Moshe Soloff shares advice on SEO for e-commerce and B2B businesses, including how product pages and online catalogs can earn visibility in search and AI recommendations.
Useful for practical SEO advice on product pages, online catalogs, and AI search visibility for B2B e-commerce.
Best for
B2B e-commerce SEO agencies or guest expert sessions on AI search visibility for product pages fit.
Not for
A consumer snack brand wanting sponsored short-form video, unboxing, discount codes, or guaranteed grocery audience does not fit.
Key takeaways
Choose local or national SEO based on whether customers need to be nearby.
Search is moving from getting clicked to being the recommendation.
Product pages need FAQs, honest comparisons, customer stories, videos, and reviews, not just title and price.
Check first: Confirm scope, duration, compensation, and audience evidence before booking or contacting them.
Main ideas
The right SEO depends on whether customers need to be close by · Search is moving from getting clicked to being the recommendation · Product pages are the asset most businesses ignore
Moshe Soloff shares practical advice on SEO for e-commerce and B2B businesses.
Their posts focus on helping product pages and online catalogs answer buyer questions and get found in search and AI recommendations.
Follow their work for clear explanations of how changes in buying behavior affect what businesses should put on their websites.
Soloff connects shifts in search behavior to specific changes businesses can make.
For example, they explain how an AI-ready product page can give buyers context through FAQs, honest comparisons, customer stories, videos, and useful reviews—not just a title and price.
For industrial businesses, they show how searchable pages for product specifications and part numbers can help buyers find catalog items and generate inquiries.
Their post on local versus national SEO adds a simple decision rule: choose based on whether the customer needs to be nearby.
Across these examples, their work translates broad SEO changes into concrete website and catalog choices.
Moshe Soloff writes about how people find and buy products online, and what that demands of a company's website.
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The recurring argument is that the website, its product pages, and its catalog are sales assets rather than brochures, and that the deciding factor is unglamorous clarity and data hygiene.
Another thread is the shift from earning clicks to being the answer an AI engine gives: ranking well and being chosen have become different things.
The right search strategy follows the business model, not fashion—a business whose customers must be nearby needs local visibility, while one that can ship anywhere competes nationally.
Concrete examples run through the posts, from locksmiths and kosher restaurants to Grainger and Fastenal, and from locksmith searches to part-number searches at 11 p.m.
The right SEO depends on whether customers need to be close by
Moshe Soloff splits search strategy by business model rather than treating SEO as one method.
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Local SEO is about appearing in map and location searches, where a result from another state is useless to the searcher.
It is easier to win because the business competes only with others in its area, and it suits shops, service businesses with a physical location, and anything where proximity matters.
National SEO is about standard search results, where location is irrelevant because the product or service can be delivered anywhere.
It suits e-commerce brands, software companies, and content-driven sites, but the tradeoff is competing with everyone, which demands more time, content, and authority.
Their decision rule is blunt: if the customer needs to be near you, use local SEO; if the customer can be anywhere, use national SEO.
A search for "locksmith" or "kosher restaurant" signals local intent: the searcher needs someone nearby right now.
A search for "accounting software" or "what is a kosher restaurant" carries no location requirement, so it belongs to national SEO.
Two businesses in the same industry can need completely different strategies and both be right.
Search is moving from getting clicked to being the recommendation
Moshe Soloff argues that the buying journey has changed shape.
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The old path was search, scroll, compare, click, maybe buy.
The newer path is ask, get a small set of recommendations, buy.
That makes the contest about being selected rather than being visited.
A site can rank well on Google and still lose traffic because an AI summary answered the buyer's question before they arrived.
They do not treat this as the death of SEO.
The brands winning are cited as the source, which requires detailed and trustworthy product information, real expertise instead of generic filler, honest comparisons, reviews and FAQs that actually answer questions, and content that educates rather than only sells.
Moshe Soloff describes ChatGPT recommending products to shoppers and Shopify merchants already being inside that system with no extra setup, so most have no idea it is happening.
Clean product titles, accurate price, stock and variant data, categories that match how people search, and off-site signals such as reviews and mentions help get a product picked.
