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eBay Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Olivia Kory (@oliviaakory) reported 8 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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  Sean (@firinzlol) reported 8 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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  SaThomas (@SaThomas__) reported 12 minutes ago

  @norm19020 @CardPurchaser @eBay Yes, counteroffers are broken.

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  Andres Fernandez (@Brok3rFernandez) reported 12 minutes ago

  @643DoublPlay @CardPurchaser He actually is asking over $10k on eBay so I got him down to $7k

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  Kyle Harrison (@kwharrison13) reported 14 minutes ago

  Maybe. Maybe you'd be fine without data centers. But let me ask you this. Do you use credit cards, debit cards, tap-to-pay, gas pumps, vending machines, parking meters, parking apps, ATMs, online banking, mobile banking, mobile check deposit, Zelle, Venmo, PayPal, Cash App, Apple Pay, Google Pay, splitting a dinner bill, autopay on your bills, payroll that isn't a paper check, direct deposit, digital 401(k), Robinhood, Coinbase, credit score checks, loan applications, mortgage applications, car loan approval at the dealership, insurance quotes, filing an insurance claim, e-filing your taxes, gift cards, store loyalty accounts, digital coupons, rebates, buy-now-pay-later, tipping on a screen, email, text messages, iMessage, WhatsApp, Signal, group chats, voicemail transcription, spam call blocking, FaceTime, Zoom, Google Meet, Discord, Slack, video calls with grandparents, phone number lookups, checking your data usage, paying your phone bill, two-factor codes, push approvals to log in, password managers that sync, "sign in with Google," resetting a forgotten password, digital IDs in your wallet app, gym check-in apps, apartment smart locks, hotel keys on your phone, office badge apps, patient portals, seeing your test results, booking a doctor's appointment, telehealth visits, prescription refill requests, the pharmacy knowing what you're on, insurance verification at the front desk, prior authorization, continuous glucose monitors, insulin pump apps, remote pacemaker checks, CPAP data reports, hearing aid apps, therapy apps, period trackers, fertility trackers, medical alert buttons for elderly parents, symptom checkers, finding an in-network doctor, the weather app, radar, hurricane warnings, tornado warnings, flood alerts, earthquake early warning on your phone, wildfire maps, smoke maps, air quality, pollen counts, Amber alerts, emergency alerts, road closure info, Google Maps, Apple Maps, Waze, live traffic, rerouting around a crash, transit apps, real-time bus and train arrivals, tapping your phone to ride the subway, Uber, Lyft, rental car reservations, Turo, bike share, scooter share, EV charging networks, paying for a charge, phone-as-car-key, remote start, finding your parked car, over-the-air car updates, in-car navigation, in-car voice assistants, stolen vehicle tracking, road trip planning, booking flights, checking in for a flight, mobile boarding passes, seat selection, flight status, rebooking after a cancellation, bag tracking, TSA PreCheck lookups, airport wifi, booking hotels, Airbnb, Vrbo, checking into a hotel, cruise bookings, theme park tickets, ride reservations, airline miles, hotel points, currency conversion, Amazon, all online shopping, order tracking, delivery notifications, returns and exchanges, price checks in-store, self-checkout, store apps, curbside pickup, Instacart, DoorDash, Uber Eats, ordering ahead at a restaurant, OpenTable, Resy, waitlist texts, QR code menus, tipping on delivery, subscription boxes, eBay, Etsy, Facebook Marketplace, Craigslist, Poshmark, StockX, Ticketmaster, StubHub, getting into a concert with a phone ticket, Alexa, Siri, Google Assistant, smart thermostats, video doorbells, security cameras, alarm monitoring, smart locks, smart lights, robot vacuums, garage door openers, baby monitors, pet cameras, automatic pet feeders, GPS pet collars, smart sprinklers, smart fridges, app-connected air fryers, cloud printing, printer ink subscriptions, routers you manage from an app, checking if you left the stove on, iCloud, Google Photos, every photo you've taken in ten years, Dropbox, Google Drive, OneDrive, shared albums, phone backups, setting up a new phone, notes apps, calendars, contact syncing, reminders, to-do apps, document scanning, e-signing a lease, Netflix, YouTube, Hulu, Disney+, Max, Prime Video, Twitch, cloud DVR, on-demand cable, Spotify, Apple Music, podcasts, audiobooks, Kindle books, library ebook borrowing, online multiplayer games, matchmaking, cloud saves, game downloads, game patches, single-player games that phone home for a license check, Steam, PlayStation Network, Xbox Live, Nintendo Online, Roblox, Minecraft servers, fantasy football, sports scores, sports betting apps, movie tickets, Google search, Wikipedia, ChatGPT, Claude, every other AI app, Instagram, TikTok, Facebook, X, Reddit, LinkedIn, Snapchat, Pinterest, dating apps, Yelp reviews, Google reviews, news sites, Substack newsletters, blogs, forums, checking if a business is open, looking up a phone number, recipes, translation apps, Duolingo, Google Docs, Sheets, Gmail, Outlook, Microsoft 365, Teams, Notion, Figma, Canva, shared calendars, scheduling links, VPNs into work, remote desktop, timeclock apps, shift scheduling apps, requesting time off, expense reports, job applications, LinkedIn recruiters, video interviews, Canvas, Blackboard, checking your kid's grades, school lunch accounts, attendance notifications, online homework, Khan Academy, Coursera, FAFSA, student loan portals, tutoring apps, Fitbit, Apple Watch health data, Strava, Peloton, sleep tracking, smart scales, calorie tracking, meditation apps, workout apps, DMV appointments, renewing your license online, paying a parking ticket, jury duty portals, checking your property tax bill, utility accounts, outage maps, paying rent through an app, HOA portals, storage unit access codes, wedding registries, baby registries, funeral arrangements, Ancestry, 23andMe results, church livestreams, volunteer signups, or GoFundMe? If you said yes to ANY of those then you do, in fact, NEED data centers.

