Giann Quijano is a creative and growth strategist who builds AI-enabled tools and marketing systems for e-commerce brands, connecting performance data, creative testing, and technical execution.
Giann Quijano is a creative and growth strategist focused on e-commerce, connecting marketing data, creative testing and AI-enabled tools to build working growth systems.
Their posts range from interactive strategy apps and automated marketing workflows to a plan for building audiences before Q4 advertising costs rise.
Follow them for ideas on turning growth hypotheses into practical tools and operating changes.
Quijano’s distinctive angle is to treat growth strategy as something to build and operate, not just measure.
In a post about AI-built tools and client deliverables, they describe making interactive strategy apps, creative systems and data tools with Claude as an engineering partner.
Another post outlines a Q3 audience-building plan for Q4: acquire and warm audiences before holiday competition drives up costs.
Giann Quijano's recurring argument is that e-commerce growth should be built, not just measured.
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Across the posts, they urge strategists to act as builders who use AI to create agentic growth systems, test creative against retention cohorts, start Q4 acquisition in Q3, and rebuild operating models so growth does not depend on the founder. They also argue that client deliverables should be live, data-fed tools rather than static decks.
From measurer to builder
Quijano argues that measuring and reporting are no longer enough.
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The e-commerce strategist should be a builder who combines creative direction, retention analysis, and AI-assisted development.
The reasoning is speed and control: waiting for an engineering ticket or an approval layer keeps tests from shipping while creative hooks go stale.
In this model, one person can function as a growth team rather than a checklist manager.
In the post that opens "In the new economy being a measurer isn't enough," Quijano describes a T-shaped operator who diagnoses funnel friction as a creative director, maps retention cohorts as a data scientist, and builds bespoke AI automation agents as a developer. They contrast shipping solutions in hours with waiting weeks for engineering approval, then ask whether the reader is still measuring the funnel or building the engine.
Later posts turn the same argument into build practice: writing project context files, generating tests for interface changes, and running multiple agents in parallel.
Quijano treats AI agents as an operating architecture, not just productivity tools.
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The problem they identify is the testing queue: a growth idea can wait two weeks for development, by which time the creative hook is stale and acquisition costs have risen.
The proposed fix is agents that execute, test, and deploy, shrinking the distance between a hypothesis and live data from weeks to minutes.
In this model, the growth architect writes project context files and validation tests rather than code.
In the Claude Code post, Quijano describes autonomous sub-agents querying retention cohorts in real time.
If a landing-page variant produces a reported 12% lift in 90-day lifetime value, the agent reallocates 30% of Meta ad spend to that path through the marketing API.
A custom command reads raw ad performance, cross-references Shopify checkout data, and rewrites product-page CSS and copy to match the winning creative angle.
Quijano reports that a conversion-rate baseline of 4.6% reached 11.2% in a single sprint.
Creative should be part of a loop with retention and unit economics: test hooks systematically, read ad performance against cohort data, and use winning angles to shape the pages and offers that convert. A creative win matters only if it improves lifetime value or lowers blended acquisition cost, not if it produces a short-term click spike.
In the $3M brand case, Quijano says the work shifted from random asset testing to a systematic, hook-led iteration framework tied directly to retention cohorts, while funnel changes lifted average order value by 28% through bundled merchandising and post-purchase upsells.
In the agentic post, the same loop rewrites product-page copy to match the winning creative angle.
The Q3/Q4 post applies it to timing: feed custom audiences and retention cohorts with high-intent creative angles before holiday bidding starts.
Quijano argues that most e-commerce brands treat Q4 as a sudden emergency, switching on acquisition in October and paying peak costs for cold traffic during the most expensive weeks of the year.
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The alternative is to treat Q3 as a sponge phase: buy attention while summer and early-fall CPMs are lower, build first-party data, pre-qualify custom audiences, and warm the retargeting pool.
Q4 then focuses on high-margin conversion rather than expensive cold acquisition.
Ask for the budget levels, channel mix, and measurement window before assessing the plan.
The post names three moves: lower-CAC arbitrage, audience pre-qualification, and retargeting warm-up. Quijano writes that by November, the retargeting pool should be packed with users who already trust the brand, lowering blended CAC while competitors bid against each other for scraps.
