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.
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.
Key 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.
Check first: Ask about their current work and verify contracting entity, capacity, and any performance claims.
Main ideas
A report that arrives after the moment has passed is an autopsy, not a strategy · Let software do the watching; keep human judgment for the calls that move the number · Measure blended revenue across every place money moves, not one channel's ROAS
Jeremy Fenderson writes about profitable growth for direct-to-consumer brands, drawing on work across paid media, client partnerships, and go-to-market strategy at Adquadrant.
Their posts argue that teams should act on a joined-up view of their data rather than wait for separate channel reports.
They left Adquadrant after nine years; ask about their current work before engaging.
Follow their work for practical examples of how customer buying patterns can change a media plan, especially when sales move between DTC and Amazon.
Fenderson connects analytics to a specific decision and its timing.
In one post, they describe a platform that watches data from sources including Shopify, Amazon, advertising platforms, and email, then flags changes such as rising customer acquisition cost or a converting creator video.
In another, they explain how a brand’s Amazon share reportedly grew from about 20% to more than 45% while its Shopify dashboard looked healthy.
They describe responding by treating Amazon as a primary storefront, measuring blended MER across DTC and Amazon, and feeding the buying pattern back into paid strategy.
The Amazon figures are directional and anonymized; ask for independently verified results if performance claims are central to a decision.
Their broader point is that customers do not follow a company’s channel structure: teams need to notice where purchases actually happen and act before delayed reporting makes the opportunity harder to capture.
Across three LinkedIn posts, Jeremy Fenderson argues about how growth work should be run and where judgment belongs.
Read more
The recurring positions: measure the whole business, not one platform’s dashboard, because customers buy where they choose; make data trigger action while the opportunity is live, rather than recounting the month after it has passed; and treat people and repeatable processes as what survives an agency role change. The posts move from an agency-leadership retrospective to a product argument for automated monitoring, while keeping the same priority: act on what the data shows.
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.
Read more
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.
The post calls the old model “That's not strategy. That's an autopsy,” and argues for a system that puts a brand’s next best move in front of the team as soon as it emerges.
Let software do the watching; keep human judgment for the calls that move the number
Fenderson describes a two-part method.
Read more
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.
The post gives four alert types: a creator’s video crossing a converting threshold, a channel’s return jumping above target, an email flow’s revenue dropping overnight and CAC creeping on a campaign.
Fenderson’s closing formulation is “Monitoring isn't the feature.
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.
Read more
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.
In the client case described, Amazon’s share of a brand’s revenue grew from roughly 20% to above 45% over eighteen months, while the Shopify dashboard still looked healthy.
Customers were finding the brand on TikTok and Meta, then buying on Amazon.
Fenderson calls the figures directional and anonymized, and says the pattern appears across the portfolio more often than expected.
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.
Read more
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 post’s summary line is “your customer doesn't care about your channel org chart.” Measurement changed first; the channel plan followed.
The case’s sequence—blended data reveals the shift, then the channel plan is rebuilt—shows how Fenderson orders these decisions: measure the whole picture first, then change the plan.
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.
Read more
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.
Fenderson says they joined in the agency’s early days and helped shape paid media, client partnerships and, most recently, go-to-market strategy, all aimed at growing DTC brands profitably.
They name the leaders developed along the way as what they are most proud of.
The post closes with an open-ended “stay tuned for what's next,” rather than a settled role.
The emphasis shifts across the posts: the Adquadrant retrospective centers on people and process, while later posts focus on a monitoring product and blended measurement. The goal—growing DTC brands profitably—stays the same, but the vehicle changes.
If you searched Jeremy Fenderson to learn who they are and what work they are accountable for, start here — it names the agency they helped build, the functions they ran and the fact that the role has ended.
Fenderson announces the end of a nine-year run at Adquadrant, having joined in the early days and helped build the agency alongside Warren Jolly, Arjun Jolly and Courtney McCall.
The post lists the scope they covered over that period: paid media, client partnerships and, most recently, go-to-market strategy, all focused on one outcome — growing direct-to-consumer brands profitably.
They dwell not on the client roster but on the internal culture and the leaders developed inside it, describing a framework they intend to carry forward.
