Case study 03 / Infrastructure software

Unmonitored market signals into an always-on intelligence system

The client builds technical infrastructure software, in a category where the buying conversation happens in code repositories, technical forums and community threads long before anyone fills a form.

Always on
six signal sources monitored continuously, with no manual checking
829
upstream signal rows normalized in the measured period
150
delivered into production channels, 18% of what was captured
82%
filtered out before it reached a human. That is the product
Before / after

Not a strategy problem. An execution problem.

The team knew the market, the technical conversations and the channels where demand was forming. The signals underneath were spread across six platforms and, in practice, not monitored at all, because the manual time cost was too high.

Before / the starting point
After / the standing system
Monitoring
No consistent manual monitoring. The time cost made it unaffordable
One always-on system across six sources, running without anyone checking
Signal quality
Raw signals were useful but overwhelming, and nothing built on top of dirty signals would matter
Contextual scoring and ICP filters at every stage, with the pass rate measured per source
Identity
Anonymous activity. A signal with no name attached goes nowhere
A de-anonymization layer resolves identity where possible, then qualifies it against ICP criteria
Delivery
Nothing arrived anywhere. Whoever remembered to look, looked
Only action-ready signals route into platform-specific team channels
The signal intelligence layer

Four stages, and a measured pass rate at each one

01
Ingest

Six sources: code repositories with their issues, forks and stargazers, technical forums, the professional network with posts and reactors, blogs and RSS, newsletters, and video.

829 rows normalized
02
Qualify

Contextual scoring plus ICP filters decide whether a signal is relevant enough to become an alert candidate.

Pass rate measured per source
03
Resolve identity

A de-anonymization layer attaches a name and a company where it can, then qualifies that record against ICP criteria.

4% reach final qualification
04
Route

Only action-ready signals reach the team, in platform-specific channels, so the context arrives with the alert.

150 delivered
Selectivity

The system's job was to say NO

82% of captured signal rows never reached the team. What arrived was a usable decision stream rather than more noise, which is what was asked for.

Signal rows normalized
829
Delivered into production channels
150
18%
82%

filtered out before a human saw it. A prioritization layer, not a firehose.

Stage or sourceQualification rateRate
Professional network posts reaching alert level
61%
Video signals, alert pass rate
21%
Post reactors, final qualification
12%
Resolved de-anonymized identities, final qualification
4%
Blog and RSS content, alert pass rate
0.5%
Denominators shown at every stage. A 0.5% pass rate is not a broken source, it is a source telling you where not to spend attention.
Where is your buying conversation happening while nobody is watching it?
If it doesn't surface at least 3 pipeline leaks you'd pay to fix, you don't pay.
Get the diagnostic
What it changes for a CMO

The questions a leader asks in the Monday meeting change

Before
Now
"Who is checking the forums this week?"
"Which signals produce the strongest quality?"
"Did anyone see that repository activity?"
"Which channels deserve expansion?"
"Are we missing the conversation entirely?"
"What activation layer do we build on top, now that the signal foundation exists?"
What we do not claim

This case is still early from a revenue-attribution standpoint. There is no confirmed response-rate or meeting lift yet, so we do not claim one. What is real already is the infrastructure and the operating leverage: zero consistent manual monitoring became one always-on system with measured selectivity at every stage. This was a GTM infrastructure project.

Client quote
Marketing lead, infrastructure software company
How we measure

Every figure is pulled from the operating systems themselves: the CRMs, the sending platforms, the engagement tools, the Demand Compass™ ledger, with baselines drawn from the initial audits, and was shared with each client before being shared publicly. Reply classifications are human-reviewed; positives exclude auto-replies. Scored records retain per-lead evidence links (the signal, the post, the source), so claims can be audited row by row. We publish the cohorts that produced zero. The finding is the product. We do not report "influenced pipeline" and we do not publish multiples we can't show the math for.

See the demand you can't see. Then the leaks that lose it.

The audit shows you the demand you can't see. The diagnostic shows you why your system misses it. The engine makes sure you never miss it again.