How TechFlux cut cost-per-lead 72% and tripled qualified pipeline in 90 days
A mid-market workflow SaaS was spending ₹6L a month on Google Ads with no visibility into which keywords actually produced closed revenue. We rebuilt the account around buyer intent, fixed attribution, and redesigned the B2B landing experience.
The challenge
TechFlux was 18 months into their Series A and scaling paid acquisition hard. Google Ads spend had climbed from ₹2L to ₹6L a month over two quarters, but pipeline had gone sideways. The internal team knew something was broken — they just could not prove which campaigns were producing deals.
Our audit surfaced three structural problems. First, 41% of impressions were from irrelevant generic terms like 'workflow tool' and 'automation software' with no qualifier, pulling in enterprise searchers looking for Salesforce-class products. Second, Google's smart bidding was optimising to 'Demo Request' form fills, but 60% of those fills were students, competitors, or freelancers — none of which the sales team ever closed. Third, there was no handshake between Google Ads conversions and closed-won revenue in HubSpot, so Google was optimising toward the wrong signal.
The board was asking for a CAC payback target of under 14 months. Actual payback was 31 months and worsening. They had six weeks to show a different trajectory or the channel budget was going to get cut.
Our approach
Phase 1 — Audit and attribution rebuild (weeks 1-2)
We wired up offline conversion imports from HubSpot back into Google Ads, mapping every form fill to deal stage. Smart bidding now had real revenue signal, not just MQLs. We also moved GA4 to measurement-protocol events for server-side tracking so iOS and ad-blocker users stopped vanishing from the funnel.
Phase 2 — Account restructure around buyer intent (weeks 3-5)
We collapsed 42 ad groups into 11 tightly-themed clusters by buyer intent. Generic head terms moved into a suppressed-bid exploration campaign; high-intent comparison and jobs-to-be-done terms got 80% of the budget. We added a 600-line negative keyword list harvested from the search terms report and competitor branded terms we had no right to bid on.
Phase 3 — Landing page surgery (weeks 4-8)
The existing landing page was a homepage clone. We built four intent-matched pages — one per buyer persona — each with a specific hero, social proof from a matched customer, and a booking calendar instead of a form. Average time-on-page went from 24 seconds to 1m 48s, and demo-to-SQL rate doubled.
Phase 4 — Optimisation and weekly revenue reporting (weeks 9-12)
Once the data was clean we ran tCPA down from ₹22k to ₹8k in four steps, pausing any ad group that could not hold volume at the new bid. Weekly reports moved from impressions and CTR to pipeline sourced and closed-won by campaign. The CEO now reviews one page on Monday morning instead of a Looker dashboard with 34 widgets.
The results
Calculated on accepted SQLs, not raw form fills.
Pipeline value attributed to Google Ads in HubSpot.
Driven largely by landing page and persona match.
Slight reduction despite 3x pipeline — efficiency compounded.
Inside the board's 14-month target.
“We were spending close to ₹6L a month on Google Ads with no clear line to pipeline. Within 90 days Digitaso cut our cost per lead from ₹26,000 to ₹7,400 and we could finally see which campaigns produced closed revenue, not just form fills.”
What we got wrong — and what we'd do differently
Not everything worked on the first attempt. The original persona-matched landing pages we built had a booking calendar embedded above the fold, and conversion rate actually dropped 18% in the first week. Turned out B2B buyers in the ₹1-4 crore ACV band wanted to read the full use-case before committing to a call — we had over-optimised for velocity. We moved the calendar below a short case study section and recovered, then exceeded, baseline.
The other honest note — offline conversion imports took us almost three full weeks to stabilise because HubSpot's deal-stage timestamps were unreliable for deals that had been manually moved. We eventually rebuilt the pipeline on the underlying HubSpot timeline events rather than the deal-stage property. Worth knowing if you try to replicate the playbook.
Services used in this engagement
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