You finally convinced leadership to expand your ABM program. More budget, more target accounts, more opportunity.
Then reality sets in.
Your team that was laser-focused on 15 accounts is now responsible for 150. And the personalized outreach that made you successful is suddenly impossible to replicate. Now, sales is asking why they’re not seeing the same engagement. And you’re stuck making impossible choices about where to focus your limited time and resources.
Here’s the problem: most SaaS marketers try to scale ABM by doing the same things for more accounts. They spread their efforts evenly across their target list, hoping something sticks. Or worse, they prioritize based on gut feel, whoever’s loudest in the sales org, or static criteria set during annual planning.
There’s a better way.
The accounts worth your attention right now are already telling you who they are – through their behavior. How they’re consuming your content, what they’re researching, how many people from their organization are engaged. You just need to know what to look for.
This blog will walk you through a practical framework for letting buyer behavior guide your ABM prioritization – so you can scale without losing the focus that made your program work in the first place.
Why Your Current ABM Prioritization Isn’t Working
Let’s be honest: most ABM prioritization happens once a year in a planning meeting. Someone pulls a list of accounts that fit your ICP, sales weighs in on who they want to target, maybe you layer in some intent data, and boom – that’s your target account list for the next 12 months.
But that list is outdated the moment you finalize it.
The static list problem
Markets shift. Budgets freeze. Champions leave. But your target account list stays the same. You planned to go hard after Account X in Q1, so you keep nurturing them in Q3 – even though they’ve gone completely dark. Meanwhile, Account Y just hired a new CMO who’s been reading your content for two weeks straight, and they’re sitting in your “low priority” tier.
The firmographic trap
Company size and revenue don’t tell you if someone’s actually in-market right now. You can waste months on a “perfect fit” account that has zero buying intent, while a slightly smaller company actively evaluating solutions gets ignored because they don’t check enough boxes on your ICP scorecard.
The “spray and hope” approach
When you can’t figure out who to prioritize, the default is treating everyone equally. Same nurture emails. Same webinar invites. Same ads to your entire account list. Your best opportunities get the same attention as accounts that will never buy, and nothing moves the needle.
Here’s what makes this even harder: in SaaS, only 8% of your website visitors actually identify themselves, according to our 2025 SaaS Mini Report. That means 92% of the people researching your product are doing it anonymously. You can’t just rely on form fills and demo requests to tell you who’s engaged.
So how do you prioritize when most of your buyer activity is invisible?
You need to look at behavior, not just identity. And you need a system that adapts in real-time, not once a year.
Rethinking Account Tiers: A Framework for Flexibility
Here’s the fundamental shift: not all accounts need (or deserve) the same level of attention at the same time.
That might sound obvious, but most ABM programs don’t actually operate this way. Once an account makes it onto your target list, they get the standard treatment – regardless of whether they’re actively evaluating solutions or just passively browsing.
The solution isn’t to create more rigid segmentation. It’s to build a system where accounts can move fluidly between different levels of engagement based on what they’re actually doing.
Think of it as a spectrum, not fixed buckets
One way to approach this is with three broad categories:
High-touch accounts are showing clear buying signals right now. Multiple stakeholders are engaged, they’re consuming decision-stage content, and they’re moving quickly. These accounts get personalized outreach, custom content experiences, and direct sales engagement. You might have 5-10% of your accounts here at any given time.
Mid-touch accounts are showing interest but not urgency. Maybe one person is researching, or they’re still consuming early-stage content. These accounts get targeted nurture, relevant content recommendations, and monitoring for signal changes. This might be 20-30% of your list.
Programmatic accounts are on your target list but haven’t shown meaningful engagement yet. They get broad air cover – ads, general nurture, awareness content – while you watch for signals that they’re warming up. This is the majority of your accounts.
Here’s the important part: these aren’t permanent assignments. An account that’s been sitting in programmatic for six months can jump to high-touch in a week if the behavior changes. And an account that goes dark after initial engagement should slide back down rather than continuing to get high-touch treatment.
Your tiers may look different
The exact percentages and definitions will vary based on your deal complexity, target audience, and sales cycle. A startup with two marketers will tier differently from an enterprise team with dedicated ABM managers. The key isn’t copying someone else’s framework – it’s having a systematic way to allocate your limited resources based on real signals.
The critical principle: make your tiers dynamic. Behavior should determine priority, not your annual plan.
The Behavioral Signals That Actually Matter
So what should you actually be looking for? Not every engagement signal means the same thing. Someone downloading a top-of-funnel ebook is very different from someone binge-reading product briefs and watching demo videos in the same session.
Here are the behavioral signals that actually indicate buying intent:
Signal #1: Content consumption depth
It’s not about how many assets someone downloads – it’s about how deeply they’re engaging. Are they skimming your blog post for 30 seconds, or are they spending 8 minutes reading your product comparison guide?
This is where anonymous behavior becomes crucial. Most of your buyers won’t fill out a form, but they’ll spend significant time consuming your content if they’re seriously evaluating. When someone from a target account is binge-reading multiple pieces of content in a single session – jumping from a use case page to a product brief to a customer story – that’s a signal worth paying attention to.
According to our 2025 SaaS research, briefs have the highest content binge rate at 21.5%, meaning buyers who start with a brief are most likely to consume additional content in the same session. That pattern of sustained engagement tells you something a single form fill never will.
Signal #2: Engagement breadth across the buying committee
One person researching could mean anything. Three people from the same account engaging with your content over two weeks? That’s a buying committee forming.
