eBay Outage Map

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eBay Issues Reports

Latest outage, problems and issue reports in social media:

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  T-Bob Hebert (@TBob53) reported 4 minutes ago

  I remember getting this issue in a giant haul from eBay when I was younger

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  Mr8000 (@mr8mil) reported 5 minutes ago

  A few weeks back I bought this card from a tip on @enzo_tcg discrod. This card comes from the Blue Trial Deck of FW from 2023, basically one of the first leader cards of all FW. Hard to find in EN, this is the only version I could get my hands on EBay. Even if this card does not meet expectations down the road, I love the “vintagy” look of it. Source and user name: dissociated, thanks for bringing this into attention. Appreciate it!

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  Sean (@firinzlol) reported 6 minutes ago

  @ThePokeMD It’s fine had a similar issue before. eBay rep basically said if it is a fake card then it will always get rejected so this “miscategorization” isn’t related to authenticity etc

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  Alex Thompson (@sierrastrades) reported 7 minutes ago

  TL;DR Sales down, profits up, cash lower because they turned the eBay options into real shares. • Sales: $780–800M vs $972M last year. Drop is mostly Switch 2 anniversary, store closures, and selling France. • Operating income: $150–170M vs $66M. Core operations got a lot more profitable. • Net income: $290–310M vs $169M. Includes ~$238M eBay gain, minus ~$75M digital-asset loss. • Cash: $5.05–5.07B vs $8.69B last year. Cash fell because they converted the eBay derivatives into 43.4 million eBay shares, now worth about $4.95B. • Full results drop September 8.

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  Brandon Coty (@cotycollects) reported 10 minutes ago

  Just bought 180 tested video games across generations for about $2.50-$3 / game. The previous batch was $2/game. Sounds crazy when you can see on eBay some of these only selling for $6-8. But I also see something that made me question this. 52 minutes south of me a video game store exists that sells these games for at least $20+ The same games you’d comp on eBay for much less. Why? Because it’s hard to trust random sellers and in this niche I’ve see people want to buy from people they like, people they trust and people they vibe with. Therefore I’m buying a disc refurbishment machine and offering a “warranty” to my future customers where if a disc stops working, we’ll fix it to the best of our ability. I’m also packaging games with the consoles we test and increasing the prices and will have a branded website off of marketplace platforms. Does this take reselling to the next level? Yes. Will it be difficult for me? No. It’ll actually be quite fun and we’ll act a little bit like game informer for retro games. If it doesn’t workout? Cool. If it does? That’s a fun way to resell.

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  Olivia Kory (@oliviaakory) reported 16 minutes ago

