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eBay is laying off about 800 employees, or 6% of its full-time workforce, saying the move is a push to align with its strategic priorities. It comes a week after the company announced it was acquiring second-hand clothing app Depop from rival Etsy for $1.2 billion. Depop is popular with millennials and Gen Z, and is part of eBay’s bid for younger consumers, who are gravitating to second-hand shopping online for sustainability and financial reasons. eBay Inc. (EBAY) was trading up 3.3% in midday trading at the time of this writing. This is eBay’s third round of layoffs since 2023. The online second-hand retailer cut 1,000 jobs in 2024 (9% of its workforce), after it cut 500 jobs in 2023 (or 4% of its workforce), per TechCrunch. We are taking steps to reinvest across our business and align our structure with our strategic priorities, which will affect certain roles across our workforce,” a spokesperson for eBay tells Fast Company. “We are grateful for the contributions of the employees impacted and are committed to supporting them with care and respect. The Silicon Valley-based online retailer has also been heavily investing in artificial intelligence. The eBay spokesperson said the cuts are not AI-related. eBay financials This latest round of layoffs comes just two weeks after eBay reported strong fourth-quarter earnings for 2025, with revenue coming in at $2.97 billion, beating estimates of $2.88 billion; and adjusted earnings per share (EPS) of $1.41, beating estimates of $1.35. “2025 was a milestone year for eBay, and our results reflect the strength of our strategy and the disciplined execution behind it,” eBay CEO Jamie Iannone said in that earnings release. “As we continue to harness AI to elevate thecustomer experience worldwide, eBay is in the strongest position it has been in years.” eBay has a current market capitalization of $40.2 billion.
Category:
E-Commerce
AI has not changed the importance of judgment in product leadership. What it has changed is the cost of getting it wrong. Early in my career, I learned a principle that still guides how I think about building products: The strongest decisions rarely start with perfect data. They start with conviction, a hypothesis shaped by experience, customer insight, and pattern recognition. What ultimately separates high-performing product organizations from average ones is how quickly and confidently instinct is validated. That validation is the true role of product analytics, and increasingly, it is where AI amplifies its value. Analytics tests whether what you believed would happen actually did, and to inform what you do next. When treating analytics as a decision engine rather than a reporting layer, it fundamentally changes how teams operate. ANALYTICS SPRAWL REDUCES CLARITY Across nearly every organization I have worked in, regardless of size or industry, one pattern shows up with remarkable consistency: analytics sprawl. Google Analytics, Amplitude, Mixpanel, Adobe Analytics, and Pendo are all excellent tools, adopted with good intent to solve real problems. However, when allor even severalcoexist within a single organization, they often create fragmentation that undermines decision-making. The issue is not the tools themselves, but the absence of a clear leadership decision to standardize. When analytics lives across multiple platforms, each with its own methodology and definitions, even basic questions become difficult to answer. AI magnifies that problem. Ask a simple question like, How many monthly unique visitors do we get? With data spread across multiple analytics platforms, there is no clean answer. You cannot aggregate the numbers. There is no deduplication. Slight differences in definitions erode trust. Teams stop discussing insights and start debating whose data is correct. That is not a tooling failure. It is a decision-making failure. INCONSISTENT DATA SCALES CONFUSION This challenge matters even more in an AI-driven world because AI depends on coherence. Models train on ambiguous metrics. If foundations are inconsistent, AI will scale confusion faster than any human ever could. Especially in organizations with multiple business units and products, analytics must start before dashboards, instrumentation plans, or AI ambitions. It starts with clarity. This comes from understanding what decisions must be made with confidence and what questions must be answered consistently across teams. Once that is established, everything else follows. Selecting the right product analytics platform is based on business requirements, not convenience. That platform may differ by context. In fact, I have yet to implement the same analytics tool twice. What stays the same is the discipline required to make analytics and AI effective at scale. Instinct may start the journey, but data must validate it. Tool sprawl is a leadership choice rather than a technical inevitability, and shared definitions matter far more than dashboards or models. Analytics and AI only matter when they improve decisions. When that foundation exists, AI becomes a true force multiplier, and organizations gain speed, trust, and the ability to scale. Insights surface faster, patterns emerge sooner, and teams spend far less time reconciling data and far more time acting on it. Leaders move from reacting to signals to shaping outcomes. Without that foundation, AI simply makes bad analytics louder. A SIMPLE CHALLENGE FOR LEADERS If you lead product, technology, or digital teams, here are three simple questions to consider: How many analytics tools does your organization use across your products? Do your teams share the same definitions for basic metrics? Can you answer a question once and trust the answer everywhere? If those answers vary, the issue is not analytics or AI. It is decision-making. If your AI strategy is ahead of your analytics foundations, you are scaling uncertainty, not intelligence. Darren Person is EVP and chief digital officer of Cengage Group.
Category:
E-Commerce
The average long-term U.S. mortgage rate slipped this week below 6% for the first time since late 2022, good news for home shoppers as the spring homebuying season gets rolling. The benchmark 30-year fixed rate mortgage rate fell to 5.98% from 6.01% last week, mortgage buyer Freddie Mac said Thursday. One year ago, the rate averaged 6.76%. The average rate has been hovering close to 6% this year. This latest dip, its third decline in a row, brings it closer to its lowest level since Sept. 8, 2022, when it was 5.89%. Mortgage rates are influenced by several factors, from the Federal Reserves interest rate policy decisions to bond market investors expectations for the economy and inflation. They generally follow the trajectory of the 10-year Treasury yield, which lenders use as a guide to pricing home loans. The 10-year Treasury yield was at 4.02% at midday Thursday, down from around 4.07% a week ago. Mortgage rates have been trending lower for months, helping drive a pickup in home sales the last four months of 2025, but not enough to lift the housing market out of its slump dating back to 2022, when mortgage rates began to climb from pandemic-era lows. Sales of previously occupied U.S. homes remained stuck last year at 30-year lows. And more buyer-friendly mortgage rates this year werent enough to lift home sales last month. They posted the biggest monthly drop in nearly four years and the slowest annualized sales pace in more than two years. Still, with the average rate on a 30-year mortgage now below 6% as the annual spring homebuying season begins, it could encourage prospective home shoppers who can afford to buy at current rates to shop for a home this spring. Assuming rates stay below 6%, buyers and sellers are going to start getting back into the market, said Lisa Sturtevant, chief economist at Bright MLS. March is when the spring homebuying season typically begins to ramp up and with rates at a three-and-a-half year low, it could be a barn burner of a spring homebuying season. Alex Veiga, AP business writer
Category:
E-Commerce
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