Edgar Bitencourt shares advice on growing e-commerce brands through paid advertising, creative testing, offers and copywriting. Bitencourt's posts also show how they use AI tools to support paid-media and agency work.
Most useful for e-commerce paid-advertising and creative-testing advice, with AI workflow ideas and human approval.
Best for
A DTC e-commerce brand with paid social, clean tracking, stock, and Q4 creative testing needs.
Not for
A B2B software or professional-services firm seeking sponsored LinkedIn posts for buyer signups.
Key takeaways
Copywriting helped scale an e-commerce brand from low five figures to seven figures in six months.
Their three-week Q4 and BFCM plan links creative and offers to inventory, budgets, tracking, site checks.
AI uses reviews and support tickets to develop ad hooks and check prior tests.
Check first: Ask for audience survey results covering job roles and company size, and recent post-level results.
Main ideas
The offer and the copy decide who wins, not the targeting · Hire experts, then get out of the way · Winning ads are found in customer material, not written from scratch
Edgar Bitencourt writes about the practical work of growing e-commerce brands: shaping offers and copy, testing ads, and finding the business bottleneck that is actually limiting growth.
More recently, their posts show how they apply AI to paid-media and agency workflows, with human approval before changes affect client accounts.
Read their work for specific operating ideas, not just ad tactics.
Bitencourt links marketing decisions to the systems and skills behind them.
In a post on learning copywriting, they say copywriting helped scale an e-commerce brand from low five figures to seven figures in six months, and offer a practice routine: study effective ads, rewrite passages by hand, then test headlines and landing pages.
Their three-week Q4 and BFCM plan makes the same practical case at campaign level, linking creative and offers to inventory, budgets, tracking and site checks.
They also argue that growth depends on solving the right problem, not simply doing more.
Their AI posts extend this operational focus: one describes using reviews and support tickets to develop ad hooks and check prior tests, while emphasizing that they approve proposed actions before those actions affect accounts.
These examples make their work useful to e-commerce founders and paid-media teams weighing where to focus, what to test and which repetitive tasks might be automated.
Edgar Bitencourt argues that e-commerce growth depends on the offer and the words around it—not on finding a winning product or adjusting ad settings.
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They describe paid media's daily work as repetitive checking that should be handed to AI under written rules and human approval. Their recurring ideas span offer and copywriting craft, a testing culture, peak-season operations, hook mining from customer reviews and past test logs, and how much judgment should remain with the person paying for the ads.
The offer and the copy decide who wins, not the targeting
Bitencourt's core claim is that e-commerce is just transactions and copywriting is the craft of engineering them: most founders study products, while winners study offers.
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A product sits on a shelf, they argue; an offer makes someone feel stupid for not buying.
Their method is specific: study three books closely (Gary Halbert's The Boron Letters, Alex Hormozi's $100M Offers, David Ogilvy's Ogilvy on Advertising), rebuild passages from memory as Benjamin Franklin taught himself to write, and continually test headlines and landing pages.
They argue that founders should focus on mastering the skill rather than watching revenue in month one: those who stress about month-one revenue quit in month two.
They credit scaling an e-commerce brand from low five figures to seven figures in six months to copywriting, not a winning product or Facebook ad tactics. In a separate post, they describe a client growing from under $3,000 a month to $180,000, now capped, they say, by the client's supply chain rather than demand.
Bitencourt argues that micromanaging a paid-media partner is expensive: a brand pays for expertise, then blocks it from working.
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They prefer clients who delegate the account entirely, accept that nobody knows what will test well until it runs, and keep communication focused on results. Anxiety makes people cover themselves instead of taking smart risks, they argue, and the freedom to test makes the account profitable.
The client they describe almost entirely avoids meetings, communicates through monthly Slack updates, gives complete freedom to test ugly ads, beautiful ads, advertorials, statics, video and copycat angles, and has never sent an instruction about the ad account in nearly three years. They contrast this with owners who demand certainty before testing or treat an ad partner like a vending machine.
Winning ads are found in customer material, not written from scratch
The raw material for good ads already exists in customer reviews, support tickets and comments under ads that lost, they argue.