Vague names, messy variant URLs, thin pages, and no footprint beyond the store quietly reduce the chances.
The comparison is Amazon, except the shelf is curated by AI and placement cannot be bought.
Product pages are the asset most businesses ignore
Most e-commerce product pages were built for an older internet: a title, a few bullets, a price.
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Moshe Soloff argues that is no longer enough.
When someone asks an AI engine for the best product for a specific need, the engine is not scanning the title.
It is looking for context: who the product is for, what problem it solves, how it compares with alternatives, what real customers experienced, and what buyers usually ask before purchasing.
A page that cannot answer those questions loses to one that can.
The fix is to treat product pages as small resource hubs rather than simple listings.
Many businesses pour money into blog content while leaving the pages closest to a sale outdated.
A page can be rebuilt with detailed FAQs that address real objections, comparison sections that do not dodge hard questions, customer stories with specific outcomes, video showing the product in use, and reviews that go beyond "great product, five stars." Moshe Soloff frames the catalog itself as possibly a business's biggest untapped search asset.
Industrial catalogs are a sales channel hiding in plain sight
Moshe Soloff points out that overall B2B sales barely grew while B2B e-commerce grew much faster: buyers did not disappear; they buy differently.
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Younger buyers expect to look up a part number late at night and order it without a call.
Many industrial sites still answer with a phone number, "call for pricing," and a catalog that cannot be searched.
They call industrial SEO one of the most overlooked opportunities in e-commerce because people searching by part number and specification already know what they want.
A large catalog becomes a large number of entry pages, each a way for a ready buyer to arrive.
A website should be the company's best sales rep: answering questions, showing pricing where possible, ranking for the parts people search, and bringing in quote requests while the team sleeps.
Grainger, at almost 80% of sales online, and Fastenal, at 61%, are held up as companies that saw the buyer change and built for it.
Moshe Soloff also describes seeing distributors land real quote requests from large manufacturers simply by showing up when someone searched for the exact part they needed, without running ads.
A catalog of 10,000 products becomes 10,000 pages and 10,000 ways in.
The deciding factor is boring fundamentals, not tactics
Across the posts, the same point keeps returning: whether the goal is a ranking or an AI recommendation, the underlying requirements are the same and unglamorous.
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Clear product titles that say what the thing is rather than what sounds clever, clean data for price, stock, and variants, categories that match how people actually search, and a presence outside the company's own site through reviews, mentions, and trust signals. These are the basis for being found and chosen, which is why Moshe Soloff frames recommendation visibility as earned through fundamentals rather than bought through placement.
The same checklist appears in the discussion of ChatGPT product recommendations and the local-versus-national breakdown. The practical starting point is getting clear on what the business actually needs before choosing tactics.
This is a clear starting point for the AI search question: how shoppers now discover products and what makes a product recommendable.
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Moshe Soloff describes a shift from a search-and-compare funnel to an ask-and-get-recommendations funnel.
The post says ChatGPT now recommends products to shoppers, Shopify merchants are already inside it without extra setup, and being chosen matters more than being clicked.
The practical checklist is deliberately unglamorous: clear product titles, clean data for price, stock, and variants, strong categories that match how people search, and a real off-site presence, including reviews, mentions, and trust signals.
Vague product names, messy variant URLs, thin product pages, and no footprint beyond the store reduce the chance of being picked.
The comparison to Amazon is useful: AI curates the shelf, and placement is not for sale.
For brands planning a campaign
For a B2B e-commerce SEO agency, this post offers a ready-made guest-session outline: audit product titles, data hygiene, category language, and off-site trust before discussing rankings. It also helps set client expectations: recommendation visibility comes from strong fundamentals, not ad placement.
This explains what AI looks for on a product page and why pages close to a sale matter more than another blog post.
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The post argues that most e-commerce product pages were built for an older internet: a title, a few bullets, and a price.
When someone asks AI for the best product for a specific need, AI looks beyond the title for context: who the product is for, what problem it solves, how it compares with alternatives, what real customers experienced, and what buyers usually ask before purchasing.
If the page cannot answer those questions, AI moves to one that can.
