# Mark Tran

Mark Tran shares practical advice on improving Google and Meta ads for ecommerce brands, with a focus on finding new customers and reducing wasted spend.

- Profile: https://b2binfluencer.marketing/creators/mark-tran/
- LinkedIn: https://www.linkedin.com/in/mark-tran-00328235
- LinkedIn followers: 863
- Topics: artificial-intelligence, ecommerce, paid-ads, meta-ads, google-ads, performance-max
- Based on 11 dated posts, latest 2026-09-28

## Summary

Mark Tran is useful for practical Google and Meta ads advice on finding new customers and cutting wasted spend.

- Best for: Ad measurement or paid media software platform selling to DTC ecommerce operators.
- Not for: Consumer brands seeking shopper facing content, direct purchases, and broad reach.
- Check first: Ask for impressions, engagement, profile visits, and audience demographics beyond the 863 follower figure.

### Takeaways

- Two Meta accounts counted returning customers as new, hiding repeat purchases behind strong reported results.
- Branded search reported 25x ROAS, while Performance Max was closer to 5x.
- For small Performance Max budgets, narrow the product feed before reviewing individual products.

## Main ideas

- **Separate new-customer growth from repeat purchases**: Tran argues that platforms can favor cheap conversions from people who would have bought anyway. A strong reported ROAS is not proof that prospecting found new customers; separating new and returning buyers can make the numbers look worse while giving a clearer measure of acquisition.
- **Look beneath blended ROAS for demand the brand did not create**: Tran questions account-wide returns lifted by branded searches, which often come from people already seeking the brand. The argument is to distinguish capturing existing intent from finding new buyers, then judge budgets with that difference visible.
- **Spend less broadly when the budget cannot support enough learning**: Tran’s small-budget advice is to reduce the number of products before expecting an algorithm to optimize well. Product-level results, not a large feed or a reassuring dashboard, should guide what gets removed or more budget.
- **Match purchase-audience windows to how long customers take to buy again**: Tran distinguishes purchase events from general site activity when explaining Meta’s 730-day retention window. The change is not equally useful for every business: a longer purchase cycle can make a longer past-buyer exclusion valuable, while a short cycle may already fit within the old window.
- **Prepare for AI labels without treating them as a ban on AI creative**: Tran presents automatic labeling as a consumer-trust and production issue, not a reason to stop using AI-generated creative. The practical response is to identify where generation or editing occurs, check live ads, and make sure creative and media teams know what to expect.

## Work to read

- [Branded search was inflating a bedding brand's ROAS](https://www.linkedin.com/posts/mark-tran-00328235_ecommerce-paidads-googleads-activity-7468161546079158272-Uwfz)
- [Two Meta accounts were counting returning customers as new](https://www.linkedin.com/posts/mark-tran-00328235_your-best-meta-campaign-can-look-like-it-activity-7475705624400658432-1BoW)
- [What Meta's 730-day retention window actually changes](https://www.linkedin.com/posts/mark-tran-00328235_metaads-ecommerce-activity-7462649777705566208-ttBP)
- [Running Performance Max on $40 a day means subtracting, not adding, products](https://www.linkedin.com/posts/mark-tran-00328235_googleads-ecommerce-pmax-activity-7467377049230381071-cflo)
- [Meta now labels AI-generated ad creative automatically](https://www.linkedin.com/posts/mark-tran-00328235_meta-is-now-auto-labelling-ai-generated-ads-activity-7482954890680397824-5M52)

## Brand relationships

- **Meta** (mention)
- **Google** (mention)

## Partnerships

None of the five posts available for Mark Tran disclose a sponsorship offer, media kit, rate card, or defined partnership format. The material is operator commentary on Google and Meta advertising rather than promotional content written for brand partners, and no post names a sponsor or advertises a paid collaboration. Because there is no published sponsor policy, no disclosed sponsored example, and no verified commercial inquiry route, a brand cannot currently build a paid partnership plan from what is documented here. The practical route to working with Mark Tran appears to be a direct professional conversation rather than a published package. This is an absence of documented offers in the material at hand, not evidence that Mark Tran declines or accepts sponsorships. Evidence that would change this section: a sponsor policy page, published deliverables or rates, an example of disclosed sponsored work, or a contact route specifically for commercial inquiries.

## Alternatives

- Josh Gregory: A brand spending enough that scaling has become the problem, and looking for a structure-first programme rather than a single-account teardown. Gregory's two posts describe where growth breaks once budgets cross roughly £25–30K a month on Meta or Google: cold audiences lack a funnel; Google and Meta are planned in isolation, causing overlap and wasted clicks; and there is no lifetime-value or offer strategy to justify paying more for a first purchase. The fixes are programmatic rather than tactical: weekly creative-testing cycles, margin and customer-acquisition-cost checks before budgets rise, offers built for first-time buyers, funnel progression mapped from awareness to purchase, and landing pages made for cold clicks. The second post also offers free Google Ads audits to ecommerce brands, giving brands that want a working session alongside the content a direct route.
- Robin Tesselaar: A brand facing a measurement problem rather than a tactical one — reported ROAS looks healthy, but profit is thin. Tesselaar works through the arithmetic: ROAS is revenue divided by spend, before cost of goods, shipping, returns and fulfilment, so a 4x campaign can lose money while a 2x campaign can be the most profitable. Average ROAS hides diminishing returns, so marginal return on extra spend should determine where budget moves. Profit-on-ad-spend sets break-even at one divided by gross margin, meaning a 35% margin breaks even at 2.86x. A worked example moves an account from 39% to 51% new-customer share, with new-customer cost down 30%, while ROAS looked strong throughout. The companion post sets out a five-part Performance Max structure: protect branded traffic with a dedicated exact-match campaign so a reported ROAS collapse is visible; split asset groups by margin; feed the algorithm CRM and lifetime-value audiences; use conversion value rules so a $20 sale and a $200 sale are not treated as the same signal; and read search themes as a diagnostic. For a platform selling measurement or profit reporting, this is the closest match to the product.
- Jack Paxton: A brand facing a sudden, unexplained drop in sales that wants a diagnostic order to follow before anyone touches campaigns. Paxton's sequence is explicit: compare Shopify, GA4 and Meta to check for broken tracking, treating a gap above 10% as a reporting problem; compare the store's seven-day conversion rate with the prior 30 days to distinguish a site problem from an ads problem; look for a specific change in the ad accounts that lines up with when the numbers moved; then check seasonality and external context across the last 12 months. Working through these four checks usually turns up one culprit. The second post details a full client turnaround: a home goods brand doing $120K a month on Meta, with contribution margin under 12% and revenue flat for four months, was spending 65% of its budget retargeting the same 14-day window. Cutting retargeting to 25%, adding a three-step post-purchase sequence, raising the free-shipping threshold by $14 and removing weak feed SKUs took the brand to $197K by month four and $280K by month nine, with margin at 23%. For a brief built on before-and-after proof, that offers more concrete outcome evidence than the reviewed Mark Tran posts.

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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/