Audience Agent B2B

Area

What it does

Business value

Intent analysis

• Measure account intent strength (such as low, medium, and high) for specific products.

• Compare product interest trends over time (such as top products in the last n days).

• Identify accounts actively showing interest in specific products.

• Surface engagement patterns that combine account activity with persona coverage.

• Helps teams focus on the right accounts at the right time.

• Improves pipeline quality by prioritizing accounts with genuine purchase signals.

• Enables proactive engagement before competitors act.

Persona mapping

• Detect and rank the top personas by product intent.

• Identify personas involved in buying one or multiple products.

• Map personas to functional roles (such as Champion, Decision Maker, and Influencer) with justification.

• Validate why a given person is considered a champion.

• Ensures that the sales team engages the true decision-makers and influencers.

• Reduces wasted effort on low-impact contacts.

• Increases win rates by aligning outreach with buyer power dynamics.

Buying group evaluation

• Assess buying group size (for example, groups with more than n members).

• Measure persona coverage across accounts (for example, below x%).

• Track role distribution and coverage gaps within buying groups.

• Highlight accounts with champions identified in recent deals.

• Reveals coverage gaps that could stall deals.

• Strengthens multi-threading strategies by ensuring full role representation.

• Improves deal health tracking through group-level engagement insights.

Buying group creation & actionability

• Recommend role-to-persona mappings based on observed persona and role patterns.

• Generate a buying group role template for a specified product.

• Support template customization by including or excluding specific personas and roles.

• Validate that required roles are defined before buying groups are created.

• Reduces manual effort and risk of incomplete buying group templates.

• Ensures role coverage is validated before creation, mitigating the risk of coverage gaps.

• Turns analysis insights into immediate, operational next steps.

Concept

Explanation

Audience detection

Behind the scenes, the agent looks at first-party intent signals based on two things: how engaged people are with your brand, and the kinds of personas that they represent. The analysis looks back over the past 18 months of activity to detect product intent across everyone in the account, especially during the time leading up to an opportunity close. This analysis helps the agent to highlight the personas most likely to influence the deal.

Sometimes accounts don’t have all their opportunity data in perfect shape, which is fine and the agent can still detect product intent purely from engagement patterns.

Persona

Personas represent the types of people engaging within an account. The agent builds them by looking at job title, function, and seniority level, and then normalizing that information so it is consistent across different accounts. That way, instead of getting lost in messy titles, you can quickly see if you are reaching decision-makers, influencers, or support roles. Personas help you understand who is showing interest, not just how much interest there is.

Mapping buying group roles

After personas are mapped to roles, you bring them together into a buying group—the full team inside the account that is most likely to influence or decide on the purchase. Each role (such as decision maker, influencer, or champion) adds a piece of the picture so you have a clear view of the committee driving the deal.

The agent maps each identified persona to the role they are most likely to play for a specific product, based on job title, function, seniority, and other attributes you configure. It also shows coverage for each role so you can see which roles are well-represented and where gaps remain in your engagement strategy.

Using opportunity data to detect persona

To give you the most accurate view of who is engaging and where their interests lie, the agent approaches persona ranking and product intent according to the following:

• Best case scenario: If you can provide data like Opportunity Stage, Opportunity Close Date, and a clear Opportunity-to-Product Mapping, the agent can confidently rank personas per product.
• This ranking provides a precise understanding of engagement and interest across the account.

But the agent knows that the data is not always complete, which is OK. It includes smart fallbacks to keep things moving:

• The agent analyzes the volume of activities, giving more weight to recent ones using time decay.
• This weighting allows the agent to differentiate and rank personas, even without full opportunity data.

When it comes to linking opportunities to products, here’s how the agent handles it:

• Ideal: You provide or help the agent create the mapping table.
• If not available: the agent uses fuzzy matching to connect the dots.
• No linkage at all: The agent infers product intent based on recent activities prior to the close date.

This layered approach ensures that the agent can still deliver meaningful insights, even when the data isn’t perfect.

Opportunity analysis

The agent looks at historical opportunity data to understand which factors most strongly predict a win, and it uses three core dimensions to do that:

1. Win rate: Shows how often deals are successfully closed when certain personas are involved. If accounts with a specific persona pattern (like a technical evaluator or a VP-level decision maker) tend to convert more often, the model gives higher weight to that pattern. This information is a percentage of total opportunities, such as opportunities that are closed or won.
2. Membership rate: Measures how frequently a persona type shows up across opportunities for a given product. If certain personas consistently appear in successful deals, it indicates that they play a critical role in the buying process.
3. Persona influence: Quantifies how much a given persona contributes to the outcome, not just whether they are present, but how their engagement or activity level correlates with wins.

Together, these signals help infer which personas have the strongest impact on purchase outcomes, even when opportunity data is incomplete. Over time, it allows the system to surface high-impact personas and patterns that are most predictive of deal success, which then inform account intent, persona mapping, and buying group recommendations.

Intent

When someone visits a web page or clicks an email link related to a product, it is a signal that they are interested, and that is called intent.

The agent starts with a taxonomy, which is basically a list of the customer’s products and the keywords that describe them. This information helps the agent to understand what each piece of content or interaction is about.

Next, the agent uses that taxonomy to label visitor activity, such as which keywords or products their actions relate to.

Then, the agent looks at how deeply someone engages, such as how many pages they visit or how often they interact. It uses this information to calculate their individual intent score for specific keywords, products, or product categories. It buckets each intent score into High, Medium, or Low intent to indicate the interest strength. (Low intent: <=0.2, Medium intent: 0.2 < score <= 0.6, High intent: 0.6 < score <= 1)

Finally, the agent combines the intent scores of all people from the same company (account) to see the overall account-level intent, showing which products or topics that company seems most interested in.

Roles influencing a buying group

Each buying group is comprised of roles who contribute differently to the purchase decision, such as Decision Maker, Influencer, Champion, and End User. Each role has a varying degree of impact.

Decision Makers hold the most influence and typically control budget approvals. Influencers shape evaluation and recommendations. Champions help build internal consensus, while End Users validate the product’s fit.

By showing you these roles, the agent helps you understand who is driving the buying decision, where your engagement is strongest, and where coverage gaps might exist. This information enables you to focus on the roles that matter most for this product.

Persona or role coverage

For any given product, there is usually a set of key roles and personas that are known to influence the purchase (N personas or roles).

For each account, the agent calculates coverage by checking how many of those N roles are represented by at least one person within that account.

If all N roles are present, the account has full coverage. If only some roles are represented, the coverage is partial.

In simple terms, role and persona coverage measure how complete your buying group is for a product, based on whether all the important decision-makers, influencers, and champions are included.