Vetto AI is hiring a Marketing Experts — Planning AI Project

About Vetto

Vetto is a global platform that connects top-tier professionals to strategic Artificial Intelligence projects around the world. Our mission is to build trust, quality, and long-term value within the AI ecosystem, for both exceptional talent and companies operating at the forefront of technology.

About the project

We're recruiting marketing experts to review and improve real-world scenarios used to train AI planning assistants in an educational context. The AI model will act as a tutor — structuring marketing problems, mapping decision trees, and teaching data-driven reasoning to students. Your job is to think like a senior marketer: break down campaign or growth challenges, identify alternatives, justify decisions with concrete data, and ensure the reasoning is both rigorous and clear enough to teach.

Who can apply

Professionals from any marketing discipline — digital, growth, branding, performance, content, product marketing, or CRM.
Anyone with hands-on experience diagnosing marketing problems and making data-driven decisions.
Final-year undergraduate students in marketing, business, or communications with practical project experience are also welcome to apply.

Compensation

Payment will be US$ 52 per approved task, converted and paid in your local currency. Each task takes approximately 80 minutes, which corresponds to an effective rate of about US$ 39 per hour.

Selection & Instructions

In this application, you will answer questions following the instructions below. If selected, you will be invited to review real marketing case scenarios as part of the project.

‼️ AI is not allowed. If we spot AI use, we'll block the application. ⚠️ This application form must be completed entirely in English or Portuguese.

For the reasoning case, present a real marketing problem you diagnosed or solved — a campaign underperformance, channel decision, audience targeting challenge, growth bottleneck, brand positioning issue, etc. You may anonymize it. We are not evaluating whether your conclusion was right. We are evaluating how you think.

The case is structured in 4 parts: Part 1 — The Problem: describe the marketing problem and what data or information you had available at the start.

Part 2 — Your Journey: describe your reasoning in 3 steps. For each step, explain what you analyzed or decided and what specific metric, data point, or insight drove that decision.

Part 3 — Discarded Alternatives: for each of the 3 steps, list at least 2 hypotheses you considered but ruled out and explain what concrete data eliminated each one. "It wasn't the case" is not a valid answer.

Part 4 — Conclusion: describe the final recommendation or solution and how the evidence you gathered led to it. Also highlight 1–2 key insights — the most important findings or turning points in your reasoning: a metric that confirmed your direction, a detail that ruled out a strong alternative, or a non-obvious observation that most people would have missed.

Case Example

⚠️ This is just an illustrative example. Your application should include more detail, specific data points, and thorough reasoning for each discarded alternative.

Part 1 — The Problem A D2C brand was running paid social campaigns on Meta and Google but saw CAC increase 40% over 2 months with no change in budget. Leadership suspected creative fatigue but had no data to confirm it.

Part 2 — Your Journey Step 1: Segmented performance by channel — 90% of the CAC increase came from Meta, while Google remained stable, which isolated the problem to a single channel rather than a broad market issue. Step 2: Analyzed Meta campaign data by ad set and creative — CTR on the top 3 creatives had dropped from 3.2% to 0.9% over 6 weeks, confirming creative fatigue on the best-performing assets. Step 3: Tested 4 new creative concepts with a 20% budget split — 2 concepts recovered CTR to 2.8% within 10 days, validating creative refresh as the primary lever.

Part 3 — Discarded Alternatives

Step 1 — Alternative 1: Audience saturation across all channels / Ruled out by: Google performance held steady — saturation would have affected both channels simultaneously.

Step 1 — Alternative 2: Seasonality drop / Ruled out by: industry benchmarks for the period showed flat or growing performance for comparable brands.

Step 2 — Alternative 1: Audience targeting issue / Ruled out by: CPM and audience overlap metrics were unchanged — reach was not the problem.

Step 2 — Alternative 2: Landing page conversion drop / Ruled out by: CVR on the landing page remained stable at 4.1% throughout the period.

Step 3 — Alternative 1: Increase budget to recover volume / Ruled out by: increasing spend on fatigued creatives would have amplified the CAC problem, not solved it.

Step 3 — Alternative 2: Pause Meta entirely and shift to Google / Ruled out by: Meta historically delivered 3x the volume of Google for this brand — abandoning it was disproportionate to the problem.

Part 4 — Conclusion

Root cause was creative fatigue on Meta's top-performing ad assets. A targeted creative refresh recovered CTR and stabilized CAC within 2 weeks, without requiring budget increases or channel restructuring.

Key insights: The most critical finding was that the CAC increase was isolated to Meta — this single data point prevented a costly overreaction affecting the entire media mix. The second turning point was identifying that CVR was stable: it confirmed the problem was pre-click (creative), not post-click (landing page), which focused the fix entirely on the ad layer.