Moshe Soloff recommends treating product pages as mini resource hubs, with detailed FAQs, comparison sections, customer stories with specific outcomes, product videos, and reviews that go beyond generic praise.
The sharp observation: many businesses spend heavily on blog content while the pages closest to a sale remain outdated.
For brands planning a campaign
This is a strong example for a guest expert session on AI search visibility because it turns the topic into a concrete product-page audit. A session could walk through one page, show missing context, comparison gaps, and review quality, then give the agency a repeatable checklist.
This explains why sites can rank well and still lose traffic, and why AI citations now factor into the buying decision.
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The post maps a changed funnel: search, then an AI summary, then maybe a click, then a purchase.
Moshe Soloff says many e-commerce sites still rank on Google but are bleeding traffic because AI engines close the sale before customers reach the page.
The response is not to declare SEO dead.
Brands winning in AI search are cited as the source.
That requires detailed, trustworthy product information, real expertise rather than generic filler, honest product comparisons, reviews and FAQs that answer questions, and content that educates instead of only selling.
The goal shifts from showing up on page one to being the answer AI gives.
For brands planning a campaign
Use this to frame measurement and content strategy for agency clients: track citations and AI summaries alongside rankings, and prioritise pages that answer buyer questions before the click. It also provides a natural bridge from SEO reporting to AI visibility reporting in a guest session.
This gives the B2B e-commerce context: buyers changed how they purchase, and industrial product catalogs are an underused search asset.
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The post contrasts flat overall B2B sales with 13% growth in B2B ecommerce and $2.9 trillion in online sales.
Moshe Soloff says buyers did not disappear; they buy differently.
The post cites 73% of B2B buyers wanting to buy online and 71% being Millennials or Gen Z.
Buyers expect to look up a part number at 11 p.m. and order it, but many industrial sites still offer only a phone number, call-for-pricing messaging, and an unsearchable catalog.
Grainger and Fastenal are examples of companies that saw the buyer change and built for it.
Part-number and specification searches are high-intent, and a catalog of 10,000 products can become 10,000 pages and 10,000 ways into the site.
Moshe Soloff says distributors have landed real RFQs from manufacturers simply by showing up when someone searched the exact part, not by running ads.
For brands planning a campaign
This supports a campaign angle for industrial distributors, manufacturers, and B2B e-commerce teams: turn the catalog into searchable, indexable product pages that answer spec and part-number queries around the clock. For an agency targeting those teams, it shows Moshe Soloff can speak to buyer behaviour and revenue mechanics, not just page tactics.
This is a plain-language way to determine whether a business needs local or national SEO before choosing tactics.
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Moshe Soloff splits SEO into local and national, tying each to a business model.
Local SEO focuses on Google Maps and location-based searches such as locksmith or kosher restaurant, where a result from another state is useless.
It is more accessible because the business competes only with others in its area.
It fits brick-and-mortar stores, service businesses with a physical location, and any business where proximity matters.
National SEO focuses on standard search results, where location is irrelevant because the product or service can be delivered anywhere, such as accounting software or informational queries.
It fits e-commerce brands, SaaS companies, and content-driven sites, but means competing nationwide, which requires more time, content, and authority.
The decision rule is simple: if the customer needs to be near you, use local SEO; if the customer can be anywhere, use national SEO.
For brands planning a campaign
For an agency working across different client types, this post offers a clear segmentation conversation before deeper AI search work. It can help qualify whether a prospective client should invest in local visibility, national content authority, or both.
Choosing an SEO Strategy by Business Model: Local vs. National
Moshe Soloff says a business model determines its SEO approach.
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Local SEO targets location-based searches, such as “locksmith,” and suits brick-and-mortar and service businesses, with less competition. National SEO targets queries that aren’t tied to a location, such as “accounting software,” and suits SaaS and e-commerce businesses—but puts them up against nationwide competition.
Comparing search intent
For brands planning a campaign
Brands should first determine whether customers need to be nearby or can buy remotely. That choice shapes the right SEO strategy.
As AI engines like ChatGPT increasingly recommend products instead of simply ranking search results, the goal is shifting from earning clicks to becoming the primary answer AI cites.
