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.

When NOT to use this

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. Also skip it when you have fewer than 3 options -- a comparison with two items rarely justifies the setup cost.

Inputs

Criteria: 3-6 dimensions you care about. Specific, not abstract. "Time to first revenue" beats "speed."
Weights: How much each criterion matters, as a percentage. Must sum to 100%.
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

Score = sum(weight_i x score_i) for each criterion i

Multiply each option's score on a criterion by that criterion's weight, then add the results. The option with the highest total wins. 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.

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). C wins, but barely. A is within 3% -- worth understanding what would flip the order before you commit.

How pmtoolkit does it differently

The calculator runs sensitivity analysis: it tells you how much each criterion's weight would have to change to flip the top choice. In the example above, dropping Revenue from 40% to 32% (and redistributing) makes A win. That number is the most useful output. If a tiny weight change flips the ranking, your weights are doing the work, not the underlying scores -- and that means you should re-examine the weights before you ship the decision. If it takes a 20-point swing to flip, the ranking is robust.

Common mistakes

Too many criteria. Above 6, the weights get small and the signal dilutes. Cut the bottom three.
Weights that don't sum to 100%. Sounds obvious, surprisingly common. The math still produces a number; the number just doesn't mean what you think.
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.