Multi-Layered Intent Data: The Missing Link in Personalizing Account-Based Experiences
For years, intent data has been marketed as the ultimate advantage for GTM teams. Platforms like 6Sense and DemandBase promised to pinpoint exactly when accounts were ready to buy, seemingly offering a shortcut to sales success. But as these tools became staples in marketing and sales tech stacks, many teams realized the reality didn’t match the promise.
ABM platforms, while powerful in theory, often come with bloated feature sets that go unused, misaligned predictive scoring, and intent signals that sometimes feel more like educated guesses than actionable insights. Combine this with sky-high price tags, and it’s no surprise marketers are rethinking their approach.
It’s easy to blame ABM technology vendors, but the truth is more complex. During the rise of ABM, vendors played a critical role in educating the market and co-creating solutions with early adopters. They helped expand the category and made ABM strategies accessible to GTM teams. However, over time, many teams began leaning too heavily on these platforms to define their strategy. That’s where things started to unravel.
At its core, ABM is about deeply understanding your customers—what drives them to buy, how they engage, and what matters most to them. No platform, no matter how advanced, can replace those insights. Platforms designed for broad use cases can’t provide the specific understanding of your ICP that only you can uncover. And as budgets tighten, companies are shifting to unbundling ABM—keeping the strategy but reassessing and refining the tools.
The Overpriced Platform Problem
Marketers' frustrations with ABM platforms stem from recurring issues:
Underused Modules: Platforms often bundle features like DSPs and predictive scoring models that don’t align with every team’s needs, adding unnecessary costs.
Low Sales Adoption: Complexity or lack of trust in data means sales teams often fail to fully embrace these tools.
High Costs, Low ROI: Six-figure annual contracts are difficult to justify, especially when many features go unused.
These pain points are pushing GTM teams to explore leaner, more focused approaches that retain the benefits of ABM while avoiding bloated platforms.
Rethinking ABM: Layered Intent Without the Bloat
Despite frustrations with platforms, account-based strategies remain critical. The precision and focus ABM provides are more important than ever in today’s "less with less" environment. Instead of relying on a single tool, marketers can achieve better results by layering intent data from multiple sources.
How to Combine Intent Data Across Layers
To build a nuanced view of accounts, leverage these data types:
Zero-party data: Buyer-provided information (e.g., surveys, forms).
First-party data: Behavioral data from your website, CRM, and campaigns.
Third-party data: External signals like content consumption and keyword searches.
When aggregated and analyzed, these signals create a clearer picture of true buying intent, helping you separate noise from actionable insights.
Scrappy Ways to Aggregate and Automate Intent Data
You don’t need a six-figure platform to connect these data sources. Here’s how smaller teams can do more with less:
Use Budget-Friendly Automation Tools:
Zapier: Sync data across platforms like HubSpot, Google Sheets, and LinkedIn.
Phantom Buster: Scrape LinkedIn profile data to track engagement from economic buyers and changemakers.
Clay: Enrich and combine datasets to uncover meaningful patterns.
Leverage AI for Analysis:
Export your aggregated data to spreadsheets and use ChatGPT to identify patterns.
Example Prompt: “Analyze this data and rank accounts based on their combined intent signals, including LinkedIn engagement, website visits, and email interactions.”
Build a Custom Scoring Model:
Create your own scoring model, weighting signals unique to your ICP and buyer journey. You can use demographics, technographics, engagement data and even scoring properties from other systems to get a perfectly personalized, aggregated prioritization engine. For example:
+ points for a LinkedIn comment from a decision-maker
+ points for a visit to your pricing page.
+ points for engaging with a key piece of thought leadership.
Add the predictive fit score from any of your intelligence or business analytics solutions
+ points or add the related buying stage score from any intent platform
+ points for key down-funnel ad engagements
If you need a way to do this that’s a bit more turnkey, check out things like Common Room and Keyplay, which, by the way, are both Seattle-based companies, like me. Must be something in the water up here.
Case Study: A Scrappy Approach That Delivered Big Results
Last year, I tackled a project to create a layered intent scoring model. With a small team and a limited budget, we combined data from 6Sense, HubSpot (which also had all of our .com engagement tracked), and LinkedIn. Here’s how we did it:
Intelligence automation: Used data scraping, automation and integrations to aggregate intent signals from any relevant technology into a single view in our CRM. Phantom Buster easy key to pull in Linkedin engagement data from our company pages and our executive influencers. Scraping company news and creating a custom psychographic property was also a critical signal in our overall buying journey, and something that we could not get ‘out of the box’ with any intent tools or databases.
Scoring: Developed a custom GPT-powered model to weigh signals differently than 6Sense’s built-in scoring, which overemphasized outbound email sends. Decision-maker engagement and company news signals made all the difference in account prioritization.
Segmentation: Categorized accounts into groups like "economic buyers," "changemakers," and "top-down initiatives."
The results were game-changing:
Demo requests increased 400% in just two months.
Outbound-sourced opportunities grew significantly, reducing acquisition costs.
The key wasn’t fancy software — it was understanding what intent data mattered most and connecting the dots effectively.
Why This Matters Now
In today’s market, every dollar counts. Marketers are no longer willing to overspend on platforms that deliver diminishing returns. Instead, they’re embracing scrappy, strategic approaches to layering intent data and driving results.
By combining zero-, first-, and third-party signals using affordable tools like Zapier, Clay, and ChatGPT, you can build an intent-driven ABM strategy that works—without the bloat or the price tag.
ABM isn’t broken, but you can’t blame overpriced platforms if you’re letting your tech vendors drive your strategy. By focusing on what truly matters — layered intent signals, thoughtful segmentation, and actionable insights — GTM teams can right-size their tech investments and unlock the full potential of account-based strategies.