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AI favors businesses with comprehensive, trustworthy content that goes beyond basic descriptions.
The changing consumer journey
For brands planning a campaign
Agencies should help clients build content that demonstrates expertise and authority, making the brand a definitive source for AI summaries—not just a link.
Traditional e-commerce product pages—with a title, bullets, and price—fall short in modern AI searches.
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AI looks for context: Who is the product for?
What problems does it solve?
How does it compare with alternatives?
Winning brands treat product pages as resource hubs, with detailed FAQs, comparison sections, customer stories, and reviews.
Expanding content and context
For brands planning a campaign
Campaigns should prioritize adding depth and clarity to existing product content, turning sales pages into decision-making resources for AI and shoppers.
In industrial B2B, customers increasingly research purchases online, often searching for specific part numbers.
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Successful companies use “Industrial SEO” to structure catalogs so they rank for those searches. Their websites act as round-the-clock sales representatives, bringing in qualified RFQs.
Catalog optimization
For brands planning a campaign
Brands serving B2B manufacturers or industrial buyers should structure their sites so search engines can index individual products and parts, making the catalog a primary acquisition channel.
Whether the goal is to rank in search or earn an AI recommendation, success depends on clean data and trust signals.
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AI systems favor clear, well-structured content, including clear product titles, accurate data on stock, price, and variants, strong categorization, and a verifiable external presence.
Auditing data and content
For brands planning a campaign
Start by auditing data integrity and content clarity—the “boring fundamentals” that underpin both traditional and modern SEO.
Moshe Soloff's posts do not document sponsorships, clients, employment, ownership, affiliate arrangements, investments, or disclosed partnerships.
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They mention Google, ChatGPT, and Shopify as search and shopping platforms, and Grainger and Fastenal as examples of distributors with large online sales—not as commercial relationships.
The posts name no agency, employer, or owned business.
They repeatedly offer to review readers' stores or product pages, but do not say what business they do this work through.
Ask whether those reviews are paid and, if so, through which business.
Moshe Soloff writes about how businesses get found and chosen online.
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Start with the local-versus-national SEO post, which offers a clear decision rule: if customers need to be nearby, local visibility matters; if they can buy from anywhere, the business competes nationally.
Then read the product-page and AI-recommendation posts together.
They argue that search is shifting from earning clicks to being the answer an AI engine gives, and that product pages need enough context, comparisons, reviews, and clean data to earn a recommendation.
The industrial e-commerce post applies the same thinking to B2B catalogs, where part numbers and specifications become entry points for buyers who already know what they want.
The through-line is practical: clear titles, accurate product data, useful FAQs, honest comparisons, and a presence beyond the company's own site matter more than tactics layered on weak fundamentals.
Both write about making a company's expertise legible to search engines and AI answers, including in technical B2B and industrial sectors.
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Shafique's posts on entity understanding and keyword judgment overlap with Soloff's focus on clear information, search intent, and AI visibility.
Shafique frames the work around agency consulting for a surgical-instrument manufacturer and choosing realistic keywords when AI tools produce attractive but impractical lists. Soloff focuses more on local-versus-national strategy, product-page structure, and catalog-led e-commerce.
Both treat product pages as a central battleground in AI search.
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They argue that entities, search intent, reviews, clean product data, and useful context matter more than keyword stuffing.
Nisar M. presents a structured product-page framework, checklist, and downloadable resource for e-commerce, B2B, and SaaS. Soloff's posts offer shorter arguments grounded in local SEO, AI recommendations, and industrial catalogs.
Both argue that AI search requires understanding how an answer is assembled, not just which keyword ranks.
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Vijay's posts on testing whether ChatGPT searches the web and how queries fan out into multiple research steps complement Soloff's argument that being cited matters more than being clicked.
Vijay focuses on AEO strategy and retrieval research: how AI systems search, which sources they trust, and how to test visibility. Soloff focuses more on e-commerce product pages, local-versus-national SEO, and industrial B2B catalogs.
For a guest expert session on AI search visibility for product pages, the posts offer a reach test, not a verified audience match.
