# Abdo Mazloum

Abdo Mazloum shares practical e-commerce growth advice, focusing on paid-media performance, measurement, product data, and AI-driven product discovery.

- Profile: https://b2binfluencer.marketing/creators/abdo-mazloum/
- LinkedIn: https://www.linkedin.com/in/abdomazloum
- LinkedIn followers: 11,572
- Topics: artificial-intelligence, ecommerce, paid-ads, seo, creative-testing, paid-media, ecommerce-growth, conversion-tracking
- Based on 20 dated posts, latest 2026-10-09

## Summary

Most useful for auditing paid-media tracking, product data, and AI-commerce visibility for e-commerce growth.

- Best for: Shopify DTC brands with heavy Meta spend and flat platform ROAS needing tracking and feed audits.
- Not for: Brands seeking paid sponsorship posts, guaranteed impressions, usage rights, or standard influencer reach buys.
- Check first: Confirm whether they accept sponsorships, request written terms, and ask for recent results and audience data.

### Takeaways

- Platform ROAS credits revenue but does not show what the ads caused.
- Product data and SEO are one system, not two workstreams.
- AI discovery moves the goal from rankings to recommendations.

## Main ideas

- **Platform ROAS credits revenue; it doesn’t show what the ads caused**: Mazloum argues that campaigns can report healthy ROAS while leaving the business with little profit. Platform attribution leaves out discounts and cancellations, returns and refunds, fulfilment and payment fees, cash tied up during payback, and the lower returns that come with added spend. The measurement order is net sales, contribution margin, allowable CAC, marginal return, then controlled scale; only after that is the decision to kill, hold or scale. Before increasing spend, they check whether stock, cash flow, fulfilment and creative can support the extra demand.
- **Product data and SEO are one system, not two workstreams**: A product page can rank well and still confuse platforms, because search and commerce systems read different signals. Mazloum separates the jobs: SEO gets products found; product data tells platforms what a product is, who it’s for and whether it can be bought. Both need to draw on the same source of truth. The four parts are identity—consistent product names, categories and variants; accuracy—matching prices, availability, descriptions and feeds; structure that search engines and commerce platforms can process; and experience—content that helps someone decide.
- **AI discovery moves the goal from rankings to recommendations**: Mazloum treats AI-driven product discovery as a change in what a store needs to get right, not a new channel to bolt on. AI systems don’t evaluate a store the way shoppers do; they rely on structured product data, product schema markup, GTINs, merchant feeds, FAQs, customer reviews, and trusted brand mentions and citations. The shift is not from SEO to AI but from rankings to recommendations. A store not built to be understood by machines reduces its chances of being discovered.
- **Sequence AI-commerce work by revenue today and importance tomorrow**: Mazloum pushes back on treating every gap as a transformation project. They split the work into three groups: problems already costing revenue, such as feed errors, unclear product pages, hidden reviews and confusing policies; conversion blockers, such as checkout friction, disconnected reporting, inconsistent pricing and poor customer-service handoffs; and future infrastructure, such as agent-ready catalogues, secure checkout, fraud controls, reliable commerce data and AI channel measurement. Two filters determine priority: the impact on revenue today and the importance of the work as AI-led buying grows.
- **Fix the foundation before moving budget**: Mazloum argues that most brands start at the wrong layer in paid media. They put unit economics first—real CAC, margin, payback, returns and MER target—then signal infrastructure such as CAPI, server-side tracking, enhanced conversions and pixel audits, followed by the creative system, with enough hooks, angles, variants and fresh creative. Bidding and scaling come last. Starting with the budget is why spend rises while margin thins.
- **Marketing keeps renaming the same customer behaviour**: Mazloum is sceptical of the acronym cycle—SEO, GEO, AEO, LLM SEO, AI SEO—and argues that the industry keeps rebranding the same human behaviour while customers still ask whether a product solves their problem. What matters is unglamorous and stable: understand customer problems, create content around real buying intent, answer questions clearly, build trust faster than competitors, and show up where attention is moving.
- **Mazloum draws a firm line around what a platform has confirmed**: Mazloum doesn’t let a platform announcement claim more than it supports. When Meta expanded its use of business-shared activity into Feed personalisation and Meta AI responses, they noted that Meta had not said better tracking would increase organic reach or drive Meta AI recommendations—only that shared activity would influence more than ads. The recommendation was to fix consent, deduplication and value accuracy rather than chase the personalisation headline. In the AI-shopping post, they likewise make a conditional claim—a store not built to be understood by machines reduces its chances of discovery—and offer a checklist to test readiness, not a guarantee.