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But the six jobs behind a winning ad usually fall to whoever has a spare hour—which is nobody.
They treat creative volume as a supply problem rather than a craft problem: how fast a team can produce and test variations.
Their process includes an approval gate and review step, since generating variants is not the same as deciding which ones run.
Using an AI assistant, they report reading 1,463 reviews and 94 support tickets to produce 33 testable hooks in customers' own words, checking a log of 137 past tests that showed a planned bundle offer had already lost twice in March, and catching a purchase event firing twice on two of five checkout paths before a new brand spent anything.
They report 62 items proposed, 55 approved and 7 declined with reasons.
In a separate post, they describe going from rebuilding a competitor's ad by hand to producing ten brand-specific variations in under an hour with a tool that analyses ads and competitor ads; the revenue and ad-count claims come from the vendor.
AI proposes, a human approves, and the thresholds are numbers
Bitencourt describes paid media as a checking job—pacing, search terms, feed health, creative fatigue and a weekly report that eats a day—and argues that the ceiling on a paid-media desk has nothing to do with skill.
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Their answer is to write rules as numbers before an assistant touches a budget: a budget shift it may make alone, a cost-per-acquisition move it must flag, and a campaign it may never pause unilaterally.
They say they would never switch off the approval gate: when spending someone else's money, it is part of the job, not a preference.
The trade-off is having to remember to start the check.
In one account, an assistant flagged creative fatigue before return on ad spend moved: frequency crossed 4.0 overnight and click-through fell 38% on the week.
It drafted replacement creative and attached its reasoning; Bitencourt approved it, and it went live.
Elsewhere, they report an assistant opening the channel unprompted about a 38% ROAS drop, and a month-end workflow in which one assistant reconciled 23 accounts across Meta, Google and TikTok, produced 23 client notes with the rough months left blank for clients' own words, sorted 39 client emails and tracked 29 creatives due.
As the scope grows, the posts show the same boundary: early examples automate a single account check; later ones cover reporting, client emails, reconciliation and outbound. They never hand over the craft argument—that offers, copy and judgment decide outcomes—and describe automation as freeing media buyers to do that work, not replacing it.
Find the real bottleneck, even when it is the uncomfortable part of the business
A recurring argument is that running a store means identifying the one critical problem between you and your goal, rather than solving the wrong problem well.
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Bitencourt argues that a priority cannot be to learn something—it must be to ship something—because learning feels productive and is often procrastination with better branding. Related posts frame fear as a compass pointing toward growth, not a stop sign, and urge radical responsibility instead of blaming the algorithm, supplier or economy.
They describe spending almost a year on marketing while the brand sold out of product every second month, because fixing the supply chain felt unfamiliar and uncomfortable. Their test: imagine being locked in a room with a laptop and told to generate revenue in 30 days, then compare that list with what you actually worked on today.
Serve one narrow customer, and treat generosity as strategy
In a list of perspective shifts, Bitencourt argues that focusing on one specific customer clarifies messaging, product and offer, and that an abundance mindset toward customers pays back: never refuse a refund, and send free products when customers are struggling.
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They pair this with celebrating small wins as proof that the model works, finding partners strong where you are weak, and treating a written vision as a practical tool for keeping attention on the problem rather than magic.
They say getting the first offer right mattered more than the $50K day that followed: the offer proved repeat customers would compound. They also describe a partnership that unlocked scale only when someone else took over supplier calls and logistics.
Marketing and sales alignment: a guest argument Bitencourt published
This is a credited piece by Ev Oputa, not Bitencourt's own argument; they chose to circulate it.
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It argues that marketing and sales bicker like siblings: marketing feels its brand and lead work is unappreciated, while sales feels marketing does not understand quotas. The fix, according to the post, is shared goals measured on revenue impact, shared CRM and analytics data, and one narrative from first impression to closed deal.
The post frames the tension through dialogue ("We gave you the leads!" / "These leads are trash!") and lists the benefits of alignment: higher-quality leads, shorter sales cycles, stronger customer experience and more predictable revenue growth. It credits Ev Oputa.
