What Would Prove That Intent Accuracy Improved After 60 Days?

To show that intent accuracy improved after 60 days of customer feedback, you need a fixed definition of accuracy, comparable evaluation data, and scores captured before outcomes were known. A rising conversion rate alone does not establish that the intent model became more accurate. Better sales follow-up, a new offer, or a different audience can also change conversion.

This article presents a proposed evaluation method. It does not claim that Cortex has completed a 60-day accuracy study or achieved a particular improvement. The required anonymized model and outcome data were not available for this article.

Define the prediction before choosing a metric

An intent score can mean several things. It might rank accounts by likelihood of becoming qualified opportunities, estimate purchase probability within a period, or indicate relative strength of observed research. Those meanings are not interchangeable.

If the score is only a ranking, evaluate whether higher-ranked records contain more relevant outcomes at a practical review capacity. If it is a probability, evaluate both discrimination and calibration: does it distinguish outcomes, and do predicted probabilities resemble observed frequencies? If it is a descriptive activity index, avoid calling it a purchase probability.

The business should specify the target event and horizon. A qualified conversation within a short window is different from a completed enterprise purchase within a long window. Changing the target halfway through the evaluation changes the question.

Why overall accuracy can mislead

When purchases are rare, a rule that predicts no purchase for everyone can appear accurate while helping sales very little. That is why a single headline accuracy percentage can be uninformative.

For a prioritization workflow, precision at the team’s review capacity may be more useful: among the records sales can work, how many reach the defined outcome? Recall asks how many eventual positive outcomes the selected group captures. These measures reveal different tradeoffs.

Keep the capacity fixed when comparing rankings. A system selecting a tiny group can increase precision while missing many useful prospects. A system selecting nearly everyone may capture most buyers while doing little to prioritize work. Neither tradeoff is automatically right; the choice depends on sales capacity and economics.

Preserve the baseline before feedback begins

Store the baseline model version, score timestamp, eligible audience, source definitions, and qualification rules. Retain an evaluation sample that is not used to tune the model. If the team repeatedly looks at the same test results and changes the model accordingly, that sample gradually becomes part of the training process.

At the 60-day milestone, evaluate the updated and baseline approaches on a suitable common holdout or prospective cohort. Use only features available when each prediction would have been made. An outcome recorded later must not leak into the earlier score.

The exact design depends on the business and technical setup. A common holdout supports a cleaner comparison of scoring approaches, while a prospective deployment test also captures the operational consequences of using them. Document which question the chosen design answers.

Sixty days of feedback is not sixty days of outcomes

A model may receive feedback for 60 days while many opportunities remain open. That is especially likely in long sales cycles. Evaluating revenue too early can make recently scored records look worse simply because they have had less time to close.

Use mature outcome windows or a method that accounts for delayed outcomes. Report pending opportunities separately. If the available evidence supports only qualification prediction at that point, publish that result rather than calling it purchase accuracy.

Record when feedback was actually received, not merely when the event happened. A won deal backfilled after the evaluation cutoff was not available to improve the model earlier. That distinction matters when explaining what the system could have learned by a specific date.

Audit the labels before celebrating the model

Customer feedback is valuable only when it describes the right event. No response, wrong territory, no budget, lost to a competitor, and operational inability to fulfill are different situations. A single rejected status can blur market fit with execution failure.

Review a sample of records with sales and marketing. Check whether different reviewers apply the qualification rule consistently. Track changes to the definition. If the company relaxes qualification requirements during the study, a higher positive rate may reflect easier labels rather than better prediction.

Include customers who were not on the original list through the appropriate approved workflow, but preserve their origin. They may teach useful patterns without proving that the original campaign acquired them.

A hypothetical evaluation outcome

Imagine a model that identifies more qualified accounts within the same weekly review capacity after a feedback period. That would be encouraging evidence for the ranking task if the cohorts and definitions were comparable.

Now imagine that revenue per account falls because the selected opportunities are smaller. The model may have improved at its specified target while becoming less useful for the company’s economic goal. The response is to revisit the target and value tradeoff, not to hide the revenue result.

Alternatively, qualification precision may stay flat while sales effort falls. That operational improvement could matter, but it should be reported as reduced review effort rather than increased predictive accuracy. Clear naming protects the value of the actual finding.

How Cortex’s learning promise should be evaluated

Cortex’s positioning centers on customer-specific learning from chosen sources, leads, customers, and deal outcomes. According to the supplied brief, scoring can begin about 24 hours after subscription. Evidence of improvement requires subsequent reliable feedback and an appropriate evaluation period.

Proprietary learning means the interpretation can become specific to a customer’s niche, offer, geography, and sales cycle. It does not mean improvement is automatic every calendar day. Poor labels, changing markets, and incomplete observations can all limit what the system learns.

Ask for a review process that can identify those limits. A trustworthy partner should be able to discuss when a model should be revised, when evidence is inconclusive, and which decision a score is intended to support.

The inputs a real benchmark needs

A publishable study would require anonymized score snapshots, model versions, feature cutoffs, outcome timestamps, qualification definitions, feedback arrival dates, cohort membership, and appropriate permissions for aggregate reporting. It would disclose counts, evaluation dates, uncertainty, and whether the comparison was controlled or observational.

Talk with Cortex about a 60-day evaluation plan. A useful plan begins with a prediction you can define and a business decision you are willing to test.