Video Summary - Vê-me a construir uma LOJA de 100.000€/ mês do ZERO em 1h

Goal & Business Model (Dropshipping Ecommerce)

Target outcome: €5,000/month profit by building an online store from zero using dropshipping.
Core strategy logic: Don’t reinvent the wheel—identify markets and products already generating demand, then replicate what’s working (store UX, USPs, themes, creatives) and scale winners using Meta Ads.

Core Framework / Decomposition (Math → Operating Plan)

Conservative profit → revenue math

Assume a 10% profit margin
To make €5,000 profit, you need €50,000 revenue in 30 days

Daily revenue / order model

€50,000 / 30 ≈ €1,667/day
Some subtitles indicate ~€1.3k/day
Order-based estimate:
- ~13–20 orders/day
AOV (average order value) assumption:
- ~€60–€100

“Market TAM” simplification (example: Portugal)

Population cited: ~10M
“Advertising audience” estimate: ~4–5M
Need only 13–20 daily orders from that audience (“crumbs from the big players”).

90-Day Plan Structure (Phases Mentioned)

Market identification
Competitor analysis
Store construction
Product research / validation
Advertisements (Meta Ads testing + scaling)
Campaign analysis + daily profit tracking
Order fulfillment (dropshipping supply chain)

Playbooks & Tactics (Actionable Rules)

1) Market Identification: “Momentum” Playbook

Pick markets showing proof of demand via competition:

Select markets with multiple competitors actively running ads
Competitors have consistent ad volume (assumption: they’re getting returns)

Budget constraint rule (choose low-CPC geographies first):

Example budget: €2,000
Prioritize markets with low CPC
CPC examples:
- Germany: €0.70–€1.00
- Portugal: €0.02–€0.04

Country / payment-method feasibility:

Example (Portugal): require MB Way and Multibanco
Avoid markets where payment complexity harms cash flow on a small budget (example given: avoid PayPal complexity if it impacts cash flow with a €2k budget)

2) Competitor Analysis: Replication Playbook (5–7 Competitors)

Select 5–7 competitors per chosen market and analyze:

Number of active ads
Number of products in their store

Produce “sheet” artifacts:

Themes/design patterns
USPs used
Shipping methods
Color / offer-badge styling cues

Store building rule:

If multiple competitors use a theme (example: Impulse), use the same theme.
Shopify themes mentioned:
- Symmetry, Broadcast, Impulse, Impact

3) Product Research / Win Probability (Meta Ad Library Logic)

Goal: find products profitable enough but not yet fully optimized for the competitor’s audience.

Evidence sources:

Facebook Ad Library (similar logic for Google)

Key insight:

Meta traffic is cold / discovery
Google tends to carry higher intent (“demand”)

Product selection KPIs (numeric thresholds)

To avoid “long-beaten” campaigns:

Example logic: don’t pick ads running for ~9 months if you want efficiency (the algorithm already learned their audience).

Two numeric entry criteria:

Competitor should have: ≥ 25 active ads
Competitor store should have: ≥ 1,000 products

Why these numbers:

They suggest real ad/testing spend (reduces “fake momentum” risk)
For beginners, reaching 1,000 products takes time—so it acts as an implicit filter for winners.

Meta Ads Operating System (Testing Volume → Kill/Keep → 48-Hour Decisions)

Budget & campaign structure

Platform: Meta Ads (Facebook/Instagram; WhatsApp placement mentioned as part of ecosystem)

Testing volume rule:

Run 5 campaigns per day
Each day:
- find 5 new products/collections
- launch 5 new campaigns

Example beginner setup (for €2,000 total budget):

Country example: Portugal
€10 per campaign per day
Total: 5 campaigns/day × €10 = €50/day

CBO vs ABO guidance:

Use CBO (Campaign Budget Optimization) early
Hierarchy:
- Campaign → ad set(s) → creatives
Example:
- CBO with one ad set
- 5–7 creatives per campaign

Creative formats & launch rules

Creative types mentioned:

Single image
Collage
Carousel
Videos

Standardization rule:

Use a consistent mix (example includes: single image + collage + carousel + 2 videos)

Campaign launch time rule:

Start around 00:01 (midnight + 1 minute) to run full-day exposure
Avoid launching only during “business hours,” since audiences are active at different times.

