Earlier in my ABM career, I assumed personalization depth was the fix. Deals weren’t moving because we weren’t being specific enough. When the content marketing team ships a personalized deck to a top-tier account, Sales is usually optimistic because the content genuinely seems relevant—it references their industry challenges, their tech stack, and recent earnings call comments from the buying committee. But weeks pass and the deck is not achieving any meaningful engagement.
Sounds familiar? Yes, because it’s the pattern.
Common examples of this are:
- Account-specific landing pages attract visitors, but are not driving any pipeline.
- A case study from their vertical doesn’t interest the target list.
- ROI calculators built for specific industries that give us web visits but not qualified leads.
It is incredibly frustrating when we do everything the ABM playbooks told us to do and yet, nothing moves.
The question isn’t whether we executed well, because we probably did, but why does bottom-of-funnel content sit unused even when it’s perfectly personalized?
Because we’re personalizing to the wrong thing.
What a Conviction State Is
A conviction state is the psychological position a buyer occupies right now — defined by the question they’re trying to answer, not by where they sit in a pipeline.
One state: “We know our current approach has limitations. We’re open to change. But we’re not convinced the disruption is worth it.” People in this state need evidence that the cost of staying put is higher than they assumed.
Another state: “We believe change is necessary. Now we need to build the case internally. How do we justify this to Finance, to IT, to the exec team?” These people are figuring out how to get it across the line within their organization.
We need to address two questions before deciding what content we need: “Who are we targeting?” and “what are they trying to figure out right now?”
If we only answer the first question we are missing the mindset of the people. What that framework misses: the conviction state isn’t about the account. These convictions exist in individuals, and there is no monolithic account moving through stages.
Each person occupies their own psychological position. Collapsing that into an account-level view loses the only part that matters.
Conviction Lives in Individuals
Someone at a $50M company and someone at a $1B company from different industries can be in the same state: for example, both figuring out how to prove ROI to a skeptical CFO, or navigating the same problem with IT or procurement.
The question they’re trying to answer is the same.
This is a real example I learned in the AI ABM course from Forge X. A pharma operations director and a manufacturing engineering manager can occupy the same conviction state because they share the same strategic trigger. Not the same vertical, but they both have the same question.
If we cluster by shared challenges instead of shared identity, this happens: The pharma buyer reads content built for that conviction state and thinks, “This is exactly where we are.” So does the manufacturer. The shared trigger is the cluster.
What This Makes Possible
When you build content around conviction states, a single piece can serve hundreds of people across dozens of accounts because those people are all trying to answer the same question.
Content built around “how to justify a vendor change to a skeptical CFO” works for every account with a stakeholder in that state: $50M or $1B, manufacturing or pharma, early pipeline or late. The logos are irrelevant. The shared psychological position is everything.
You don’t need a personalized deck for each logo when your content addresses the specific reasoning problem people in that state are facing.
Consensus Quality is the Real Metric
For a deal to close, you need alignment across the entire buying group. Until recently, we could only count how many stakeholders we’ve touched. Not only that, this approach has been used to build useless attribution models that forced marketing teams to be tied to unrealistic metrics of success (like demanding enterprise MQLs from organic social) but also didn’t help to move individual stakeholders through conviction states.
Recent advances in technologies allow us now to measure the quality of consensus and respond to the question: do all the stakeholders share the same understanding of the problem? If the answer is yes, that coherence turns conversations into velocity. You can now engineer and measure this by combining conviction-state keyword design with cross-tool signal validation across conversation intelligence, contact-level advertising, and behavioral data. When the same conviction-state language surfaces across all three for the same contacts at the same account, you have a consensus quality read that changes how you prioritize and respond.
Engineering the Consensus Signal
Most ABM programs go blind in the gap between “we’re touching multiple stakeholders” and “those stakeholders are aligned.” The tools to close that gap exist, but they are not magical; you need to connect them with a methodology.
The starting point is a conviction-state keyword library. Before you run any campaign or track any signal, you design the specific language that maps to each psychological position in the buying arc. These aren’t generic intent terms. They’re phrases that only appear when someone is at a specific point in their thinking.
Someone rationalizing an investment uses language like “building the business case,” “getting CFO sign-off,” or “how do we justify this to the board.”
Someone still in evaluation says things like “comparing our options,” “what does implementation look like,” or “how does this fit with what we already have.” The language is specific enough that when you hear it or see engagement with it, you know where that person is.
Once the library is designed, you track it across three surfaces. I will set an example with three technologies that allow you to track at a contact-level: Common Room, Gong et Influ2.
Common Room is the hub, aggregates buying signals across channels and connects with Gong natively, facilitating that the keywords and topics coming up in calls with each stakeholder land on the same contact record as the behavioral signals.
Once Common Room tells you which contacts are showing conviction-state signals and where they appear to be in their thinking, Influ2 comes into play by serving specific content to those individuals in the buying group and the engagement pattern tells you whether your reading was right.
Two things this still requires upfront: the keyword library needs discipline and is only as good as the specificity of what you’re tracking. Vague terms map to everything and will not work. And, connecting signals across tools is currently a manual or near-manual process. There are revenue platforms that can help merge the view.
But remember, the methodology itself lives with you, not the software and traditional content marketing builds awareness, but Content ABM builds alignment.