Weighted Scoring

Pick the criteria that matter, weight them, score each option against each criterion. Use when your decision has more than two axes and the axes aren't equally important.

When to use this

You're comparing options across multiple dimensions that don't reduce to "impact" and "effort" -- partnership choices, vendor selection, market entry, build-vs-buy. The dimensions matter unequally (revenue probably matters more than support burden) and you want that asymmetry visible in the score, not hidden in someone's head.

Limits

You don't have well-defined criteria yet. Weighted scoring assumes you know what matters; if you're still figuring that out, run a Kano survey or a jobs-to-be-done interview pass first. A two-option decision can also use weighted scoring if the criteria help explain the trade-off.

Inputs

Criteria: 3-6 dimensions you care about. Specific, not abstract. "Time to first revenue" beats "speed."
Weights: How much each criterion matters. Use percentages totaling 100% for a clear allocation; PM Toolkit normalizes by the total weight.
Scores per option: A rating for each option on each criterion. Pick a scale (1-5 or 1-10) and use it consistently across criteria.

The math

Raw score = sum(weight_fraction_i x score_i)
PM Toolkit score (%) = 100 x sum(weight_i x score_i / scale_max_i) / sum(weight_i)

Multiply each option's score on a criterion by that criterion's weight, then add the results. The option with the highest total ranks first under those assumptions. The weights act as a translator: a 9-out-of-10 on a criterion that matters 40% contributes more than a 9-out-of-10 on a criterion that matters 10%.

A worked example

Say you're a platform PM evaluating three partnership opportunities. You care about four things: revenue (40%), strategic fit (30%), time to integrate (20%), and ongoing support burden (10%). Score each option 1–10, with higher always better. For time to integrate and support burden, a higher rating means less time or burden.

Criterion (weight)	Partner A	Partner B	Partner C
Revenue (40%)	8	6	9
Strategic fit (30%)	6	9	5
Time to integrate (20%)	7	4	6
Support burden (10%)	5	7	8

Partner A = (0.4 x 8) + (0.3 x 6) + (0.2 x 7) + (0.1 x 5) = 3.2 + 1.8 + 1.4 + 0.5 = 6.9.

Partner B = (0.4 x 6) + (0.3 x 9) + (0.2 x 4) + (0.1 x 7) = 2.4 + 2.7 + 0.8 + 0.7 = 6.6.

Partner C = (0.4 x 9) + (0.3 x 5) + (0.2 x 6) + (0.1 x 8) = 3.6 + 1.5 + 1.2 + 0.8 = 7.1.

Ranking: C (7.1), A (6.9), B (6.6). PM Toolkit displays these as 71%, 69%, and 66% of the maximum score. Review how reasonable changes in weights or ratings affect the close ranking.

In PM Toolkit

The calculator normalizes ratings by each criterion's maximum, so different scales can be combined. Its sensitivity view summarizes the spread of ratings and their weighted influence; it does not calculate an exact weight at which the winner changes.

For a manual check, lower Revenue from 40% to 32% and increase the other weights proportionally to 34%, 22.67%, and 11.33%. The scores become approximately A 67.53%, B 66.80%, and C 68.47%: C still leads. State how the remaining weights change whenever you test an alternative.

Common mistakes

Adding overlapping criteria that count the same benefit twice. Keep criteria that contribute a distinct decision factor.
Confusing raw and normalized scores. PM Toolkit divides by the total weight; a manual weighted sum needs weights expressed as fractions totaling 1.
Treating subjective scores as numeric truth. "Strategic fit = 7" is a judgement. Document why you gave it a 7, or the next person can't audit the decision.
No sensitivity check. If you don't know whether your ranking is robust to a small weight change, you don't know if you're picking a winner or picking your weights.
Anchoring weights to the answer you wanted. If you set Revenue to 40% because Partner C is the strongest on revenue, the framework isn't deciding anything.