Peak season is an operating problem before it is a marketing problem
Bitencourt's Q4 guidance treats the quarter as a system with predictable failure modes—suppliers slipping timelines, ads overspending overnight, sites crashing and teams missing details—and argues that winners simply spot and fix problems fastest.
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The four pillars are a message that stands out (clear outcome in three seconds, emotion before logic, a reason to buy from this brand rather than Amazon, and real urgency without fake timers); creatives built to stop the thumb (one-second stop, product plus emotion, UGC with proof, offer overlays); media buying that amplifies proven winners instead of testing in November; and an offer strong enough to carry the traffic. They argue that validating creative and offers in advance matters more than finding a clever new tactic.
The post closes with a three-week plan: week one audits the past 90 days and locks hooks, inventory and cutoffs; week two produces static and UGC batches, builds the email calendar and sets prospecting and retargeting budgets by day; week three checks tracking, feeds, product pages, checkout speed and on-call ownership. The stated expectation is 16-hour days and daily standups during the period.
This post shows how Edgar Bitencourt would help a store plan for peak season, laying out their Q4 structure from offer design to the three-week build.
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Bitencourt frames Q4 as an operating problem, not a marketing one.
They identify the failure modes they expect — suppliers slipping timelines, ads overspending at 2am, sites crashing and teams missing details — and argue that winners spot and fix problems fastest.
The post lays out four pillars: a message that stands out (a clear outcome in three seconds, emotion before logic, a reason to buy from this brand rather than Amazon, real urgency and no fake timers); creatives that stop the thumb (a one-second stop, product plus emotion, UGC with proof and offer overlays); media buying that amplifies proven winners rather than testing in November; and an offer strong enough to carry the traffic.
The three-week plan: week one audits the past 90 days and locks hooks, inventory and cutoffs; week two produces static and UGC batches, builds the email calendar and sets prospecting and retargeting budgets by day; week three checks tracking, feeds, product pages, checkout speed and on-call ownership.
For brands planning a campaign
Use it as a checklist against a brand's existing Q4 calendar.
For a paid-acquisition brief, focus on validating creative and offers in October rather than testing during the sale, and separating cold, warm and retargeting budgets with daily caps.
The 16-hour day and standup cadence describe their expected workload, not a promise about an engagement.
The clearest account of how Bitencourt thinks about creative volume: the bottleneck they aim to solve and the tool they use.
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The post tackles a familiar agency problem: a client sends a competitor's ad and asks for something better, prompting revisions, Slack threads and asset sourcing.
Bitencourt says teams running creative across many accounts face this weekly.
They describe testing an AI tool that took them from rebuilding an ad by hand to producing ten variations for the client's brand in under an hour.
They say the tool is trained on large-scale ad spend, identifies ads with untapped potential and generates iterations.
The revenue claims and offer of 1,000 ads in exchange for commenting come from the vendor's marketing, reproduced in the post; they are not independently verified.
For brands planning a campaign
For a brand seeking faster creative iteration, Bitencourt frames the work as a supply problem — how quickly a team can produce and test variations — rather than a craft problem. Ask how the process works, who reviews generated variants and how many make it to a real test; the post covers generation, not filtering.
Explains Bitencourt's view that offers and copy, not targeting tricks, decide who wins.
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Read it before evaluating their campaign advice.
Bitencourt says the six-figure scale-up came from copywriting, not finding a winning product or hacking Facebook ads.
Their argument: e-commerce is transactions, copywriting engineers them, and most founders study products while winners study offers — a product sits on a shelf, while an offer makes someone feel stupid for not buying.
The learning method is specific.
First, study rather than read three named books: The Boron Letters by Gary Halbert, $100M Offers by Alex Hormozi and Ogilvy on Advertising by David Ogilvy.
Second, write by hand using the Benjamin Franklin method: read a passage, put it away and reconstruct it from memory.
Third, test headlines and landing pages continuously, focusing on mastering the skill rather than watching month-one revenue.
For brands planning a campaign
For offer-positioning work, this shows how Bitencourt would approach a brief: rework the offer and copy before adjusting bids or audiences. It also sets a concrete expectation for how they train marketers around them, useful for a brand that wants to retain the capability after an engagement.