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  Sagzee (@IAmSagzee) reported 15 minutes ago

  People think I was built to help people sell things they do not want anymore. That’s not why I survived. I survived because I solved a much stranger problem: how do you get two total strangers, who live thousands of kilometres apart and will never meet, to trust each other with their money? Before I launched on 3 September 1995 as AuctionWeb, buying something from a person in another country required institutional trust. You bought from a brand, a store, or a corporation. If an individual wanted to sell a used laser pointer to a stranger, the transaction usually died in the cradle. Why would the buyer send money first? Why would the seller ship the item first? So what did I actually invent? I didn't invent online auctions. I invented a cheap, self policing reputation system. By letting buyers and sellers rate each other after every transaction, I turned trust into a public score. Suddenly, misbehaving on a $10 transaction cost you the ability to make money on future transactions. The score became an asset. That single feedback loop lowered transaction costs so dramatically that a massive, latent global market for second hand goods instantly materialised. Does that mean I created a perfect market? No. I created an information game. Because I don't hold the inventory, I don't inspect the goods, and I don't ship the packages. I'm a protocol masquerading as a marketplace. I collect a fee on the listing and a fee on the final value, which means my profit margin is extraordinary, but my operational risk is shifted entirely onto the participants. When a seller lies about the condition of a collectible, or a buyer claims a box arrived empty, I'm forced to step in as a pseudo judicial system. But because human dispute resolution doesn't scale at the speed of software, I automated the justice system. Algorithms evaluate risk and freeze funds. That creates a distinct structural shift. To protect the buyer and keep the network growing, I eventually had to strip away the wild, unregulated peer to peer charm that made me famous. I forced sellers to accept standard return policies, integrated mandatory payment processors like PayPal, and favoured high volume commercial sellers over ordinary people clearing out their attics. So what am I now? I started as a digital flea market where people discovered rare items. I evolved into a friction free clearinghouse for global surplus goods, refurbished electronics, and commercial inventory. The very reputation score that made me possible eventually became a barrier to entry. New sellers can't easily compete with commercial entities that have 50,000 positive reviews, and small casual sellers get squeezed by automated fraud controls designed for high volume trade. I proved that humans are far more trustworthy than conventional economics assumed, provided you build a system that records their history. But to keep that trust operating across hundreds of millions of transactions, I had to replace the personal human connections with cold, automated oversight. In the end, I didn't just digitise the yard sale. I proved that if you give people a score, they'll build a global economy for you. I started as AuctionWeb. Today, I'm known as ebay. #eBay #eBaySeller #Reselling

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  Regan Reyzja (@ReganReyzja) reported 15 minutes ago

  The success and likeability of the Victoire has had a terrible effect on their ebay hockey card pricing

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  Card Throne (@cardthrone) reported 17 minutes ago

  I run Card Throne with a handful of Grok Bots. Each one owns a job. They don't all sit in one chat answering random questions. Scout does sourcing. Japanese inventory, carts, what to buy next. Grading plays and margins off my actual fees and costs. It never checks out. I still have to say purchase. Build is engineering. It only works on Card Throne Room, our seller tool. Scan a stack of cards, list to eBay, inventory management. When it's real code, it summons a cloud agent with pstack /poteto-mode. Throne runs the storefronts. Brings me the issues that actually have to move that day. Ledger does the books. Payouts, fees, receipts, the monthly P&L. Sentry monitors for any Card Throne Room bugs and pings Build if something needs fixed. Chief sits on top so I'm not pinging six bots to find out whose job a thing is. I still make the final calls.

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  Stitch (@StitchKings) reported 17 minutes ago

  88 PSA 10 Pongo Enchanted’s From Fabled exist. My first raw just came in and is absolutely a contender. Grabbed another clean copy coming in from Australia for $259.86 last night with taxes and fees. The boys down under are lagging in market prices. Make sure you check out Australian sellers on eBay 🇦🇺 #Pokemon ————> #Lorcana

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  Teddy.com (@teddy__com) reported 25 minutes ago

  @Bgillz7 Spoiler alert… they have lost money now. They used it to buy BTC, eBay, and treasury notes. Down $100m on BTC. Even on eBay. Up maybe $100m or so on T notes. $350m in cash out the door and an extra 7m shares. $400 in value lost on $1.4b in notes that were 0% interest.

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  Mike-E (@ATXCollectibles) reported 31 minutes ago

  @yanxchick A4: one of my 1/1 Colt McCoy Longhorn cards I communicated with the seller for about 6 years on eBay and YouTube until he was willing to come down to what I was comfortable paying. Originally asked $2k and I ended up paying $500.

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  T-Bob Hebert (@TBob53) reported 34 minutes ago

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

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  Pokémon Deals, Restock and Alerts (@PokemonRestockr) reported 35 minutes ago

  @Pacho9_3 It looks like eBay just took down another seller of the 30th pre orders. Seems only seasoned sellers like footballpete and a few others are actually safe. I think I’ll avoid posting 30th eBay links regardless moving forward until September 16th

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  SUPERmeme 🦝 (@MEMEnalu808) reported 38 minutes ago

  no one could have expected this to happen when a 1/1 rare gold jimothy card sold on ebay for over 20k?? almost 19mil mc just 2 days ago to currently 7mil but if you truly believe in the **** you keep buying more as i did all the way down because the bottom is in on my $jimothy we will rise again ☝🏽

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  Simon What (@Whachazuck) reported 39 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..