Quijano argues that scaling an e-commerce brand is not only a math problem.
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ROAS, CAC, and creative fatigue matter, but so do founder dependence, fragile procedures, and weak unit economics. A predictable, data-driven business is valued differently from a founder-dependent hustle, and the operator's job is to build the machine that creates enterprise value and gives the founder time back.
Quijano describes a founder whose revenue had flattened around $3M, whose ad accounts were burning cash, and who had not eaten dinner with four kids in two months.
The engagement restructured creative strategy into a hook-led framework tied to retention cohorts, re-engineered the funnel to lift average order value by 28% through bundles and post-purchase upsells, and wrote standard operating procedures so the founder stopped being the operational bottleneck.
Quijano says the brand was positioned for a strategic exit sixteen months later.
Live, data-fed deliverables instead of static decks
Quijano prioritizes client deliverables that are usable, current, and inspectable.
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Strategy decks become interactive web apps, proposals become live URLs that update with data, and each tool is documented with what it does and how it was built. They draw a line between being an engineer and being a creative strategist: the skill is knowing what to build and why it adds value, then using AI to ship it.
In a post about the previous thirty days, Quijano lists more than twenty-five tools, apps, and client deliverables built with Claude as an AI engineering partner.
Examples include StockPulse, inventory monitoring for DTC agencies aimed at preventing profitable campaigns from being paused when clients stock out; DealPulse, a real-estate deal analyzer; Potion Lab, a product-match quiz; a twenty-two-angle creative diversity matrix built on performance-creative frameworks with over $300M in spend; and a digital product storefront with eleven products.
Quijano states plainly, 'I'm not an engineer.
I'm a creative strategist,' and says the work is documented with what it does and how it was built.
Answers the first question a newcomer has: what does this person actually make?
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The post is a dated inventory of shipped work, not a general claim about capability, making it the quickest way to assess the range and the problems each build targets.
Quijano lists more than twenty-five tools, apps, and client deliverables built in thirty days with Claude as a build partner, grouped into categories: SaaS tools such as StockPulse (inventory monitoring for DTC agencies, aimed at preventing campaigns from being paused when clients stock out), DealPulse (a real-estate deal analyzer), and Potion Lab (a product-match quiz); client strategy decks delivered as interactive web apps that update with live data instead of static PDFs or slide links; and ad and creative systems, including a twenty-two-angle creative diversity matrix assembled from performance-creative frameworks, dark-funnel pages for CPG and beauty brands scaling past $50M, and a storefront with eleven products.
They say they are not an engineer but a creative strategist who describes the problem, directs the architecture, and ships the same day, and invite direct messages for project work.
This is a portfolio and pitch post, so ask for examples and results from comparable projects.
For brands planning a campaign
For a brand that wants internal tooling rather than another strategy deck, this is the clearest indication of what a working engagement produces: dashboards, proposal apps, and creative systems that clients keep and use. A brief could apply the same treatment to the brand’s reporting and proposal process, not just paid media.
Explains how the AI-enabled growth engine is meant to work in practice.
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It is the most specific method post in the set, naming the pipeline, commands, and decisions the system is supposed to make on its own.
The post argues that growth teams are limited by architecture rather than headcount, and that a testing queue costing two weeks per experiment leaves creative stale and raises acquisition costs.
It describes autonomous sub-agents that query retention cohorts in real time and, when a landing-page variant is producing a reported 12% lift in ninety-day lifetime value, reallocate 30% of Meta ad spend to that path through the marketing API.
It also describes a custom /analyze-hooks command that reads ad performance data, cross-references it with Shopify checkout data, and rewrites product-page CSS and copy to match the winning creative angle.
Quijano reports a conversion-rate baseline of 4.6% reaching 11.2% in one sprint, and recommends standardising context in project files, generating dozens of tests per interface change, and running parallel agents through git worktrees.
Ask for supporting data before relying on the reported performance figures.
For brands planning a campaign
This is the closest thing to a blueprint for a campaign aimed at improving LTV-based budget reallocation, a creative-to-page feedback loop, and a testing cadence measured in minutes. A brand could ask for this architecture, scoped to its own data sources, and assess the proposal against these named mechanisms.
It is a short, concrete calendar argument and a simple test of whether a brand's planning timeline matches the advice.
