Sales Rep Scorecard Template

What is a sales scorecard?

A sales scorecard is a short, weighted grade for each rep against targets for their role. It answers two questions a quota number alone cannot: is this rep's result repeatable, and what should the next one-on-one be about?

The name gets used for three different tools, and ORM has a page for each. Pick the one that matches your question.

What should a sales rep scorecard include?

Six metrics cover a B2B SaaS account executive: three outcomes the business needs and three leading indicators that predict next quarter. Each has one formula, so two managers scoring the same rep get the same answer.

The "low score usually means" column follows Pete Furseth, ORM's COO: pressure on deal size or win rate usually signals competition, and a low deal count is a pipeline generation problem. The stale definition is the one ORM uses for meaningful activity: a change in stage, close date or amount. Floors and targets are illustrative defaults. Set your own from the last four to eight quarters.

Activity counts (calls, emails, meetings booked) are left out of the score on purpose. They are easy to raise, and a rep can hit every activity target and still miss. If you want them, track them next to the card, not in it. For a frontline manager's card, which sits one level up, see how to measure a frontline sales manager.

How the weighted score works

Each metric scores 0 at its floor and 100 at its target, on a straight line between them, capped at a maximum. The weighted score is the sum of weight times score, divided by the weights that have data. 100 means on target across the board.

In the workbook the floors, targets, caps and weights come from the Setup tab with SUMIFS on the rep's profile, so the formulas above show the Ramped AE values typed in. The cap stops one great metric from hiding a weak one: without it, Tom's 35.7% win rate would score 182 instead of 120 and add six points to his card from a single metric. A blank input drops that metric and rescales the rest, so a rep with no commit on record is scored on five metrics, not punished on six. The caps (120, or 110 for forecast accuracy and stale pipeline) and the rating bands are illustrative defaults too: change them on the Setup tab.

What is in the template, tab by tab

• Start here. Fill-in order, the color legend, how scores, weights, coaching focus and forecast bias are calculated, and Google Sheets steps.
• Setup. Period name and dates, the commit snapshot, the stale window in days, the four rating bands, and the metric table: weight, floor, target and max score for each of the six metrics in each of three profiles (Ramped AE, Ramping AE, Enterprise AE). A check shows whether each profile's weights add to 100.
• Reps. Up to 30 reps: name, team, profile (dropdown), tenure in months, full quarterly quota and pipeline created target.
• Actuals. The period's inputs per rep: bookings counted to quota, pipeline created, Stage 2 exits and advances, wins, losses, the commit called at the snapshot, open deals and stale open deals.
• Calc. The working columns: six rates, six scores, weights used, points, points below target, weighted score, coaching focus, rating, rank, rank on attainment alone, and forecast bias (Under-called, Over-called or Exact).
• Scorecard. The team view: one row per rep with the six rates, weighted score, rating (color coded), rank, attainment rank and coaching focus, a team row, and a bar chart of weighted scores.
• Rep view. The one-on-one page. Pick a rep from the dropdown to see each metric's actual, floor, target, score, weight, points in the score and points below target, the inputs behind them, and a notes box.
• Team. Team attainment, pipeline, conversion, win rate and stale share, team vs average rep forecast accuracy, median attainment, the attainment distribution in four bands, ratings count and average score by profile.

How to set up the scorecard, step by step

1. Open it. In Excel, open the file. In Google Sheets, upload it to Drive and choose Open with, then Google Sheets. Start from the blank copy, or overwrite the example.
2. Write down the definitions first. Which bookings count to quota (new and expansion ARR but not renewals, say), what Stage 2 is in your CRM, and when the commit snapshot is taken. In one ORM implementation, it took five to seven iterations to agree which field held the closed-won booking amount.
3. Set the profiles. On Setup, adjust weights, floors and targets for each role. Base targets on your own last four to eight quarters, not on a vendor benchmark. Keep each profile's weights at 100.
4. Add the reps with their full quota and a pipeline created target. A common start for the target is quota divided by your win rate on pipeline created, then adjusted by segment.
5. Enter the actuals after the period closes, or weekly for a running view. Pull every number from the CRM report you use for the forecast, so the card and the forecast agree.
6. Read the Scorecard tab, then the Rep view for each one-on-one. Start the conversation with the coaching focus, and compare the score rank with the attainment rank.

Sales rep scorecard example: Q3 2026, eight reps

This is the example file at the end of Q3 2026 (July 1 to September 30). The team, deals and quotas are illustrative, made up to show the mechanics. Eight reps share a $1,900,000 quarterly quota and booked $1,614,000, so the team reached 84.9%.

