How to Score Buyer-Intent Signals: A Practical Framework

1The five-dimension scoring rubric

Start here. Score every signal, regardless of where it came from, on these five dimensions. Each is rated 0 to 5 based on how strongly it is present, then multiplied by its weight. Sum the five weighted scores for a total out of 100.

Once you have a total, sort it into a tier and pair the tier with a specific action. Without a tied action, a score is trivia.

2Why enterprise intent scoring does not fit here

Most of what ranks for buyer-intent scoring is written for a specific buyer: a demand-gen team with a marketing analytics stack already in place. Demandbase's own guide to buyer intent leans on website analytics, email engagement, and CRM data, then recommends dividing qualified leads into tiers "based on their scores (e.g., hot, warm, cold)." That tiering idea is sound and this guide borrows it, but the inputs assume infrastructure most small teams do not have.

Saber's glossary entry on intent scoring is explicit about the mechanics: a scoring model assigns different weights to signals based on historical conversion data, for example "a pricing page visit might receive 15 points while a demo request earns 40 points," with the score recalculating as new behavioral signals arrive. That is a legitimate approach if you already have the pricing-page and demo-request data to weight in the first place. If your signal is a Reddit comment or a review on Capterra, none of those inputs exist yet, and no amount of enterprise tooling manufactures them.

That same guide puts a number on the account-level alternative: G2 Buyer Intent, which tells you which companies are browsing your category, typically runs $15,000 to $50,000 a year, and the signal only resolves to the company level, not a named person you can actually reply to. The rubric in this guide scores the opposite kind of signal: a specific, public, individual statement, which is free to read and, done well, tells you exactly who to talk to and what to say.

Whether the signal comes from a form fill, a Reddit thread, or a review site, the ratio holds: most of what looks like demand is not ready yet. Scoring exists to separate the minority that is from the majority that is not, without burning a rep's day reading every single one in full.

3The five dimensions, one at a time

Each dimension answers a different question about how close the author is to a decision. Score them independently before summing, because a post can be high on one dimension and low on another, and the mix is what the weighting is for.

Specificity of the ask (20 points). "CRMs are interesting" is a zero. "Looking for a CRM under $500/month that handles a 10-person outbound team" is close to a five. The more the author names a category, a constraint, or a specific competitor, the higher this scores. Vague enthusiasm for a topic is not intent, it is noise wearing intent's clothes.

Stated urgency (20 points). Deadlines, contract-renewal dates, and phrases like "before Q3" or "this week" push a signal up fast, because they imply the decision has a forcing function rather than an open-ended someday. A post with no timeline language at all, even a specific one, should score low here regardless of how detailed the rest of it is.

Budget or paying-competitor signal (25 points, the heaviest weight). This is the single strongest predictor across every source we reviewed for this guide, which is why it carries the most points. Someone already paying a named competitor has cleared the hardest part of a sale: they believe the category is worth money. A stated dollar figure or an approved budget line does the same work. Weight this one heavily and the rest of the rubric mostly falls into place.

Decision-maker signal (20 points). A founder, an operations lead, or someone who says "I need to present this to my boss by Friday" can act on a decision. A student asking for a class project, or a person clearly researching on behalf of someone else with no stated authority, cannot, no matter how specific or urgent the language sounds. This dimension exists to catch the false positives the other four miss.

Recency (15 points). A pricing complaint from three years ago is context, not a lead. Weight recent activity, inside roughly the last week, above older posts, and treat a bump of new replies on an old thread as a partial recency signal even if the original post is old.

4Two worked examples, scored end to end

Here is the rubric applied to two paraphrased, anonymized signals from different sources, to show how the same five dimensions produce very different totals.

Example A, a Reddit comment in a SaaS subreddit: "We've been on [Competitor] for 14 months and support has gone downhill since their acquisition. Our contract renews in three weeks and my director already approved switching if we find something in the ~$400/month range. What should we look at?"

Total: 96/100, Immediate tier. Every dimension is present and strong. This is exactly the kind of signal that should reach a human within hours, not sit in a queue.

Example B, a two-star G2 review: "I like the dashboard but the reporting export breaks constantly. We're stuck for now since our renewal isn't until next fiscal year, just wanted to warn other buyers."

Total: 56/100, Hot tier, just barely. This reviewer already pays a competitor and names a specific, credible gap, which is real signal. But they explicitly say the timeline is not now, which caps the urgency score and pulls the total down from where the specificity alone might suggest. The right move is not outreach today, it is logging the account with a follow-up reminder near their renewal window, when the same signal will likely re-score into the Immediate tier.

5DIY manual scoring vs. automated scoring

The rubric above works by hand. The honest question is how long that holds up.

Start manual. Score ten to twenty real signals by hand using this rubric before you automate anything. It tells you two things fast: whether your weights actually predict which conversations turn into pipeline, and whether the volume of real signal in your category is even worth automating yet. Automating a rubric that has not been tested against real outcomes just produces a faster, more confident version of a wrong number.

6Calibrating your weights over time

The weights in this guide, 25 points for budget and paying-competitor signals, 20 each for specificity, urgency, and decision-maker signals, and 15 for recency, are a reasonable starting point, not a law. Saber's intent-score research describes the same principle for behavioral scoring models: signals get reweighted "based on historical conversion data," and the weights that matter are the ones that actually predict a close, not the ones that feel intuitively important.

In practice, that means keeping a simple log: the score you assigned, the tier it landed in, and what actually happened afterward, a reply, a meeting, a closed deal, or nothing. After you have thirty or forty scored signals with known outcomes, check which dimension shows up most often in the ones that converted. If decision-maker signal turns out to correlate with conversions far more than recency does in your category, move points from recency to decision-maker and re-run the rubric going forward.

Do this quarterly at minimum. A scoring rubric that never gets checked against real outcomes eventually just measures how confident it sounds, not how accurate it is. For the wider practice this framework sits inside, see our guide to what demand intelligence actually is, and for the highest-value single signal type this rubric can score, see switching-intent signals.

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