# Giann Quijano

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.

- Profile: https://b2binfluencer.marketing/creators/giann-quijano/
- LinkedIn: https://www.linkedin.com/in/giannquijano
- LinkedIn followers: 15,341
- Topics: artificial-intelligence, ecommerce, paid-ads, meta-ads, ecommerce-growth, creative-strategy
- Based on 11 dated posts, latest 2026-09-29

## Summary

Giann Quijano is most useful for building AI-enabled e-commerce growth systems and practical tools.

- Best for: A DTC e-commerce brand with ad, Shopify checkout, and retention data wanting a working experimentation roadmap.
- Not for: A one-off paid creator collaboration priced on reach and engagement without data access or process change.
- Check first: Confirm data access, case data, built tools, documentation, maintenance, and security reviews.

### Takeaways

- They built twenty-five tools, apps, and client deliverables in a thirty-day build log.
- They treat Q3 as the Q4 acquisition window before holiday competition drives up costs.
- They connect ad performance data to product-page changes to shorten insight-to-test distance.

## Main ideas

- **From measurer to builder**: Quijano argues that measuring and reporting are no longer enough. 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.
- **Agentic loops as the growth engine**: Quijano treats AI agents as an operating architecture, not just productivity tools. 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.
- **Hook-led creative tied to retention cohorts**: Quijano argues against random asset testing. 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.
- **Q3 is the Q4 acquisition window**: 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. 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.
- **Operating model over ad tweaks**: Quijano argues that scaling an e-commerce brand is not only a math problem. 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.
- **Live, data-fed deliverables instead of static decks**: Quijano prioritizes client deliverables that are usable, current, and inspectable. 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.

## Work to read

- [A thirty-day build log of twenty-five tools](https://www.linkedin.com/posts/giannquijano_i-built-25-tools-apps-and-client-deliverables-activity-7438747157282619392-zCVE)
- [Building an agentic growth engine with Claude Code](https://www.linkedin.com/posts/giannquijano_creativestrategist-ai-ecomm-activity-7437898225321369601-cOGv)
- [Treating Q3 as the Q4 acquisition window](https://www.linkedin.com/posts/giannquijano_most-e-commerce-brands-treat-q4-like-a-sudden-activity-7497821307015831552-YMjw)
- [Rebuilding a $3M brand's operating model](https://www.linkedin.com/posts/giannquijano_most-founders-think-scaling-an-e-commerce-activity-7499131380207919104-fFGd)
- [From measurer to builder](https://www.linkedin.com/posts/giannquijano_strategy-performancemarketing-growthstrategy-activity-7463325898788884480--7NA)

## Brand relationships

- **Claude** (mention)
- **Meta** (mention)
- **Shopify** (mention)

## Partnerships

Giann Quijano’s posts describe agency-style project work, not a sponsorship or brand-partnership program. 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.

## Alternatives

- Waleed Bin Wasim: Choose Waleed Bin Wasim when the growth bottleneck is the creative message, not the build. 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.
- Athens Ramseyer: Choose Athens Ramseyer when the unresolved question is which number the growth engine should buy against. 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.
- Josh Henry-Hicks: Choose Josh Henry-Hicks when new-customer growth is failing and media structure is the suspected cause. 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.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/