E-commerce · Mumbai, India
E-commerce case study: organic revenue +212%
A 2,400-SKU home décor store grew organic revenue 212% in nine months after we fixed crawl waste, rebuilt category content and made product data machine-readable for AI shopping answers.
Direct Answer & Strategic Overview
A 2,400-SKU home décor store grew organic revenue 212% in nine months after we fixed crawl waste, rebuilt category content and made product data machine-readable for AI shopping answers.
Key Takeaways
- • Expert strategies tailored for Crawl control
- • Data-driven approach without relying on guesswork
- • Focus on sustainable, long-term search visibility
- • Fully aligned with modern Answer Engine and AI search requirements
Client snapshot
| Field | Detail |
|---|---|
| Client | D2C home & living store |
| Industry | E-commerce |
| Market | Mumbai, India |
| Services used | Technical SEO + content + AI SEO |
| Engagement length | 9 months |
The challenge
When d2c home & living store came to AVR Web Consulting, growth had stalled for reasons that were structural, not cosmetic. The audit surfaced four blocking issues:
- • 68% of crawl budget was spent on faceted filter URLs that returned near-duplicate pages
- • Category pages had 40 words of copy and no schema, so they lost to marketplaces
- • Zero visibility in AI assistants for 'best budget X in India' style prompts
- • Product titles duplicated across variants, splitting authority
What we did
Step 1: Crawl control
Canonicalised facet combinations, blocked 11,000 parameter URLs and rebuilt the XML sitemap by template so Google spent its budget on money pages.
Step 2: Category content system
Wrote answer-first buying guides on the top 40 category pages — 600-900 words each with comparison tables, sizing guidance and FAQ blocks.
Step 3: Structured product data
Added Product, Offer, AggregateRating and FAQPage schema across the catalogue so both Google and AI shopping assistants could parse price, stock and specs.
Step 4: Authority building
Manual outreach placed 34 links from Indian home décor, interiors and lifestyle publications over seven months.
Step 5: AI answer targeting
Built 18 comparison and 'best of' pages structured as extractable answers, then tracked citations weekly across ChatGPT, Gemini and Perplexity.
Results after 9 months
| Metric | Before | After | Change |
|---|---|---|---|
| Organic sessions | 38,400/mo | 94,700/mo | +147% |
| Organic revenue | ₹11.2L/mo | ₹34.9L/mo | +212% |
| Top-10 keywords | 212 | 1,046 | +393% |
| AI assistant citations | 0 | 47/mo | New channel |
In the client's words
"We had spent two years buying traffic. AVR made the catalogue itself the traffic source, and now AI shopping assistants recommend us by name." — Head of Growth, D2C home & living brand
What this means for similar businesses
- • Crawl waste is an invisible ceiling — resolving facet bloat before investing in new content is the highest-ROI move for large catalogues
- • Category pages written as genuine buying guides outperform thin product-list pages even against well-funded marketplaces
- • Product schema that publishes price, stock and ratings in machine-readable form is the entry ticket for AI shopping answer inclusion
- • Sustained authority building through relevant editorial links compounds alongside content work to produce gains that paid spend alone cannot replicate
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