An Intent Score Is Useful Only When It Changes a Specific Decision

An intent score should help your team decide what to review, what question to ask, or which next step to offer. A high number without a defined action can create confidence without creating value. Before comparing providers’ scores, ask what each number actually means.

For some services, a score ranks observed activity. For others, it reflects customer fit or an estimated likelihood of an outcome. Those meanings require different evaluation methods. A score of ninety is not automatically a ninety percent chance of purchase.

Define the decision boundary

Choose one workflow, such as selecting accounts for a weekly sales review. Specify eligibility, review capacity, and the minimum information sales needs. Then determine how the score will influence that workflow.

If sales can work only a limited queue, evaluate the relevance of the selected group at that capacity. If the score triggers a different message, evaluate whether the message fits the buyer’s situation. If it estimates a probability, ask how that probability was calibrated.

A single score may not serve every purpose. The account most likely to respond is not necessarily the account with the greatest potential value. Keep those objectives visible instead of forcing them into one unexplained ranking.

Ask for reasons at the right level

Sales usually needs practical context, not model internals. Relevant product interest, territory fit, recency, and the type of engagement can help prepare a conversation. The explanation should reflect what the evidence actually supports.

Account-level interest should not become a claim about a named employee. Household matching should not be described as verified individual identity. A score based on a broad topic should not be presented as confirmation that a purchase is underway.

Useful explanations also identify uncertainty. If a signal is old or identity linkage is weak, the team should be able to see that before choosing an action.

Test the score against a simple alternative

A sophisticated model should be evaluated against a practical baseline. That might be the current routing process, a fit-only rule, or a recency-based queue. The baseline should represent a real alternative the business could use.

Keep outcome definitions and follow-up conditions comparable. If the scored group receives much better treatment, its results do not isolate the predictive value of the score. A prospective test can help distinguish the value of selection from the value of the intervention.

Do not judge the model solely on records it was trained to recognize. Preserve an appropriate evaluation set or assess future cohorts with scores captured before outcomes occur.

A hypothetical queue decision

Imagine two accounts with similar intent scores. One needs a product your company supports and operates in your territory. The other shows stronger activity but requires an unsupported service. A fit gate should prevent the second account from displacing the first merely because it is busier.

Now imagine an account with excellent fit but limited observable activity. It may still deserve attention through another strategy, such as an established relationship or referral. Low observed intent is not proof of no demand.

The score is one input to a decision system. Eligibility, commercial value, and the cost of acting remain relevant.

Make score changes interpretable

Track model versions and meaningful source changes. A score rising after a new data feed is added may not represent a real change in the buyer’s behavior. A threshold that worked previously may select a different share of the audience after recalibration.

Review the distribution and actual selected records after important changes. Preserve the ability to explain why a prospect moved into or out of a queue. This helps sales trust the process and gives marketing a way to diagnose surprises.

Avoid celebrating average score increases as a business result. The useful question is whether decisions improved under a stable outcome definition.

Where Cortex adds customer context

Cortex’s two-way intent model is intended to become specific to the customer’s niche, offer, geography, and sales cycle through feedback. That makes the success definition especially important. The model should learn what your business means by a good opportunity.

Bring won and lost outcomes, qualification reasons, and appropriate customer examples into the process. Customers beyond the original list can contribute learning without being counted as campaign acquisitions.

Discuss your scoring decision with Cortex. Start with the action you want to improve and ask how the score will be interpreted, evaluated, and revised when the evidence changes.