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Moshe Soloff had 5,545 LinkedIn followers on 2026-10-09.
Their posts address e-commerce and B2B search problems: product pages that answer buyer questions, AI recommendations and citations, online catalogs and part-number search, B2B ecommerce growth, and local versus national SEO.
That content is relevant to an agency selling SEO for product pages, but the posts do not identify followers or viewers by job, employer, seniority, industry, or geography.
Before booking, ask for recent post results on AI search or product-page topics and whether Soloff has audience survey results or another documented source for professional attributes.
Treat follower count as a reach signal, not proof that followers are e-commerce SEO decision-makers.
LinkedIn followers
5,545
LinkedIn profile follower count at observation · 2026-10-09
The five posts describe no sponsorship program, media kit, rate card, or published partnership terms.
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Each closes with a direct invitation to get in touch — "If you want a second set of eyes on your store, feel free to reach out" and "Drop a message and let's figure it out together" — suggesting an informal audit or consulting conversation about product pages, catalogs, and AI-search visibility rather than a defined paid-placement format.
For a paid collaboration, brands would need to negotiate the terms, deliverables, and pricing directly; the posts set no expectations for scope or cost.
Ask directly whether Moshe Soloff accepts sponsorships and what terms they offer.
A B2B e-commerce SEO agency wants a guest expert session on AI search visibility for product pages, aimed at its own clients and prospects.
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Decision: book Moshe Soloff, once scope, duration, and compensation are agreed.
The topic match is direct.
Two of the five posts deal specifically with what AI looks for on a product page.
One asks when the reader last read their own product page and argues that AI looks beyond the product title for context: who the product is for, what problem it solves, how it compares with alternatives, what real customers experienced, and what buyers usually ask before purchasing.
The post also names a failure mode the agency’s clients will recognise: businesses pour budget into blog content while the pages closest to a sale stay outdated.
The other post covers the discovery shift from search-scroll-compare-click to ask-get-three-recommendations-and-buy, with a checklist of clear product titles, clean data for price, stock, and variants, categories that match how people search, and off-site presence such as reviews and mentions.
A third post explains why sites can still rank on Google and lose traffic, reframing the goal as being cited rather than merely ranking.
The commercial format suits a guest session, though it is not pre-packaged.
Every post ends with an open invitation to get in touch, usually offering a second set of eyes on a store or product page.
No media kit, rate card, or published session terms appear in the posts, so agree scope, duration, and compensation directly rather than assuming a standard package.
For a session where the agency brings its own attendees, the topic fit matters more than audience reach.
The only audience measurement available is a follower count; before using the session for distribution, ask for audience details by job, employer, seniority, or industry.
When to choose a specialist
A consumer snack brand wants a paid sponsored short-form video series with unboxing clips, a discount code, and a guarantee that most viewers are grocery shoppers aged 18 to 34.
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Decision: decline, or treat the proposal as unscoped and unsupported by the available posts.
Four checks fail.
First, format: the posts show no video work, on-camera presenting, or short-form content; they are written posts only.
Second, commercial fit: no sponsorship program, media kit, rate card, or published partnership terms appear, and none of the posts is a disclosed brand partnership.
The repeated offer of a second set of eyes on a store reads as an audit or consulting conversation, not paid placement; the deliverables and pricing needed for sponsorship are not established.
Third, audience: no platform demographics, engagement metrics, audience survey, or video performance data support a guarantee about viewer age or shopping behaviour.
Fourth, subject matter: the posts address whether product pages and catalogs get found and chosen; entertainment-led reach is a different job.
If the snack brand’s real need is to structure its product pages and catalog so AI engines can recommend its products, that is the conversation the posts support—not an unboxing series.
A worked campaign example
Worked angle for the agency brief: a 45-minute practical session called something like “What AI reads on your product page before it recommends you,” delivered to the agency’s client and prospect list.
Opening, about five minutes.
Use the funnel shift as the hook: search, then an AI summary, then maybe a click, then a purchase.
Sites can rank well and look fine, yet still lose traffic because the answer arrives before the visit.
This sets up the problem without declaring SEO dead.
Body, about thirty minutes, built around one live audit.
