In this episode
Anya Cheng, founder and CEO of Taelor, explains how an AI-powered men’s clothing rental subscription generates proprietary data that neither traditional retail nor general-purpose AI models can replicate — and how that data is being turned into demand prediction for fashion brands and retail buyers. Cheng, previously at Meta, eBay, and Target, argues that the differentiator in the next decade is not the model but the dataset behind it: because Taelor sees what customers actually choose (absent discount distortion) and how garments physically hold up after repeated wear and wash cycles, it can tell brands what will sell rather than what sold last year. The conversation connects that data advantage directly to the industry’s overproduction problem, where a large share of clothing is never sold at all.
Key takeaways
- The algorithm era has ended; proprietary data is the moat. Cheng argues that anything built on a general large language model is easily replaced, so the durable advantage is data that is hard to find anywhere on the internet — a marketer’s customer insight, a dentist’s imagery, an agile coach’s team performance history.
- Rental reveals true preference; discount retail reveals price sensitivity. When a shopper buys at 10–19% off, the purchase signals the promotion. In a subscription where the fee is already paid, the selection signals what the customer genuinely wants.
- A rental model measures garment quality directly rather than taking a brand’s word for it. Taelor observes which garments shrink after roughly five wears and which loosen after roughly five washes — turning sustainability from a marketing claim into an observed property.
- Last year’s sell-through is a poor predictor of next year’s fashion demand. Brands design roughly two years ahead using prior-year sales data, and in fashion the signal frequently inverts — skinny jeans being Cheng’s example.
- Cheng cites industry figures that 40% of the world’s clothing goes unsold and 30% of that ends up in landfill, and that the fashion industry generates 20% of the world’s polluted water — the waste problem is upstream in production, not only downstream in consumption.
- The customer is not buying clothing; they are buying an outcome. Taelor’s core segment turned out to be socially active men — salespeople, consultants, professors, pastors, single men — who need to close a deal or earn a second date, not men who care about clothes.
- Narrowing the ideal customer profile is what lifted retention above 90%. The initial assumption that any man was a prospect gave way to a specific, occasion-driven segment, which then reshaped the service design and improved the LTV:CAC ratio.
- A hybrid model keeps the customer out of the prompt-engineering business. Human stylists review AI selections before shipment and act as the service layer, so customers describe a goal — a date, a conference — rather than specifying garments.
- Agility means getting feedback sooner, not moving faster. Cheng’s framing: build the bicycle so you learn the car was never needed, and use AI prototyping to test a hypothesis before spending engineering effort.
Chapters
- 0:00 — Can personalization and sustainability coexist in retail?
- 1:32 — Anya Cheng on Taelor: AI stylists, rental subscription, no shopping or laundry
- 2:51 — What big-tech experience taught her about strategic frameworks
- 5:24 — Reconciling consumption and sustainability with AI styling
- 6:44 — Proprietary data: true preference and true garment quality
- 8:29 — Inside the customer experience: goals first, garments second
- 11:34 — Logistics, quality assurance, and why the algorithm era ended
- 13:49 — Measuring success: retention, the 80/20 rule, and who actually buys
- 15:58 — How Taelor measures sustainability impact
- 16:48 — 40% unsold, 30% to landfill: fashion’s prediction problem
- 18:36 — Turning rental feedback into demand prediction for brands
- 19:22 — Where the share economy goes beyond fashion
- 21:21 — What’s next: partnerships and predictive data
- 22:37 — Staying agile: build the bicycle, not the car
Why proprietary data, not the model, is the competitive moat
Cheng draws a hard line between the last technology era and this one. The previous era belonged to the algorithm — Google’s ranking, Netflix’s recommendations — while the next two decades, in her framing, belong to whoever holds unique data. Most companies will not build their own foundation models; they will use a handful of commercially available ones. That makes the model itself a commodity input. What remains defensible is data that cannot be scraped or inferred: the context a customer volunteers, the outcome they were trying to achieve, the physical behavior of a product after real use.
What a rental model can measure that retail cannot
Two distortions disappear inside a subscription. First, price stops confounding preference: because the monthly fee is already paid, a customer’s selection reflects what they want rather than what was marked down. Second, the product returns. Taelor sees the same garment across multiple customers and multiple wash cycles, which means quality is measured rather than claimed. Cheng contrasts this with the prior state of sustainability marketing, where the brand asserting the loudest claim was treated as the most sustainable.