## Work to read

- [Meta tracking data is no longer just feeding your ads](https://www.linkedin.com/posts/abdomazloum_your-meta-tracking-data-is-no-longer-just-activity-7471547248720519168-y2XB)
- [Shopify just made your store readable for AI agents](https://www.linkedin.com/posts/abdomazloum_shopify-just-made-your-store-readable-for-activity-7466473736570044416-OQVU)
- [SEO and product data cannot work separately anymore](https://www.linkedin.com/posts/abdomazloum_seo-and-product-data-cannot-work-separately-activity-7493275155679367169-9yaG)
- [If your e-commerce SEO is working less than it used to, AI shopping is probably the next visibility gap](https://www.linkedin.com/posts/abdomazloum_if-your-e-commerce-seo-is-working-less-than-activity-7473706529112662017-Gt_F)
- [These are the exact benchmarks I use when auditing enterprise eCommerce sites](https://www.linkedin.com/posts/abdomazloum_these-are-the-exact-benchmarks-i-use-when-activity-7463197125137432576-7aYk)
- [I would not scale an ad account on platform data alone](https://www.linkedin.com/posts/abdomazloum_i-would-not-scale-an-ad-account-on-platform-activity-7492912791784251393-I3_2)
- [I spent 100 hours compiling my best performance marketing cheat sheets](https://www.linkedin.com/posts/abdomazloum_i-spent-100-hours-compiling-my-best-performance-activity-7459573644202160128-rOM-)

## Partnerships

Across 13 LinkedIn posts published between 8 April and 12 August 2026, Abdo Mazloum names no media kit, rate card, sponsor policy or booking route, and discloses no paid brand engagement. The only promotional mechanics are self-operated: several posts gate a downloadable asset behind a comment trigger—comment “PAID” for a paid-media cheat sheet or “40” for a bundle of 40 frameworks—with replies limited to a stated window and, in one case, available only to a connection. This shows how they distribute assets and capture leads, not what a brand can buy. Ask directly about scope, deliverables, review and approval rights, disclosure language, exclusivity and timing; their posts do not specify these terms.

## Alternatives

- Tom Shivers: Choose Tom Shivers when the engagement centres on selling through AI agents rather than auditing what a store already sends them. The posts explain the OpenAI and Stripe Agentic Commerce Protocol and Google's Universal Commerce Protocol: how agents consume product feeds, compare items, build carts and complete checkout through delegated payments while the retailer remains merchant of record. They spell out the retailer's steps: supply OpenAI with a structured product feed, implement ACP checkout endpoints, support delegated payments, keep pricing, inventory and fulfilment consistent, and treat the Merchant Center Next feed, local inventory feed and structured data as determinants of eligibility for AI Mode and agent-led checkout. They also identify a checklist most brands lack: which feed fields an agent reads before recommending a product.
- Bivas Kumar Roy: Choose Bivas Kumar Roy when the need is a direct answer to why Meta reports more purchases than Shopify. The posts examine the same investigation Mazloum's tracking post points to, at greater length and with the discrepancy as their sole focus. They distinguish tracking problems from attribution problems, then cover attribution windows and click-through versus view-through credit; Pixel and CAPI deduplication using consistent event IDs verified in Events Manager; duplicate purchase events from refreshes or unreliable triggers; non-customer orders; incorrect event revenue or currency; payment redirects from PayPal and Klarna; and consent-state limitations for EU traffic. The distinction helps scope the work: fixing duplicate events will not resolve attribution differences, and changing attribution settings will not fix broken purchase tracking.
- Shubham Shukla: Choose Shubham Shukla when the need is storefront and checkout guidance for a non-specialist audience, such as internal enablement content or a design brief for a team that needs the basics laid out. The posts offer a ten-point checklist for frictionless checkout (one-page checkout, guest checkout, payment-method range, auto-fill, mobile-friendly flow, transparent pricing, security indicators, clear error messages, BNPL and distraction-free layout) and a paired checklist for SEO-friendly store design (site structure, mobile-first, page speed, clean URLs, navigation, product-page design, headings, internal links, image alt text and trust signals). Each point is plainly stated and can be turned into a design requirement without further translation.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/