Anya Cheng is the Founder and CEO of Taelor, an AI-powered menswear rental and styling platform at the intersection of fashion, data, and artificial intelligence. Rising to prominence in the 2010s after leading product teams at Meta, eBay, Target, and McDonald’s, she became known for scaling digital products that touched hundreds of millions of users while bridging consumer behavior, growth, and personalization. Today she is widely regarded as an influential figure in fashion tech and serves as faculty at Northwestern University, translating operating experience into curriculum on integrated marketing and product strategy.
Previously, as a senior product leader at Meta, eBay, Target, and McDonald’s, she owned global initiatives that drove measurable business outcomes across eCommerce, food delivery, and retail. At McDonald’s she helped lead the global rollout of mobile ordering to thousands of stores, transforming how customers interacted with a brand serving more than 60 million people per day. At Taelor, her team has raised approximately $2.3 million in pre-seed funding, achieved over 10 million marketing impressions with zero ad budget, and earned recognition such as Inc.’s 2025 Best in Business – Best Startup category and Webby Award honors.
Her career highlights include award‑winning marketing campaigns at Sears and Kmart, scaling cross‑border digital commerce at eBay, and driving omnichannel experiences at Target that combined stores, mobile, and online into a unified customer journey. As founder of Taelor, she has built an AI-driven styling engine that mixes acquired competitor data, human stylists, and feedback loops from thousands of garment rentals to improve recommendations and reduce fashion waste. Along the way she has been named to Girls in Tech’s “40 Under 40,” delivered a TEDx talk on perseverance, and built a following of more than 28,000 professionals who track her work across AI, circular fashion, and consumer technology.
As a book author, startup advisor, and frequent podcast guest, Cheng documents the path from Taiwan to Silicon Valley and distills lessons on resilience, go‑to‑market execution, and human‑centered AI. As a teacher at Northwestern University and a sought‑after speaker at industry events like NRF and SF Tech Week, she helps the next generation of founders and operators understand how to turn data, storytelling, and product intuition into enduring companies.
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The framework Meta uses in PM interviews to separate great product thinkers from idea generators.
“Nobody used the feature besides a product manager,” Anya Cheng tells me. “Why?”
She’s describing a project from her time at Target. The team wanted to build store GPS—beacon-powered navigation so customers would never forget an item on their list. They spent six months and millions of dollars mapping every item location in stores with different layouts and footprints. They geo-fenced the shelves. They built the feature. They launched it.
“Come on,” she says. “Mom is going to a Target store to get lost. They want to go to a store wandering around and buy stuff.”
The Target moms didn’t need efficiency. They needed escape. The Starbucks inside is the feature. The cup holders on the cart are the feature. The permission to wander for an hour away from noisy kids is the feature. The team had solved the wrong problem perfectly.
Anya Cheng is the founder and CEO of Taelor, an AI-powered menswear rental subscription. Before founding Taelor she was Head of Product at Meta for Facebook and Instagram Shopping, Head of Product at eBay for Latin America and Africa, led mobile and tablet e-commerce at Target, and was Senior Director at McDonald’s launching their global food delivery apps. She teaches product management at Northwestern and has won 20-plus industry awards. The Target GPS story is one she uses to teach the most important lesson she knows: the quality of your execution is irrelevant if you’re solving the wrong problem.
“If you are taking away the value prop,” she says, “then your product is just not going to be popular.”
Target’s value proposition isn’t convenience. It’s discovery. It’s the opposite of a GPS. The beacon team understood the technology. They understood the implementation challenge. They just didn’t understand why moms go to Target.
I ask Anya how she avoids the same trap. How she decides what to build and—more importantly—what not to build. Her answer is a framework she’s used at Meta, eBay, McDonald’s, and now Taelor.
It starts with the Facebook PM interview question: if you’re the product manager of X, what feature would you launch? She’s been on both sides of this question hundreds of times. The candidates who fail are the ones who answer it.
“Two types of person,” she says. “One type will be out of the interview loop right away. The other will at least get to the second level.”
The first type jumps to solutions. I’d build this, I’d build that. Ideas are cheap. ChatGPT can come up with ideas. That’s not the job.