Why fashion’s overproduction is a prediction problem
The waste Cheng describes is created before anyone shops. Brands commit to designs roughly two years in advance, and the primary input available to them is what sold last year — a signal that in fashion often reverses. The result, by her figures, is that a large share of production never sells and much of it is landfilled. Taelor’s proposed intervention sits at that design decision: supply brands and retail buyers with contextual demand signals from real wear, so the forecast is based on what customers chose and how the garment performed rather than on a backward-looking sales file.
Who Taelor actually sells to — and how that changed the product
The company launched with a broad assumption and corrected it. The customers who stayed were men whose work or social life requires meeting people: sales professionals, consultants, professors, pastors, single men. Cheng’s characterization is that the product is a chance to succeed — the wingman, the gadget supplier behind the superhero — not clothing. That reframing changed the service: stylists became text-accessible, the intake question became the customer’s goal rather than their taste, and the experience was redesigned around occasions like a date, a conference, or a client meeting.
How AI and human stylists divide the work
The AI narrows roughly 30,000 garments across about 150 brands against a stated goal. Human stylists then review the selection before shipment and remain available by text, which is where the counterintuitive recommendations come from — the blazer a customer said he didn’t need, the printed shirt that became a conversation opener at a conference. Cheng’s stated reason for keeping humans in the loop is that customers should not have to become prompt engineers to get a good result.
Where the share economy goes beyond clothing
Cheng treats apparel rental as a late entrant to a shift that already happened elsewhere: DVDs to streaming, CDs to Spotify, cars to rideshare, homes to short-term rental, and increasingly appliances and sports equipment. Her answer to the squeamishness about wearing someone else’s clothes is that shared use is already the default in restaurants, hotels, and grocery bags. The driver, in her view, is not environmental preference but time: laundry and browsing are hours people would rather spend elsewhere.
FAQ
What is Taelor? Taelor is an AI-powered men’s clothing rental subscription. For a monthly fee — around $100 — members receive a rotating selection of garments styled to their stated goals, return them without doing laundry, and can purchase pieces they want to keep at a discount.
How does clothing rental reduce fashion waste? Two ways, per Cheng. Renting and buying secondhand reduce new purchases, and the wear-and-return data lets brands predict demand more accurately, which cuts the unsold inventory that drives the industry’s waste problem at the production end.
Why does Anya Cheng say proprietary data matters more than AI models? Because general-purpose large language models are broadly available, any capability built only on a model can be replicated. Data that cannot be found on the internet — real customer context, verified product performance — is what remains defensible.
What makes rental preference data better than retail purchase data? In discount-driven retail, a purchase may reflect the markdown rather than the shopper’s taste. In a subscription where the fee is already paid, the customer’s selection reflects genuine preference, making the signal cleaner for prediction.
How does Taelor measure success as a subscription business? Retention is the primary metric, running above 90%, alongside lifetime value relative to customer acquisition cost. Cheng credits the improvement to identifying the specific customer segment that values the service most rather than treating all men as prospects.
What does Anya Cheng say it takes to stay agile? Build the smallest thing that generates real customer feedback — the bicycle rather than the car — so you learn early whether the product is needed at all. She notes AI prototyping now makes that testing cheap enough to do before committing engineering effort.
About Anya Cheng
Anya Cheng is the Founder & CEO of Taelor, an AI-powered men’s clothing subscription service promoting sustainable fashion. A Silicon Valley entrepreneur, she has been recognized among “Girls in Tech 40 Under 40” for her expertise in tech product management and marketing. Anya played a pivotal role in launching Facebook and Instagram Shopping at Meta, led new business expansion at eBay, and helped grow McDonald’s global food delivery. She also shaped Target’s mobile commerce and has led teams in AI, product management, UX, and marketing across Fortune 500 companies. Her work has earned 20+ prestigious awards, including the Webby Award for Best Shopping App, Best Mobile App Award, and The Communicator Award. A best-selling author, adjunct professor, and two-time TED speaker, she lectures at Northwestern University and 500 Global and is a sought-after keynote speaker. Anya’s award-winning venture, Taelor, won first place at Draper University’s Startup Competition and was named a Startup to Watch by Bay Area Inno. She holds a Master’s in Integrated Marketing Communications from Northwestern University and an MBA from the University of Chicago Booth School of Business.