The most detailed account of what Bitencourt says occupies a paid-media week, with numbers attached.
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It shows what a working relationship with them covers.
Bitencourt argues that winning ads draw on customer reviews, support tickets and comments under failing ads, while the six jobs around a winning ad fall to whoever has a spare hour — which is nobody.
They describe assigning those jobs to an AI assistant inside Slack, starting with their own supplement brand.
Reported output: 1,463 reviews and 94 support tickets read, producing 33 testable hooks in customers' own words; a test log of 137 past tests across accounts that revealed a planned bundle offer had already lost twice in March; 151 samples shipped to TikTok Shop creators, with 59 posting, 57 still inside the window, 35 quiet past three weeks, and a nudge drafted for each; and a tracking check that caught a purchase event firing twice on two of five checkout paths before a new brand spent anything.
On approval, they report 62 items proposed, 55 approved and 7 declined with reasons, and say nothing touches a client, creator or ad account without their yes.
The post is a paid partnership with Viktor.
For brands planning a campaign
This is a concrete example for a campaign that needs creative testing.
The hook-mining approach — turning reviews and support tickets into testable angles, then keeping a searchable log of past tests — is worth copying whether or not Bitencourt is involved.
The tracking audit also matters for a Q4 launch: double-firing purchase events distort the optimisation a scaling brand depends on.
Ask how they would structure the first month and which of the six jobs they would tackle first.
A concise account of the rules Bitencourt would apply to an ad account and where they draw the line on automation.
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Useful for brands weighing whether to hand spend to an automated system.
Bitencourt describes paid media as repetitive checking — pacing, search terms, feed health, creative fatigue and a weekly report that eats a day — and says the ceiling on a paid-media desk has nothing to do with skill.
Their answer is written thresholds: a budget shift the assistant can make on its own, a cost-per-acquisition move it must flag, and a campaign it can never pause unilaterally.
They say they would not switch off the approval gate: the assistant proposes, Bitencourt approves, then it goes live.
On someone else's spend, they say, that is the job, not a preference.
By week two, they report, anomaly reports stopped needing a second look.
The post is a paid partnership with Viktor.
For brands planning a campaign
For a brand considering AI-assisted account management, this is the governance model Bitencourt advocates: numeric thresholds set in advance, a named human gate on anything that spends money, and no autonomous pausing. Ask for the eight detailed rules in the image and include them in the contract or onboarding document.
Shows the client behaviour Bitencourt rewards, giving brands the clearest signal of what they need to get their best work.
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Bitencourt describes a client of nearly three years who grew from under $3,000 a month to $180,000, with the client's supply chain, not demand, setting the ceiling.
They credit the growth to a client who avoids meetings, communicates monthly on Slack, gives them complete freedom to test ugly ads, beautiful ads, advertorials, statics, video and copycat angles, and has never sent an instruction about the ad account.
Bitencourt contrasts this with owners who hire experts and then micromanage, calling it expensive, and argues that anxiety makes people cover themselves rather than take smart risks.
For brands planning a campaign
Sets expectations for the working relationship.
A brand that wants quarterly alignment calls and pre-approval on every creative will have a different engagement from one that delegates the account and reviews results monthly.
The growth figures come from the agency's account of one client and are not broken out by channel or spend.
A concrete example of Bitencourt using monitoring to catch creative fatigue early.
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Edgar Bitencourt says the recurring expensive habit across a reported $30M in ad spend and 80+ brands is that nobody checks the account until standup.
They describe asking an AI employee to check pacing each morning on one account.
On day twelve, it flagged creative fatigue before ROAS moved: frequency crossed 4.0 overnight, and click-through rate fell 38% that week.
The creative swap was drafted with the reasoning attached.
They approved it, and it went live.
They call the approval gate non-negotiable for paid media.
The post is a partnership with Viktor.
For brands planning a campaign
A worked example of the fatigue threshold and response Bitencourt would bring to a scaling account: watch frequency and click-through as leading indicators rather than waiting for return on ad spend to fall, and draft replacement creative in advance. The $30M and 80+ brands figures are their own claims.
