What problem does a qualification scorecard solve?
It makes two reps describing the same deal produce the same assessment. Qualification frameworks give teams a shared vocabulary, and then every rep applies that vocabulary differently. One rep calls a director who likes the product a champion. Another reserves the word for someone who has advocated internally without being asked. Both mark the deal qualified, and the pipeline carries two very different things under one label.A scorecard removes the interpretation. Each criterion has fixed scoring bands, and the bands describe observable buyer behavior rather than rep opinion.
What criteria belong on the scorecard?
Seven, each scored 0, 1, or 2, with weights that reflect what your closed deals actually show.| Criterion | Score 0 | Score 1 | Score 2 | Weight |
|---|---|---|---|---|
| Economic buyer access | No contact identified | Identified, no meeting | Met in a working session | 20% |
| Quantified business pain | Assumed by the rep | Stated by a user | Quantified by the buyer in dollars | 20% |
| Compelling event | None known | Soft internal goal | Dated event with a consequence | 15% |
| Budget status | Unknown | Being requested | Allocated for this period | 15% |
| Buying process mapped | Unknown | Partially known | Steps and owners documented | 10% |
| Champion strength | No advocate | Supportive contact | Has sold internally unprompted | 10% |
| Competitive position | Behind or unknown | Even | Preferred by the evaluators | 10% |
What should each score band trigger?
Different management actions, not different probability percentages.| Score | Meaning | Required action |
|---|---|---|
| 80 to 100 | Fully qualified | Build a dated close plan and hold the date |
| 60 to 79 | One material gap | Name the gap and the action to close it in two weeks |
| 40 to 59 | Multiple gaps | Manager or SE joins the next buyer conversation |
| Below 40 | Not qualified | Move out of the forecast until a criterion improves |
Why does scorecard discipline change the forecast?
Because unqualified pipeline distorts every ratio built on top of it. Pipeline coverage is the obvious casualty. A team at 4x coverage where a third of the pipeline scores under 40 has less real coverage than a team at 2.5x with a clean book, and no coverage ratio will tell you which situation you are in.The distortion also shows up in deal size. The pattern to check for is pipeline carrying an average deal size of $80,000 against closed-won deals averaging $40,000. That gap comes from deals entered at aspirational value and never rescored as the scope narrows. A scorecard with a quantified-pain criterion catches it, because a buyer who has quantified the pain in dollars has also implicitly sized the deal.
Watch aging alongside score. Across ORM's customer base, more than 10% of pipeline has gone twelve months without being touched. Stale pipeline is exactly what a scorecard would grade low, which is why the aging rule and the scorecard belong in the same review.
How do you keep reps from inflating their own scores?
Compare average scores by rep against their actual win rate, then have the conversation.Self-scoring is unavoidable at scale, so the control has to be measurement rather than approval. Pull average scorecard values by rep and put them next to close rates from the same period. A rep whose deals average 78 while closing at the team rate is scoring generously. A rep averaging 52 and closing above the team rate is scoring conservatively and probably has better deals than the pipeline shows.
Two more controls help:
- Manager spot-checks a random sample of five scored deals per rep each month, scoring them independently. - Any criterion at 2 requires a one-line justification naming the evidence, such as the date of the economic buyer meeting.
The justification field does most of the work. Reps score honestly when they have to name the meeting that happened.
How does the scorecard fit alongside weighted pipeline?
Scorecards judge individual deals, weighting judges the portfolio, and neither substitutes for the other. A weighted pipeline view applies historical conversion rates by stage, which is a reasonable way to value a large group of deals and a poor way to assess any single one. Stage weights say nothing about whether this particular deal has an economic buyer.Run both. Use the scorecard when deciding what a rep should do next week. Use the weighted view when deciding whether the quarter needs more pipeline. When the two disagree, and they will, the disagreement is the signal: a heavily weighted stage full of low-scoring deals is exactly the situation where a quarter looks safe and then misses.
Recalibrate the weights twice a year against closed-won and closed-lost data. Criteria that predicted outcomes last year may not predict them after a pricing change, a segment shift, or a new competitor.
Frequently Asked Questions
What is a deal qualification scorecard?
A short, weighted checklist that converts qualification into a number. Each criterion is scored on a fixed scale, the scores are weighted, and the total maps to an action such as advance, work the gap, or disqualify. Its purpose is to make qualification comparable across reps rather than dependent on individual judgment.
How many criteria should a qualification scorecard have?
Six to eight. Fewer than six misses real risk. More than eight and reps stop completing it honestly because it takes too long. Each criterion should be answerable in under fifteen seconds by someone who knows the deal.
Should reps score their own deals?
Yes, with calibration. Rep self-scoring is the only way to get coverage across the whole pipeline. The control is a manager spot-check on a sample each month and a comparison of average scores by rep. A rep whose deals score well above the team average and close at the team rate is inflating.
What is the difference between a scorecard and a qualification framework like MEDDIC?
A framework tells you what to find out. A scorecard tells you what the answers are worth. MEDDIC identifies the economic buyer as something to establish. A scorecard assigns that a weight and forces a number, which is what makes it usable for pipeline-level decisions.
How do you set the weights on a scorecard?
Run a win-loss regression against your own closed deals if you have the volume. If you do not, start with equal weights, collect two quarters of scored deals, then compare average criterion scores on won deals against lost deals. The criteria with the widest gap earn the highest weight.
See how ORM turns these insights into action
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