# Jeremy Fenderson

Jeremy Fenderson works on profitable growth for direct-to-consumer brands. Fenderson's posts describe experience in paid media, client partnerships, and go-to-market strategy, alongside work on analytics that helps teams spot when to scale campaigns or adapt to marketplace sales such as Amazon.

- Profile: https://b2binfluencer.marketing/creators/jeremy-fenderson/
- LinkedIn: https://www.linkedin.com/in/jeremyfenderson
- Topics: artificial-intelligence, ecommerce, leadership, paid-ads, dtc-ecommerce, ai-in-marketing, paid-media, amazon-marketplace
- Based on 11 dated posts, latest 2026-08-12

## Summary

Jeremy Fenderson is most useful for paid media, blended measurement, and marketplace growth work for DTC brands.

- Best for: A scoped paid media and analytics engagement focused on blended measurement and marketplace growth.
- Not for: A campaign buying creator-style sponsored placements with a rate card and defined deliverables.
- Check first: Ask about their current work and verify contracting entity, capacity, and any performance claims.

### Takeaways

- Fenderson says reports arriving after the moment are an autopsy, not a strategy.
- They describe monitoring fourteen data sources and turning them into decision-linked alerts.
- Amazon share reportedly grew from about 20% to over 45% while Shopify looked healthy.

## Main ideas

- **A report that arrives after the moment has passed is an autopsy, not a strategy**: Fenderson’s complaint is not that agencies measure badly, but that they measure too late. The monthly cycle described in the post has a strategist opening nine dashboards on the fifth of the month, reviewing what already happened and writing a deck about it. By then, the creative has fatigued, the creator has cooled off and the budget is spent. The priority is to shorten the gap between a signal appearing and someone acting on it, so spend and creative can be adjusted while a creator or channel is still working.
- **Let software do the watching; keep human judgment for the calls that move the number**: Fenderson describes a two-part method. The platform watches many data sources at once and raises alerts—called Signals in the post—when something crosses a threshold worth acting on. People then focus their hours on the small share of decisions that change the outcome. The thesis: software handles the work, while judgment becomes the edge. The strategist stops pulling reports and starts making calls. Each named alert is tied to a time-sensitive decision, distinguishing it from a dashboard.
- **Measure blended revenue across every place money moves, not one channel's ROAS**: Fenderson argues that channel-by-channel reporting can obscure what is happening to a business because acquisition and purchase often happen in different places. When customers discover a brand on one platform and buy on another, each platform’s dashboard can look reasonable while the underlying shift goes unrecorded. The priority is blended measurement across the business, with discovery and purchase reported separately.
- **Follow where the customer already buys, even if it means rebuilding a side channel as a storefront**: This is the action Fenderson draws from the measurement argument. Once blended data showed Amazon had become the main place the brand's customers bought, the plan changed: the listing and ad structure were rebuilt as a primary storefront rather than left as the channel the brand would "get to later," spend was judged on blended MER across DTC and Amazon instead of per-channel ROAS, and the finding was fed back into the paid strategy. The reasoning is that measuring only the channel you want customers to buy on will miss the one they have chosen.
- **The team and the repeatable framework are what outlast any single role**: In a retrospective on nine years at Adquadrant, Fenderson presents culture, leadership development and a documented internal framework as the period’s lasting contributions, rather than any individual client win. Their advice to the next group of leaders: lean on the people around them, think bigger about what they can achieve and back themselves. This is the only post focused on how a business is built and staffed, rather than how it measures and acts.

## Work to read

- [After nine years at Adquadrant: what Fenderson built and why the chapter closed](https://www.linkedin.com/posts/jeremyfenderson_after-9-incredible-years-my-chapter-at-adquadrant-activity-7472016735114690560-SHpF)
- [The case against the monthly report: monitoring versus noticing](https://www.linkedin.com/posts/jeremyfenderson_most-agencies-find-the-chance-to-scale-a-activity-7490598303026339840--HnC)
- [A brand's revenue crossed from DTC to Amazon, and no single dashboard showed it](https://www.linkedin.com/posts/jeremyfenderson_one-of-our-biggest-brands-quietly-expanded-activity-7490745982629023744-W-MJ)

## Brand relationships

- **Adquadrant** (employment)

## Partnerships

Jeremy Fenderson's three posts focus on agency-side growth work — paid media, client partnerships, go-to-market strategy, and analytics for DTC brands — rather than sponsored content. They describe work such as running paid media, reading blended MER across direct-to-consumer and marketplace channels, and reworking Amazon listings and ad structure, but offer no published partnership terms. Brands should ask directly whether Fenderson offers creator-style promotion and, for any engagement, confirm the scope, deliverables, usage rights, disclosure, success measures, rates, turnaround time, exclusivity, approval steps, and booking process in writing before budgeting. The work described looks more like a consulting or agency engagement built around a brand's growth programme than an influencer package with a fixed menu of placements; no official partnership inquiry route appears in the posts.

## Alternatives

- Chris Marrano: Choose Chris Marrano when the campaign’s bottleneck is creative throughput rather than measurement. These posts describe a working chain: a Facebook ads agent writes every campaign, asset, and performance signal to a database; a strategist agent triggers when an asset hits five active ads, gets paused, or shows fatigue inside a set window, pulls the top-selling products and top-performing ads, and writes a creative brief from real performance data; a designer agent produces new ads from that brief using image generation; a human creative strategist reviews and revises. The stated belief is that advantage no longer sits in clever account structure but in signal quality, creative volume, and fast feedback loops, and that Meta’s Andromeda update changed how ads are selected and scaled. If the brand’s problem is the three-day lag between spotting fatigue and shipping a replacement, this is the closer match to the work.
- Amy Leffingwell: Choose Amy Leffingwell when the campaign makes TikTok Shop the growth channel rather than one line in a cross-channel picture. These posts treat creators as a performance channel and examine what that changes: customer acquisition, attribution, and checkout collapse into a single motion; there’s no Meta-style auction dynamic or Google-style keyword intent curve; and a creator-driven sale is traceable to the content that triggered it and to SKU-level performance. One post argues that the planning question shifts from how much a brand can spend before CAC breaks to how many creators it can activate before hitting operational limits, and that brands should model creator-driven CAC and contribution margin the way they model paid media. The supporting argument cites TikTok Shop’s $500M in US Black Friday-through-Cyber Monday sales, live shopping becoming a real conversion layer, larger brands entering, and creators monetizing through commerce rather than content alone. For a TikTok Shop plan built around creator-led acquisition, this addresses the decision more directly than a blended-measurement engagement.
- Joshua Graham: Choose Joshua Graham when the campaign’s real question is unit economics and what the brand can afford to spend, not which alert to wire up. One post works through why consumer giants paid $3.8 billion for Thorne and a reported $1.2 billion for Grüns: strong customer economics, clear positioning, repeat purchase behavior, and a place in someone’s routine. It cites Grüns as running at a minimum 3x fully burdened LTV-to-CAC. The post also sets the cost backdrop, noting Meta’s average price per ad rose 9% in 2025 and another 12% year over year in the second quarter of 2026, and argues that raising what a customer is worth gives a brand a higher allowable CPA and room its competitors lack. The second post takes on the campaign’s attribution problem—Meta manufactures demand Amazon ultimately harvests—and reasons about what to do when clean isolation isn’t available. If the engagement is meant to inform acquisition economics and a future sale, this material is closest to that conversation.

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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/