A6 · Evidence case · public-case-study (Pocus 2024)

Composite PQL score combining product usage signals (feature activation, frequency, team size expansion) with firmographic fit; score drove SDR call prioritization

SDR connect rate +55%; conversion from PQL to opportunity +39%; false-positive rate (SDR called, no intent) dropped from 48% to 21% — Usage-frequency signals outpredict usage-breadth signals. How often someone uses a core feature matters more than how many features they've touched.
moderate evidence self-reported Last updated 2026-06-18

Source trust note: Results are reported by the implementing company or a vendor's reference customer. Direction is credible; magnitude may be overstated due to selection effects.

What was built

Composite PQL score combining product usage signals (feature activation, frequency, team size expansion) with firmographic fit; score drove SDR call prioritization

Company type: PLG SaaS (developer tools)

Tier map

Tier 1 execution: product-led pipeline optimization

Tier 1 · Execution C6 Agentic hub →

Human-in-the-loop design

SDR reviews top 20 accounts daily from scored list; agent updates scores hourly from product telemetry

Results

SDR connect rate +55%; conversion from PQL to opportunity +39%; false-positive rate (SDR called, no intent) dropped from 48% to 21%

Quality caveat: these results are self-reported — treat as directional signal, not precise benchmark.

Source trust

self-reported public-case-study (Pocus 2024)

This case is rated self-reported. The implementing company or a vendor reference customer is reporting their own results. Direction is credible — these teams built something real and measured it. Magnitude may be inflated due to selection effects (teams who had good results are more likely to share them).

Failure mode observed

Score staleness: product telemetry pipeline lagged 4 hours, causing SDRs to call accounts that had already churned or converted

Transferable lesson

Key lesson: Usage-frequency signals outpredict usage-breadth signals. How often someone uses a core feature matters more than how many features they've touched.

How to cite

@misc{shalvi_gtm_evidence_firmographic_behavioral_composite_scoring_2026,
  author    = {Singh, Shalvi},
  title     = {Composite PQL score combining product usage signals (feature activation, frequen — Agentic GTM Evidence Case},
  year      = {2026},
  note      = {Source trust: public-case-study (Pocus 2024). Methodology: moderate.},
  url       = {https://shalvisingh.com/gtm/evidence/firmographic-behavioral-composite-scoring}
}

Singh, S. (2026). *Composite PQL score combining product usage signals (feature activation, frequen — Agentic GTM Evidence Case*. GTM World Model. Retrieved from https://shalvisingh.com/gtm/evidence/firmographic-behavioral-composite-scoring

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