ActiveCampaign VP of Product Marketing Jackie Palmer’s Exclusive Interview with MarTech Pulse on Autonomous Marketing

Jackie Palmer, VP of Product Marketing at ActiveCampaign, has 20+ years of MarTech experience, shows how disciplined product marketing, AI innovation, and customer obsession fuel scalable growth and autonomous engagement.

Can you share your transition from baking aspirations to 20+ years in MarTech, highlighting key roles at Infor, Demandbase, and now ActiveCampaign that shaped your product marketing leadership?

It definitely wasn’t a straight line. In elementary school, I was convinced I was going to open a tea and scone shop. I loved the idea of building something thoughtful and personal. I later realized that waking up at four in the morning wasn’t for me, but that instinct to create something meaningful and customer-focused never went away.

I found my way into tech somewhat unexpectedly, starting in customer Analytics before marketing automation was even a defined category. I’ve always had a strong analytical mindset, and early on, I focused on understanding customer behavior and how businesses could connect with them more effectively. As marketing automation emerged, it felt like a natural progression. It brought together data, personalization, and one-to-one engagement in a way that aligned with both the analytical and strategic sides of my background.

At Infor, I deepened my experience in enterprise technology and complex product portfolios. At Demandbase, I helped scale the business from a single-product company into a multi-product platform, refining positioning and aligning product marketing closely with revenue growth. Now at ActiveCampaign, that journey has come full circle. We’ve evolved from traditional Email Marketing into autonomous marketing, where AI actively suggests and optimizes how businesses engage with their customers. It’s the same core mission I’ve been drawn to throughout my career—helping businesses better understand and connect with their customers—but the technology that powers it has become far more intelligent.

With your deep background in account-based marketing and customer experience, how does Active Intelligence leverage AI to evolve beyond traditional automation for SMBs in 150+ countries?

Traditional automation is built on static workflows and predefined rules. It relies on the marketer to understand what they need to implement to achieve their goals. What we’re building with Active Intelligence goes much further; it’s designed to understand intent, ingest and learn from behavioral data, and continuously optimize campaigns based on performance and goals.

For SMBs, that’s powerful. Many don’t have the resources to fine-tune campaigns across email, SMS, and WhatsApp manually. With Active Intelligence, marketers can describe their goal in plain language, like increasing repeat purchases or improving retention, and AI agents translate that into audiences, content, and cross-channel execution.

The result is a shift from manual configuration to goal-based orchestration. Instead of asking, “How do I build this workflow?” marketers can ask, “How do I drive more repeat purchases?” and let the system help execute against that objective.

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Following ActiveCampaign’s acquisition of Feedback Intelligence, how will this transform raw AI conversations into continuous learning loops for better intent detection and user outcomes?

Historically automation has relied on fixed rules and static reporting. Feedback Intelligence enables us to move beyond that model and treat every AI interaction as a learning opportunity. Each conversation contains signals about the user’s intent, satisfaction, friction, and outcomes. By structuring and analyzing those signals, we can continuously train and evolve our AI agents to meaningfully improve performance.

Marketers are increasingly prioritizing systems that don’t just automate tasks but actively improve over time. By embedding feedback loops into Active Intelligence, we can evaluate conversations at scale, identify patterns behind successful engagement, and automatically refine intent detection and recommendations. This gives us deeper visibility into what’s actually driving results, not just surface-level engagement metrics.

The outcome is AI that adapts in real time. As user behavior and market dynamics shift, the system continues to learn and optimize in context. Instead of requiring constant manual adjustment, marketers gain an infrastructure that grows smarter with every interaction and becomes more reliable over time.

Your work scaling Demandbase from single to multi-product post-acquisitions doubled revenue to over $250M how did maturing product marketing from 2 to 8 team members drive that hyper-growth?

When we began integrating acquisitions at Demandbase, the complexity increased overnight. We moved from a single-product narrative to four distinct product lines serving four unique personas; two product marketers simply couldn’t carry that load. One product alone required dedicated ownership from more than one person, and as we expanded into new industries, we also needed vertical expertise. Scaling the team to eight allowed us to build both the overarching platform story and the individual product and industry narratives—while partnering closely with sales and customer success to support a true land-and-expand motion.

