Carbon Arcโs early read on May SMB payrolls is out.
Our latest Payrolls Flash points to another firmer than anticipated monthly workforce expansion in May, just like it did in April, and March.
To understand how we turn Carbon Arcโs SMB Workforce data asset into a usable signal, send us a DM.
I thought the Everlane acquisition was Shein picking through the discarded brand bin. But looking at the consumer data, one man's trash is truly another man's treasure.
By income, generation, and brand overlap, Everlane gives Shein a new brand new customer base on the cheap.
Shein's acquisition of Everlane made headlines. @CarbonArcAI data tells the real story.
Across the top 50 cross-shopped brands for each retailer, only one appears on both lists.
@SHEIN_Official's customer base is 50% Gen Z, 53% from households under $45K. @Everlane's customer base skews older, with higher household incomes.
Shein's customer base shops frequently, spends less. Everlane's shops deliberately and spends more.
See the full data-driven rationale in the thread.
@business@BoF@hm@ZARA@Reformation
๐๐ฒ๐๐๐ถ๐ป๐ด ๐ผ๐ป ๐ ๐ฎ๐ ๐ฝ๐ฎ๐๐ฟ๐ผ๐น๐น๐? ๐๐ฝ๐ฟ๐ถ๐น ๐ฆ๐ ๐ ๐ฑ๐ฎ๐๐ฎ ๐ถ๐ ๐๐ผ๐๐ฟ ๐ฒ๐ฎ๐ฟ๐น๐ ๐๐ถ๐ด๐ป๐ฎ๐น.
Workforce expanded M/M for the first time since 2023. Y/Y growth at an 11-month high. Regional and sector divergences worth watching.
Try Lenses free for 30 days with code ๐ฃ๐ฅ๐ข๐๐จ๐๐ง๐ฏ๐ฌ: https://t.co/YnNZDY5VyW
@CarbonArc@Kalshi
#SMB #Payrolls #PredictionMarkets #AlternativeData
๐๐ฟ๐ฒ ๐น๐ผ๐๐ฒ๐ฟ-๐ถ๐ป๐ฐ๐ผ๐บ๐ฒ ๐ฐ๐ผ๐ป๐๐๐บ๐ฒ๐ฟ๐ โ๐ฟ๐๐ป๐ป๐ถ๐ป๐ด ๐ผ๐๐ ๐ผ๐ณ ๐บ๐ผ๐ป๐ฒ๐โ ๐ฎ๐ ๐๐ต๐ฒ ๐ฒ๐ป๐ฑ ๐ผ๐ณ ๐๐ต๐ฒ ๐บ๐ผ๐ป๐๐ต?
We used Carbon Arcโs ZIP-level credit card data to show March+April Y/Y changes in late-month spend ratios for low-income vs. high-income geographies. The results?
Lower-income ZIPs showed stronger late-month gas-station spending and the sharpest relative deterioration in discount merchandise, restaurants & QSR, and a pullback in traditional grocers. This is consistent with lower-income budget stress in a high gas price environment.
Try Lenses free for 30 days using code ๐๐๐จ๐ก๐๐๐ฏ๐ฌ: https://t.co/nTxv2TIg3K
Last night we were proud to support the @Stocktwits Cashtag Awards at the NYSE! ๐ Great people, great energy, and exactly the kind of community we build for.
๐ Get access to private data with Lenses. Use code ๐๐๐จ๐ก๐๐๐ฏ๐ฌ: https://t.co/nTxv2TIg3K
#CashtagAwards #StockTwits #Fintech
๐๐ฎ๐ฟ๐ฏ๐ผ๐ป ๐๐ฟ๐ฐ'๐ ๐ ๐๐ฃ ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ ๐ถ๐ ๐ป๐ผ๐ ๐น๐ถ๐๐๐ฒ๐ฑ ๐ผ๐ป ๐๐ต๐ฎ๐๐๐ฃ๐งโ๐ ๐๐ฝ๐ฝ ๐๐ถ๐ฟ๐ฒ๐ฐ๐๐ผ๐ฟ๐.
Search "Carbon Arc" under "Apps" and start querying real consumer transaction data - credit card spend, foot traffic, payroll signals, and more - directly inside ChatGPT.
No exports. No tab switching. Just signal.
Ready to explore? Start today with one month free. Use code ๐๐๐จ๐ก๐๐๐ฏ๐ฌ: https://t.co/lEIrfJXYVP
Crude oil tankers likely bound for India account for ~48% of crude tankers with last known calling locations inside the Gulf. Here's where they are, and how long they've been circulating.
Maritime Data gets you the whole list, including those bound for China and beyond: https://t.co/YnNZDY5VyW
@howardlindzon@mansourtarek_ I would love for you to try out @CarbonArcAI and get your feedback. We give access to payrolls, credit card, web traffic data and more from a MCP server or consumption based to analyze these markets. Happy to get you setup on a trial
@obermattj is right here. There is always a strong desire to isolate predictions from the noise. As liquidity grows in markets that focus on KPIs, Carbon Arc gives you payroll, credit card, app usage and web traffic signals to forecast company KPIs for $20/mo.
This is the same transaction data hedge funds have been using to build their world models, and now we are allowing everyone to build theirs
@plur_daddy@mtndrew Are you looking for any other search methods beyond natural language? Iโm curious if you feel their work flow is an explore problem or their presentation of the data
Wow, this tweet went very viral!
I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs.
So here's the idea in a gist format: https://t.co/NlAfEJjtJV
You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.