The closing line points to something next rather than a settled destination, making this a transition in progress, not a current role.
For brands planning a campaign
Before scoping an engagement, confirm their current affiliation and available capacity.
The post establishes deep agency-side experience across paid media, partnerships and go-to-market, but it also shows their nine-year tenure has ended.
Verify who is contracting and under what banner rather than assuming the prior agency structure still applies.
This post answers what Fenderson believes about how growth work should be run, the clearest statement of method in these posts.
Read more
Read it to understand the operating thesis before evaluating the analytics claim in the third post.
Fenderson contrasts two ways of working.
In the old way, a strategist opens nine dashboards on the fifth of the month, reviews what already happened and writes a deck about it — late enough that the creative has fatigued, the creator has cooled off and the budget is spent.
The article's line for that is blunt: "That's not strategy.
That's an autopsy."
The replacement watches fourteen data sources at once — Shopify, Amazon, TikTok Shop, Meta, Google, Klaviyo, Gorgias and the ad library — and converts them into what the post calls Signals: a creator's video crossing a converting threshold, a channel's return jumping above target, an email flow's revenue dropping overnight, and CAC creeping on a campaign.
Four concrete alert types are given, each tied to a decision with a deadline.
The closing claim is the useful one: monitoring is not the feature; noticing is.
The strategist's job shifts from pulling the report to making the call.
For brands planning a campaign
Use this as the framing question for an analytics or media engagement: which decisions should come to us rather than be hunted for, and who is accountable for acting within the window? It also sets a testable expectation: a useful reporting layer produces named, decision-linked alerts, not a broader dashboard.
This worked example shows the blended-data argument in practice rather than as a principle.
Read more
Read it to see how Fenderson reasons from a number to a changed plan.
A brand's Amazon share of revenue moved from roughly 20 percent eighteen months earlier to above 45 percent, while the Shopify dashboard still looked healthy.
Customers discovered the brand on TikTok and Meta, then bought on Amazon, where a card was already on file and Prime shipping was available.
Acquisition spend on one channel was funding the sale on another, and channel-by-channel reporting hid it.
The response was three moves: rebuild the Amazon listing and ad structure as a primary storefront, read blended MER across DTC and Amazon instead of per-channel ROAS, and feed the "where they actually buy" finding back into the paid strategy.
The closing principle is that measuring only the channel you want customers to buy on will miss the one they have chosen.
Fenderson says the numbers are directional and anonymized and that the pattern recurs across the portfolio.
For brands planning a campaign
This is the closest match to a brief built around blended MER and marketplace expansion, and it supplies concrete first moves: treat Amazon as a storefront rather than a side channel, and judge spend on blended MER. Treat the percentages as illustrative and ask for the underlying account data before using them in a forecast.
Watch the whole business, then act on what moves first
Fenderson argues for rebuilding agency work so data tells the team where to act instead of making them wait for a monthly report.
Read more
They describe a platform that reads fourteen sources at once — including Shopify, Amazon, TikTok Shop, Meta, Google, Klaviyo, Gorgias and the ad library — and raises "Signals" when a creator's video starts converting, a channel's return runs above target, an email flow's revenue drops overnight or CAC creeps on a campaign.
The strategist's job shifts from pulling the report to making the call.
The thesis: the work becomes software while judgment becomes the edge.
Multi-source monitoring that feeds proactive alerts, with human decision-making on top
For brands planning a campaign
This is a pitch for shorter decision cycles: adjust spend and creative while a creator or channel is still working, rather than after the month is compiled. A brand evaluating an engagement should ask what triggers action, who watches the signals and how quickly a decision gets made.
In a described client case, a brand's Amazon share grew from roughly 20% to about 45% of revenue over eighteen months while the Shopify dashboard still looked healthy.
Read more
Customers were discovering the brand on TikTok and Meta and buying on Amazon, so the team was paying to acquire customers on one channel and handing the sale to another.
No single platform report caught it.
The response was to read blended MER across DTC and Amazon rather than channel-level ROAS, and feed the "where they actually buy" signal back into the paid strategy.
Fenderson's summary line: the customer does not care about the channel org chart.