Prioritization, customer focus and team alignment4
E-commerce offers, copywriting and growth2
Paid-media testing and seasonal preparation2
Creative research and iteration2
E-commerce offers, copywriting and growth
Bitencourt argues that growth depends less on finding a winning product than on making the offer and its copy compelling.
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They recommend studying strong copy, rewriting it by hand, and testing headlines and landing pages. One case post attributes growth from low five figures to seven figures in six months to copywriting; another describes a client growing from under $3,000 to $180,000 per month, while noting supply-chain limits.
Bitencourt teaches through specific business examples and practical exercises. Their results are self-reported, so ask for supporting benchmarks.
For brands planning a campaign
For a DTC brand, assess whether a campaign can sharpen the offer and customer message—not just promote the product. Pair copy variations with a test plan, and check inventory capacity before scaling demand.
Bitencourt presents paid acquisition as a cycle of testing creative, offers and account performance.
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They advocate trying varied formats and angles rather than demanding certainty before testing. Their Q4 guidance covers inventory and deadlines, creative production, email, tracking, budgets and contingency planning in a three-week preparation sequence.
The Q4 post offers a checklist-style operating plan; the client-growth post emphasizes giving a capable partner room to experiment.
For brands planning a campaign
A brand preparing for BFCM can assess Bitencourt for a coordinated paid-social and creative-testing plan: define the offer, develop varied hooks and formats, validate tracking and inventory, then schedule tests and backups before peak season.
Bitencourt describes turning competitor ads into brand-specific variations and using customer reviews, support tickets and past test results to find potential hooks.
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One post describes an AI tool that generated ten ad variations from a competitor example; another details reviewing customer feedback and prior test logs before proposing new hooks.
The examples move from research to proposed creative. Ask what evidence supports the reported tools and outcomes, and how they would validate performance for your brand.
For brands planning a campaign
Useful for brands with a creative backlog or scattered customer insights. Ask for a process that traces each concept to customer input, adapts rather than merely copies competitor work, and records test outcomes.
Several posts describe AI handling monitoring, reporting, reconciliation, client updates and other repeatable work.
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The recurring boundary is human approval: the AI proposes changes, while Bitencourt says they approve them before they affect client accounts, ads or communications. They also argue that automation should free media buyers to focus on judgment and strategy.
For brands planning a campaign
For a brand or agency considering AI in campaign operations, evaluate the approval gates, permitted actions, error checks and escalation process—not just the promised time savings. Treat these posts as product-partnership examples, not neutral comparisons of tools.
Across business-advice posts, Bitencourt urges founders to identify the real bottleneck, focus on a specific customer and take responsibility for decisions rather than chase tactics.
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They also argue that marketing and sales need shared goals, data and messaging. Their examples include recognizing that stock shortages, not marketing, were limiting growth.
For brands planning a campaign
These ideas may suit a campaign that needs stronger offer-to-operations coordination. Before increasing spend, test whether supply, customer understanding or sales follow-through is the constraint.
Edgar also promotes GoMarble AI and Atria, but the nature of those relationships is not stated. Ask them to clarify their commercial relationships with these businesses.
Viktor
Disclosed partnership
Edgar describes posts about Viktor’s AI employee tools as partnerships and demonstrates their use in paid-media and agency workflows.
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The posts say they propose actions and approve them.
Explore Edgar Bitencourt's posts one thread at a time.
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Start with the offer and copywriting argument: they credit copy — not product hunts or ad settings — for scaling an e-commerce brand.
Then read the client case, where they argue an owner should hire expertise and stay out of the account.
From there, follow either the testing-and-peak-season track (creative variation, Q4 preparation, offer strength before traffic) or the automation track, where they describe AI assistants handling repetitive paid-media checks under written thresholds and a human approval gate.
If you read only one, choose the post on paying for expertise and then blocking it.
Later posts are largely paid tool partnerships; treat performance figures as Bitencourt's or the vendor's reported numbers.
Their recurring operating rules cover how creative is sourced from customer reviews and past test logs, and which decisions stay with a person.
Both target the same DTC e-commerce growth audience and teach in the same shape: a numbered list of what brands get wrong, with each item paired with a short corrective.
