Jeffrey P. Snider engagement report
@JeffSnider_EDU - 141K followers on X
Measured over 37 original posts from a 30-day window, last computed on September 27, 2026.
Engagement
A typical post picks up 89 interactions against 141K followers, an engagement rate of 0.063%. Measured over 37 original posts, its engagement rate beats 46% of 21,185 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 8.6K times each, and 1.03% of those impressions turn into an interaction. That is about 6.11% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, with activity on roughly 73% of days. Most posts go out around 22:00 UTC, and Tuesday is the busiest day of the week. Of the 37 posts sampled, 81% carry an image or video, 59% are part of a thread and 19% link out. The account's strongest tracked post pulled 681 interactions, about 7.7x its own typical post.
Measured over 37 original posts from a 30-day window, last computed on September 27, 2026.
Compared with accounts its own size
Jeffrey P. Snider's engagement rate beats 46% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 8 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 42% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.063%, Jeffrey P. Snider sits above the 25th percentile of the 213,098 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.147%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.147% |
| 75th percentile | 0.698% |
| 90th percentile | 2.61% |
| 99th percentile | 82.9% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 22:00 UTC, and Tuesday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 81% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 19% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 81% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 19% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts.
- Its average post runs 609 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Sep 7, 20267.7x their median
If the AI trade breaks, everyone pictures a 2000-style crash. The more likely outcome is quieter, and in some ways worse. Not a crash. A decade of going nowhere. Look at the last bubble, but ignore the obvious tech names. Look at Coca-Cola and Walmart. Rock-solid businesses, no hype. At the peak they traded near 50 times earnings. Here is what happened next. They did not collapse. They churned sideways for ten years or more. Because the price was already so far ahead, earnings had to double, even triple, just to grow back into it. Walmart eventually fell to 15 times earnings without the story ever breaking. That is the real risk in an overpriced stock. Not that it crashes tomorrow, but that it does nothing for a decade while the fundamentals slowly catch up.
- Sep 10, 20263.0x their median
The American consumer has a story: spending is holding up, stadiums are full, gasoline sales are at record levels. There is just one problem. Those numbers can look strong even while consumers are buying less. Circle K just showed it. Fuel revenue jumped 33%, but US fuel volumes fell 1.6%. Americans are driving less, visiting stores less, and cutting back on discretionary purchases. That is not resilient demand. That is what demand destruction looks like when higher energy costs start consuming the household budget. The consumer is still spending. They just have less to spend.
- Sep 7, 20262.2x their median
Everyone keeps saying the Fed is going to hold rates higher for longer. The swap market, one of the biggest markets in the world, is quietly betting the opposite. Ten and thirty year swap spreads are still sitting near the lowest levels in their entire history. That is not noise. That is the market pricing where short term rates actually end up over time. If the swap market believed the Fed's own narrative, spreads would move to reflect higher for longer. They have not. Instead they compressed further as oil prices rose, which is a bet against the Fed's story, not for it. The message underneath all the technical language is simple: rates go lower, and stay lower, for a long time. The forward curve is not buying the headlines.
- Sep 8, 20262.2x their median
Everyone keeps asking whether inflation is about to break out. There is a market built for exactly that question. Billions of real dollars positioned on it every single day. And it has been screaming the opposite answer for a month and a half. The TIPS market prices inflation protection directly. When breakeven rates rise, demand for protection rises. When they fall, it does not. During the worst of the Iran energy shock, breakeven rates barely moved above their normal range. Now, even as the conflict lingers, they have collapsed to the lowest level since October 2024. That is not what inflation risk looks like. That is demand destruction. The Fed keeps talking about inflation risk anyway. Because it is not looking at the market. It is looking at models that have been wrong over and over again.
