I build the revenue engine underneath your marketing — attribution that survives contact with your sales team, pipelines that don’t leak, and demand gen measured to closed-won instead of clicks.
6yrsest
Running growth in B2B SaaS
$40k+est
Monthly ad spend managed
3xest
Pipeline from the same budget
100%
Of spend traced to pipeline
The stack I actually run
None of these are marketing problems. They are all measurement problems wearing a marketing costume.
“Half our contacts say ‘Other Campaigns’.”
HubSpot sets Original Source exactly once, at contact creation, and never revisits it. One malformed UTM and that contact is mis-attributed permanently — and every channel report downstream inherits the error.
“Three dashboards, three pipeline numbers.”
GA4, the CRM, and the spreadsheet your CFO actually trusts all disagree. So the meeting gets spent litigating whose number is right instead of deciding what to do.
“Sales says the leads are bad.”
Marketing says sales doesn’t follow up. Without lifecycle stages, routing rules and response-time SLAs instrumented, both claims are unfalsifiable — so the argument never ends.
“We cut a channel last quarter.”
Based on a number nobody fully trusted. And you will never find out whether it was working, because the measurement died along with the budget.
Demand on top. Operations underneath. Measurement proving both. Most teams buy these from three different vendors and spend the year translating between them.
Where pipeline comes from.
Paid & Performance
Spend across Google, Meta and LinkedIn managed to CAC and pipeline — not impressions, not MQLs.
Organic & AI Visibility
Technical SEO plus GEO/AEO, so you get cited by the answer engines your buyers now ask first.
Outbound Systems
Clay-enriched lists and signal-triggered sequences. Sent because something happened, not because it is Tuesday.
The system underneath it.
CRM Architecture
Lifecycle stages, deduplication, routing and SLAs built to survive contact with a real sales team.
Data Pipelines
CRM, product, web and ad platforms unified into one queryable layer in BigQuery.
Lifecycle Automation
Scoring, nurture and an MQL→SQL handoff that doesn’t quietly drop a third of the leads.
Proof it actually worked.
Attribution
UTM governance → Original Source integrity → closed-won, traced per channel and per rupee.
Dashboards
One view leadership actually opens, built on the warehouse instead of pasted screenshots.
Experimentation
Tests sized before they run, cohorts held properly, forecasts you can defend in a board meeting.
Pick a channel. Watch it resolve all the way to revenue. This is the model I build — and yes, this site runs it, which is how I can tell which LinkedIn post produced your visit.
Pick a channel
Illustrative model
Sample data, shown to demonstrate the measurement model — not a claim about anyone’s results.
LinkedIn → revenue
closed loopPipeline
₹18.4L
CAC
₹12.1k
Read
Best pipeline per hour spent. Scale the format, not the frequency.
Building this for your stack is the RevOps buildout. Finding out why you don’t have it yet is the audit.
Clients described by shape rather than name, and the figures shown are the ones I am able to publish.
Half of all contacts were landing in ‘Other Campaigns’ and no channel report could be trusted. We rebuilt UTM governance, repaired source integrity, and got every rupee of spend traced to pipeline.
52% → 97%est
Contacts with a usable source
100%
Spend traced to pipeline
Outbound was a volume game with a falling reply rate. We rebuilt it around enrichment and trigger signals, so sequences fired because something happened rather than because it was Tuesday.
1.1% → 4.6%est
Reply rate
−58%est
Cost per qualified conversation
Paid was optimising toward the cheapest lead, which meant the algorithm was actively hunting for the worst ones. We re-pointed the whole account at pipeline value and let it find fewer, better buyers.
9% → 34%est
MQL → SQL
−41%est
Cost per qualified lead
Three ways in. Almost everyone should start at the top — it is the cheapest way to find out whether I am any good.
I take apart how you measure revenue and hand you back the map.
Deliverable
Audit document · UTM spec + validator · 90-minute walkthrough
Best first step. You keep every artifact even if we never speak again.
I build the system the audit proved you were missing.
Deliverable
Working system · Documentation · Team handover
Scoped from the audit, so there are no surprises in week five.
I run the demand gen and own the system it runs on.
Deliverable
One person accountable for the entire loop
For teams without a growth lead who need the function to exist now.
Not sure which one you need? That is what the call is for. I will tell you if the answer is “none of these”.
If yours isn’t here, it is a better use of twenty minutes than of email.
That is the normal starting condition — it is why the audit exists. I have never opened a CRM that was in good shape. If yours were clean you would not need me.
Almost never. Most teams already own more capability than they use; the failure is configuration and discipline, not tooling. If I do recommend a change, I will show you the specific thing your current stack cannot do.
The audit produces a written leak list inside two weeks. Fixes to UTM hygiene and routing usually land in the same fortnight, because they are configuration rather than engineering.
Yes, and it is often the best setup. They keep making the campaigns; I make the measurement trustworthy enough for you to judge them fairly.
That is the fractional engagement. I would rather own the whole loop than optimise a channel while somebody else breaks the tracking.
For the right team, yes. The fastest way to find out is the same call.
A 20-minute call. Bring your worst dashboard. I will tell you what is broken and whether you need me at all.