That structure was critical to unlocking growth. As the portfolio matured, customers could adopt multiple solutions within a unified ecosystem rather than stitching together diverse vendors. The value proposition became clearer: shared data, streamlined workflows, and faster time to impact. For customers, that meant easier expansion. For us, it translated into higher lifetime value and more predictable revenue acceleration. The hyper-growth came from aligning product marketing to that expansion engine.

At ActiveCampaign, I’ve applied many of the same principles, even with a more concentrated portfolio. We support multiple personas and expansion paths across offerings like WhatsApp, SMS, Postmark, and ActiveCampaign HQ, so product marketing must balance focus with flexibility. We use a “major and minor” model—each team member owns a primary area while also contributing to shared initiatives or adjacent products. We also run buddy programs that pair product marketers with specific teams from our Customer and Growth org, matching them to a region or segment to provide dedicated support. And we have team captains who own cross-product priorities–things like community programs and our product launch process–so those initiatives have clear accountability. That structure creates depth without silos, ensures coverage as we grow, and builds a mature function capable of supporting both platform storytelling and revenue expansion.

Looking to 2026, what autonomous marketing advancements do you predict for Active Intelligence, especially in real-time campaign optimization across email, SMS, and social channels?

We’re moving toward a world where campaigns continuously optimize themselves in real time and increasingly initiate the next best action on their own. Subject lines, content, send times, and even channel mix will be adjusted dynamically based on engagement and predictive signals, with autonomous interactions enabling the system to act rather than simply recommend.

 Rather than manually sequencing channels, the platform will determine where each customer is most likely to engage—whether that’s email, SMS, social, or messaging apps—and adjust accordingly. The shift is from reactive optimization to forward-looking orchestration, where outreach aligns to emerging intent before performance declines or opportunities are missed.

I also expect tighter collaboration between specialized AI agents. Segmentation, content, and performance agents will operate as a coordinated system, continuously sharing signals and refining outcomes. That narrows the gap between insight and execution while freeing marketers to focus on strategy and creativity instead of manual coordination.

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For MarTech decision-makers seeking revenue growth, what strategies from your GTM advising at Scale Venture Partners would you recommend to maximize AI-driven deliverability and multi-channel ROI?

The teams seeing the most impact from AI aren’t treating it as a feature layer on top of existing campaigns—they’re using it to fundamentally speed up how they learn. The cost of experimentation has dropped so dramatically that there’s little reason not to test aggressively. Segmentation, sequencing, creative, send times—these can all be optimized continuously. If something doesn’t work, you adjust quickly. If it does, you scale it immediately. My advice to any martech leader: don’t be afraid to try things. Ask your peers, even across industries, what they’ve experimented with and let that inform your own approach. That velocity becomes a growth advantage.

At ActiveCampaign, we’ve intentionally leaned into that approach. Over the last year, we’ve operated with the freedom of a smaller company while maintaining the discipline of a mature team. We test broadly in both product and go-to-market, using real performance data to refine targeting and messaging in near real time. That same philosophy supports how we’re building our autonomous marketing platform.

Deliverability and multi-channel ROI ultimately come down to relevance. Earlier in my career, personalization relied on rules-based models that had to be manually recalibrated. Today, AI continuously interprets behavioral signals and automatically adjusts messaging, cadence, and channel mix. When communication reflects real customer intent, engagement strengthens, inbox placement improves, and conversions follow. In an environment where customers expect personalization by default, the advantage goes to teams willing to let AI learn—and act—at machine speed.

What practical suggestions do you offer MarTech leaders for generating sustainable revenue through AI agents that provide verified insights beyond basic metrics like thumbs-up ratings?

Start by defining success in terms of business outcomes, not surface-level engagement. Conversion lift, revenue impact, and retention tell a far more meaningful story than clicks alone.

It’s also essential to build closed-loop systems where AI recommendations are measured against real performance data. That accountability is what enables continuous improvement.

Finally, AI needs to provide context. When marketers understand why a recommendation is being made and what outcome it supports, AI becomes a trusted growth partner, not just a productivity tool.

Thank you, Jackie Palmer,  for taking the time to share your insights with us.

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