# Gavin Trippe

Gavin Trippe writes about how Shopify brands measure marketing performance, forecast revenue, and decide where to spend across media channels.

- Profile: https://b2binfluencer.marketing/creators/gavin-trippe/
- LinkedIn: https://www.linkedin.com/in/gavintrippe
- LinkedIn followers: 2,058
- Topics: artificial-intelligence, ecommerce, marketing-measurement, attribution
- Based on 14 dated posts, latest 2026-10-08

## Summary

Most useful for connecting Shopify brand measurement to budget choices, forecast diagnosis, and channel spend decisions.

- Best for: A Shopify or DTC brand that missed a forecast or needs channel-level finance numbers.
- Not for: Paid sponsorship or consumer video for a broad product launch audience.
- Check first: Confirm audience composition and engagement data if your campaign depends on reach.

### Takeaways

- Check actual media spend against plan before blaming the forecast model.
- Look beyond platform attribution when products convert at different speeds.
- Give finance channel-level ranges and make forecast assumptions visible.

## Main ideas

- **Check whether the planned spend happened before blaming the forecast**: Trippe argues that a forecast describes a plan, so a miss cannot be diagnosed fairly until actual spend is compared with planned spend across media partners. Someone must own follow-through: an agency cannot control budgets it does not manage.
- **Look beyond platform attribution when products convert at different speeds**: Trippe warns that short attribution windows can make quick-converting products or channels look better than slower ones, even when the slower bet contributes more revenue. They recommend checking broader evidence, such as an MMM or incrementality results, before reallocating spend.
- **Give finance channel-level ranges and make the assumptions visible**: A blended return figure does not tell a finance team what each channel may produce. Trippe argues for channel-level high and low estimates, with a specific explanation when results fall outside the range. Longer-term forecasts should reflect uncertainty in budgets, promotions, and launches.
- **Plan media around when its effects arrive, not just when a sale happens**: Trippe argues that spending more on the sale date will not create immediate demand in every channel. The plan should account for conversion lag: build reach with slower channels early, then shift toward remarketing as the sale approaches.
- **Measurement needs an owner who turns results into budget changes**: Buying measurement tools is not the same as using their findings. Trippe argues that someone must connect test results and models to a channel-level recommendation, implement the change, and check whether spend followed it. They make a related distinction about AI: running an MMM is easier than validating it enough to trust with major budget decisions.

## Work to read

- [Check whether the spend went out before blaming the forecast model](https://www.linkedin.com/posts/gavintrippe_first-thing-id-do-if-your-shopify-brand-activity-7509977663927869441-gRFI)
- [Answering the CFO when you only have a blended ROAS](https://www.linkedin.com/posts/gavintrippe_cmo-were-at-a-24-blended-roas-marketing-activity-7510307542321807363-MHTM)
- [When the platform’s fastest-converting product is not the most valuable one](https://www.linkedin.com/posts/gavintrippe_we-have-a-shopify-client-that-sells-two-products-activity-7509612792073592832-KdNi)
- [Where the measurement stack stalls between the tools and the buying team](https://www.linkedin.com/posts/gavintrippe_your-shopify-marketing-team-should-not-have-activity-7511817653724905472-89pN)
- [Black Friday spend has to start weeks before Black Friday](https://www.linkedin.com/posts/gavintrippe_please-dont-be-the-shopify-brand-that-waits-activity-7513591484563013632-8p50)
- [Building a revenue forecast when the plan isn’t agreed yet](https://www.linkedin.com/posts/gavintrippe_cfo-we-need-the-2027-revenue-forecast-for-activity-7512933383824556032-391M)
- [AI can run an MMM; it can't be trusted to move $5M](https://www.linkedin.com/posts/gavintrippe_claude-debugging-my-code-thats-the-smoking-activity-7511047460136361985-h1yz)

## Brand relationships

- **Selling on Giants** (mention)

## Partnerships

Gavin Trippe's posts show a personal LinkedIn presence and seven posts about marketing measurement, budget allocation, and revenue forecasting for Shopify brands, including client-service work on media mix modelling and spend adherence. The only outside appearance mentioned is an interview with Will Haire on the Selling on Giants eCommerce Show, described as a conversation rather than a sponsored placement. The posts don't mention sponsorship formats, a media kit, a rate card, or published partnership terms, so the formats list is empty. Brands considering a paid collaboration should confirm terms and availability directly; the posts provide no official partnership or inquiry route.

## Alternatives

- Jack Paxton: Paxton is the better choice when the brand wants to start inside its own ad account rather than with a measurement model. The posts show two relevant situations. First, a sudden sales drop that needs a fast, ordered diagnosis: check whether tracking is broken (for example, Shopify, GA4 and Meta disagreeing by more than 10%), then whether the store is converting, then what changed in the ad accounts, then seasonality and outside context—and isolate one culprit rather than four. Second, a Meta account where blended ROAS looks acceptable but the business is not improving: Paxton's published case is a home goods client spending $120K a month at 2.8x blended ROAS and under 12% contribution margin, with 65% of Meta budget retargeting the same 14-day window. The changes described were reducing retargeting to 25% and pushing the rest into Advantage+ prospecting, building a post-purchase sequence, raising the free-shipping threshold after checking average cart value, and cutting weak SKUs from the Shopping feed. The reported result was $197K in month four, $280K by month nine, and 23% contribution margin. That is the right match when the failing lever is in-account execution and per-order economics, and success can be measured in weeks by margin and revenue.
- Nicole Livermore: Livermore is the better choice when the forecast miss traces to offer economics rather than media execution or measurement. The post argues that a promotion can convert beautifully and still be a poor growth decision once tariffs, higher landed costs, fulfillment, media spend, borrowing costs, and the cash needed to replace inventory are counted. A 20% discount priced against 2025 margins comes out of a much thinner 2026 contribution margin; the business paid more, and often earlier, to bring the inventory in, while each discounted unit recovers less cash toward the next purchase order. That is the more useful starting point for a brand whose revenue held up but cash and margin did not, and whose question is whether the promotion should run at all on current economics. A second post covers customers using AI search to compare and shortlist providers, which matters if the brand also needs to know whether buyers are finding it through AI answers rather than its own channels.
- Cody Wittick: Wittick is the better choice when the underlying defect is customer classification inside Google Shopping rather than the cross-channel model. An audit of a roughly $10.8M DTC brand found that about 95% of the branded Shopping traffic examined had no useful new-versus-returning customer classification. That makes the cost of acquiring a new customer unanswerable and lets repeat purchases count as acquisition wins, making the channel look healthier on blended ROAS than its acquisition economics warrant. The recommended order is to confirm purchase events come from a clean source, separate new and returning customers where possible, keep customer lists current so Google is not working from stale data, and judge prospecting against new-customer economics rather than blended platform ROAS. That is the right first move when a brand suspects its reported acquisition performance is inflated by existing customers. A second post covers flooding Q4 with creator content—product seeding to hundreds of micro creators with no brief, capturing untagged posts, locking in usage rights, and running the best posts as ads through the creator's handle—which is relevant if the bottleneck is creative supply for paid social rather than measurement.

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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/