  "All models are wrong, but some are useful." My friend @josephfwyer wrote the best summation I've seen of how we view the world at Haus. I'm saving you a click and posting his full substack article below. Please tell us where we are right, but more importantly, tell us where we are wrong! The Hitchhiker’s Guide to Marketing Measurement and Decisions A map of the Haus Analytics Platform The first two posts in Decisions and Data have been about what not to do: don’t trust statistical significance to make budget calls for you. Fair enough, but now we’re going to go from “stop doing that” to “do this instead.” So this post is the map. It’s the whole path from the data everyone starts with in marketing measurement to a system that recommends decisions and checks its own work. I’m really proud to say this is what Haus Analytics has built. We’ve walked down this intellectual map the last few years and now I want to walk you down it. Each stop on the path deserves its own deep dive, and those are coming. Today is just the strategic view of the problems and solutions. Last-click attribution has big shortcomings Speed-running last-click attribution real quickly: You click on a digital ad on a advertising platform. Later you buy something on the advertiser’s site, where a snippet of code reports the conversion back to the platform. Roll that up and you get the reporting most marketers watch daily. Suppose for a campaign there was a thousand clicks and a hundred tracked purchases on just a hundred dollars of spend. You’ve acquired those conversions for one dollar each. Streams of this kind of attribution data are flowing constantly and reporting ultra granular data. The problem is when you ask if this advertising spend “caused” those conversions. Around 2011, the economist Steve Tadelis was consulting at eBay and looked hard at their paid search spend. The ad in question sat at the top of search results page when you searched “eBay,” directly above the free organic link to eBay. His hypothesis was that people typing “eBay” into a search engine were largely already on their way to buying something at eBay. Radical, I know. He got his chance to look deeper when Ebay shut down brand keyboard bidding while negotiating with a major search engine. After 3 months of data had populated, he found that organic clicks rose to absorb almost all of the traffic the ads had been getting credit for. eBay was paying approximately $20M/yr to the search engine for customers it was already getting. He eventually published the results in a peer-reviewed journal. The word for what attribution measures is correlation. The word for how much customer activity advertising causes is “incrementality”: purchases that happen because of the ad and would not have happened otherwise. Modern targeting makes the gap between attribution and incrementality on digital channels worse because platforms can model each user’s propensity to buy your product and show ads to the people most likely to purchase anyway. Hold on to this: attribution is biased, but it is also fast and absurdly detailed. I’ll come back to it later. Experiments are where incrementality comes from If you want to know what customer activity an ad caused, you need a comparison: people who saw it versus equivalent people who didn’t. That’s called an experiment. Keeping track of individuals in experiments can be messy (although some ad platforms can pull it off) so a workhorse in this industry is the geographic experiment. You can randomly assign market areas in a country into receiving advertising (treatment) or not (control). Then you simply look at the difference in conversions between the two. Randomization removes that attribution correlational bias that tricked Ebay into lighting money on fire (and many, many other companies even today). On average, the differences between the treatment and control regions wash out, so the estimate is centered on the true incrementality. That sounds really simple, so why does Haus have all these experimentation scientists? It’s a lot of work to improve precision without messing up accuracy. Precision is the measure of how far off the estimate can be when it is off. Think of it like darts. A tight cluster in the upper right of the dart board is precise but inaccurate. A loose scatter centered on the bullseye is accurate but imprecise. Randomization gets your cluster centered on the bullseye. What the science team does all day is making the cluster tighter without dragging it off the bullseye. If you have a great experiment design and analytical model then you will get a precise estimate on the real number. At eBay, the real number for brand search keywords was close to zero. But that’s one company at one moment. I’ve seen brand search come back near zero for one advertiser and strongly incremental for another. You don’t know until you test. So everyone needs to test to find how much money they are leaking. As much as I love experiments, they have limitations. An experiment tells you the causal effect at the spend level you tested. That’s only one data point! You can’t trace a curve through one data point and you need a curve to allocate budgets because every channel eventually hits diminishing marginal returns. Causal MMM, debiasing the model Marketers have been fitting models to trace diminishing return curves for a long time. Media mix models (MMMs) trace your sales on your spend across multiple ad channels and let statistics tell you how much each is driving. In theory this gives you the full diminishing curve for every channel at once. In practice, MMM has two mortal flaws. Multicollinearity. Businesses tend to turn all their channels up and down together, so when sales change, the model struggles to tell which channel did it. Seasonality. Businesses spend the most going into Black Friday and Christmas season, which is exactly when people’s propensity to buy surges without needing ads. So were the gains in sales caused by the ads or the season? All models are wrong, but some are useful. MMM is very wrong, but very useful because it can go all the way to a budget recommendation when an experiment can’t. So we want to fix MMMs. We do that by stopping treating experiments and MMM as rival methodologies and instead merge them together. You ran a geo experiment and learned that at last quarter’s spend, a particular ad channel truly drove some specific number of purchases. We require the model’s curve for that channel to pass through the experiment data point. We call this experiment calibration. Pinning one channel’s curve to ground truth also disciplines the others, because the remaining sales have to be explained by the remaining channels plus organic demand. The seasonal bias gets squeezed out the same way. That’s causal MMM (cMMM): the curve-tracing power of an MMM, anchored to the causal truth of the experiments. And Haus refreshes it weekly instead of the traditional once-a-quarter-two-quarters-later read. Causal attribution, debiasing the daily feed A weekly model of whole channels is still too slow and too coarse for the person making changes every day. Remember what attribution had going for it: daily, granular, ad-level. Attribution is wrong, but what if how wrong is predictable? So apply a similar calibration move we did with cMMM but to Attribution. If your experiments show that only five percent of the purchases the channel claims are truly incremental, then discount its daily feed by 95%. Do that per channel. You can’t have experiments everywhere so let a model reason about how the causal correction shifts over time and across spend levels. Now the daily numbers marketers already watch become numbers they can trust. We also show the raw platform-reported figures next to the corrected ones, so you can always see what the feed said and what we did to it. Architect, where measurement becomes decisions Everything up to this point is measurement, and measurement adds no value unless it impacts decisions. We’re now going to get into the most exciting part of the Haus stack. We call it Architect. It takes the experiments, the causal MMM, and the causal attribution feed, and turns them into specific recommendations: move this much budget from here to there. Then, it measures what happened after you made the move and reports back within a couple of weeks. Did revenue improve? By how much? Continually updating its expectations and recommending again. Across the first companies acting on these recommendations so far, the average adopted Architect recommendation has improved conversions by 10%. Some companies have stacked changes and watched the gains compound. Architect earned the trust to make those changes by tracing the logic through the experiment, cMMM, and cAttribution data. That is why we’ve been working so hard to mak every layer underneath an automated and scalable system. An experiment alone is an isolated data point. A calibrated model alone is a forecast. The loop of recommend, act, verify, and update is the thing that turns marketing measurement from a reporting function into the situation room where strategic decisions are made. One more note, since the obvious question in 2026 is “why not just have an AI do all this?” Some companies will sell you exactly that right now. Personally, an AI reading raw attribution data inherits every bias in it and without each layer built, tested, debiased, and made explainable, you can’t trust if it’s going to work or know why it told you to move the money. The entire stack is what makes an automated recommendation trustworthy. Wrapping it up That’s the map! Attribution gives you speed and detail but with debilitating bias. Experiments remove the bias but are solitary data points. cMMM extends the causal truth to actionable curves. cAttribution pushes causal truth back into the daily feed. Architect reads the outputs of the whole stack and closes the loop by recommending and verifying decisions. In the coming weeks I’ll talk through some stops on the map with some illustrative math and examples of where things can go wrong. If you only take one thing from the altitude view, take this: never trust a marketing number that hasn’t been anchored to an experiment somewhere.