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Both treat retention and repeat purchase as the profit engine behind acquisition spend, and write for operators rather than agencies pitching services.
Ahmad stays inside email: welcome flows, segmentation, discount discipline, post-purchase sequences and zero-party data.
Bitencourt's writing centres on paid acquisition and where offers, copy and creative tests come from.
Choose Ahmad for lifecycle and retention detail, Bitencourt for media buying, creative sourcing and peak-season execution.
Both write for people running Meta ads for e-commerce brands and teach by naming failure modes rather than tactics: expecting day-one winners, chasing vanity metrics and treating one good campaign as proof of a system.
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Her checklist format is close to Bitencourt's rules-before-the-account-touches-budget framing.
Rafiq's material focuses on the media buyer's own discipline and career, especially how they judge campaigns.
Bitencourt takes a higher-level view, covering the owner's relationship with the person running the account and the handoff of routine monitoring to AI.
Choose Rafiq for buyer-level habits, Bitencourt for delegation, approval gates and offer positioning.
Both argue that a narrow channel specialist beats a generalist agency, drawing on experience running their own stores and seeing the P&L.
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Both describe internal quality control in concrete terms and use their own reported numbers as evidence.
Mei's agency only sells email, while Bitencourt's posts cover team structure — four-person pods, three QA checks and hiring from larger agencies — alongside paid media, creative volume, customer-review research and AI handling repetitive checks. Choose Mei to learn who should own email and how that team is staffed; choose Bitencourt for paid acquisition, offer and creative testing.
The only measured audience figure for Edgar Bitencourt is a LinkedIn following of 22,527 accounts, recorded on 7 October 2026.
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That tells a buyer the audience exists at a meaningful scale on one platform, but not who is in it.
The posts are written for e-commerce brand owners, paid-media buyers, and agency owners — they work through ad testing, offer positioning, BFCM preparation, and AI-assisted media-buying workflows — which is a content-appeal signal, not a measurement of who follows or reads the account.
No platform demographics (geography, age, gender) and no audience survey (employer, job title, seniority) are published in the posts here, so for a DTC Q4 campaign you cannot yet confirm how many followers actually run paid acquisition or control budget.
Concrete next step: ask Edgar for audience survey results covering respondent job roles and company size, and for recent post-level results (impressions, engagement, comments) on the closest comparable topics and formats, such as BFCM offer prep, creative testing, and paid-social scaling.
Until then, treat the follower count as reach on LinkedIn and the topic list as the basis for a hypothesis about relevance, not as verified audience composition.
LinkedIn followers
22,527
LinkedIn account record for Edgar Bitencourt · 2026-10-07
Edgar Bitencourt's LinkedIn partnerships are mostly disclosed or promotional collaborations with marketing-AI vendors.
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The tools run inside paid-media processes, and posts report what they did — reconciled accounts, flagged creative fatigue, drafted client notes — before routing readers to a comment-keyword call to action. The posts consistently state that the tool proposes, they approve, and nothing reaches a client or ad account without their approval.
Disclosed AI-vendor partnership post
Buyer requirement
Expect the post body to include an explicit "In partnership with [vendor]" line, a comment-keyword CTA and a first-comment link to the vendor's signup page.
The vendor owns the free-trial or credit offer ($100 credits, no card is the pattern used); the creator's post drives traffic rather than closing the deal.
Budget for the vendor landing page, not Edgar's profile, as the conversion destination.
Workflow carousel featuring a named tool
Buyer requirement
Provide account access, the numeric thresholds the tool may act on and the escalation rules it must follow.
The published rule set separates actions (budget shifts the tool can make), flags (CPA moves it must report) and prohibitions (never pause a campaign alone), with human approval required before anything goes live.
Brands need written thresholds and a named approver to fit this workflow.
Creative-tool demo (ad cloning, ad generation)
Buyer requirement
Expect a before-and-after story: a competitor ad arrives, and the tool produces ten brand-fitted variations in under an hour.
A lead-capture CTA follows (comment a keyword to get access).
The content addresses agency owners, paid-ads leads, ecommerce owners and creative strategists, and assumes an existing creative pipeline and multiple client accounts rather than a single first campaign.