Quijano argues that most brands treat Q4 as a sudden emergency, switching on acquisition in October and paying peak costs through the year's most expensive eight weeks.
The proposed alternative is to treat Q3 as a sponge phase: buy top-of-funnel users while summer and early-fall costs are depressed, fill custom-audience pools and retention cohorts with high-intent creative angles before holiday bidding starts, and enter November with a warm retargeting pool so BFCM spend can go toward high-margin conversion instead of cold acquisition.
The post names three moves — lower-CAC arbitrage, audience pre-qualification, and retargeting warm-up — and asks how the reader's brand is approaching pre-BFCM acquisition now.
For brands planning a campaign
Engagement timing changes the value of the work.
A brand hiring in Q3 is buying cheap attention and building the audiences that make Q4 efficient; a brand arriving in October is buying a rescue.
This is also a useful screening question: brands whose budget cycles do not allow early spend will not get the result described.
Answers what a full engagement looks like from the outside.
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It is the only case narrative in the set that runs from the initial problem to an exit, and shows how the scope extends beyond media buying.
Quijano recounts a founder whose revenue had flattened around $3M, with ad accounts burning cash and the founder absorbed in day-to-day operations.
The described engagement had three parts: replacing random asset testing with a hook-led iteration framework tied to retention cohorts; re-engineering the funnel to lift average order value by 28% through bundled merchandising and post-purchase upsells, improving the lifetime-value-to-acquisition-cost ratio; and writing standard operating procedures so the founder stopped being the operational bottleneck.
The post says the brand was positioned for a strategic exit sixteen months later, at a valuation reflecting predictable data-driven growth rather than founder dependence.
Ask for supporting evidence and documentation of the founder’s circumstances before treating these outcomes as established.
For brands planning a campaign
For a brand constrained by founder overload or unpredictable revenue rather than creative volume, the scope shifts to operating procedures, merchandising, and retention economics alongside paid acquisition. The engagement can address the operating model, not just campaigns.
Explains the overall argument and why the AI tooling matters.
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Read it last: it frames the other four posts but contains the fewest specifics of its own.
Quijano argues that the e-commerce strategist role is splitting into two high-leverage directions — creators who code and do their own reporting, and developers who market — and that measuring alone is no longer enough.
The post describes a single practitioner covering three roles at once: creative director diagnosing funnel friction, data scientist mapping retention cohorts, and developer building bespoke AI automation agents.
It contrasts shipping a solution in hours with waiting weeks for an engineering ticket or creative approval, and closes by asking whether the reader is still measuring the funnel or building the engine.
It is a positioning argument; unlike the tooling posts, it names no specific client, metric, or build.
For brands planning a campaign
Useful mainly as a hiring screen. It explains why a brand might bring in one operator instead of adding headcount across three functions, making it relevant to a brief about consolidating a growth team rather than adding a specialist.
AI-built growth systems and the growth-architect role3
Hook-led creative testing tied to retention cohorts3
Speed and deployment velocity as competitive advantages3
Live, data-fed client deliverables instead of static decks2
Pre-BFCM acquisition as a lower-cost demand-building phase1
Unit economics and operating model over ad tweaks1
AI-built growth systems and the growth-architect role
Giann Quijano argues that e-commerce strategists should move from measuring and reporting to building the systems that run growth.
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The posts describe using Claude Code to create autonomous sub-agents, custom commands, test suites, and parallel agents that connect advertising platforms, Shopify checkout data, and retention cohorts. The goal is to close the gap between a growth hypothesis and a live, data-generating test.
Describes writing project-level context files, automating validation with dozens of tests per interface change, and using parallel branches so multiple agents can build offer-page variants at once.
The posts cite outcomes such as a conversion-rate jump from 4.6% to 11.2% in one sprint, but provide no underlying test logs, spend levels, or client names.
For brands planning a campaign
A brand can ask Giann Quijano to design an AI-enabled growth architecture, not just a media plan. Ask them to show the built tools, data connections, and handoff documentation—not just make the case for AI.
Pre-BFCM acquisition as a lower-cost demand-building phase
Giann Quijano treats Q3 as a preparation phase for Q4, not a waiting period.