Two reps over quota land on Watch. Tom booked 102.5% of quota, third on attainment alone, but created 73.3% of his pipeline target, advanced 4 of 12 deals out of Stage 2, and 6 of his 15 open deals have not moved in 30 days. Sam booked 117.1% on an enterprise quota, but created 65.7% of his pipeline target and called a $300,000 commit against $410,000 booked: 73.2% accuracy, under-called. Both quarters were good. Both next quarters are at risk, and the scorecard says so while there is still time to act.

A ramping rep can be on track at 24%. Chris is in month 7 and booked 24.0% of the full $200,000 quota. On the Ramping AE profile, attainment carries 15% of the weight with a 50% target, and pipeline created carries 30%. Chris created 108.3% of his pipeline target and advanced 7 of 15 deals, so he scores 94.4. His attainment stays visible against full quota, the way ORM measures ramp.

The team forecast hides the reps. The team's commit was $1,550,000 against $1,614,000 booked, 96.0% accurate. The average rep was 85.1% accurate. Over-calls (Jordan, Dana, Lena) and under-calls (Maya, Priya, Sam, Chris) cancel out in the total. A team number that looks precise can sit on top of reps who are each guessing. For the team-level measure over several quarters, use the forecast accuracy scorecard.

Jordan's card shows why one metric is not enough. Jordan created 130% of the pipeline target, the best on the team. The other five metrics are weak: 76.0% attainment, 38.5% stage conversion, 20.8% win rate, an over-call, and 29% stale. Lots of pipeline that does not convert is a qualification problem, which is where the coaching starts.

How do you weight a sales scorecard by role?

Weight each role by what it is hired and paid to do, and give each role its own floors and targets. The template ships with three profiles. All numbers are starting points, not benchmarks.

Separate profiles matter because motions differ. Pete makes the same point about stage weights in a forecast: new business, expansion, enterprise and SMB pipelines behave differently, and one set of weights for all of them produces surprises at quarter end. A scorecard with one weight set does the same to people. If you have SDRs, give them their own card built on meetings held and qualified pipeline sourced; their metrics do not fit an AE's six.

For context on targets: Ebsta's 2025 benchmark puts the B2B new-logo win rate at about 19% (Ebsta and Pavilion, 2025), and Gartner reports that only 7% of sales teams reach 90% forecast accuracy or better, with a median of 70% to 79% (Gartner). A 90% target for each rep's commit is demanding, which is why its floor sits at 70%.

How do you score ramping reps fairly?

Measure ramping reps against their full quota, and lower the target and the weight, not the quota. That keeps the number honest and the score fair.

Pete's reasoning: if a company cuts a new rep's quota and then measures attainment against the reduced number, the rep looks more productive than they are. ORM measures ramp as bookings divided by the full quota equivalent, by role, region and tenure. In B2B SaaS, ORM generally sees reps take 15 to 18 months to become fully productive, and there is no single ramp curve: SMB reps, AEs and senior AEs ramp at different speeds. Pete calls the distinction assigned quota (what you ask a rep to sell) versus productive quota (what you should expect them to sell).

In the template, that is the Ramping AE profile: attainment is still full-quota attainment, but its target is 50% and its weight is 15%, while pipeline created and stage conversion carry more. Move a rep to Ramped AE when your own tenure data says they should be fully productive. To find the right attainment target by month of tenure, see quota attainment for ramping reps.

How to use the scorecard in a one-on-one

Open the Rep view, start with the coaching focus, and agree on one action for the metric costing the most points. A scorecard that ends in a grade and no action is a report card. If a rep stays on Coach now for two quarters and the territory, ramp and pipeline supply check out, the next step is a written sales performance improvement plan built on the same metrics.

• Review leading metrics weekly, outcomes monthly or quarterly. Pipeline created, stage conversion and stale share move week to week. Attainment and win rate are only meaningful over a period.
• Read the points, not only the score. Points below target show where the gap sits. Tom's 33.2 points below target split into stage conversion (12.5), pipeline created (10.7) and stale pipeline (10.0). He gets 4.8 back from metrics above target, which is how he lands on 71.6.
• Talk about the bias. An under-called commit is as much a forecast problem as an over-call. The Rep view shows the direction.
• Force decisions on stale deals. Pete's advice when deals sit without moving: a no today is better than a no three months from now.
• Mid-quarter, focus beats activity. If a rep is behind at week six, find the gap (deal size, win rate, pipeline or time to close), then work the open deals with a credible path to close. "Sell harder" does not work halfway through a quarter.

Cadence matters for the forecast too. Digital Bloom reports 87% forecast accuracy for companies that track pipeline weekly, against 52% for those that track it irregularly (Digital Bloom, 2025). To keep the in-quarter pace view next to the card, pair it with the sales tracker template. The team-level version of these leading metrics, read every week against seasonal targets, is the weekly business review.