For brands planning a campaign
An engagement measured only in platform-level ROAS can hide where revenue actually lands. Ask for blended measurement across marketplace and DTC before setting spend, and reporting that attributes discovery and purchase separately.
In the same case, Fenderson rebuilt the Amazon listing and ad structure once Amazon had become the main place customers bought, rather than leaving it as the channel the brand would "get to later."
Read more
The change followed the measurement change: once blended data showed where the sale happened, the channel plan was rebuilt around it.
Channel-plan redesign driven by observed purchase location
The figures are directional and anonymized. Ask for audited client data before using the percentages to forecast results.
For brands planning a campaign
Marketplace work — listings, ad structure and channel economics — belongs inside the engagement, not appended to DTC media buying. A brief for this kind of work should say who owns marketplace execution and how it reports against the same targets as DTC.
Agency culture and leadership as part of the offer
Announcing the end of nine years at Adquadrant, Fenderson traces a path across paid media, client partnerships and, most recently, go-to-market strategy, all aimed at growing DTC brands profitably.
Read more
They credit the culture, the leadership pipeline and a repeatable framework as the things worth carrying forward, and advise the next leadership group to lean on the people around them, think bigger than the stated goals and back themselves. The closing note: an open-ended "stay tuned for what's next."
Leadership development and a documented internal framework as the durable asset
For brands planning a campaign
Evaluate Fenderson on how a partnership is staffed, handed off and held to a standard, not only on media tactics. Team building and go-to-market leadership are part of the offer.
Fenderson names one business relationship: nine years at growth agency Adquadrant, working across paid media, client partnerships and go-to-market strategy for direct-to-consumer brands.
Read more
Two other posts describe agency work and an analytics platform built around "Signals," but name no company behind them.
Adquadrant
Employment
Fenderson writes that joining Adquadrant in its early days led to nine years at the agency, spanning paid media, client partnerships and, most recently, go-to-market strategy to grow DTC brands profitably.
Read more
Their post announces that this chapter has closed, making the employment historical rather than current. They credit founders Warren Jolly and Arjun Jolly, and colleague Courtney McCall, as people they built the business alongside.
Jeremy Fenderson writes on LinkedIn about how DTC growth work should be run: measure the whole business rather than one platform's dashboard, let monitoring trigger action before an opportunity passes, and treat the team and a repeatable process as the part of an agency that outlasts any single role.
Read more
To follow the argument in order, start with the post on a multi-source monitoring platform and its four alert types, then read the client case in which revenue shifted toward Amazon while the Shopify dashboard still looked healthy, and finish with the retrospective on nine years at Adquadrant, which shifts from measurement to culture and leadership. Across all three, the through-line is acting on what the data shows while it still matters; the emphasis moves from agency building to product and measurement.
Graham approaches the same P&L problem as Fenderson from the other end: where demand is created and where the sale is recorded.
Read more
Graham's post on Meta demand being harvested by Amazon covers the same discovery-on-one-platform, purchase-on-another pattern Fenderson describes. Both argue that acquisition economics look worse than they are when each platform is judged on its own dashboard, and both write for operators deciding how aggressively to spend.
Graham builds the argument around customer lifetime value, repeat purchase and allowable CPA, using acquisitions like P&G's purchase of Thorne to show what strong unit economics eventually buy.
That post also traces the operating implications of the deal for a DTC brand.
Fenderson focuses on operating cadence: how fast a signal turns into a decision, what triggers action, and how to rebuild a channel plan once blended data shows where customers buy.
Read Graham for how to make each customer worth more, and Fenderson for how quickly the business notices and responds.
Marrano makes the same case for compressed feedback loops in paid media.
Read more
His post on connecting AI agents into a closed loop argues that the win is not one agent doing one task but the handoffs.
His podcast summary says the advantage is no longer account structure but signal quality, creative volume and fast feedback loops.
Like Fenderson, Marrano treats AI as infrastructure that removes lag between knowing something and doing it; both say the agents support rather than replace the team.
Marrano's example is a creative production chain: a fatigue signal triggers a request for new ads, a designer agent builds them, and a human approves them.