Track account-level engagement, not just individual behavior. When you see multiple job functions from the same company – say, a product manager, an engineering director, and a VP – all consuming content, you’re watching a deal take shape in real-time.
This is especially important in SaaS, where buying decisions typically involve multiple stakeholders across different functions. You need visibility into the full account, not just your primary contact.
Signal #3: Content topic progression
Pay attention to the journey, not just the destination. An account that starts with awareness content (“What is X?”) and progressively moves toward decision-stage content (pricing pages, ROI calculators, product documentation) is telling you they’re getting serious.
Our data shows that SaaS buyers spend the most time on webinars (5+ minutes), reports, and analyst reports – but demos account for nearly 2 minutes of engagement time by anonymous buyers specifically. When someone moves from educational content to product-focused content, especially demos and briefs, that’s directional momentum.
The shift in content topics is often more telling than the volume of engagement.
Signal #4: Time compression
Some accounts warm up slowly over months. Others go from cold to hot in two weeks. Both patterns matter, but they require different responses.
When you see an account suddenly accelerate – going from zero engagement to multiple team members consuming content across several sessions in a short window – that’s urgency. They’re likely evaluating multiple vendors right now, and speed matters.
Look at both recency and frequency together. An account that engaged heavily three months ago but has gone quiet isn’t the same as an account engaging right now.
Connecting the dots with revenue intelligence
Here’s the challenge: these signals are scattered across your entire content ecosystem. Blog posts, interactive demos, product pages, videos, PDFs. If you’re only tracking form fills and demo requests, you’re missing most of the picture.
This is where revenue intelligence comes in. You need a way to stitch together anonymous browsing behavior, known visitor activity, and account-level patterns across every touchpoint – then surface the accounts that are actually showing buying intent.
Without that, you’re back to guessing. With it, you can see which accounts are moving and prioritize accordingly.
Building Your Dynamic Prioritization System
Knowing what signals to look for is one thing. Building a system that actually uses those signals to prioritize accounts is another.
Here’s how to put this into practice:
Step 1: Define what “engagement” means for your business
Stop using vanity metrics. “Number of page views” or “email opens” won’t tell you who’s ready to buy.
Get specific about what engagement looks like at each stage. For example:
- Early engagement might be: 2+ minutes on educational content, multiple blog visits
- Mid-stage engagement might be: consuming a product brief, viewing pricing page, watching a demo
- Late-stage engagement might be: multiple stakeholders active, reading technical documentation, returning to the site 3+ times in a week
Your definitions will differ based on your sales cycle and product complexity, but the key is being explicit about what counts.
Step 2: Create your scoring framework
Once you know what signals matter, assign relative weight to each. Not all behaviors are equal.
A simple framework might look like:
- Content binge session = high signal
- Multiple stakeholders from same account = high signal
- Demo view by anonymous visitor = medium signal
- Single blog read = low signal
- Time since last engagement = decay factor
You don’t need a complex algorithm. Start simple and refine as you see what actually correlates with closed deals.
Step 3: Set thresholds for tier movement
Define what needs to happen for an account to move between tiers. Be specific.
For example:
- Programmatic → Mid-touch: Account hits engagement threshold (e.g., 3 content pieces consumed, or 2+ people active)
- Mid-touch → High-touch: Multiple buying signals in compressed timeframe (e.g., demo view + pricing page + 3 stakeholders active within 2 weeks)
- Any tier → Programmatic: No meaningful engagement for 60 days
The thresholds should be clear enough that anyone on your team can understand why an account moved.
Step 4: Build the feedback loop with sales
Here’s where most ABM programs break down: marketing identifies hot accounts, but sales doesn’t find out until it’s too late.
You need automated alerts that notify sales immediately when an account hits your engagement thresholds. But not all alerts should be created equal – customizing thresholds based on account context reduces notification fatigue and helps sales prioritize the right conversations.
For example:
- A contact from an open opportunity interacts with content? Lower the threshold and fast-track them to sales immediately – they’re a fast-moving buyer
- Someone from a target ABM account starts binge-reading product content? Trigger an alert so the account owner can strike while they’re hot
- An account that doesn’t fit your ICP perfectly? Require higher engagement before notifying sales
You can apply the same logic to existing customers showing high engagement, specific high-value campaigns, or accounts consuming bottom-of-funnel content like analyst reports or comparison guides.
The key is giving sales context, not just notifications. “Account X just consumed 4 pieces of content including a demo and pricing page in the last 48 hours” is actionable. “New lead score: 75” is not.
Step 5: Review and adjust weekly, not quarterly
Your prioritization should be a living system, not a set-it-and-forget-it model.
Look at your tier distribution every week. Are too many accounts stuck in programmatic? Are high-touch accounts actually closing? Which signals are proving most predictive?
Adjust your thresholds, refine your scoring, and keep iterating. The goal isn’t perfection—it’s continuous improvement based on what’s actually working.
Conclusion
Scaling ABM doesn’t mean doing the same thing for more accounts. It means getting smarter about where you focus.
The shift from static, planning-based prioritization to dynamic, behavior-based prioritization isn’t just more effective – it’s the only way to scale without burning out your team or diluting your impact.
Your target accounts are already telling you who deserves attention right now. They’re showing you through what content they consume, how deeply they engage, how many stakeholders are involved, and how quickly they’re moving. You just need the systems in place to listen.
Start by auditing your current target account list. How many are actually engaging with your content – even anonymously? How many have gone completely dark? Which ones are showing multiple buying signals that you’re missing because you’re still working off last quarter’s prioritization?
The accounts worth your attention are revealing themselves. The question is whether you’re set up to see them.