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They argue brands should buy top-of-funnel attention while summer and early-fall CPMs are lower, feed custom audiences and retention cohorts with high-intent creative, and warm the retargeting pool before BFCM auction pressure peaks. Q4 can then focus on high-margin conversion instead of expensive cold acquisition.
Saturate the market with high-intent creative, build first-party data, and pre-qualify custom audience pools before November.
The post does not name a budget level, channel mix, or measurement window. Ask for a plan tailored to the brand on those terms.
For brands planning a campaign
For a DTC brand planning a seasonal campaign, this suggests shifting acquisition spending earlier and defining Q4 success by margin and retargeting efficiency, not just last-click ROAS. A campaign designed to warm audiences before the holiday window should specify the required data and creative assets.
Hook-led creative testing tied to retention cohorts
Across the posts, creative is part of a loop with retention and unit economics, not a standalone asset.
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Giann Quijano describes moving from random asset testing to a systematic, hook-led iteration framework tied to retention cohorts. The loop also connects ad performance data to product pages, allowing winning creative angles to inform copy and page design.
Cross-references raw ad performance with Shopify checkout data, then rewrites page copy or CSS to match the winning creative angle.
For brands planning a campaign
A campaign brief should ask for a defined hook taxonomy, a cohort-based performance read, and a process for updating product pages when creative angles win. This approach better suits a brand that can share retention and checkout data than one seeking only top-of-funnel creative concepts.
In a case-style post about a flatlining $3M brand, Giann Quijano argues that scaling is not just a math problem.
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The work described includes restructuring creative strategy, lifting average order value through bundles and post-purchase upsells, and building standard operating procedures to remove the founder as the operational bottleneck. The stated result is a more predictable business positioned for an exit.
Re-engineer the funnel, institutionalize operations, and tie creative iteration to retention cohorts.
The post presents a success story without naming the brand or providing financials. Ask for evidence supporting the claims from Giann Quijano's other client work.
For brands planning a campaign
Giann Quijano is a fit for a brand that wants to change how its growth engine operates, not just run more ads. A campaign brief should include AOV, LTV:CAC, and founder-dependency goals, and test whether the brand wants the operational changes that precede an exit.
Live, data-fed client deliverables instead of static decks
Giann Quijano describes replacing static PDFs and slide decks with interactive web apps and live URLs.
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The posts list 25+ tools, apps, and client deliverables built in 30 days, including SaaS tools, strategy decks, creative systems, CRM dashboards, and a digital product storefront. Each proposal is described as a live URL that corrects itself with data.
Build client strategy as web apps, package proposals as live URLs, and document each tool with what it does and how it was built.
For brands planning a campaign
This points to a working style focused on usable tools and dashboards, not just recommendations. Ask for live examples, documentation, and clarity on who maintains the tools after handoff.
Speed and deployment velocity as competitive advantages
Giann Quijano repeatedly frames speed as a moat.
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The posts describe removing the testing queue, shipping solutions in hours instead of weeks, and increasing deployment velocity by letting agents handle smoke tests and edge-case checks. The contrast is between teams waiting on development cycles and operators who build infrastructure that guarantees optimization.
Ask how the described automations are reviewed for security, privacy, and platform-policy compliance.
For brands planning a campaign
A brand can test this approach by asking for a time-boxed experiment roadmap with named owners and deployment steps. Its value depends on the brand's technical access and review process supporting fast releases without creating compliance or quality risks.
The posts name no sponsor, affiliate, employer, or client brand.
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The brands mentioned are platforms Quijano builds and advertises on: Claude and Claude Code for AI tooling, Meta for paid acquisition, and Shopify for store and checkout data.
They also describe unnamed client work: interactive strategy decks sent as live URLs, dark-funnel pages for CPG and beauty brands past $50M, and a turnaround of an unnamed $3M e-commerce brand positioned for an exit.
Tools built in a 30-day sprint—StockPulse, DealPulse, Potion Lab, and a 22-angle creative diversity matrix library—are not identified as Quijano's own products or client deliverables, so ask which they are before treating them as brand relationships.
Claude
Mention
Giann Quijano describes building growth automation with Claude and Claude Code: autonomous sub-agents that read retention cohorts and shift Meta ad spend through the Marketing API, plus a custom command that rewrites product-page copy against winning creative angles.