Creative testing philosophy (“bleed fast, scale later”)

High test volume because waiting is not viable with small budgets
Decision logic:
- Kill quickly if early signals fail
- Scale quickly when performance meets criteria

Campaign Analytics: KPIs, Thresholds, Timelines

Metrics tracked (explicit)

CPC
CTR (mentioned but treated as secondary early)
ATC (“add to carts”)
Purchases
ROAS
Breakeven ROAS (BE threshold)

Breakeven rule

Recommended breakeven range: 1.5–1.7 ROAS
Example used: breakeven = 1.5
- Below BE → loss
- At BE → break-even
- Above BE → profit

48-hour evaluation rule

Judge campaigns on 48 hours / 2-day windows
Purpose: avoid reacting to single-day randomness (“good days/bad days”).

Day-1 kill rule (early validation)

After €10 spend, on Day 1 check:

CPC target: €0.2–€0.4
- If CPC is “crazy” (example: ~€1 in Portugal), kill immediately
ATC threshold: ≥ 2 add-to-carts on Day 1

If criteria not met → shut down immediately.

Day-2 decision rule

By Day 2:

Campaign should show positive ROAS
If ROAS is not positive → kill
If positive and above BE → increase budget
- Example scaling: €10 → €15, later €15 → €25 (and up further)

Daily Execution Cadence & Record-Keeping

Daily workflow:

Launch 5 campaigns/day
Keep only campaigns that meet the criteria
Add 5 new campaigns daily

Daily log requirement (profit sheet): Track daily revenue, ad spend, product costs, and results.

Fill the sheet daily to preserve clarity and improve decision accuracy.

Fulfillment & Supplier Sourcing (Dropshipping Operations)

Sourcing model: not AliExpress

Use private providers / private suppliers

Supplier selection criteria:

Price
Shipping time (fast is critical)
Quality tradeoffs (avoid “lowest price only”)

Prime Flow (platform concept mentioned)

A tool/platform (Prime Flow) that:

Connects to Shopify
Imports product listings
Runs an internal supplier “auction” with quotes in up to 24 hours

Auction dynamic:

Suppliers bid with product + shipping costs
You select the best offer
The supplier ships and updates order status in the lifecycle

Operational support mention:

Supply chain/account manager checks tracking inactivity (e.g., no tracking movement for 3 days)

Supplier decision rule (avoid customer-support costs)

Don’t optimize only for price if it increases delivery times
Example: 2–3 extra days can become expensive via:
- more customer emails/support
- more claims

Shipping time preference:

“Fast as possible”; example target: 7–10 days lead time.

Financial Model Estimates & Timeline

Ads spending example

If ROAS = 2 and you want €50k revenue:

ROAS of 2 implies ad investment around €25k
€25k / 30 ≈ €833/month

Campaign count and ramp time (example)

Worst-case scenario:
- 83 active campaigns
- producing ~1 sale/day
Testing capacity:
- 5 new campaigns/day
Ramp time:
- 83 / 5 ≈ 17 days

Realistic expectation

Month 1 might not reach full €5,000 profit
But it builds baseline data and enables scaling in later month(s).

Key KPIs & Numeric Targets (Collected)

Profit target: €5,000/month
Margin assumption: 10%
Revenue target: €50,000/month
Order target: ~13–20 orders/day
AOV assumption: ~€60–€100
Budget example (setup): €2,000
Ads operating model:
- 5 campaigns/day
- €10 per campaign/day
- CBO strategy
- 5–7 creatives/campaign
- Launch time: ~00:01
Product entry criteria:
- Competitors: ≥ 25 active ads
- Catalog: ≥ 1,000 products
Day-1 thresholds:
- CPC: €0.2–€0.4 (kill if ~€1 in Portugal)
- ATC: ≥ 2 add-to-carts
Breakeven: ROAS 1.5–1.7 (example: 1.5)
Evaluation cadence: every 48 hours
Scaling examples: €10 → €15, then €15 → €25
Supplier quotes: up to 24 hours

Examples / Concrete Scenarios Cited

Portugal CPC feasibility:
- Portugal CPC: €0.02–€0.04
- Germany CPC: €0.70–€1.00
Product-ad longevity avoidance:
- Avoid products whose ads ran since January 1 (~9 months), since competitors have already optimized audiences.
Expected early performance pattern:
- Early days may look like loss/loss/loss → break-even → occasional profit/loss
- Profit later as winners scale and compensate for testing losses.

Presenters / Sources

Tiago (presenter / co-instructor)
Afonso (presenter / participant)
Tomas (mentioned once in analogy: “as Tomás said…”)

Vê-me a construir uma LOJA de 100.000€/ mês do ZERO em 1h

Key takeaways