The focus is asset volume and handoff design in ad accounts.
Fenderson's example covers portfolio-wide monitoring across fourteen commerce and marketing systems, with alerts going to a strategist who makes the call.
Their examples include email flow revenue dropping and CAC creeping, not just creative fatigue.
Choose Marrano for creative output and agent design, and Fenderson for whole-business signal coverage and channel-level decisions.
Leffingwell argues that creator-driven commerce needs its own operating model rather than being bolted onto existing channel thinking, close to Fenderson's point that customers do not care about the channel org chart.
Read more
They make the case that TikTok Shop collapses acquisition, attribution and checkout into one motion, and that brands should model creator-driven CAC and contribution margin as they do paid media. Fenderson makes a comparable case for blended MER across DTC and Amazon rather than channel-by-channel ROAS.
Leffingwell writes about whether to enter TikTok Shop now, arguing that waiting removes leverage rather than reducing risk.
They end with an invitation to planning conversations before the next planning cycle.
Fenderson writes about measurement and rebuilding the channel plan after the fact, using a client case where purchase location had already shifted.
Go to Leffingwell for a case about when to enter a new commerce channel, and Fenderson for reallocating spend once the data shows where a brand's customers already buy.
For a campaign on DTC paid media, blended measurement, and marketplace expansion, Fenderson’s posts cover relevant topics: one describes combining channel data to spot shifts in where customers buy, and another describes using performance signals to guide campaign decisions.
Read more
To assess buyer fit, request recent audience data relevant to the campaign and documented survey results or another source for audience job roles and seniority.
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.
Read more
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.
A brief for a scoped paid media and analytics engagement focused on blended measurement and marketplace growth is a strong match.
Read more
The clearest evidence is the client case described in the third source: Amazon grew from roughly 20% of a brand’s revenue to above 45% over eighteen months while the Shopify dashboard still looked healthy, because customers discovered the brand on TikTok and Meta and bought on Amazon.
The response involved three moves: rebuild the Amazon listing and ad structure as a primary storefront rather than a side channel; read blended MER across direct-to-consumer and Amazon instead of channel-by-channel ROAS; and feed the “where they actually buy” finding back into the paid strategy.
A second post describes the operating method behind that work: monitor fourteen named data sources and turn them into decision-linked alerts, so spend shifts while a creator is still converting or an email flow is still broken—not on the fifth of the month, when the creative has already fatigued.
A third post supports the scope of an engagement: nine years at Adquadrant covering paid media, client partnerships, and most recently go-to-market strategy, all aimed at growing direct-to-consumer brands profitably, alongside the culture and internal framework described as the durable part of that work.
Before contracting, verify the contracting entity and available capacity: the nine-year tenure has ended, and the post points toward something next without naming it.
Also request the account-level data behind the marketplace percentages; the post labels them directional and anonymized.
When to choose a specialist
A campaign buying creator-style sponsored placements is a poor fit based on what Jeremy Fenderson’s posts say.
Read more
Picture a skincare brand wanting three disclosed posts plus a story frame inside six weeks, with a published rate card, named deliverables, a turnaround commitment, defined usage rights, and audience demographic targets.
The posts document no sponsorship format: no rate card, media kit, deliverable list, turnaround, exclusivity term, approval step, booking process, or inquiry route—and no sponsorship announcement.
They cover agency-side commentary about client growth work, not consumer-facing promotional content, so there is no example of what a placement from this account would look like or who it would reach.
They also say nothing about audience demographic or professional-role composition, so a requirement to reach, say, US women aged 25–40 shopping for skincare cannot be assessed.
Before budgeting for placements, confirm format, terms, and audience data in writing, or choose a creator with documented placement formats and measured audience.
Another weak fit is a short reactive campaign needing a signed quote within days: without a rate card or booking route, fast contracting may be difficult.
A worked campaign example
A mid-size home-fragrance brand sells direct on its own site and on Amazon.
Amazon’s share has risen from about 18% to roughly 40% of revenue in a year, channel reports each look acceptable, and nobody can state the brand’s blended MER.
The ask is to fix measurement and act on it.
A workable angle: an eight-week “where the money actually moves” engagement, structured in four steps.
