eBay Outage Map

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eBay Issues Reports

Latest outage, problems and issue reports in social media:

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  Germainrain (@germainraine) reported 5 minutes ago

  @cmpayneful @GameStopOnChain Consider this a rabbit hole, but if you choose to go down, will lead to more… like XRP. Surprisingly this stuff connected to Epstein, which is why the DOJ is gonna be important. If I were to guess I think eBay was permitting trafficking kids likewise to JP Morgan

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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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  norm19020 (@norm19020) reported 7 minutes ago

  @CardPurchaser Is anyone else having issues trying to make offers on @ebay this morning? I get a message saying “we are experiencing a technical problem”

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  Olivia Kory (@oliviaakory) reported 11 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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  J & C Collectibles (@JC_colleccoast) reported 11 minutes ago

  🚨 SOS Card Peeps! Need some help ASAP please. 🚨 I received an offer on My Acuff 1/1 card on eBay. The guy offered me a fair price and is now negotiating with me…I’m totally fine with that, but in our chat (on eBay) the user name keeps changing…when I click on the first name at the top of the chat it seems like he is a legit seller/buyer. When the other name pops up, it says “error” Is someone trying to scam me or is this some kind of issue with eBay’s messaging?!? Thanks in advance!

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  ☆ (@LucilleFilm) reported 16 minutes ago

  @drivenbyfilms ppl are saying the screener was for an already released film but even then, this could still get him in trouble. there's a reason why ebay listings don't go up until months after release and awards szn.

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  DogsBeAwesome (@Glasz_Half_Full) reported 19 minutes ago

  @mikelectricstuf @FedEx Before brexshit I used to have a small ebay store that sent parts overseas. Royal Mail had the same problem too.

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  Kat 🦋 (@ArtemusBlue) reported 20 minutes ago

  @Kurumi96Neko @WholesomeMeme Earphone jack to USB-C adaptor, you can get them for like £3 off Ebay ✨ Problem solved, and it even extends your cable a bit!

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  🕯️ (@sixlinekenner) reported 23 minutes ago

  resolution to not buy more dolls this week broken by $50 ebay pullip #yay

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  Simon What (@Whachazuck) reported 23 minutes ago

  @davidareader @yeehahboogie @eevblog Ebay seem to have bought the Inpost contract from Vinted, not been option for while on Vinted. Standard US monopoly their basic search is full of issues, zero innovation but buy up the competition, or their main postage method anyway Need to rename as eDropshipbay since 2004..

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  Uma Kompton (@RIPUmaKompton) reported 24 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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  Sj (@Pkrguy23) reported 24 minutes ago

  @clarkknowscards DM me and I will tell you how I got out of eBay jail from their error which plagued me for almost 6 months. Should work the same for you.

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  dave (@davejones3334) reported 25 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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  Wandering Biku. Professional cynic. (@BikuWandering) reported 26 minutes ago

  So, buying DVD's from eBay is cheaper than ordering movies to stream. Plus you get to keep them when the grid goes down 👍👍👍

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  Jake (@gauchojake) reported 27 minutes ago

  @__tinygrad__ I just went down this rabbit hole on eBay yesterday. It’s insane.