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They also say more than 25 tools, apps, and client deliverables were built in 30 days using Claude as the AI engineering partner.
Meta is the paid acquisition channel Quijano works in: reallocating a share of Meta ad spend to landing pages with stronger 90-day LTV and overhauling a client's Meta campaigns as part of a wider operating-model rebuild.
Shopify checkout data feeds Quijano's creative-code feedback loop, where ad performance data is cross-referenced with store data to adjust product-page copy and CSS.
Giann Quijano writes about building e-commerce growth systems, not just measuring them.
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Start with the posts on AI-built growth engines and the growth-architect role, where they argue that strategists should ship the tools and automations behind a test rather than wait on a development queue.
Then move to the creative-testing material on hook-led iteration tied to retention cohorts, the post on treating Q3 as the buying window for Q4, and the case write-up about a flatlining brand, where funnel economics and founder-independent operations mattered more than ad tweaks.
Across these posts, Quijano states a principle, then names the tool, script, or campaign change that puts it into practice.
The performance figures and client outcomes are self-reported; ask for supporting documentation before relying on them.
Ramseyer also puts retention and unit economics at the center of DTC growth, arguing that revenue-based lifetime value, blended averages, and averaged acquisition cost hide the cohorts that determine whether scaling works.
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Both treat creative as a performance input measured against customer value, not as a standalone asset.
Ramseyer writes mainly about measurement definitions and TikTok Shop social commerce, including brand-owned shoppable content feeding GMV Max. Quijano focuses on the tools and agents that connect ad platforms, checkout data, and retention cohorts.
Henry-Hicks also diagnoses DTC growth through acquisition structure: declining new-customer revenue, creatives that educate rather than assume familiarity, audience segmentation, and moving spend up the funnel.
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Both frame the advertiser's problem as more than a simple conversion-rate fix.
Henry-Hicks focuses on media structure and account controls, such as excluding existing customers, using ViewContent as the conversion event, and tightening brand exclusions in Performance Max. Quijano looks beyond the ad account to build the automation and operating model for running tests.
Giann Quijano has a LinkedIn following of 15,341, observed on 11 October 2026.
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Their posts target e-commerce founders, DTC operators, and growth or creative strategists, with examples covering AI-enabled growth engines and Claude Code workflows, Q4 acquisition timing, retention cohorts and unit economics, and removing the founder as an operational bottleneck.
This makes the content a strong fit for a campaign about building an AI-enabled growth engine, but the audience fit is unmeasured: no platform demographics, engagement rates, or audience survey results are published.
Ask for recent LinkedIn audience demographics and engagement data for comparable AI and e-commerce growth posts, plus survey results if the campaign requires information on job function, seniority, or company size.
Giann Quijano’s posts describe agency-style project work, not a sponsorship or brand-partnership program.
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They build tools, client deliverables, and growth systems for e-commerce brands and agencies, using AI as an engineering partner, and say proposals now ship as live web applications rather than static decks.
They also describe a client engagement that re-engineered unit economics and creative testing, with the brand eventually positioned for an exit, and argue that growth teams should be rebuilt around AI-driven experimentation rather than added headcount.
The only commercial contact route in the posts is an open invitation to send a direct message about new project work.
A brand considering a paid collaboration should ask directly about scope, deliverables, and terms; the posts publish no official partnership page, rate card, or sponsor policy.
Strong fit for the architecture work, conditional on data access, technical access, and a 90-day validation runway.
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Brief
A DTC e-commerce brand wants to build an AI-enabled growth engine to reduce CAC and scale revenue. It can provide ad-platform performance data, Shopify checkout data, and retention cohorts, and wants a working experimentation roadmap rather than a media plan.
Reasons
A campaign using an agentic loop that connects ad data to checkout and retention cohorts, then acts on the combined signal, matches the most concrete method described in the posts. Giann Quijano describes sub-agents querying retention cohorts in real time and reallocating 30% of Meta spend when a landing-page variant produces a reported 12% lift in ninety-day lifetime value. They also describe an /analyze-hooks command that reads ad performance, cross-references Shopify checkout data, and rewrites product-page CSS and copy to match the winning creative angle.
The experimentation goal matches the hook-led iteration framework tied to retention cohorts, rather than random asset testing. The framework appears in both the AI growth-engine post and the $3M brand case narrative.
