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Cold DM Calculator

Resource · Last updated July 14, 2026 · By the ColdDMCalculator team

Cold DM Campaign Scorecard: Rate Your Campaign Health

Score your campaign across five dimensions before you send a single message. Each dimension has weighted criteria — rate yourself honestly and the total score tells you whether the campaign is ready to launch.

Scoring dimensions

For each criterion, give yourself 1 point (met) or 0 points (not met). Multiply the dimension subtotal by its weight to get the weighted score. Sum all weighted scores for your total.

Targeting quality

Weight: 25%
  • Audience is defined by role, industry, and company size (not just 'everyone')
  • You can articulate their specific pain or goal
  • Your offer is relevant to their current situation
  • List is sourced from reliable data (not scraped or purchased randomly)
Score: ___ / 4 × 25%

Script quality

Weight: 25%
  • First line is personalized to the recipient
  • Message is about them, not you
  • One clear idea per message (no feature dumping)
  • CTA is low-friction (question, not a meeting request)
  • Tone is human, not salesy or robotic
Score: ___ / 5 × 25%

Volume planning

Weight: 15%
  • Daily send volume is within safe platform limits
  • You have capacity to handle projected replies
  • Campaign timeline is realistic for the volume planned
  • Volume is phased (start small, scale after validation)
Score: ___ / 4 × 15%

Conversion funnel

Weight: 20%
  • Reply rate target is based on data, not hope
  • Positive reply rate target is realistic
  • Booking process is simple (scheduling link, not back-and-forth)
  • Follow-up sequence is written and timed before launch
Score: ___ / 4 × 20%

Cost efficiency

Weight: 15%
  • Campaign cost includes tools, time, and list research
  • ROI is calculated at conservative rates
  • Break-even volume is within planned campaign volume
  • Kill criteria are defined before launch
Score: ___ / 4 × 15%

Score interpretation

Score rangeRatingAction
85–100StrongCampaign is well-planned. Launch with confidence.
70–84GoodMinor gaps to address. Review weak areas before launch.
50–69ModerateSignificant gaps. Fix before launching at scale.
Below 50WeakCampaign is high-risk. Rework targeting, script, or plan.

Total score calculation

Sum the weighted scores from all five dimensions. Maximum possible score: 100. Use the calculator to validate your forecast alongside this qualitative score.

How to Use This Resource

  • Complete the scorecard before your first send — ideally a few days before launch so you have time to fix gaps.
  • Score honestly. A high score based on generous self-assessment doesn't protect you from poor results.
  • Re-score mid-campaign with actual metrics to identify which dimension needs improvement.
  • Pair with the Risk Checklist for a complete pre-launch review.

This resource is for educational planning purposes. Results vary based on execution, audience, and platform rules.

Related: All Resources · Risk Checklist · Script Scorecard · Calculator

Frequently asked questions

When should I use this scorecard?

Use it twice: once before launch (to catch planning gaps) and once mid-campaign (to diagnose underperformance). Pre-launch scoring prevents most avoidable failures.

What if my score is low across multiple dimensions?

Start with targeting. Bad targeting makes every other metric worse. If your audience is wrong, a great script and perfect volume won't save the campaign. Fix targeting first, then script, then volume.

Can I use this for ongoing campaigns?

Yes. Score your campaign every two weeks using actual metrics instead of targets. The scorecard becomes a diagnostic tool that tells you which dimension to improve next.

How does this relate to the risk checklist?

The risk checklist catches compliance and execution risks. The scorecard measures planning quality across five dimensions. Use both — the checklist is a binary pass/fail, the scorecard gives you a gradient score.

Score your campaign now.

Use the calculator to validate your quantitative assumptions.

Forecasts are estimates based on user-provided assumptions. Results are not guaranteed.