u/lepchas

Facebook ads went down just now. Tried opening a couple of accounts all not opening sorry try again and some don't have any campaigns.

Hey 👋 looking for a few Shopify owners to test out our ecomm platform

Dm if interested.

Zucks posted this...

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Will Cathcart just announced that he's stepping down as the head of WhatsApp after 7 years leading the app. Will's been one of Meta's most important and effective leaders, helping to bring WhatsApp to over 3 billion people and championing privacy for our community. I'm super grateful for his partnership and contributions over these years. Will is transitioning to a new role within Meta where he'll build new products from the ground-up -- I'm excited to continue to work together closely.

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Kunal Shah will join Meta as WhatsApp's next leader. Kunal built CRED into one of India's most important technology companies, and he brings the kind of builder mentality and global perspective that will serve him well in running the world's biggest messaging app. I look forward to working with Kunal to continue to make WhatsApp the best service for billions of people and millions of businesses.

This for the Roas lovers ..2 cases.

**Campaign A — Low spend, Low ROAS, but profitable**

- Ad spend: $300

- Revenue: $600 (ROAS = 2)

- COGS (20% of revenue — high-margin product, e.g. digital/info product): $120

- Shipping: $0

- **Profit = 600 − 120 − 0 − 300 = $180**

**Campaign B — High spend, High ROAS, but losing money**

- Ad spend: $4,000

- Revenue: $24,000 (ROAS = 6)

- COGS (75% of revenue — thin-margin physical product): $18,000

- Shipping: $2,500

- **Profit = 24,000 − 18,000 − 2,500 − 4,000 = −$500**

Campaign B has a much "better" ROAS (6 vs. 2) and 40x the revenue, but it actually **loses** $500, while Campaign A — tiny spend, mediocre ROAS — nets a real $180 profit.

The lever here is **margin**, not spend or ROAS. High ROAS on a low-margin product can still bleed money once COGS and shipping eat most of the revenue. Low ROAS on a high-margin product can be solidly profitable even at small scale.

This is why agencies/brands that only report ROAS on a dashboard can completely mask whether a campaign is actually making money — you need margin and absolute profit in the same view to know.

Second e.g below 👇

**Campaign A — ROAS 6**

- Ad spend: $1,000

- Revenue: $6,000

- COGS (40% of revenue): $2,400

- Shipping: $400

- **Profit = 6,000 − 2,400 − 400 − 1,000 = $2,200**

**Campaign B — ROAS 4**

- Ad spend: $5,000

- Revenue: $20,000

- COGS (40% of revenue): $8,000

- Shipping: $1,500

- **Profit = 20,000 − 8,000 − 1,500 − 5,000 = $5,500**

Campaign B has a worse ROAS (4 vs. 6) but generates **$5,500 profit vs. $2,200** — more than double.

Why: ROAS only measures revenue per ad dollar, not the absolute size of the win. Campaign A is more "efficient" per dollar spent, but it's spending so little that the efficiency doesn't translate into real money. Campaign B spends 5x more, sacrifices some efficiency, but converts that spend into far more total profit.

This is the core flaw: **ROAS is a ratio (rate), profit is an absolute (dollar amount)**. A high ratio on a small base can lose to a lower ratio on a larger base — same logic as why a 50% return on $100 ($50) loses to a 20% return on $1,000 ($200).

I personally think meta has screwed up royally by relying too much on AI not only in the ads but within the meta software the ads manager , to payments , to the actual placements and relevance of content to users..

Payments not working this is India specific.

With all this happening and unstable performance I would like to know if anyone has moved his budget out of meta to some other channel and has been successful.

Especially in Ecommerce purchase ads.

A reported case in China has brought renewed attention to a growing issue in ecommerce: return systems that are built for speed and convenience can also be exploited at scale when fraud controls fail to keep up.

In the case, a woman allegedly carried out over 1,000 fraudulent returns over four years using multiple accounts and a wide range of purchased goods, with total losses reportedly close to 900,000 yuan.

The method was not technically complex. It reportedly involved ordering items, receiving them through standard delivery, replacing them with different or older goods, and then initiating returns through normal platform processes.

What makes the case notable is not just the volume, but how ordinary the process appears. No hacking. No system breach. Just repeated use of standard ecommerce workflows.

That raises a bigger question for the industry: how many systems are designed to assume good faith at scale, even when patterns of abuse are repeatable?

Return fraud is difficult to control because ecommerce platforms are balancing two competing priorities at once: making checkout frictionless and making returns effortless. Both improve customer experience, but both also reduce the natural barriers that help detect or discourage abuse.

Most fraud detection tools look for clear red flags, but return abuse often doesn’t show up as a single obvious signal. Instead, it appears as small patterns spread across accounts, devices, and transactions.

On their own, these signals can look completely normal, especially in categories like fashion where returns are already expected.

That makes consistent detection difficult, even when the behavior is repeated over long periods.

The impact is also unevenly distributed. While platforms design the return experience, sellers often absorb the cost when something goes wrong. That includes lost inventory, disputed refunds, and returned items that may not match what was originally shipped. Couriers, meanwhile, are typically not in a position to verify contents in detail at the point of pickup.

So responsibility becomes fragmented: platforms control the system, but merchants and logistics partners often carry much of the risk.

The broader challenge for ecommerce is finding a balance between customer trust and system integrity. If returns become too strict, customers lose confidence. If they are too lenient, the system becomes vulnerable to repeated abuse.

The difficult part is that there is no single signal that solves this. It requires layered detection, better data sharing, and more targeted risk controls that don’t punish legitimate customers but still catch repeat patterns earlier.

Meta down ?? 👎 These guys can't manage and keep up their own platform and we keep paying them for our sales goals. 😂

Looking for some Ecommerce store owners who want a free audit of meta ads ,site Conversion optimization.

Dm.

How many facing this

How many facing this issue??

We noticed our old format flex ads sales dropping after the new update so we switched to the new format where you select media to go through the whole steps of image generation etc , you can specify urls , primary text and crop for each asset now, specify what placements to show .we see our sales recover however since these ads are new they will take some time to stabilize. The old formats will fail at some point as I think the algo is looking at signals from the new style ads.

The new ads we started have a learning of 0 to 10 instead of 50.

Also earlier flex didn't give the breakdown of how much each media is spending along with other metrics now it works in the new format which is super helpful.