Step one, weeks one and two: build a blended view across Shopify, Amazon, Meta, Google, and the email platform, with a written definition of blended MER so the number can be compared month to month.
Step two, week three: agree on five alerts, each with a threshold, a named owner, and an attached decision—a creator video crossing a converting threshold, a channel’s return running above target, an email flow’s revenue dropping overnight, CAC creeping on a campaign, and marketplace share shifting against total revenue.
Step three, weeks four and five: rebuild the Amazon listing and ad structure as a primary storefront, since the blended data shows that is where the sale lands, and redirect acquisition spend toward the channels where customers actually discover the brand.
Step four, weeks six to eight: hold a weekly decision call and document one reallocation or creative decision each week while the underlying campaign is still live.
Measure success by blended MER against the brand’s own baseline, time from signal to decision, and marketplace share tracked against total revenue rather than in isolation.
The brand supplies read access to the connected sources, historical data, and one decision-maker who can act during the weekly call.
The source case describes its percentages as directional and anonymized, so establish the brand’s own baseline rather than treating them as a forecast.
Confirm the contracting entity, current capacity, and written terms before the engagement begins; the posts show a transition in progress rather than a settled role.
Choose Chris Marrano when the campaign’s bottleneck is creative throughput rather than measurement.
Read more
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.
The tradeoff is scope and evidence.
These posts cover an automation chain and the argument behind it, not the marketplace shift or blended cross-channel view the campaign needs: there’s no Amazon share case, combined direct-to-consumer and marketplace revenue view, or alert-by-alert operating method with thresholds and named owners.
They describe a builder’s own agent stack and a podcast conversation about where AI in marketing is heading, not a client engagement with measured outcomes.
A buyer would be relying on a described process, not a track record.
The chain also depends on image-generation tools and agent plumbing the brand would have to own or buy.
A campaign that mainly needs one trustworthy blended MER number will get less from this than from a monitoring-and-decision engagement.
Choose Amy Leffingwell when the campaign makes TikTok Shop the growth channel rather than one line in a cross-channel picture.
Read more
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.
The tradeoff is that these posts make a strategic argument, and their closing call to action is a front door.
Both offer free TikTok planning calls and make the case for moving now rather than documenting a defined scope, deliverable list, or result.
They show no blended cross-channel measurement build, alerting or operating cadence for acting on signals, or client case with numbers.
Amazon share and blended MER, the center of the campaign’s stated need, sit outside their scope.
A brand that already believes in TikTok Shop and needs the measurement and reallocation discipline to run it is unlikely to find that here: the posts make the case for the channel, not the machinery for operating it.
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.
Read more
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.
The tradeoff is informed analysis of other companies’ numbers, not an operating engagement.
The posts describe patterns across brands and market data, citing publicly announced acquisitions and reported ad-price increases; they show no client build, monitoring or alerting system, or result a buyer can point to before contracting.
The Meta and Amazon halo post is explicit that isolating the effect is still mostly a black box, varies by brand and category, and that tools like Triple Whale offer only a directional read.
A brand seeking a defensible blended MER figure should ask for an operating engagement that can deliver one.
Choose Graham for the economic frame and an honest account of what cross-channel attribution can and cannot prove; choose an operating engagement if the need is implementation.
Jeremy Fenderson's three LinkedIn posts describe a platform that watches fourteen commerce and marketing data sources and turns them into decision-linked alerts; a client case in which a brand's Amazon share of revenue grew from about 20% to more than 45% while its Shopify dashboard still looked healthy; and a retrospective on nine years at Adquadrant covering paid media, client partnerships and go-to-market strategy.
The posts do not include publication dates, and Fenderson describes the revenue percentages in the Amazon case as directional and anonymized.
Similar creators include Joshua Graham, Chris Marrano and Amy Leffingwell.
The posts include no video, audio, newsletter or long-form writing.
No sponsorship format, rate card, deliverable list or booking route appears in the posts here, and the posts provide no audience demographic or professional-role data, so campaign fit rests on documented growth work rather than on placement formats.
The posts do not state what Fenderson is doing next, and the Adquadrant role ended rather than being current.
