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Handbook

Part III · The data · Chapter 9

Scoring: who to contact first

Fit plus timing, built as a rubric you can explain and checked against deals you actually won.

10 min read

Most teams have more accounts than time. Scoring decides who gets contacted first. A good score is simple enough to explain to a sales rep in one breath, and checked against the deals you actually win. A score nobody trusts gets ignored, however clever it is.

What you'll take away

  1. 01Why fit and timing should be scored separately, then combined.
  2. 02How to build a points rubric that sales can read and challenge.
  3. 03How to check a score against past wins before trusting it.
  4. 04How to turn the score into a daily priority list and a routing rule.

Fit and timing are different questions

Fit asks whether a company would be a good customer at all, based on stable facts like size, industry and tools. Timing asks whether now is a good moment, based on signals that come and go. Mixing them into one number hides which one is driving it. Score them separately, then decide how to combine them.

Contact nowstrong fit, active signalWatch closelyweak fit, active signal:check fit by handNurturestrong fit, no signal yet:steady, low-pressure touchesLeave aloneweak fit, no signalfit (how well they match the ICP) →timing (signals) →
Fig 1. The top-right corner is the daily priority list. The other three corners each need a different, cheaper treatment.

Build a rubric you can explain

Start with points for a handful of attributes, using your won-deal analysis from chapter 3 to set them.

AttributePointsWhy
Headcount 50–250+20Where most wins came from
Industry: manufacturing+20Highest retention
Uses a tool we integrate with+15Shorter sales cycle
Headcount over 1,000−20Long cycles, procurement-heavy
Signal: new finance lead in last 90 days+25 (timing)Strongest historical trigger
Signal: visited pricing page this week+30 (timing)Active evaluation
  • Keep it to five to eight attributes. More rarely makes it better and always makes it harder to explain.
  • Use negative points for things that make deals worse, not just missing positives.
  • Make timing points decay as the signal ages.
  • Write the reason next to each line. When sales asks why an account scored high, the answer should be readable.

Check it against reality

  1. 1

    Score last year's accounts

    Apply the rubric to the companies you contacted last year, using the data as it was then if you can.
  2. 2

    Compare by band

    Split into high, medium and low scores. Did high-scoring accounts book meetings and win deals at a higher rate?
  3. 3

    Look at the surprises

    Wins that scored low and high scorers that went nowhere show which attributes are wrong.
  4. 4

    Adjust and repeat quarterly

    Markets and products change. A score that was right last year may not be now.

If the top band doesn't convert noticeably better than the bottom band, the score isn't working, however sensible it looks. Fix it before anyone relies on it.

A score is only as good as the difference it predicts. Measure that difference.

Using the score

  • A daily list: the top accounts by combined score, capped at what the team can actually work that day.
  • Routing: high-fit accounts to experienced reps, high-timing ones to whoever can respond fastest.
  • Spend: deep research and personalised outreach only for the top bands; lighter automation for the rest.
  • Feedback: let reps flag scores that look wrong, and review those flags monthly.

Try it

Back-test a score

You scored 1,200 accounts contacted last year. Results by band:

  • High (top 300): 24 meetings, 6 deals
  • Medium (600): 30 meetings, 5 deals
  • Low (300): 12 meetings, 4 deals

Is the score working? What would you look at next?

Write your answer down first, then reveal the worked answer

Meeting rates: high 8%, medium 5%, low 4%, so the score helps at booking meetings.

Deal rates: high 2%, medium about 0.8%, low about 1.3%. The low band won more deals per account than the medium band, which suggests the score predicts interest better than fit.

Next: study the four low-band wins. What do they share that the rubric penalises or misses? A common culprit is a size penalty that is too harsh, or a missing attribute such as a particular industry. Adjust, re-score last year, and compare again. A strong answer separates meeting rates from deal rates instead of reading only one.

Go deeper

Practise the skills