What Pete Furseth watches on a rep scorecard

Pete Furseth, ORM's COO, works with B2B SaaS revenue teams on forecasts, quotas and capacity plans. These points shaped the template.

• The distribution, not the average. Plan for average attainment of 75% to 80%, with a tail below and a tail above. A scorecard should show who is in each tail. The Team tab counts reps in four attainment bands.
• Change in the data, not only the data. A close date that moves is the best signal a deal will slip, and a deal that slips into the next quarter is less likely to close even in commit. Stale share and forecast bias on the card catch the rep-level version of this.
• Pipeline value is not closed value. Most deals close for less than their value in the CRM. One example: an average open deal of $80,000 against an average closed-won deal of $40,000. A rep's pipeline created target should reflect what actually closes.
• Symptoms point to causes. Pressure on deal size or win rate usually means competition. A low deal count means pipeline generation. Use the coaching focus to pick the right fix.
• Assumptions get written down. Ramp by role and region, average sales price, win rates and days to close should be documented each year, with a note on why they changed. The Setup tab is that record for the scorecard.

Common sales scorecard mistakes

• Attainment as the whole card. In the example, ranking by attainment alone puts Sam second and Tom third. The weighted score puts them fourth and sixth, because their pipeline is short.
• Activity counts in the score. Calls and emails are inputs a rep can inflate. Score the result of the activity: pipeline created and stage conversion.
• One weight set for every role. A first-year rep scored like a tenured one lands on Coach now every quarter, whatever they do.
• No cap. Without a maximum score, one outlier metric lifts a weak card into a good rating.
• Only the team total. Results are concentrated. Ebsta and Pavilion found that 14% of sellers drive 80% of revenue, and that missed quota rates rose to 78% from 69% (Ebsta and Pavilion, 2025). A healthy team number can hide most reps behind target.
• Too many metrics. Past seven, every metric has a weight too small to change behavior. Six is a good default; drop one by setting its weight to 0.

ORM's take: score the rep, forecast the quarter

A weighted scorecard is the fairest simple way to compare reps. It rewards next quarter's pipeline as well as this quarter's bookings, it treats ramping reps honestly, and it puts one coaching point on the table. Its limit is that it looks back: it grades a period that has happened.

ORM looks forward. It snapshots the pipeline every day, measures how each rep's deals actually move through stages, and forecasts each rep, segment and the company to period end, with ramp measured from your own history. ORM targets 95% forecast accuracy. The scorecard tells you who to coach. The forecast tells you what that coaching has to deliver this quarter.

Common questions

What is a sales scorecard?

A sales scorecard is a one-page view that grades each rep on a short list of metrics against targets for their role. A good one mixes outcomes, such as quota attainment and win rate, with leading indicators, such as pipeline created and stage conversion, and weights them into one score. Managers use it to compare reps fairly and to pick what to coach next.

How do I make my own sales scorecard?

Pick five to seven metrics a rep can influence, half outcomes and half leading indicators. Give each a weight that adds to 100, a floor that scores 0 and a target that scores 100. Score each metric on that line, cap the upside, and add up weight times score. Set separate weights and targets for each role. The template above has all of this built.

What KPIs should be on a sales rep scorecard?

For a B2B SaaS account executive: quota attainment against full quota, pipeline created against target, stage conversion, win rate on decided deals, forecast accuracy on the rep's own commit, and the share of open pipeline that has gone stale. SDRs need a different card built on meetings held and qualified pipeline sourced. Activity counts belong in the background, not in the score.

What are the top 5 sales KPIs?

For a SaaS sales team, the five that explain most quarters are quota attainment, pipeline created, win rate, average deal size and sales cycle length. The last four are the levers behind attainment: when a quarter slips, one of them moved. On a rep scorecard, stage conversion and forecast accuracy usually replace deal size and cycle length, because a rep controls them more directly.

How do you weight a sales scorecard?

Weight by what the role is paid and coached to do. A ramped AE might carry 35% on attainment and 20% on pipeline created. A rep in the first year carries less on attainment and more on pipeline and conversion, because bookings lag. Enterprise reps carry more on forecast accuracy, since one large deal moves the quarter. Keep every weight at 10% or more, or drop the metric.

What is the difference between a sales scorecard and a sales dashboard?

A dashboard shows what is happening across the team, usually live, with many charts. A scorecard grades each rep against targets on a few chosen metrics, once a week or once a period, and ends in a score and a rating. The dashboard is for watching the business. The scorecard is for the one-on-one and the quarterly review.

More free tools

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