Reporting and analytics tool promotion
Buyer requirement
The pitch is time saved on manual cross-platform reporting (Meta, Google, GA4, Shopify) and one-click audits of spend, revenue, ROAS trends and CAC. Brands need consolidated platform access, clean tracking and naming conventions already in place; the format assumes the buyer is replacing manual Monday reporting, not building reporting from scratch.
Free resource distribution (agent or prompt collections)
Buyer requirement
Access is gated behind a comment or direct-message step, and no price is stated. These posts deliver downloadable collections — for example, 50 role-specific Claude agents across research, SEO, content, demand generation, prospecting and conversion — so brands should treat them as lead magnets that start a relationship, not scoped engagements with deliverables.
A DTC e-commerce brand with a working paid-social programme, clean tracking and stock to sell, looking for a Q4 paid-acquisition and creative-testing plan.
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Bitencourt’s posts address that need directly, laying out four peak-season pillars — message, creative, media buying and offer — and a three-week build.
The plan audits the previous 90 days, locks hooks and inventory cutoffs, produces static and UGC batches, builds the email calendar, sets prospecting and retargeting budgets by day, then QAs tracking, feeds, product pages, checkout speed and on-call ownership.
Creative volume is treated as a supply problem: their posts describe mining hooks from customer reviews and support tickets, then checking a log of past tests before briefing anything — preparation a store needs before BFCM.
In another post, they report catching a purchase event firing twice on two of five checkout paths, the kind of fault that distorts optimisation as spend scales.
The working relationship matters as much as the plan.
Bitencourt describes a client who grew from under $3,000 to $180,000 a month, gave them freedom to test ugly ads, advertorials and copycat angles, never instructed them on the ad account, and communicated monthly on Slack rather than in quarterly calls.
They call micromanaging an expert expensive.
A campaign that hands over the paid-social desk and creative-testing loop, reviews results monthly and names an approver for anything that spends or reaches a client fits their stated approach.
Their governance is specific enough for an onboarding document: numeric thresholds for what a tool may change, what it must flag and what it may never do unilaterally, plus human approval for everything.
The commercial format is a disclosed vendor partnership, not a neutral recommendation.
Their promoted posts follow a set pattern: an explicit “in partnership with” line, a process described with numbers, a comment-keyword call to action and the vendor’s own landing page as the conversion point.
If the plan uses a tool, budget for the vendor page and treat the post as the traffic and credibility source.
Before signing, ask for audience survey results covering respondent roles and company size, post-level results on BFCM and creative-testing topics, and the rates and terms behind the partnership format.
When to choose a specialist
A B2B software or professional-services firm looking for sponsored LinkedIn posts to reach buyers and drive signups to its own product.
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The audience information is one figure — 22,527 LinkedIn followers recorded on 7 October 2026 — with no demographics report or audience survey, so reach into a defined buyer group remains unproven.
The content targets e-commerce operators, media buyers and agency owners, covering offers, creative testing, BFCM preparation, month-end agency admin and AI-assisted media workflows.
A B2B software buyer choosing a platform is a different reader from the one these posts address.
The commercial format also runs the wrong way for a straightforward placement.
In vendor posts, Bitencourt uses someone else’s tool and reports what it did; the vendor’s page converts, and the relationship is disclosed as a paid partnership.
Buying a sponsored post from them reverses that pattern.
Their posts include no media kit, rate card or partnership inquiry route, so ask for the pricing and terms before scoping a placement.
A campaign seeking an editorial recommendation between competing tools conflicts with the disclosed-partnership format and its comment-keyword calls to action.
Two related briefs are weak matches.
A firm seeking an execution-only media buyer who follows instructions on the ad account does not match the conditions Bitencourt says work best: freedom to test, no account-level instructions and monthly reporting.
A brand whose constraint is not acquisition — one that sells out every second month or has a retention problem — is also a poor fit.
Their advice is to address the bottleneck first: in one post they describe wasting a year on marketing while stock shortages capped growth, and argue that the job is to solve the most critical problem, not the most familiar one.
A worked campaign example
Need: scale a DTC store’s paid social into BFCM without spending the quarter’s testing budget in November. This is the approach Bitencourt describes using to run accounts.
