Precision over volume: Rethinking ABM in a noisy market

Digital content floods every channel. Trends emerge and disappear before the last one has properly landed. In 2024 alone, an estimated 402.74 million terabytes of data were created every day. 

More signals. More dashboards. More automation. Meanwhile, attention is stretched thin. Engagement is harder won, and ROI is under sharper scrutiny.

Against this backdrop, ABM (account-based marketing) is reasserting its relevance. Not because it’s new, but because it offers something increasingly rare: focus. The assumption that more data automatically leads to better outcomes hasn’t held up. More data doesn’t mean better decisions. And access to information isn’t the same as understanding it.

ABM works best when data serves the strategy, not when it replaces it. Meaningful results depend on focusing on the people behind the accounts, not just the signals they generate.

When data drives strategy (and why that limits impact) 

There’s no question that modern technology has strengthened ABM. 

Information that once required hours of manual research is now available in seconds. Marketers no longer need to trawl through sprawling datasets to identify target accounts or track engagement. Adoption reflects that shift: 70% of marketers have implemented ABM, with 64.1% reporting revenue growth as a result.

Recent AI advancements have accelerated this momentum even further. AI-driven personalisation is quickly becoming embedded in modern ABM programs, helping teams interpret engagement signals, anticipate intent and tailor experiences in real time. IBM, for example, reported tripling its account identification and doubling its top-tier engagement through AI-powered personalisation in a recent campaign.

The capability is impressive. But capability isn’t the same as strategy. Too often, intent data, automation and dashboards are treated as stand-ins for genuine insight. Complex human decisions are reduced to behavioural signals. What gets clicked becomes more important than why it matters.

Without a clear understanding of customer priorities, pressures and past interactions, campaigns can become highly precise in targeting, yet generic in impact. ABM was never meant to be about tracking everything. It was meant to focus on the signals that shape real commercial outcomes.

Why ABM continues to cut through

ABM’s momentum isn’t accidental. It’s a response to the market we’re operating in.

Customers are navigating a steady stream of templated outreach, much of it AI-generated and indistinguishable. In a crowded market, relevance is what earns attention, and precision is what keeps it.

ABM changes how resources are deployed. Instead of spreading effort thinly across broad segments, it concentrates investment on a defined set of high-value accounts - aligning marketing activity directly to revenue priorities and commercial outcomes. It’s about making deliberate investments where the potential impact justifies the strategy.

AI has strengthened this approach. It brings speed, sharper pattern recognition and the ability to personalise at scale. But without a clear strategic foundation, more data simply creates more complexity.

The most effective ABM programs combine both. Strategy defines which accounts matter and why, while AI allows teams to act on that strategy with consistency and precision.

Deep curiosity: ABM’s real differentiator 

Data and AI may have accelerated ABM’s growth. But it’s not what makes it effective.

An ABM strategy built purely on numbers risks missing what actually drives decisions inside an organisation. Data can tell you who engaged, what they downloaded and when they returned to your site.

• It won’t tell you what success looks like for that stakeholder internally.
• It won’t reveal the political dynamics shaping the buying group.
• It won’t fully explain the commercial pressures behind the enquiry.

That requires something more deliberate. The real differentiator in ABM strategy is what we call ‘deep curiosity’.

Deep curiosity means moving beyond surface-level signals and investing time in understanding your target accounts properly — their strategy, market context, leadership priorities and pain points. 

Metrics matter, but they won’t tell you everything you need to know about your audience. When teams understand the commercial reality their accounts are operating in, engagement shifts from simply reaching the right audience to addressing the issues that actually matter to them.

That’s how you build an ABM strategy that creates a genuine opportunity for your sales team.

Extending alignment to customer success and product 

At its core, ABM has always relied on tight alignment between marketing and sales. The two teams work together to workshop four foundational elements:

1. Establish clear roles and responsibilities
2. Agree on key account selections
3. Share and interrogate account data and intel
4. Develop shared success metrics

But the opportunity doesn’t stop there. Bringing customer success and product teams into the model strengthens ABM’s impact even further. 

Sales understands the opportunity. Customer success understands the lived experience. Product understands the roadmap. Together, they see the bigger picture.

With a shared understanding and AI-enabled insights to support it, organisations can establish more personalised engagement with key accounts, enable earlier identification of risks and opportunities, and ultimately, create stronger long-term relationships built on relevance, trust and value.

AI joins the team line-up 

ABM has always been a team sport. Its strength lies in alignment—marketing and sales prioritising the same accounts, working from the same intelligence and aiming for the same outcomes.

What’s changed is the volume of information those teams are working with.

In a market shaped by overwhelming data and limited attention, AI-enabled tools have become essential contributors. They help surface patterns and prioritise effort with far greater efficiency than manual processes allow.

But tools alone don’t win the game. As competition for attention intensifies, the advantage lies in where to apply your marketing effort and why. AI expands what ABM can do, but it doesn’t determine what’s commercially meaningful for stakeholders. That still requires human judgment.

ABM works best when it’s designed for people. Technology should support curiosity, alignment and relevance, but it’s a genuine understanding of the people behind the account, and what matters to them, that ultimately makes it effective.