Pick a product page from one of the agency’s clients in advance and assess it against five questions drawn from the material: who is this product actually for; what problem does it solve; how does it stack up against the alternatives a buyer would consider; what did real customers experience; and what do buyers ask before purchasing?
Score the page out loud and show where AI would move on to a competitor that answers better.
Then rebuild one section on screen, ideally the FAQ or comparison block, so attendees see the difference between a page that describes a product and one that answers objections.
Extend the discussion to catalog-level thinking for industrial and B2B attendees.
Bring in the B2B e-commerce numbers as context: flat overall sales against double-digit online growth, and a buyer base that increasingly wants to look up a part number at 11 p.m. and order it rather than call.
The key point is that a catalog of ten thousand products can mean ten thousand indexable pages and ten thousand ways in—and that distributors have picked up real RFQs from appearing in exact part searches rather than from ads.
This gives larger clients a reason to act beyond one product page.
Close, about ten minutes, with the fundamentals and next steps.
The unglamorous checklist is the takeaway: clear product titles, clean data for price, stock, and variants, categories that match how people search, and real presence outside the store through reviews, mentions, and trust signals.
Vague product names, messy variant URLs, thin pages, and zero off-site footprint quietly cost selection.
Frame the goal as being the answer AI gives, not only a page-one result, and offer attendees a follow-up page review as the handoff.
Agree scope and compensation with Moshe Soloff directly before promoting the session; no published session or partnership terms are available.
A better choice than Moshe Soloff when the agency's clients are industrial or medical manufacturers and the session should focus on making an existing company easier for AI to understand.
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Azhan describes a surgical instrument manufacturer with strong backlinks and real certifications that ChatGPT and Google AI still misread.
The repair was to clarify who the company is, what it manufactures, its location, product categories, certifications, expertise and history, then get important pages indexed again.
A second post separates useful AI speed from missing SEO judgment: a prospect's AI-generated keyword list had high volume but no difficulty, intent or sense of who already ranks.
For a room of B2B manufacturers, that entity-and-intent framing may land harder than a product-page audit.
The tradeoff is distance from the specific product-page task.
Azhan's posts show entity repair and keyword strategy for a manufacturer's website, not a walkthrough of how AI reads a product page or a catalog of SKUs.
The agency would need to adapt the material to product-page content, comparisons and FAQs.
Before booking, ask about session format, availability and commercial terms.
A better choice when the agency wants a structured product-page optimization framework it can hand to clients as a checklist.
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Mohsin's post lays out a broad framework covering entity attributes, query-intent mapping, title and URL structure, schema, image optimization, reviews and user-generated content, internal linking, variant handling, discontinued products and AI search readiness.
A second post argues that AI search rewards clear category positioning, specific outcomes and credible proof rather than generic product pages.
That structure could suit a session built around a repeatable audit template rather than one live page teardown.
The tradeoff is demonstration depth.
The framework is presented as a list and a mind map, not as a close reading of a real product page with before-and-after evidence.
The examples lean toward consumer e-commerce and enterprise SaaS, such as coffee machines and Docusign, rather than B2B e-commerce catalogs.
The agency would need to supply its own client page and testing to make the session concrete.
Ask about session terms and availability before booking.
A better choice when the agency wants clients to test whether AI search is retrievable for their category before rewriting product pages.
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Aabhas gives a concrete diagnostic: pick real buyer questions, run them in ChatGPT, watch whether web search is triggered, map query fan-outs and study which sources get cited.
The post states ChatGPT averages 7.6 fan-out searches per prompt and cites an analysis of 22,881 AI citations where many cited domains had low domain authority.
A second post explains why starting from keywords can miss the retrieval process and the decisions AI is actually researching.
For an agency whose clients may be optimizing pages that AI never retrieves, this is a useful first session.
The tradeoff is implementation detail.
Aabhas focuses on AEO strategy, retrieval and citation testing rather than rebuilding product-page content, comparison blocks or catalog structure.
The agency would need to pair the diagnostic with a separate implementation walkthrough.
Ask for a B2B e-commerce product-page example, and confirm session format, availability and commercial terms before booking.
