Ron Shah ✓

@obviceo · Paramus, NJ · joined 18 Dec 2021

Co-Founder & CEO of @my_obvi | $100M+ in Sales | Advisor & Investor in 32+ Brands | SAAS Go To Market Expert | Grab a Time: https://t.co/ROAOHc1vnB

23 018Followers

3 573Following

11 114Posts total

6.7KViews on collected posts

Últimas publicaciones

Ron Shah ✓@obviceo22 Sep

🤖 I committed to posting one way I'm using AI every day. Here's tip #50 The product that stops the scroll is almost never the product that gets bought. I learned this from someone who's signed 176 brands over $50M GMV. In catalog-heavy categories, fashion, jewelry, footwear, 460 views · 3 likes · 1 reposts · 1 replies Open on X →

Ron Shah ✓@obviceo18 Sep

I've spent 9 years and $100M+ figuring out BFCM. Every year, brands wait until the busiest week on the calendar to test something new. CPMs spike 300%+. Every mistake costs full price. And by the time they've figured it out, the window's already closed. This year, there's a 902 views · 5 likes · 0 reposts · 1 replies Open on X →

Ron Shah ✓@obviceo18 Sep

@obviceo The operator vs assistant distinction is the interesting part. Writing and thinking are useful. But catching an $88K mistake while you sleep is a different level of value. 4 views · 0 likes · 0 reposts · 0 replies Open on X →

Ron Shah ✓@obviceo17 Sep

🤖 I committed to posting one way I'm using AI every day. Here's tip #49 You can still tell when an email was written by AI, even from brands that swear they've dialed it in. Their customers can tell too. Everyone knows the fix by now, or thinks they do. Give it context. Brand 761 views · 3 likes · 0 reposts · 1 replies Open on X →

Ron Shah ✓@obviceo17 Sep

@obviceo The line I'd draw is access rather than capability. That charge was findable because something could actually read the ledger, and in ecom most of the same catches are sitting in 3PL and billing exports that nothing is connected to. 12 views · 0 likes · 0 reposts · 0 replies Open on X →

Ron Shah ✓@obviceo16 Sep

@obviceo That $88,400 catch is a pretty compelling case for AI operators, mate. 8 views · 0 likes · 0 reposts · 0 replies Open on X →

Ron Shah ✓@obviceo16 Sep

The honest AI stack for a DTC operator after 30 days of real testing: ChatGPT writes. Claude thinks. Perplexity Computer does the work. The first two are assistants. The third one is an operator. Only one of them found the $88,400 duplicate charge in my bookkeeping while I was 1.3K views · 11 likes · 1 reposts · 3 replies Open on X →

Ron Shah ✓@obviceo14 Sep

@obviceo The real test is not replacing five salaries - it is whether customer, inventory and creative signals become decisions before the founder becomes the integration layer. If the AI only produces more dashboards, you have bought another bottleneck. 13 views · 0 likes · 0 reposts · 0 replies Open on X →

Ron Shah ✓@obviceo14 Sep

If you want to test it on your own operations, I have a link for other operators. https://t.co/4J6GkvOKQP Full disclosure this is part of a Perplexity Computer partnership. Everything above is real. If it did not happen, it did not go in the thread. 133 views · 0 likes · 0 reposts · 0 replies Open on X →

Ron Shah ✓@obviceo14 Sep

The pitch behind AI agents is that one operator can run all four seats. After 30 days the honest read is: 2.5 out of 4 today, probably 3 in 90 days, maybe 4 in a year. That is still the fastest return on headcount I have ever seen in this business. Not because the tech is 152 views · 1 likes · 0 reposts · 2 replies Open on X →

Ron Shah ✓@obviceo14 Sep

Seat four: media buyer. Not there yet. It reconciles the money side but it is not making bid changes or launching creatives on its own. I would not hand it my ad account. Human still runs that seat. 54 views · 0 likes · 0 reposts · 1 replies Open on X →

Ron Shah ✓@obviceo14 Sep

Seat two: creator manager. I asked it to source 20 creators for Obvi's menopause launch with rationale for each pick. It came back with 20 picks, 19 send-ready outreach scripts, each one written in the voice of the arena that creator lives in. Fitness gets one script. Menopause 64 views · 0 likes · 0 reposts · 1 replies Open on X →

Ron Shah ✓@obviceo14 Sep

Seat three: retention lead. The one I am still testing. It is running our win-back segment analysis and pulling repurchase gaps against LTV. Early read is strong but I want another 30 days before I put it on the honest stack for this seat specifically. 60 views · 0 likes · 0 reposts · 2 replies Open on X →

Ron Shah ✓@obviceo14 Sep

The honest stack after 30 days of testing: ChatGPT for writing. Claude for thinking. Perplexity Computer for doing. The first two are assistants. The third one is an operator. It has my Gmail, my banking, my Shopify, my ad accounts connected. It goes and does the work. 245 views · 1 likes · 0 reposts · 1 replies Open on X →

Ron Shah ✓@obviceo14 Sep

Seat one: finance ops. Perplexity Computer reconciled my ad spend against invoices for 30 days. It caught a $88,400 duplicate COGS charge sitting in a tie-out that nobody had flagged. That is a full salary line found in one run. It also stands up a 13-week cash flow view and 75 views · 1 likes · 0 reposts · 1 replies Open on X →

Ron Shah ✓@obviceo14 Sep

The four seats every DTC brand needs and nobody under $10M has: 1. Finance ops 2. Creator manager 3. Retention lead 4. Media buyer That is the hire list. That is the salary math. That is why lean brands just skip whole functions and hope. 568 views · 4 likes · 0 reposts · 1 replies Open on X →

Ron Shah ✓@obviceo14 Sep

Every founder I know has the same problem and it is not CAC. It is that a real ops team costs $400K a year and you cannot afford it until you are already past the point where you needed it. I have been running an honest test for 30 days on whether one operator with the right AI 2K views · 11 likes · 0 reposts · 4 replies Open on X →

Frente a cuentas del mismo tamaño

17 publicaciones de los últimos 90 días, junto al rango de 10K–100K seguidores. por debajo de sus pares en alcance y en interacción.

Visualizaciones medianas133esta cuenta1 001mediana de 10K–100K

Alcance, %0.58%esta cuenta3.95%mediana de 10K–100K

Interacción, %0.82%esta cuenta1.55%mediana de 10K–100K

Métrica	Esta cuenta	Mediana de 10K–100K	Proporción
Visualizaciones medianas por publicación	133	1 001	0.13×
Alcance (visualizaciones ÷ seguidores)	0.58%	3.95%	0.15×
Tasa de interacción	0.82%	1.55%	0.53×

Otras cuentas de este rango → Comparar con otra cuenta → Cómo se construyen estas referencias →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

24514 Sep

152

133

1.3K16 Sep

1217 Sep

761

418 Sep

902

46022 Sep

Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.82%14 Sep

3.33%

1.56%

1.85%

1.97%

0.00%

1.20%16 Sep

0.00%

0.00%17 Sep

0.53%

0.00%18 Sep

0.67%

1.09%22 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.55%.

What the audience does

Likes55.6%40 in total

Reposts2.8%2 in total

Replies26.4%19 in total

Bookmarks15.3%11 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Últimas publicaciones

Frente a cuentas del mismo tamaño

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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