one segment and one motion, and prove it. A number you can defend from a small, clean pilot will carry you further internally than a sprawling program nobody can explain.
When you scale, scale the process, not just the spend. What strained us as we grew across product lines was never budget; it was operational consistency. And learn to speak in your CRO’s language. Bring engagement metrics to a revenue leader, and you’ll lose the room in under a minute. Talk pipeline, velocity, and win rate.
Two things on AI, since nobody is building a program right now without it.
Learn enough to interrogate the output. You don’t need to build models, but you should be able to explain why an account scored the way it did. “The platform said so” is not an answer that survives the room. That’s honestly part of why I went back to school for it.
And resist the volume instinct. AI has made it nearly free to produce more of everything, and more was never the ABM strategy. Use it for account research, buying group mapping, and summarizing engagement into something a rep can actually read before a call. If it just helps you send more to more people, you’ve automated the exact behavior ABM was invented to fix.
Be patient with the timeline and impatient with the learning. Real results take quarters, but you should know within weeks whether your message is landing and your list is right. Both are fixable fast if you’re willing to look.
About Anjeli Jackson
Anjeli Jackson is a marketing leader specializing in ABM programs, 6sense, digital marketing, and data-driven B2B strategies. At OpenText, she leads ABM programs and the 6sense platform, driving cross-functional alignment and revenue growth. She launched the company’s first stage-based ABM campaign, generating 27% marketing-influenced revenue in 30 days, while personalized 1:1 campaigns increased account engagement 5.4x and web personalization boosted demo form fills by 600%.
