Case study · Demand Engine

The list had ten thousand contacts. It had almost no pipeline.

B2B Technology & ITES · Lead Generation & Pipeline Fuel

Duration
6 months
Location
Delhi
Model
List build + enrichment
Focus
ICP-matched pipeline fuel

What we walked into

A technology firm with a busy SDR team working a bloated, unverified list. Bounce rates were high, reps burned hours on poor-fit accounts, and the marketing-to-sales handoff leaked badly. Volume wasn't the problem: relevance was. This is a Demand Engine break, where intent isn't being captured or qualified before it ever reaches an SDR.

The engineering work

  • Re-defined the Ideal Customer Profile with the sales team
  • Rebuilt the target-account list with verified, enriched contacts
  • Layered in intent and trigger signals to prioritise outreach
  • Set a clean MQL-to-SQL qualification framework and handoff

What this engine is designed to move

Metric
Target
Benchmark range
MQL-to-SQL conversion
13% to 30%+
13% average; 39 to 40% top quartile
Sales-ready leads
+50%
+50% with strong nurture
Cost per qualified lead
-33%
-33% with better qualification
Lead attribution loss
-40%
-40% via process audit
> The principle

“A list of ten thousand contacts isn't a Demand Engine. It's noise with email addresses. The engine is judgment: the right accounts, verified, with a reason to believe they might buy now.”

Client details are anonymised and described by sector and region only. Improvement targets shown are illustrative and modelled on published B2B lead-generation and MQL-to-SQL conversion benchmarks. They represent the scale of change this engine is designed to pursue, not audited results from a completed engagement. Verified, named results are published only with client consent as engagements mature.

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