# Richard Wyatt

Richard Wyatt shares practical ecommerce advertising advice, especially on Google Shopping, Performance Max and product-feed strategy. The examples show how to separate low-traffic products into Shopping campaigns and use Onefeed labels and tracking data to assess performance.

- Profile: https://b2binfluencer.marketing/creators/richard-wyatt/
- LinkedIn: https://www.linkedin.com/in/richardwyatt
- LinkedIn followers: 2,256
- Topics: ecommerce, performance-max
- Based on 11 dated posts, latest 2026-08-24

## Summary

Useful for hands-on Google Shopping and Performance Max campaign structures using Onefeed feed labels and product-level data.

- Best for: An ecommerce brand running Google Shopping and Performance Max that wants feed segmentation and campaign splits.
- Not for: A reach-led sponsored placement or influencer package with audience scale and defined deliverables.
- Check first: Ask for recent comparable engagements, scope, fees, and deliverables before committing.

### Takeaways

- Performance Max misses products with no impression history, so run Shopping alongside it.
- Use Onefeed custom labels for products with fewer than two sales and 1,000 impressions.
- The example reports 50 to 60 percent more Shopping revenue, not a benchmark.

## Main ideas

- **Performance Max misses products with no impression history, so run Shopping alongside it**: Wyatt's central claim is that Performance Max is so focused on warm traffic that it is effectively blind to items that have not yet built up demand signals. Products with almost no impressions stay that way, and the revenue they could contribute never appears in the PMax account. The fix they propose is structural rather than a bidding tweak: use the feed to identify those items and give them their own Shopping campaigns, then let proven items return to Performance Max. Decisions about which items belong in which campaign draw on sales, impression, conversion and, where available, margin data, plus basket-level tracking to catch products converting on site through other channels.
- **The product feed is the control layer, and new items need a scheduled test window**: A second recurring method runs through the same post: campaign eligibility is set in the feed rather than negotiated campaign by campaign. Labels based on sales history, margin or product creation date determine which campaign type an item can enter, while separate campaigns absorb material that does not belong in the main structure. This makes segmentation repeatable as a catalog changes.
- **Read a performance drop from campaign-level signals before changing strategy**: When an ecommerce account underperforms, Wyatt's approach is diagnostic first: pull a fixed set of campaign-level readings that point to a cause, rather than starting with a strategy rewrite. Each signal maps to a different explanation for a dip.
- **Customer-level tracking — new versus existing buyers and repeat purchase — sits underneath the feed decisions**: Wyatt argues that most ecommerce businesses lose sight of customer lifetime value, the cost of acquiring a new customer, and how to bring existing customers back, because those numbers get buried in day-to-day operations. The data layer that answers those questions also makes product-level segmentation possible: basket-level tracking records what was bought, who bought it, and whether the buyer was new or returning.
- **Platform choice shapes the work: Shopify-first, with Magento treated as a known cost**: Wyatt writes about the engineering behind the tooling, not just the strategy, and the platform determines how much friction that work carries. Shopify is where the app is built and where their attention sits; Magento is described as difficult to work with and thinly used. There is a tension worth noting: they frame Shopify as displacing legacy ecommerce platforms while still running a Magento app project.

## Work to read

- [Running Google Shopping alongside Performance Max with Onefeed labels](https://www.linkedin.com/posts/richardwyatt_heres-a-free-bit-of-info-for-anyone-interested-activity-7348426050156081153-Npfl)
- [Onefeed's AI deep-dive summary for diagnosing a performance drop](https://www.linkedin.com/posts/richardwyatt_well-weve-got-some-great-news-for-the-onefeed-activity-7402700611600674816-NwYb)
- [Basket-level tracking, customer lifetime value and repeat purchase in Onefeed](https://www.linkedin.com/posts/richardwyatt_onefeed-new-feature-alert-most-customers-activity-7416471135439097856-UnCq)
- [A public request for a Magento test store to finish an app](https://www.linkedin.com/posts/richardwyatt_i-am-hoping-someone-can-help-i-was-let-down-activity-7383489148931899392-1RRa)
- [Two days debugging Shopify GraphQL, OAuth and Remix authentication](https://www.linkedin.com/posts/richardwyatt_theres-a-special-place-in-hell-for-the-person-activity-7353480343544336384-hzmu)

## Brand relationships

- **Onefeed** (disclosed_partnership)
- **Shopify** (mention)
- **Magento** (mention)

## Partnerships

Richard Wyatt's five available posts show hands-on ecommerce advertising work and a software product, Onefeed, which they discuss building and using: feed labels, campaign segmentation, basket-level tracking, a Shopify app, and a campaign-analysis feature. The posts do not describe a sponsorship or paid-partnership program, sponsored placements, audience packages, rates, deliverables, exclusivity terms, or an application process. This points to a consulting or software-led relationship rather than a media buy. For a campaign involving Google Shopping and Performance Max, contact them through the LinkedIn profile already listed and ask about rates, deliverables, exclusivity, and how to scope the engagement; no standard partnership route or deliverable is published.

## Alternatives

- Martin Davies: Choose Martin Davies when the engagement starts with diagnosis rather than feed construction: a brand whose sales have slowed and which cannot tell whether the cause is weaker market demand, a competitor or its own panic changes. The published advice is to run an impression share report by day over the past 30 days, or from before the dip: steady impression share points to lower demand in the market, while falling share points to competitor activity. The strategy should change accordingly. A second post addresses whether brand traffic should be excluded from Performance Max. It describes a third-party brand script that reports brand versus non-brand clicks, conversions and share of brand revenue over time, so a brand can see whether brand traffic is absorbing too much of its PMax delivery and move it into search campaigns with a brand negative instead.
- Robin Tesselaar: Choose Robin Tesselaar when the decision hinges on whether the account is actually making money. Two posts argue that most Google Ads accounts look profitable without being profitable: ROAS is revenue divided by ad spend, before cost of goods, shipping, returns and fulfilment, so a 4x ROAS campaign can lose money and a 2x campaign can be the most profitable one. The material gives a break-even rule (target ROAS of 1 divided by gross margin, so 35% margin means break-even at 2.86x), names new customer acquisition cost as the metric that reflects reality, and lays out three ways ROAS misleads: it ignores margins, rewards warm returning buyers, and hides diminishing returns through blended averages. A companion post sets out the account structure that makes Performance Max behave: a dedicated exact-match brand search campaign to force PMax onto cold traffic, asset groups split by margin, CRM and lifetime-value audiences fed in as signals, conversion value rules so a $200 sale is not treated like a $20 sale, and search themes used as a diagnostic rather than a growth lever. Use this framing when a brand needs to test whether a Shopping-and-PMax restructure is producing genuine incremental profit or is being carried by repeat buyers.
- Josh Gregory: Choose Josh Gregory when a brand needs more than Google Shopping: a brand scaling paid media past roughly £25–30K a month across Meta and Google and seeing ROAS decline, acquisition costs climb and results swing. The posts describe where that money usually goes wrong: no funnel built for cold audiences once warm buyers run out, channels planned in isolation so Google and Meta overlap and waste clicks, and no lifetime-value or offer strategy to justify paying more for a first purchase. They then set out the structure that supports scale: weekly creative testing cycles, margin and acquisition-cost checks before budgets rise, first-time-buyer offers, a mapped funnel from awareness to purchase, and landing pages built for cold clicks. A second post explains the dilution that follows spending too quickly, with the algorithm broadening targeting and conversion rates falling. It also offers a small number of free Google Ads audits over the following month, giving a brand an explicit route to an initial diagnostic.

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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/