The second type starts with personas. She gives me the birthday product example. Three personas: the birthday person who wants to be surprised, the close friends who want to organize and are afraid of forgetting, and the acquaintances who just want to say happy birthday. Each has distinct pain points. Each pain point sits on a spectrum of severity, frequency, and relevance to Facebook’s unique position.
“Then you come up with selecting criteria,” Anya says. “Which pain point is more painful? Which pain point has more people with that pain point? Which pain point is Facebook more relevant to solving versus other people?”
The criteria filter the problem space before you ever touch solutions. Then when you do generate solutions, you filter again: which solution solves the problem best, which takes fewer engineering hours, which fits the direction of the business?
“Up to here,” she says, “I haven’t told you anything about the solution.”
She brings up the same framework when she tells me about Google Shopping versus Facebook Shopping. Same goal: sell things online. Completely different products. Google’s mission is organizing the world’s information, so Google Shopping became price comparison. Meta’s mission is bringing the world closer together, so Facebook Shopping became community commerce—friends selling bicycles from their backyard, influencers sharing product recommendations.
“Exactly the same goal,” she says. “But totally different product because it’s different mission of the company.”
The mission is the highest-level selection criterion. It determines which problems are yours to solve and which aren’t. The Target beacon team forgot this. They selected a problem—moms forgetting items—that was real but irrelevant to why people went to Target in the first place.
Anya’s own origin story follows the framework precisely. At Meta, she was dealing with imposter syndrome—a Taiwanese immigrant surrounded by Ivy League engineers. She needed to look good. She tried Stitch Fix (had to buy everything), Rent the Runway (had to browse 100,000 garments). She realized fashion companies designed for fashion lovers, not for people who wanted to get ready and get on with their day.
So she did product 101. Interviewed people. Found that her real persona wasn’t women like her—it was busy men. Sales guys, consultants, pastors, executives. People who didn’t care about fashion but cared deeply about the outcomes fashion enabled: getting a job, closing a deal, landing a date.
The MVP was a Shopify landing page with a stock photo of blue shorts. A realtor from San Diego put his email in, waited two months, found Anya on LinkedIn, and called her. They bought clothes from Macy’s during a Christmas sale and shipped from the post office.
“Became our first customer,” she says. “The MVP still worked.”
It worked because the hypothesis was right. The problem was real. The selection criteria—not the solution—validated the business. Everything that followed—the 150 brand partnerships, the AI-augmented styling, the circular fashion model—was built on the foundation of understanding what the customer actually needed.
She tells me about another failed product: eBay’s AI-powered listing tool. Snap a photo of a bicycle, AI writes the description. Built it. Shipped it. Nobody used it. Small sellers on eBay have sentimental attachment to their items. They want to write their own descriptions. Efficiency wasn’t the pain point. Pride was.
“If you don’t deeply understand the customer persona, the insider psychology, the job to be done,” she says, “it’s just very hard to build a great product.”
I bring up vibe coding—the trend of PMs building functional prototypes with AI tools on weekends. Her intern did exactly this: came back with three working features built in a weekend. Her response was blunt.
“This is how exactly at Meta we don’t hire people.”
The features might have been good. But they were selected by enthusiasm, not criteria. The intern skipped the framework—the personas, the pain points, the filtering—and went straight to building. AI made it possible to skip the hard work. And skipping the hard work is exactly the failure mode that produces Target store GPS.
“In the old time,” Anya says, “you have three ideas and you have to go convince your engineer and designer. And they will challenge your logic. But now you can skip all of this.”
The challenge was the quality filter. Removing it doesn’t make you faster. It makes you wrong more efficiently.
I ask Anya what she wants product leaders to take away from all of this. She doesn’t hesitate.
“We are all problem solvers,” she says. “Go to the meeting. Forget that you are a designer, forget that you are PM, and really focus on thinking about what problem can be solved.”
The solutions will come. They always do. The hard part—the part that separates a Target beacon from a Taelor, a failed eBay listing tool from a 10-million-impression marketing flywheel—is choosing the right problem in the first place. Not the coolest one. Not the most technically interesting one. The one that actually matters to the person on the other end.
Selection criteria over ideas. Every time.
