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“We’re focusing on differentiating by blending expert content and storytelling into the product journeys seamlessly, trying to help the customer find the right products but also for AI discoverability.”
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tl;dr – what you’ll get:
- Competing in SEO & GEO against bigger domains: how The Sports Edit focuses on where they’re different: exclusive brands, stronger ranges such as running, and specialist comparison content. That thinking also shapes how they’re approaching GEO and visibility in LLMs.
- How to run experimentation without a big team or budget: a small team scaled up testing after changing their tech stack; testing matters more, not less, when dev budget is tight.
- Investing in modern AI vendors: why they moved from Optimizely (which they used via M&S) to Visually, plus the AI-native PIM and AI tools they’re watching.
How The Sports Edit competes as a multi-brand retailer: SEO, content strategy & experimentation with Sara Davis, Ecommerce Manager.
The Sports Edit is in an interesting position. In search, they’re up against their own parent company Marks & Spencer (which they can never beat for domain authority), the D2C sites of the brands they sell and third party marketplaces. And they run experimentation without a dedicated CRO team.
Key discussion points:
The multi-brand role. Be the expert source and the one-stop shop for advice and a curated range. Stay on top of price. Make post-purchase service seamless.
The content problem. Shoe models update every year, so comparison content needs regular upkeep. Sarah’s candid that they’re good at launch content but haven’t cracked maintenance yet. AI is one area they’re exploring to help.
Organic strategy. Working with SEO agency Novos, they focus on unique brands, range strengths like running, and product and brand comparisons. Sarah’s team also runs sport strategy across M&S.
How the two sites split. The Sports Edit is the performance channel (top-end running shoes and high-performance apparel), while M&S is more commercial. Higher-priced products need richer content, so video, FAQs and page layout all matter more.
Personas and content. An expert advice blog pillar, long-tail comparison content (Nike vs. Puma, for example), and FAQs, which started on PLPs with PDPs next. Running is the first focus area.
First-party data. Sarah sees this as largely untapped. They capture preferences at newsletter signup, and surveyed customers before relaunching the loyalty programme earlier this year.
How experimentation works. It sits with Sarah and covers merchandising blends, recommendations and A/B tests. Ideas are proven in the testing tool first, then handed to dev to build properly.
Why Visually rather than Optimizely. It’s Shopify native, tests can be built in minutes using its AI generator, and the Contentsquare integration makes deeper analysis easier.
Where test ideas come from. GA and Contentsquare data, other retailers, D2C sites, Visually’s inspiration library and insight shared with M&S. The PDP gets most attention because that’s where PMax, Google Shopping and paid social traffic lands, and it’s increasingly important for LLMs.
Tech on the radar. Emfas, an AI-native PIM they’ve used for just over a year to scale content and work more efficiently, and Alhena, an AI tool they’ve seen in demos, for AI-generated FAQs and virtual try-on.
⭐ Chapters:
- [02:05] Sarah’s background and The Sports Edit
- [03:30] The multi-brand role versus D2C brand sites
- [07:20] Keeping expert content current as products change
- [10:00] Performance versus commercial channel positioning
- [17:20] Personas, comparison content and FAQs for LLM-era discovery
- [22:00] First-party data and loyalty programme insight
- [27:10] Experimentation without a dedicated CRO team
- [33:20] How test ideas are sourced and prioritised
- [37:40] Learning from failed tests and the next stage of segmentation
- [41:45] Tech that impressed: AI-native PIM and Alhena
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