The revenue goal extends beyond media buying. The reviewed case narrative covers average-order-value lift through bundled merchandising and post-purchase upsells, lifetime-value-to-acquisition-cost improvement, and standard operating procedures that remove the founder as the operational bottleneck. The proposal is useful if the brand's constraint is the operating model, not just creative volume.
The build inventory is directly relevant to an owned growth engine. The post reports more than twenty-five tools, apps, and client deliverables built in thirty days, including StockPulse for DTC inventory monitoring, DealPulse, Potion Lab, interactive strategy decks, a twenty-two-angle creative diversity matrix, and dark-funnel pages for CPG and beauty brands scaling past $50M.
Conditions
The brand must provide raw ad performance, Shopify checkout data, and retention cohort access. Without them, the strongest mechanisms described in the posts cannot be executed as described.
The first thirty days should be a paid validation using the brand's own data. The reported numbers—a 4.6% to 11.2% conversion-rate move in one sprint, 28% average-order-value lift, and 3x deployment velocity—are author claims. Ask for the underlying case data before using them as benchmarks.
For a campaign using the described automations, ask for the built tools, documentation, maintenance plan after handoff, testing process, and security, privacy, and platform-policy reviews.
Set the commercial relationship up as project work with defined deliverables and terms. The posts document no sponsorship program, rate card, audience guarantee, or standard partnership intake; ask directly about the proposed scope and terms.
Why it matters
For this brief, Giann Quijano is selling the build-and-operate layer that runs the experiments, not commentary about AI. They are the right fit if the brand wants an owned growth engine and is prepared to give a single operator access across creative, data, and deployment. They are the wrong fit if the brand only wants a deck, a media plan, or reach.
When to choose a specialist
Poor fit.
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Brief
A beauty brand wants a one-off paid creator collaboration: four sponsored social posts and three short videos promoting a new product to a mass audience, priced on reach and engagement. The brand has no access to order-level or retention data, will not change its internal growth process, and needs brand-safe copy approval on a fixed calendar.
Reasons
The posts document no sponsorship or media-partnership program: Giann Quijano does not describe sponsored placements, paid post formats, affiliate arrangements, rates, exclusivity terms, audience guarantees, or content-approval requirements. They state only that prospective clients can send a direct message to discuss project work.
The commercial format is project work, not media inventory. The posts describe tools, client deliverables, growth systems, live web apps, client strategy decks, creative systems, and dashboards. A reach-based sponsored-post buy is a different transaction, with different deliverables and success measures.
A campaign without data access removes the data layer that makes the strongest methods work. The described methods depend on ad performance data, Shopify checkout data, retention cohorts, and deployment access. A one-off content buy without data access cannot use the agentic experimentation loop, the /analyze-hooks command, or lifetime-value-based budget reallocation.
The success metric points the wrong way. The posts argue that testing speed and operating-model change drive value. A reach-and-engagement-only campaign would not test whether the brand can reduce CAC, improve retention economics, or build a data-fed engine.
Audience fit for a mass-reach beauty buy is unmeasured. The posts report a LinkedIn following of 15,341 observed 11 October 2026, but no platform demographics, engagement rates, geography, seniority, or company-size data. Ask for audience analytics before buying on reach.
The fixed copy-approval calendar conflicts with the speed argument in the posts. Giann Quijano repeatedly frames the advantage as shipping in hours or days instead of waiting on approval queues. A slow, fixed approval cycle removes that advantage.
Better use
If the brand's real need is a launch with speed and data, rewrite the brief as a ninety-day growth-architecture project with access to ad and checkout data, then add creator content only after the testing loop is live. If the real need is paid reach, this creator is not the right transaction.
A worked campaign example
Assumed brief
A DTC brand with at least three months of ad and Shopify data wants to lower blended CAC and improve ninety-day lifetime value. The brand provides access to the ad platform, Shopify checkout, and retention cohorts, and agrees to a single operator working across creative, data, and deployment.
Deliverables
A context file with targets, stack, and approval rules.
A data map with source IDs, dates, gaps, and known quality issues.
An experiment backlog ranked by expected effect on CAC or LTV.
A hook taxonomy built from the reviewed twenty-two-angle framework.
