Darwinbox came to iExcel with a U.S. expansion budget and a stack scaling faster than its diagnostics. Eight audit surfaces, one growth roadmap.
A one-month diagnostic doesn't touch every dial. It reads every dial. Below is the surface iExcel covered — the paid platforms, the analytics layer, the CRM, and the tracking that ties them together.
Darwinbox is one of Asia-Pacific's most credible HCM platforms — HRMS, payroll, workforce management, HXM, wired into thousands of enterprises. The India-headquartered team was pushing into the U.S. market, where the category is louder, the buyer is more skeptical, and the cost of a wasted quarter is measured in six-figure ad spend.
They didn't need another agency to run campaigns. They needed a forensic read on the machine they'd already built — paid search, LinkedIn, Meta, GTM, GA4, SEO, ABM, and the HubSpot ↔ Google Ads sync — and a prescription for where to point the next twelve months of growth budget.
Our engagement was one month. Not a campaign build. A diagnostic + market research package covering eight audit surfaces and a competitor + keyword + persona research pack the operating team could actually use on Monday.
A modern HR SaaS marketing stack has eight places to leak money — paid, analytics, CRM, and ABM plumbing that has to reconcile. We audited all of them in parallel and rated each: audit complete, fix required, or working-as-intended.
Google Ads — bid strategies, device performance, keyword performance, landing pages, conversion tracking QA.
Job-title targeting, campaign structure, creative rotation, form vs. off-platform conversion setup.
Facebook + Instagram audit — audience layers, creative testing framework, event mapping through Pixel.
17 pixels installed. Bing, TikTok, and LinkedIn conversion setups incorrectly implemented — QA + rebuild recommended.
Schedule-a-demo + 1-minute engagement events, subdomain traffic gaps on explore. and blog., UTM governance for the "Unassigned" spike.
14,994 organic ranking records catalogued — sitemap, page-level backlinks, APAC filtering, HR-glossary authority mapped.
End-to-end checklist across identify → research → targeting → campaign → operational phases. Ready-to-run.
Offline conversion value workflow: lead score + GCLID → Google Ads. Bidding on quality, not clicks.
"The U.S. market doesn't reward louder ads. It rewards a stack the CFO can defend in a QBR — clean attribution, clean pipeline, clean read."
Most audits ship a slide deck. This one shipped an operating dataset. Four deliverables — competitor matrix, keyword planner, relevant-links directory, LinkedIn persona list — big enough to run against, curated enough to actually execute.
The competitor matrix wasn't a five-logo comparison. It was a 87-row market map across HRMS, HCM, payroll, HR software, workforce management, HRIS, HXM, recruiting, and recognition, with 29 records flagged as iExcel-priority for the U.S. push. The keyword planner separated 1,938 rows earning their place in paid from 199 rows earning their place in SEO — same file, two workstreams. The 272-record LinkedIn persona list broke out HR, Talent, People, Recruiting, Culture, Hiring, and DEI titles so the paid-social targeting could be built from a real list, not a hunch.
Across HRMS / HCM / payroll / workforce / HRIS / HXM / recruiting / recognition. 29 flagged as priority.
1,938 rows scored Good-for-Ads. 199 rows scored Good-for-SEO. One file, two roadmaps.
Curated links directory — HR trade press, analyst directories, review platforms, industry associations.
LinkedIn-ready: HR, Talent, People, Recruiting, Culture, Hiring, DEI. Ready to feed campaigns.
The paid stack was live across Google Search, LinkedIn Ads, and Meta — but the three accounts weren't failing the same way. Google was over-indexed on the wrong keyword layers. LinkedIn was targeting the right personas but converting off-platform in a way conversion tracking couldn't see. Meta was running audiences the Pixel wasn't fully instrumented for.
The Google Search account made the case on its own numbers. Broad-match keywords were converting at roughly double the exact-match rate and closing conversions at 89% lower cost — but the U.S. campaigns were running zero broad-match keywords. Layered on top of that: identical search intent that converted efficiently out of India cost 19 times more per conversion once it hit the U.S. account, and the highest-converting landing page in the entire program — the quote-request page, at 11.28% — was sitting behind one of the lowest click-through rates in the funnel.
We wrote each channel a separate recommendation set — bid strategy, structure, landing pages, tracking QA — because a single "here's the paid plan" doesn't work when the three channels are broken in three directions.
The quote-request landing page converted at 11.28% against WordStream's 2.35% cross-industry landing-page average — while pulling one of the lowest click-through rates in the account. The traffic reaching that page wasn't the problem. Getting more of the right traffic there was — a creative and routing fix, not a landing-page rebuild.
Seventeen tracking pixels were installed. Some of them worked. Some of them didn't. The GTM audit's real value wasn't the inventory — it was the specific finding that the Bing, TikTok, and LinkedIn conversion setups were incorrectly implemented. Live pixels, dead data.
GA4 had its own list. The Schedule_a_demo and Spend_more_than_1_minute events needed instrumentation cleanup. The explore.darwinbox.com and blog.darwinbox.com subdomains were bleeding traffic outside the main property's view. And the "Unassigned" traffic spike was a UTM governance problem, not a tracking bug — the fix is a naming convention, not a tag.
None of this is glamorous. All of it is the diagnostic that makes the next dashboard worth opening.
The single most valuable deliverable in the engagement wasn't a slide. It was a workflow: a HubSpot lead-score-to-Google-Ads offline-conversion pipeline that lets Google's bidding algorithm optimize for the leads sales actually wants, not the ones the form fires on.
The pipeline is four steps. HubSpot scores every lead against a fit + engagement rubric. The GCLID captured at form fill travels with the record. HubSpot ships the score back to Google Ads as an offline conversion value. Google's Smart Bidding starts optimizing for score, not volume.
That's the difference between a paid program that fills a CRM with garbage and one that teaches the ad platform which clicks are worth more.
Fit + engagement rubric produces a numeric score at the record level. Live scoring on ingestion.
Google click identifier captured at form fill and stored on the contact — the join key.
Offline conversion upload pushes GCLID + score value into Google Ads on a scheduled cadence.
Smart Bidding weights toward click patterns that produce high-score leads, not high-count leads.
"The dashboard is only worth reading if the pixels underneath it are honest. That was the entire first month of this engagement."
For enterprise HR SaaS platforms scaling from a home market into North America, the Darwinbox engagement is the template.
It's not a media plan. It's a diagnostic that separates working infrastructure from broken infrastructure, a research pack big enough to feed twelve months of paid + SEO + ABM work, and a scoring model that teaches Google Ads what a good lead looks like — so the next quarter's budget doesn't fund the same mistakes the last one did.
Eight audit surfaces. Four datasets. One scoring workflow. One month.
iExcel runs the audit that tells you which line items on next year's marketing budget are worth funding, and which ones are quietly broken.
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