Shimon Sandler

Yext just published "A statistical look at regional and industry variation in local SEO ranking factors". Really interesting, especially this graphic. Here are the quickest takeaways: All effects are weak. Every correlation is small (roughly −0.10 to +0.14), so treat these as gentle signals, not causes. Biggest positives, by industry: Hospitality: Has Business Hours (+0.14, strongest overall), Has Merchant Description (+0.11), Profile Completeness (+0.10), Rating (+0.09). Healthcare: Review Count (+0.12) and New Reviews/Month (+0.11) lead; several review/engagement metrics sit around +0.07 to +0.08. Retail: Review Count (+0.08) and New Reviews/Month (+0.07); smaller bumps from Total Attributes and Photo Count (~+0.05). Financial Services (FINS): Review Count (+0.08), New Reviews/Month and Rating (+0.06), plus modest lifts from Attributes/Photos (+0.05). Food & Dining: Very small signals across the board; nothing much above +0.04. Consistent positives across industries More reviews (total and recent) and higher ratings are positive everywhere. Owner review responses (year/month/week) are positive but small. Consistent negatives Slower average review response time is negative in every vertical (worst in Healthcare at about −0.07). Notable outliers In Hospitality, having a local landing page shows the strongest negative (≈−0.10), and photo count is slightly negative (≈−0.04) - unique compared to other industries. Popular Times (presence of that feature) is more helpful for Healthcare and Hospitality (~+0.07–0.08) than elsewhere. Practical gist Focus on review volume, recency, and ratings everywhere. Respond quickly to reviews (slow replies correlate with worse outcomes). Hospitality: make sure hours, descriptions, and profiles are complete; don’t rely on a local landing page to help visibility. Retail/FINS: keep attributes and photos filled out; keep reviews flowing. Food & Dining: expect weaker gains from these particular profile tweaks, other factors likely matter more. ---- What do you think? #localseo #localseotips #localsearchrankings

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I see sellers torch Sponsored Display budgets treating it like Sponsored Products Sponsored Display charges for impressions and views, not clicks You pay to show up whether buyers care or not Broad audience targeting kills conversion rates fast Start with Views Remarketing Retarget shoppers who already viewed your listing Warm buyers convert at 3x the rate of cold traffic Then layer product targeting on competitor ASINs Skip lifestyle audiences until you max out high intent traffic Sponsored Display ACoS runs 20 to 30 percent higher than Sponsored Products That's expected for retargeting If ACoS climbs without lifting total sales, you're burning cash on wrong audiences Precision targeting hits buyers who showed purchase intent Everything else is expensive brand awareness you can't afford

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Interesting data/thoughts shared here by Kevin Indig. In SEO, we know that it is important to optimise for engagement. For large language models (LLMs), it appears that optimising for time-to-answer could be more important, at least for citation frequency. I'd caveat this with the point that being cited vs being recommended by a LLM can be two completely different things, although they aren't mutually exclusive. We may well find that engagement and UX best practise/gold standard from SEO still factors in when it comes to how likely an AI model is to recommend that users go and visit your webpage/interact with you brand (as in direct people to your website/brand within the answer itself). Time will tell and I look forward to reading the detail on this one/seeing all the data from Kevin and his team.

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