A working ad-to-checkout-to-retention analysis system.
At least three shipped offer-page variants, including saved failed revisions.
A written spend-reallocation rule with guardrails.
Handoff documentation and standard operating procedures.
Phase1
Actions
Write a project context file with the brand's ROAS targets, AOV goals, technical stack, margin floor, and approval rules. The post recommends standardising context in project files.
Map the data: ad-platform performance, Shopify checkout, retention cohorts, and any post-purchase survey data. Label each source and identify what's missing.
Set one north-star metric: contribution margin after CAC, with ninety-day LTV by cohort as the main supporting metric. Don't rely on last-click ROAS alone.
Choose one funnel or product line for the first loop. Define the baseline conversion rate, AOV, and ninety-day LTV before making changes.
Decision gate
By day 14, confirm data access, baseline numbers, and a written experiment backlog. If data access is incomplete, stop and treat that as the first finding rather than guessing.
Name
Weeks 1-2: context and baseline
Phase2
Actions
Build a command that reads raw ad performance, joins it to Shopify checkout data, and ranks creative angles by retention cohort. This follows the /analyze-hooks pattern described in the post.
Create a hook taxonomy from the twenty-two-angle creative diversity matrix described in the post. Assign each ad an angle so results can be compared by angle, not just by individual asset.
Automate validation for page and interface changes. The post recommends generating dozens of tests per interface change and using parallel branches so multiple agents can build offer-page variants at once.
Ship at least three offer-page variants per cycle, each tied to a specific hook and cohort. Keep the original baseline and every revision.
Decision gate
By day 42, one variant should beat baseline on a primary or secondary metric with a clear consequence for the reader. If not, diagnose whether the problem is data quality, hook taxonomy, page build, or measurement window.
Name
Weeks 3-6: hook-to-page feedback loop
Phase3
Actions
When a variant shows a ninety-day LTV lift, write a budget rule to reallocate spend to that path. The post describes reallocating 30% of Meta spend when a variant produces a reported 12% ninety-day LTV lift. Apply the same logic, setting the exact percentage based on the brand's margin floor and risk tolerance.
Add guardrails: a maximum daily shift, minimum observation window, and pause rule if contribution margin falls below the floor.
If the calendar allows, run the Q3 sponge phase described in the post: buy top-of-funnel attention while summer and early-fall CPMs are lower, pre-qualify custom audiences, and warm the retargeting pool before BFCM auction pressure peaks.
Connect winning creative angles to product-page copy and CSS, not just ad sets.
Decision gate
By day 70, decide whether the loop is producing a repeatable CAC or LTV improvement. If it produces only one-off lifts, change the hook taxonomy or data join before scaling spend.
Name
Weeks 7-10: LTV-driven reallocation
Phase4
Actions
Document every tool, command, and dashboard, explaining what it does and how it was built. The post says every tool shipped is documented with its purpose and build method.
Write standard operating procedures so the brand can run the loop without the founder in every decision, following the operating-model work described in the $3M brand case.
Review what should run automatically and what needs human approval, especially spend changes, page changes, and customer-facing copy.
Decide whether to scale, hold, or stop based on measured results against the original baseline.
Decision gate
At day 90, compare CAC, ninety-day LTV, and contribution margin against baseline. Ask for the first thirty days of logs, failed variants, and handoff documentation; a positive story without those artifacts isn't enough.
Name
Weeks 11-13: operating handoff
Title
Ninety-day AI-enabled growth engine for a DTC brand
What to verify
The posts' performance numbers are self-reported. Verify them against the brand's data before relying on them.
Complete security, privacy, and platform-policy reviews before automation touches spend, customer data, or live pages.
No sponsorship or media-partnership terms are documented. Treat this as project work and agree on scope, deliverables, and terms.
The audience beyond the LinkedIn follower count is unmeasured. Request demographics and engagement data before assessing fit with a DTC buyer profile.
Choose Waleed Bin Wasim when the growth bottleneck is the creative message, not the build.
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In a post dated 29 September 2026, they describe a hair care DTC brand that believed it had a scaling problem: ROAS fell as spend increased.
After reviewing the creatives, they found most spoke to existing customers, warm audiences and product-aware buyers, with only around 25% built for new customers.
