High-CPC fintech vertical. Spending Rs 40L/month on Google Ads. 1.8x ROAS. Poor account structure with generic keywords competing on price.
4.2x ROAS. Rs 24L/month spend (40% reduction). 65% CPA reduction. 2.8x improvement in conversion rate through audience refinement.
Fintech is one of the most expensive verticals on Google Ads. CPCs for trading-related keywords in India regularly hit ₹200–₹600 per click, which means every inefficiency costs money fast. Stockk.trade was spending ₹40L/month — serious budget — but returning only 1.8x ROAS. The account had been built to maximise clicks and registrations, not to acquire the right users. The account structure told the story when Deepak pulled it. Three broad campaigns covered everything: "trading app," "stock market," and "demat account." All broad match keywords, minimal negatives. The account was matching to queries like "free stock market tips," "how to invest for beginners," and "paper trading practice" — none of which produce the high-intent, account-funded traders Stockk.trade needed. Audience targeting was turned off entirely. Smart Bidding had been enabled but set to Maximize Conversions rather than Target ROAS, so Google was optimising for registration volume at any cost rather than for high-value signups. The brand campaign was mixed into general non-brand campaigns, corrupting Smart Bidding's signals. Step 1 — Wasted spend audit: Ran a search term report for the past 6 months. Identified ₹8.4L/month going to queries with zero ROAS. Built an initial negative keyword list of 340 terms — added in the first week. Step 2 — Campaign restructure by intent tier: Rebuilt from 3 broad campaigns into 6 intent-based campaigns: beginners, active traders, advanced investors, product-specific, competitor conquesting, and brand-only. Each had its own budget, bid strategy, and audience targeting. Step 3 — Brand separation: Moved all Stockk.trade brand keywords into a dedicated campaign with separate budget. Added brand terms as exact match negatives in non-brand campaigns. This immediately clarified conversion data going into Smart Bidding — brand traffic converts at 8–12x the rate of non-brand. Step 4 — Audience targeting by income and behaviour: Applied demographic layering across all campaigns: income top 30%, age 25–54, interest in investing and financial products. Created remarketing lists from first-party data: pricing page visitors, incomplete registrations, KYC-complete but unfunded accounts. Step 5 — Smart Bidding on conversion value: Switched from Maximize Conversions to Target ROAS with separate targets for each intent tier. Set up conversion value rules so Google received accurate signals — funded accounts valued higher than basic registrations. ROAS improvements visible by week 6. By month 2, negative keyword cleanup and brand separation had already pushed ROAS from 1.8x to 2.4x. Monthly ad spend dropped from ₹40L to ₹24L — not because budget was cut, but because ₹16L was going to searches not producing customers. The 2.8x conversion rate improvement came from better audience targeting filtering out low-intent users, and campaign restructure ensuring each ad appeared for the right query stage.