Every company and signal in PQ Intel gets a 0-100 composite score — Hot, Warm, or Cold — so you prioritise by objective heat, not gut feeling. The scoring engine uses signal volume, signal types, and ICP keyword hits weighted by your configuration.
84|Multiple strong signals from a company that closely matches your ICP. Reach out today — these prospects are actively signalling intent.
92|Some signal activity and partial ICP alignment. Monitor the feed and engage when the next signal fires — these are developing opportunities.
97|Low signal volume or weak ICP match. Skip these for now unless the ICP configuration is tuned to your exact targets.
102|The composite scorer (composite_scorer.py) is a deterministic weighted algorithm — not an LLM call. It evaluates each company and signal on three dimensions:
Signal volume — how many distinct signals a company has produced across all platforms. A company with five signals across LinkedIn, Reddit, and HN scores higher than one with a single isolated post.
Signal types — competitor mentions and funding alerts carry more weight than routine posts. The scorer distinguishes between informational content, direct competitor comparisons, and hiring signals, applying a type multiplier to each.
ICP keyword hits — how closely the signal content matches your defined target keywords, industries, and roles. The scorer uses a weighted keyword dictionary from your ICP config: industry terms score higher than generic keywords, and role mentions amplify the score further when they match your target job titles.
These three factors are combined into a single 0-100 score. No black-box inference, no prompt drift — just transparent, reproducible scoring that you can audit, tune, and trust.
Every lead in your pipeline carries its composite score. Sort your list by score to see Hot prospects first. Filter by score tier to focus your team's energy on the right opportunities. The score is visible on every lead card, making prioritisation instant — no need to open a detail view to know who to call first.
Scores update nightly via recalc_scores.py, so a company that was Cold yesterday can become Hot today — and you'll know about it in your morning feed. The recalculation script processes every lead in parallel, so even accounts with hundreds of signals are re-evaluated within minutes.
The scoring engine applies a time-decay function to every signal. A signal from today contributes full weight to the composite score. A signal from three months ago contributes roughly 30% of its original weight. This ensures that a company with a sudden spike of recent activity jumps ahead of one with old, stale signals — even if both have the same total count.
Time-decay parameters are configurable per tenant. Teams doing fast-cycle sales can set a 14-day decay window; enterprise teams with longer sales cycles can extend it to 90 days. Adjust in your ICP settings — the next nightly run applies the new curve to all existing signals.
Your team runs a daily standup where SDRs pick their top three accounts to work. Without scoring, each SDR spends 20 minutes scrolling through leads and guessing which ones are most likely to convert. With PQ Intel scoring, they open the pipeline sorted by composite score. The top five accounts are clearly Hot — multiple signals, strong ICP match, recent activity. Each SDR picks one, reads the signal context, and sends a personalised outreach. That's 20 minutes saved per person, per day, redirected into actual selling time.
A mid-market SaaS company you've had in Warm territory for weeks posts nothing for 12 days. Then, in a single 48-hour window, they publish a job listing for a Head of Sales, a team member comments on a relevant LinkedIn thread, and their CTO asks a technical question on HN. The scoring engine detects the volume spike and reassigns the account to Hot overnight. Your SDR sees the badge change in the morning feed and reaches out while the signals are still fresh — before any competitor monitoring tool has noticed the activity.
Your company pivots from targeting mid-market fintech to enterprise healthcare. You update your ICP configuration with new industry, keyword, and geo parameters. That night, recalc_scores.py runs against every existing signal with the new weights. The next morning, your feed looks completely different — companies that were Cold are now Hot under the new criteria, and vice versa. No manual reclassification, no lost pipeline. The scoring engine adapts to your strategy instantly.
Start each day with the highest-scored prospects. No second-guessing which lead to call first — the score tells you.
143|See which score tiers have the most volume and assign SDR coverage accordingly. Balance short-term Hot outreach with Warm pipeline development.
148|Watch companies move from Cold to Warm to Hot over days and weeks. The score trend reveals which accounts are gaining momentum before they hit wide awareness.
Score your existing customer base by signal activity. Customers showing strong engagement signals and hiring activity may be ready for an expansion conversation. Quiet customers with dropping scores may need a check-in before churn risk escalates.
Run reports on score distribution across your pipeline. If 70% of leads are Cold, your ICP may be too broad or your monitoring isn't capturing the right signals. Use score analytics to tune both ICP configuration and signal monitoring scope.
Most platforms score by engagement — how often someone interacts. PQ Intel scores by ICP fit plus signal volume, so a quiet prospect with high fit still ranks above noisy ones who don't match.
164|| Capability | PQ Intel | Common Room |
|---|---|---|
| Score range | 0-100 composite | Engagement-based |
| Scoring methodology | ICP-weighted + signal volume | Engagement scoring |
| ICP keyword matching in score | Yes | No |
| Algorithm type | Deterministic weighted | ML-based |
| Hot/Warm/Cold tiers | Yes | Yes |
The composite score is not a standalone number — it's a decision signal that flows across every part of PQ Intel.
Every lead in the pipeline carries its score and tier badge. Filter by Hot/Warm/Cold to see the right subset. Sort by score to prioritise daily work. The kanban board updates scores after each nightly recalc.
The scoring engine sits directly downstream of the signal monitoring pipeline. Every inbound signal is scored in real time against your ICP configuration before it appears in your feed.
Campaign audience filters support score tier conditions. Build a campaign that targets only Hot-scored leads, or create separate sequences for Warm leads that need nurturing.
Export scored leads to your CRM via webhook or API. Every lead record includes the composite score, tier, score breakdown dimensions, and recent signal summaries — so your CRM sees exactly what PQ Intel computed.
See all integrations on the integrations page.
Scoring works hand-in-hand with ICP configuration and monitoring. Explore how:
Define your target per tenant — the scoring engine runs against your ICP config.
Signals from 13+ platforms feed directly into the scoring engine.
View and sort leads by composite score directly in your pipeline.
Target campaigns by score tier — send Hot leads different sequences than Warm.
Enrich contacts at Hot-scored accounts first — prioritise enrichment spend by score.
Find decision-makers at high-scored accounts directly from LinkedIn.
From three weighted inputs: signal volume, signal types, and ICP keyword match strength. A deterministic formula, not an LLM inference.
Hot (70+): strong ICP fit with recent signals. Warm (40-69): some fit. Cold (<40): limited signals or weak match.
Yes. Scoring weights are tied to your per-tenant ICP configuration. Adjust which dimensions are prioritised.
Yes. When you adjust ICP weights, all existing signals are re-scored on the next nightly run.
Common Room scores engagement within your product. PQ Intel scores intent signals from external platforms before prospects have heard of you.
The Hot/Warm/Cold thresholds (70+, 40-69, 0-39) are fixed defaults, but you can adjust them per tenant in your ICP configuration settings. A team targeting enterprise accounts might raise the Hot threshold to 85, while a high-volume SDR team might lower Warm to 30 to keep more leads in play.
No. The composite score is based on signal data only — signal volume, types, and ICP keyword matches. Enrichment adds contact details but does not change the score. This keeps scoring transparent and predictable: you know exactly why a company scores what it does.
The composite score is independent of pipeline stage. A lead can be Hot at Discovery and still Hot at Negotiation if signals continue. Conversely, a lead can drop from Hot to Warm if signals go stale during a long sales cycle. Pipeline stage and composite score are separate dimensions for decision-making.
Yes. Every company that generates matched signals is scored automatically, whether or not they've been added to your pipeline. You can browse scored companies from the signal feed and promote them to pipeline when ready. This means no company with buying intent slips through unnoticed.