Anya Cheng on LinkedIn: https://www.linkedin.com/in/anyacheng/
Resources
Taelor: https://www.taelor.ai
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Transcript
[00:00:00] Greg Kihlström: In an era where personalization is paramount, can a brand truly be sustainable, or are we just using new technologies to fuel the same old consumption patterns? Agility requires a willingness to challenge established business models and integrate new technologies, not just for efficiency, but for purpose. It demands that we rethink the entire value chain from production to the end of a product’s life. Today, we’re going to talk about the intersection of artificial intelligence, sustainability, and retail. We’re gonna explore how technology isn’t just a tool for optimization, it’s a catalyst for building entirely new purpose-driven business models that challenge the status quo of consumerism. To help me discuss this topic, I’d like to welcome Anya Cheng, CEO at Taelor. Anya, welcome to the show.
[00:01:32] Anya Cheng: Hello, hello. This is Anya from San Francisco. I’m excited to be here.
[00:01:36] Greg Kihlström: Yeah, looking forward to talking about all of this with you. Before we dive in, though, why don’t you give a little background on yourself and your role at Taelor?
[00:01:44] Anya Cheng: Yeah. So, used to work for Meta, eBay, and Target, and now the launching of this, the AI company a few years ago. Taelor, yeah, Taelor, we offer AI stylists to help busy men to look great, so then they don’t have to do any shopping or laundry because we also offer a clothing rental service, so they’re paying $100 per month, for example, you can wear like six to 10 clothes per month, so no more shopping or laundry. And then we take those feedback from the customer, make AI again, and we help fashion brand and retailers to predict what’s going to sell so then there will be less unsold inventory and le- less waste for the world.
[00:02:24] Greg Kihlström: Yeah. Love it. Love it. So yeah, let’s, let’s dive in here, and we’re gonna talk about a few things here, but you know, as, as you mentioned in your, in your intro, you, your background includes some leadership roles at some, some very large companies, that, that you mentioned. how does that enterprise experience inform your approach to building this, this startup and, and an impact-driven one at that?
[00:02:51] Anya Cheng: Yeah. I think that working for big tech company is, you can never convince anyone with your idea because everyone are like Ivy League school graduate from Google and Facebook before. They’re all super smart. Why do I have to believe in your idea? No way. But you can convince me with your logic.
[00:03:09] Anya Cheng: So from the tech, big tech company, the logic we call strategic frameworks are very important. And so for example, using a simple analogy, if you own a fruit stand, you sell bananas, strawberry, and apple, and one day you tell your intern, say, “Hey, why don’t you put the watermelon outside the store and take strawberry in?” And your intern say, “No, I disagree. I think we should put a strawberry in outside while we put the watermelon inside right next to the fridge because we sell juice, and juice has higher margin.”
[00:03:44] Anya Cheng: And what does this really mean? This means that both of you have the same goal, which is increase revenue, and you have different strategy. One is reduce thief, so putting watermelon, which is heavy, outside. One is increase margin, which is why putting watermelon next to the refrigerator where you sell juice. So the, of course, the tactic is different. So I think what I learned a lot is that they are like getting into the details on the detail- on the, the strategic pro- framework. It help people understand how you think and allow them to make a comment, and that’s still something we’re pretty using all the time here at Taelor. we have amazing investor, like people who are investor behind Lyft, Instacart, Spotify, Facebook, TSMC. So when we communicate with investor, they are non-experts in sustainability, not expert in AI application, so try to make it detailed so then with logic, then they can make a better comment.
[00:04:42] Greg Kihlström: Yeah. Yeah. Love it. And so you’re, you, you’ve moved into the, the fashion industry here and, and you know, there, there’s, there’s some, let’s just say some, some specifics about the, the fashion industry and, and, you could say a conflict between both driving consumption but also the sustainability, imperative that, that a lot, a lot of consumers are, are certainly feeling. How did, you know, how does Taelor’s business model use AI and, and probably other tools as well, but use AI-based tools to reconcile these two goals that’s are seemingly at opposition?
