SEO & AI search · Case study
+214% Organic Traffic in 12 Months: A BigCommerce SEO Case Study (2026)
Australian home & lifestyle retailer · BigCommerce storefront · 12-month SEO and AI search retainer · Client name withheld under NDA
Published · Last updated
The business
Client profile
The platform is named with the client's permission. Brand identifiers are redacted under NDA.
| Industry | Home & lifestyle eCommerce |
|---|---|
| Market | Australia |
| Platform | BigCommerce (Stencil theme) |
| Starting authority | Low–moderate (84 referring domains) |
| Starting organic sessions | ~1,150 / month |
| Organic share of revenue at start | Under 8% |
| Engagement | 12-month SEO and AI search retainer |
The movement
At a glance
| Metric | Start | Month 12 | Change |
|---|---|---|---|
| Monthly organic sessions | ~1,150 | ~3,610 | +214% |
| Organic revenue from search | ~$20k/mo* | ~$56k/mo | +180% (2.8×) |
| Referring domains | 84 | 218 | +159% |
| Keywords in Top 3 | 6 | 67 | +1,017% |
| Link retention at 12 months | Not applicable | 97% | Not applicable |
*Baseline ranged $18–22k/mo across the preceding quarter. Exact figures redacted under NDA.
Before the campaign
The stack we inherited
BigCommerce Stencil storefront - customised theme, not headless.
Legacy SEO app producing duplicate meta titles and descriptions across roughly 340 product pages.
Manual redirect spreadsheet maintained by the founder - 120+ URLs, several pointing to live 404s.
Flat category structure - every product sat under a single category level, so internal linking had nowhere to go.
No schema beyond BigCommerce defaults.
47 low-quality links from a previous vendor, spread across irrelevant directories and a PBN-adjacent network.
Less, but stronger
What we removed or replaced
The campaign was as much about deletion as addition. Most SEO case studies skip this part; it's usually where the real gains are.
- Disavowed and removed 47 low-quality links from the previous vendor. Filed removal requests where possible, disavowed the rest.
- Consolidated ~340 thin product pages into ~90 by merging duplicate variants and 301-redirecting the rest.
- Replaced the legacy SEO app with native BigCommerce SEO fields plus a structured data implementation.
- Rebuilt the redirect map in a proper 301 manager - fixed 120+ legacy redirects and resolved 14 chains that were wasting crawl budget.
- Restructured categories from flat to hierarchical (three levels), giving internal linking a framework to work with.
- Removed duplicated manufacturer copy from all 90 retained product pages and replaced it with original copy.
Shared delivery
How we worked with their team
- Weekly 30-minute call with their eCommerce manager - priorities, blockers, approvals.
- Their existing BigCommerce dev partner implemented theme-level changes in two-week sprints. We wrote the specs; they built. We did not have production access.
- Content approvals sat with the founder for anything touching brand voice.
- We owned the audit, keyword mapping, content briefs, outreach, schema markup, reporting and AI search restructuring.
- A shared Notion board tracked every page, redirect and placement. The client could see status in real time.
The commercial context
The starting point
An established Australian eCommerce brand in home and lifestyle products. The storefront was technically sound, but organic search contributed less than 8% of revenue and paid ads carried the business. Three specific problems: category pages were chasing head terms against marketplaces with no realistic path to ranking; product pages carried duplicated manufacturer copy across ~340 URLs; and there was no foundation - no content strategy, no internal linking, no schema, and a backlink profile polluted by a previous vendor.
Twelve months of work
What we did
- Months 1–2
Audit, cleanup and page selection.
Reviewed the backlink profile, disavowed 47 low-quality links, recovered unlinked brand mentions and mapped competitor coverage. Selected 12 category and product pages with the strongest commercial intent and realistic ranking potential. We did not target the highest-volume head terms.
- Months 3–5
Content and on-page foundation.
Consolidated ~340 product pages into ~90. Rewrote category and product copy around specific high-intent queries. Built a three-level category hierarchy and an internal linking structure connecting buying guides → product pages → comparison content. Implemented Product and FAQ schema across the catalogue.
- Months 4–9
Editorial link acquisition.
Targeted outreach to home, lifestyle and gift-guide publishers. Links were earned through original product research, seasonal buying guides and expert commentary. An agreed minimum DR threshold applied before the campaign started. Average placed link DR: 55–80.
- Months 6–12
AI search readiness.
Restructured key pages for answer extraction: clear headings, direct answers, structured data. AI referrals were tracked as a separate channel throughout.
Tools used: Ahrefs, Screaming Frog, Google Search Console, GA4, BigCommerce native SEO fields.
The reported results
The results
| Metric | Start | Month 6 | Month 12 |
|---|---|---|---|
| Monthly organic sessions | ~1,150 | ~1,950 | ~3,610 |
| Organic revenue from search | ~$20k/mo | ~$34k/mo (1.7×) | ~$56k/mo (2.8×) |
| Referring domains | 84 | 147 | 218 |
| Keywords in Top 3 | 6 | 31 | 67 |
| AI referral sessions / month | <10 | ~45 | ~130 |
Growth was not linear. Months 1–4 were flat - the cleanup and consolidation work produces nothing visible in the short term. Acceleration began in month 5 and continued through month 12.
Why it moved
What drove it
The largest contributor was the page consolidation and content rewrite - merging 340 thin pages into 90 strong ones removed a significant internal duplication problem. Editorial links then raised the ranking ceiling on those pages. Long-tail content and AI search restructuring added query coverage and produced the AI referral growth shown above.
We don't split traffic into clean percentages across these - they interact - but the order of impact is as listed.
Put the result in context
What this case study doesn't show
This is one client, not an average. Across retained clients with 12+ months of engagement, our company-wide average organic traffic growth is +30 – 57% over 12 months. This client sits at the strong end.
The starting baseline was low. ~1,150 monthly sessions and ~$20k/mo is a small base. The same percentage would be far harder from 50,000 sessions.
The market was favourable. Home and lifestyle has strong seasonal gifting demand, which supported the Q4 content push.
AI search is a small channel here. ~130 sessions per month is real growth, but it is not what produced the +214%. The +214% is organic search, reported separately.
Your starting point matters
What this might mean for you
This client started with a small organic footprint and a low-authority profile - the situation where percentage gains are largest. If your site is in a similar position, the playbook here is directly transferable: cleanup, consolidation, editorial links, AI readiness.
If your site is more established, the same playbook still applies - but the numbers look different. Expect smaller percentages, larger absolute traffic and revenue, and a greater emphasis on protecting existing rankings than on recovery.
And if you need results inside 60 days, we'd rather say it now: the first four months of this campaign were flat. Anyone promising otherwise is selling you something other than SEO.
The honest answer is that we won't know which category you're in until we've looked at your site. That's what the gap audit is for.
Your next step
Want to see how this might look for your site?
Every campaign starts from a different baseline. If you'd like to see the detail behind this one - sessions, revenue, placements and the pages that moved - we're happy to walk you through it on a call, with brand identifiers removed. We can also connect you with the client under NDA if that's useful.