- Sep 10, 20262.0x their median
The most important financial system on earth has nobody in charge of it. Not by accident. By choice. Almost every global transaction runs on dollars created offshore, outside the US. China and the world depend on them. So who is responsible for that system? Nobody. The Fed says its mandate stops at the border. In 2008, when the offshore market broke down in London, Fed officials called it a Bank of England problem. Not ours. Back in the 1960s, officials game-planned who would step in if it broke. Then they decided it did not matter. So no one took the job. 2008 proved that a disaster. More than a decade later, nothing has changed. We still run the world's reserve currency with no one accountable for it.
- Sep 23, 20261.7x their median
A private credit default used to be clean. Borrower fails, lender takes the loss, everyone moves on. Not anymore. Defaults just hit a record. But nearly half were not real failures. They were extensions. An extension just means the lender gives the borrower more time. No bankruptcy, no clean loss. The loan rolls on. And the lender's money is now trapped, sometimes earning interest that is not even paid in cash. So the fund reports income it never actually collects. Rising defaults are not just creating losses. They are making an illiquid market even harder to escape.
- Sep 16, 2026
Fed hikes. The Federal Reserve’s hawkish interpretation is the outlier – their own data doesn’t agree with it, not that it will matter to policymakers. Turns out, their own projections don't either. https://t.co/7OorkN0Qe5
- Sep 17, 2026
Everyone is watching China bail out its banks. The real story is further down the list. Insurance companies. China Life. China Taiping. China Reinsurance. Some of the biggest insurers in the country are quietly getting capital too. The official reason is falling interest rates. That is the clean, boring story. But here is the tell. China just rewrote its insurance law for the first time since 1995, with tighter scrutiny and higher capital rules. You do not rewrite 30-year-old law over low rates. You do it when you find something. And what they found sounds familiar. Reaching for yield, risky bets buried in Cayman Islands subsidiaries, hidden risk nobody priced. We called that a Western problem. Turns out China has its own version, hiding in the insurers.
- Sep 17, 2026
Stop asking whether China will boom or collapse. Both are wrong. There are two Chinas. The first is the one you know. The factory to the world. For decades China made the product and the West kept the profit. A Nike shoe. China did the labor, Nike took the margin. A VW chassis hiding under five different brands. China stayed stuck in the cheap middle. Then around 2017, China decided it was done being the factory. It wants the whole chain now, raw inputs to the brand. But owning everything means paying for everything. The foreign subsidy that carried China for decades disappears. So the real question is not boom or collapse. It is whether one China can afford to break free of the other. That answer decides the next decade.
- Sep 20, 2026
Private credit sold investors a feature that was really the whole danger. No volatility. The pitch was simple. These loans do not trade daily, so you get high yield without the swings of junk bonds. But low volatility is not low risk. It just means the risk is not being measured. A public bond that drops shows you the loss. A private loan stays marked at 98 because the manager says it will be repaid. And here is the conflict. That mark sets his returns, his fees, and his investors' confidence. So he keeps it high, right up until the day he cannot. That is why one loan going from 100 to zero is so dangerous. It makes you question every other mark still near par.
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 89 interactions against 141K followers, an engagement rate of 0.063%. Measured over 37 original posts, its engagement rate beats 46% of 21,185 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 8.6K times each, and 1.03% of those impressions turn into an interaction. That is about 6.11% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2.1 posts a day over the last 30 days, with activity on roughly 73% of days. Most posts go out around 22:00 UTC, and Tuesday is the busiest day of the week. Of the 37 posts sampled, 81% carry an image or video, 59% are part of a thread and 19% link out. The account's strongest tracked post pulled 681 interactions, about 7.7x its own typical post.
- What is Jeffrey P. Snider's engagement rate on X?
- Jeffrey P. Snider (@JeffSnider_EDU) has an engagement rate of 0.063%, based on the median interactions across 37 original posts from the last 30 days against 140,810 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.063%, Jeffrey P. Snider sits above the 25th percentile of the 213,098 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @JeffSnider_EDU have real engagement?
- Its engagement rate beats 46% of the tracked X accounts closest to it in follower count (21,185 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @JeffSnider_EDU post?
- Most posts go out around 22:00 UTC, and Tuesday is its busiest day, at roughly 2.07 posts per day across the measured window.