[00:05:24] Anya Cheng: Yeah. So, what, what we do is that we offer AI styling for people. What does it mean is that most of a guy, if you open your closet, you probably have five different brands, Lululemon, Nike, Banana Republic, and that’s about it. You have been wearing the same thing again and again.
[00:05:42] Anya Cheng: However, it’s not because most of the guy want to be Steve Job. Most of you guys actually know looking good matters for the day night, close a deal, sell houses, right? Being a marketers and so doing so, I am, I should be more looks creative versus wearing the same blue shirts every day.
[00:05:59] Anya Cheng: But who has time to do loads of shopping and laundry, right? So what we do is we offer the AI styling side, and our AI is unique because we got the proprietary data. What does it mean? When you decide to go to Target, my old company, you buy something or not isn’t because you like it, is because the price. Anything 10, 19% off, someone is going to buy it, which is very different from your Netflix show. You pay a monthly fee, so when you pick something from the show, you definitely like it, not because it’s on discount. And the same thing here, we have AI stylists work side by side with the customer. The collaboration process, usually people will get something that they like versus what’s just basically based on the price.
[00:06:44] Anya Cheng: So we know the true preference of the customer. We also know the true quality of a garment. After wearing five times, these garment shrink. After washing five times, these garment become more loose. So in, in the past, only who is more sustainable? Any brand that they advertise it more say they are more sustainable, then they are more sustainable. But now with the rental model, we actually know the true quality of a garment. So by combining the two, this is how we can help our customers. They don’t have to do shopping, but… And we work with brands, over a hundred of them, like Bonobos, John St. Murphy, Marine layer. So then we style them and also send them the clothes. And the a- when they buy the clothes, they are also secondhand, even though still like new, because typically after renting three times, customer already buy the clothes.
[00:07:34] Anya Cheng: So, but still like new, but just like airport rental car, even though it’s new, as long as it’s one time use, you got, like, 50% off. So you get, good discounts there while AI picking stuff, so then you don’t have to spend a lot of money. Like those time in celebrity, they hire, in Hollywood, they have to hire, like, sell- personal shopper to help them pick clothes.
[00:07:56] Greg Kihlström: Right. Right. And so let’s talk about, this a little bit more from the, the customer’s perspective as well. you know, h- what, what does this look like from… You know, so you’re, you’re using AI, and I believe you’re also using human stylists to s- to some degree, so in, in kind of a hybrid, hybrid mode. Can, can you walk us through how that process works from, from the customer experience? you know, where does the algorithm end and, and the human intuition begin and personalizing? You know, what, what does that look like?
[00:08:29] Anya Cheng: Yeah. From user experience, people pay a monthly fees, say $100. Then we will ask you a few questions. Number one questions is, is about your goals. Because for most of our customer, they don’t care they wear red or blue. Just like marketers, we don’t care am I, am I advertising on Meta or IGs or search engines. I care as long as we have high ROAS. So what-
[00:08:52] Anya Cheng: … what we do is, most important is a goal. For example, we have a customer say, “Hey, I’m going out date night.” So we’re like, “Oh, we send you a blazer.” The guy said, “You know, I’m more like hot type. I don’t need blazer.” “Give it a try,” our stylist say. By the end of date night, 9:00 PM, he walk out the restaurant, the girl say, “Oh, it’s a bit chilly.” “No worry, here’s my blazer.”
[00:09:15] Greg Kihlström: [laughs] Right.
[00:09:15] Anya Cheng: Suddenly he just scored a next c- the next date. Another guy, he, he is a sales guy. He went to a conference. So he say, “Oh, CiCi, send me something perfect for conference.” Our stylist with AI picking decide to send him a s- little p- the shirt with a small print of dolph- dolphin print. He’s like, “What?” And she said, “Give it a try.” He went to the conference, lot of people stop by, “Nice shirt.” It become a conversation opener, icebreaker, and he close the deal.
[00:09:46] Anya Cheng: So the goal for us is not just dress you up. It’s dress for people to achieve the final goal. Just like in marketers, we all say, like, we are not buying Coke, we are buying the freedoms. We are not buying Tesla-
[00:09:58] Anya Cheng: … we are buying the s- the status of that I’m a techie and I’m cool and I’m green, right?