Week one — offer and message.
Write the offer before the ads: the outcome in three seconds, the emotional reason before the logical one, a reason to buy from this brand rather than Amazon, and real deadlines instead of countdown timers.
Their position is that the offer, not the product, decides the result — a product sits on a shelf; an offer makes someone feel stupid for not buying.
Test headline and landing-page variants while refining the offer, using the copy study-and-rewrite method they describe rather than waiting for certainty.
Week two — creative supply.
Pull hooks from the last thousand reviews and support tickets, using customers’ own words.
Check the log of past tests so an angle that already lost is not briefed again: in their account, a planned bundle offer had failed twice in March, and the test log surfaced it before anyone wrote copy.
Use an ad-cloning step on two or three proven winners to generate variants quickly, then filter them.
Their posts show the generation step, not the filtering step, so specify who reviews the output and how many variants reach a live test.
Produce static and UGC batches alongside the email calendar, and set cold, warm and retargeting budgets by day rather than as single campaign totals.
Week three — QA and monitoring.
Check the purchase event on every checkout path, plus feeds, product pages, checkout speed and preloaded offers.
Set the numeric rules they advocate before any tool touches spend: the budget shift it may make, the cost-per-acquisition move it must flag, the campaigns it may never pause, and the named approver.
Pre-draft replacement creative so fatigue prompts a swap, not a scramble.
Their worked example is frequency crossing 4.0 overnight with click-through down 38% on the week, while replacements are already written and waiting for approval.
The brand must supply the numbers this plan depends on: monthly ad budget, current ROAS and CAC, fulfilment capacity and shipping cutoff dates.
The client results Bitencourt cites — under $3,000 to $180,000 a month, $30M in ad spend across 80+ brands, and ten variations in under an hour — are their own or their vendors’ reported figures and were not independently verified.
Consider Sara if the campaign needs a media-buyer training angle alongside the Q4 campaign plan.
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Her post turns common mistakes—expecting immediate sales, overreacting to fluctuations, and skipping testing—into a concise checklist. That could support a team building steadier testing habits before peak season.
The examples offer less direct support for a campaign aimed at Q4 peak-season planning than Bitencourt’s detailed peak-season checklist and creative workflow. Ask for a Q4 plan covering offer positioning, ad cloning, and BFCM preparation.
Consider Muhammad if the brand’s Q4 constraint is email revenue rather than paid-social acquisition.
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His posts flag neglected welcome flows, segmentation, post-purchase messages, and testing; another post argues that email strategies should reflect each brand’s audience, buying psychology, and customer journey. Those ideas could inform the email work that runs alongside a BFCM campaign.
The examples focus on e-commerce email, not paid-social buying or ad-creative production. Ask for a plan for ad cloning and BFCM media preparation before choosing this direction for Q4 paid acquisition.
Consider Mark if the Q4 decision is how to build an email operation around the paid campaign.
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His post describes a four-person specialist team and three QA checks per email; another connects email decisions to margin, acquisition costs, and store economics. His examples are relevant when the brand needs channel ownership and quality control for email during the peak-season push.
The examples concern email agency delivery, not paid-social campaign management, ad cloning, or BFCM preparation.
They are useful for the adjacent retention and execution question, but provide less direct support for the paid-acquisition decision than Bitencourt’s work.
Ask for examples of paid-social campaign management and BFCM preparation.
This profile draws on the full text of 16 LinkedIn posts published by Edgar Bitencourt between July 2025 and October 2026, read in full rather than summarised from headlines.
The account's follower figure (about 22,500) was recorded on 7 October 2026.
Comparisons with other creators use posts by Muhammad Ahmad, Sara Rafiq and Mark Mei.
Several posts disclose partnerships with AI tool vendors, so the revenue, ad-spend and time-saving figures in them are numbers reported by Edgar Bitencourt or a vendor and were not independently verified.
The posts here do not give individual dates, so the ideas are grouped by theme rather than chronology.
One post reproduces a credited guest piece, and one refers to a set of operating rules shown only in an image not included in the post text.
No audience demographics, media kit, rate card or partnership terms are published in the posts here.
