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Solution · Performance marketing (India)

Meta + Google Ads for ecommerce

D2C and ecommerce grow when paid social and search work with catalogue truth, offer clarity, and a site that can convert on mobile. We combine Meta for demand and creative velocity with Google for high-intent capture—always tying targets to contribution, not a single ROAS screenshot from a lucky week.

Who it’s for: D2C brands, fashion & lifestyle, home goods, and multi-SKU stores that need disciplined creative testing, clean catalog/PDP alignment, and scaling rules that don’t collapse on thin-margin products.

Ecommerce performance is a loop: find creative-market fit, scale what works, then protect margin as spend rises. We set up product-led creative frameworks, run structured test batches, and connect shopping/search intent where it makes sense for your category. Site speed, returns policy clarity, and checkout friction are part of the same system as the ad account.

How we approach it

Practical playbooks for Meta Ads, Google Ads, landing pages, and CRO — not generic “awareness” talk.

Pillar 1

Meta: broad vs targeted prospecting, retargeting by behaviour (view content, add to cart, initiate checkout), and creative cadence to fight fatigue in fast-moving categories.

Pillar 2

Google: brand defence, category capture, and shopping feeds where applicable—aligned to margin and stock reality.

Pillar 3

Product & offer alignment: best sellers vs long tail, discount strategy that doesn’t train customers to only buy on promos, and PDPs that match the ad’s promise.

Pillar 4

Scaling rules: step-ups with guardrails—incremental spend tests, new customer vs returning targets, and contribution-aware KPIs (not just platform ROAS).

Measurement, attribution & reporting

We emphasise platform signals (purchase, value) plus your business numbers where possible: returns, contribution margin, and inventory constraints. iOS/ATT and browser limitations mean we treat reporting as directional and stable definitions—not perfect truth. We help you read blended performance without fooling yourself.

Common mistakes (we help you avoid)

  • Scaling spend before checkout, shipping, and returns are stable—turning a media problem into an operations crisis.
  • Comparing week-to-week ROAS on small numbers; noise dominates until volume and seasonality are accounted for.
  • Over-segmenting into dozens of ad sets and starving the algorithm of exit velocity from learning.

Local intent pages

If you’re evaluating this playbook for a specific city, start here:

Related reading & services

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Related case studies

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