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  HowlingHemorrhoids (@DeadlessHick) reported 17 minutes ago

  A funny thing about ebay for me is when I get a wild hair up my *** on buying stuff, like a new HDD to replace a dead one in my server, I'll peruse the auctions with current bids ~50% below market value, and then place a max bid of ~80% market value. Often times I do this, I see that someone else has already placed a higher max bid than I'm willing to do. So I feel like a gremlin running around ebay casually helping sellers and causing the other bidders to lose a chance at a true deal. I got a $420 camera lens for $200 doing this method though.

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  Uma Kompton (@RIPUmaKompton) reported 22 minutes ago

  @ultra_kitsch @offbeatorbit Not the same as an awesome apartment ofc but I got a $300 dress with tags for $6 on eBay because the pictures were so terrible 😭 will never forget it

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  Franklin Cormorant (@FranklinCormor1) reported 23 minutes ago

  @thewebbie @amazon The fluctuating delivery times I can live with; I just expect that as a result of the many variables inherent in online shopping. The packaging quality, however, is a big problem. Amazon built its sales volume on cut-rate shipping deals with USPS and UPS that other retailers couldn't match. That could hold out unless -guess what- we suffer an inflationary period driving up energy and labor costs. Now they can't afford to pay for proper boxes, packing, and skilled workers to sort & pack properly. (For anyone with an eBay side hustle you know that's actually quite a skill). So I concur that the amount of large items shipped in cheap envelopes is increasing, meaning we get more broken and missing stuff. Amazon will keep doing this until the cost of refunds and returns overwhelm the packaging savings...and I fear the end of the "free shipping" era is near.

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  Tyler (@SeeifIhaveit) reported 25 minutes ago

  Beware of this eBay seller. Purchased a J Baez lot when he was hot and I had to cancel and refund him because it sold on Mercari probably 15 minutes prior. He purchased it literally as I was taking down the eBay listing. I messaged him and apologized and refunded him immediately. Well… he left negative feedback and said he never got the cards.

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  ᴅᴏɴᴏᴠᴀɴ² 🇺🇸🇬🇧 (@arcanedonovan) reported 25 minutes ago

  @PunchingCat @michiganstan25 i put them on ebay for my house down payment

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  marqix ☆ (@fwmarqix) reported 27 minutes ago

  My librarian Janet was running a book laundering scheme where she checked out books under dead people's names, reported them lost, pocketed the replacement fees, then resold the "lost" books online. Discovered this when my dead grandmother's library card got a overdue notice. "Grandma's been dead for three years." "Oh no, our system must have glitched," Janet said. But the book was real. Someone checked out "The Goldfinch" under her name last week. I checked records. Grandma's card had been active for three years. 47 books checked out. All "lost." All replacement fees paid from a account I'd never seen. Confronted Janet. "You're running a fraud scheme with dead people's library cards." "I'm maintaining circulation metrics for funding purposes." "You're stealing." She showed me the books budget. Cut by 40% over three years. "I'm keeping this library alive. The replacement fees fund new books. The 'lost' books get resold. Money goes back into the system." "Through your pocket." "Some administrative costs are involved, yes." She'd been doing it for five years. 200 dead people's cards. $180,000 in replacement fees. Books resold on eBay for another $90,000. All money went back into library programs. Story time. Teen literacy. Computer access. "You're Robin Hood but make it books?" "I prefer Marian the Librarian but yes." I should've reported it. But the library was thriving. Programs everywhere. New books weekly. Then an auditor noticed the pattern. Investigated. Found everything. Janet was fired. Police got involved. Community rallied. "Janet saved this library." Petition to drop charges: 4,000 signatures. Charges dropped. Janet was rehired with "enhanced oversight." She's not allowed to handle money anymore. But circulation is down 30%. Programs are being cut. Janet started a nonprofit. "Friends of the Library Foundation." Runs fundraisers. All legal now. Raised $200,000 last year. Library is thriving again. I asked if she missed the scheme. "Every day. It was elegant. But this is better. Harder, but better." My grandmother's library card finally got deactivated. Last book checked out under her name: "How to Get Away With Murder." Janet swears it wasn't her. I believe her but I also don't.

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  dave (@davejones3334) reported 31 minutes ago

  @LLMJunky @stevenharms I don’t sell much on eBay but almost every tech item I’ve had scammer issues. Luckily I didn’t lose any money and they were fairly low value items in the 100-300 range. I would not trust selling a multi thousand dollar item there.

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  Derek Huntley (@BigDerek_KU) reported 33 minutes ago

  @eBay fix your app. I’m tired of getting live notifications when I have them turned off. Beyond annoying!

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  Brent Wendland (@BrentWendland) reported 33 minutes ago

  @packaddictsnw Yeah eBay needs to do something with the tracking on these. I’ve gotten way more “buyer didn’t receive item” and have to issue a refund.