You don’t need a new scoring model.
You need to test whether the one you built in January is actually producing pipeline.
Mid-Q1 is when scoring flaws start to show. The “high-priority” accounts are not converting. Reps are cherry-picking outside their assigned tiers. Attainment gaps are widening. Pipeline feels lighter than forecasted.
That is not always an execution problem. Often, it is a scoring problem.
Your scoring model is a hypothesis
Account scoring is a prediction.
It predicts which accounts will convert, which segments deserve attention, and where pipeline will come from. But it is still a hypothesis. And Q1 results are your first real evidence.
If high-scoring accounts are not generating meetings or opportunities, your model may be wrong. If lower-tier segments are outperforming your top tier, your ICP definition may be off.
The question is not whether you have a scoring model. The question is whether it is working.
5 ways to pressure-test your model
Before Q2 planning starts, run these checks.
- Are your highest-scoring accounts actually converting?
Look at meeting rates and opportunity creation by score tier or segment. If Tier 2 is outperforming Tier 1, your prioritization needs work. - What percentage of top-tier accounts are actively being worked?
If a large share of high-fit accounts is idle, your problem is distribution, not scoring. - Are reps ignoring your score?
Compare activity levels across score bands. If reps consistently work lower-scored accounts first, they do not trust the model. - Is attainment evenly distributed?
Large attainment gaps often signal uneven opportunity. If some reps hold most of the high-scoring accounts, fairness is broken. - Has your ICP shifted since January?
New packaging, pricing, or target segments can make your scoring stale within weeks.
If you find issues in more than one area, do not wait for Q2 to reset. Adjust now.
How Gradient Works makes this practical
Pressure-testing scoring sounds simple. In practice, it usually means messy exports and manual analysis. That is where most teams stop.
Gradient Works connects scoring, assignment, and accountability so you can act on what you find.
Market Map helps you see which clusters of accounts actually look like your best customers. Instead of static firmographic filters, you get segmented groups tied to real performance. You can quickly compare conversion by cluster and validate whether your scoring logic matches reality.
If your CRM data is incomplete, your scoring will be flawed. AI Researcher fills those gaps by enriching account records with structured data from the web. That makes your ICP measurable, not theoretical.
Once you know which accounts truly matter, Carve lets you operationalize it. You can model new scoring thresholds, rebalance books, and redistribute high-fit accounts in minutes. No full territory overhaul. No weeks of spreadsheet work.
Why this matters before Q2
If you carry flawed scoring into Q2, you double down on weak assumptions. You hire against distorted opportunity. You expand into segments that do not convert.
Mid-Q1 is your correction window.
Before you add headcount.
Before you change comp.
Before you blame execution.
Fairness is not equal distribution. It is equal access to high-quality opportunity. And that starts with a scoring model that actually drives pipeline.