They recommended a creative funnel mapped to awareness levels—from unaware through problem-aware, solution-aware, product-aware and ready-to-buy—so scaling means giving the ad platform stronger creative signals, not just raising the budget.
That addresses the same buyer task as the AI-enabled growth engine brief through message-to-audience matching.
An earlier post, dated 31 July 2026, covers positioning and offer clarity for people selling marketing services, including choosing one niche and making the offer understandable in about ten seconds.
Choose them when the ad account needs better creative direction and the brand can act on it without new tooling.
The posts describe work focused on diagnosis and creative direction.
They do not say that Giann Quijano builds the data-connected systems described: there is no workflow joining ad performance to Shopify checkout and retention cohorts, or automated spend reallocation.
If you hire Waleed Bin Wasim for the creative funnel, someone else must build and run the measurement and deployment layer; otherwise, the loop stays manual.
The September engagement brings them in as Creative Strategist to build the creative funnel and messaging.
Agree on scope: creative direction and execution, rather than an owned growth engine.
Choose Athens Ramseyer when the unresolved question is which number the growth engine should buy against.
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A post dated 1 September 2026 explains the difference between revenue lifetime value, CAC:LTV and contribution lifetime value, and argues for separating new-customer CAC and conversion rate from blended figures and segmenting performance by offer, discount, channel, SKU, subscription and acquisition cohort.
It also reframes offer testing: rather than comparing a 20% discount with a 10% discount on first-order CAC, test both against 90-day and 180-day contribution, and accept a higher new-customer CAC for a higher-intent customer when the contribution justifies it.
A post dated 10 September 2026 covers TikTok Shop execution: treating the main brand profile as part of the performance engine, posting one to three shoppable brand posts a day to feed GMV Max, and recycling paid Meta creative into TikTok-native shoppable content so brand-owned content reduces reliance on affiliate-attributed sales.
Choose them when measurement definitions or the social-commerce channel—not engineering—are the constraint.
The posts offer analytical frameworks and channel tactics, but do not say that Athens Ramseyer builds tooling, code or automated systems.
The TikTok Shop outcome is described for one named brand launch, not as a repeated, measured programme.
Hiring Athens Ramseyer can sharpen economic definitions and provide a social-commerce plan, but you will still need a builder to automate reallocation and page changes.
The modeling advice also assumes enough transactional depth for cohort and 180-day contribution analysis; ask whether your data supports that analysis before applying it.
Choose Josh Henry-Hicks when new-customer growth is failing and media structure is the suspected cause.
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A post dated 16 June 2025 says the team audits around 20 brands a month and that 90% have a shrinking percentage of new customers.
It lays out four fixes: new-customer creative that sells the why through education and context; credibility borrowed from named experts such as a dermatologist, nutritionist or stylist; manual Meta campaigns with aggressive exclusion of existing and engaged segments and ViewContent as the conversion event to pull high-intent traffic up funnel; and tighter brand exclusions when running Performance Max.
A post dated 10 June 2025 reframes apparent conversion problems as visibility problems and gives concrete checks: ViewContent-optimised campaigns should at least double click-through rate, from roughly 2% on conversion campaigns to 4% or more, and traffic quality should be judged by bounce rate, time on site and pages per session rather than cost alone.
This suits a brand that needs a fast structural audit and a new-customer acquisition plan before committing to a build.
The posts describe audit findings and structural recommendations, not an experimentation system the brand can operate.
They do not describe tooling, a data join between ad platforms and store data, or automated budget reallocation.
The benchmarks come from the team's audit practice; ask to see the underlying account data before treating the figures as targets for your brand.
This work complements, rather than replaces, a build of an owned growth engine.
This profile draws on five LinkedIn posts by Giann Quijano about e-commerce growth strategy, AI tool building, creative testing, and paid acquisition planning.
The posts cover Claude Code workflows, retention-cohort testing, Q3 demand building ahead of Q4, and a client turnaround narrative.
The posts have no dates and are a selected set rather than a full archive: they contain no transcripts, named clients, or financial records.
Performance figures, tool counts, and client outcomes are Quijano's own claims and are not independently verified.
The posts publish no sponsorship program, rate card, or partnership intake.
Read any stated result as Quijano's account rather than a confirmed outcome.