[00:10:03] Anya Cheng: So that’s really what our customers are buying. And for using AI allow us to pick something from 30,000 garments, 150 brands, and then it can fit well with the customer goal. So our customer receive the clothes, wear for couple weeks. If they are traveling, they can change the address. It ship to, say, New York in a hotel. They wear whole weeks. By the end of week, they put into return envelope, give it to hotel lobby or any post office box on the street, then they can return without doing any laundry. By the time they go home, within a day or two, they get a new shipment already. And it’s circular, so then we always constantly learn, and our stylists double-check before the item ship out to the customer, and also being play a role as a customer service so our customer do not need to be a prom engineers.
[00:10:54] Greg Kihlström: You know, you, you mentioned a few things as far as the… I mean, there’s also just a logistics part of this too and, you know, any, any e-commerce business is gonna have, you know, some, some logistics and return management and all those kinds of things. But with yours, the, the returns are kind of built into [laughs] the model, right? So I would imagine… And, and so to do that, you know, not only are you using AI to, to personalize and, and, and, and style, but I would imagine you’re also using, data and AI to just handle the, the complexities and, and the logistics here. Can you talk a little bit about, about that part?
[00:11:34] Anya Cheng: Yeah. Logistics are quite straightforward because you just post office, sending there and back here. I do know, like, Rent the Runway, which is woman’s, side of b- company. They are in the same category. We know them pretty well because they are a lot bigger, so then they also use AI to double-check on quality assurance. Like, is this-
[00:11:55] Anya Cheng: … st- is it clean after washes? So they use al- also AI to do so. in our, in our case, are still relatively small, so that’s not yet applicable for us yet. but I would say the- E- everyone just need to remember nowadays, the algorithm era has ended. If you look at last, era was all about algorithm, the Google with the giant boxes there, with the black boxes behind, while the Netflix recommend you the best show. But the next 20 years in the era of AI were all mostly about unique data. If you don’t have proprietary data, then you’re just easy to re- replace by any large language model out there. And most of people are not going to build their own large model because people are going to use those three companies, Anthrophix and Geminis and, and Metas, and so ChatGPT. So it’s just only using these few. So think about what unique data you have. For example, you are a marketer, so you know very interesting insight about your customer. Maybe you are a dentist, so then you actually know, have the images of teeth who ha- that has problem. You are agile coach, so and you know how to mo- motivate your engineering team best and improve their burndown chart, and then imp- increase their velocity. So whatever things is harder to find on the internet become the goal that and the moat of your company.
[00:13:19] Greg Kihlström: Yeah. And so from a measurement standpoint as well, so, you know, as a, as a subscription service, you know, certainly customer lifetime value, you know, re- re- re-ups and, and renewals and, and stuff of subscriptions are, are critical. How do you measure success with personalization in terms of, of things like, you know, subscription renewals? You know, how do you, how do you kinda, tie those together?
[00:13:49] Anya Cheng: Yeah. So obviously being a subscription model, retention is important. We have pretty over 90% retentions, and our customer love us. But I think it’s the end of day, as a marketer, you wanna, you have to know what people, why they love you, wha- and what’s their 80/20 rules, right? We all know not every single customer is, equal. Like, what we have found, when we started, we thought, “Oh, oh, any mens are going to be our customer.” And turn out that we realize most of our customers are people who are socially active. Sales guy, professor, pastor, consultants, and single guy, people who have to meet people. They don’t care about how they look like, but they care getting a job, getting a date, and close a deal. What we are really selling is a chance to succeed. We are selling is a gadget guy behind a superhero. We are selling the fairy godmother, for them, and is wingman for them. So wh- once we realize it, we re- know that how we design our experience, for example, our human stylists, actually you can just text them and they will tell you what, they will help you on that. For example, you will, you want to, you, you will tell us like, “My date night is coming on Tuesday, and I’m going this Japanese restaurant. And by the way, here is my shoes if you can fit something for me.” So what we, we start turning the experience to be more like I’m taking care of the customers and helping them out and help them to succeed, and they really change our strategy by learning more about them. And of course, they increase lifetime value and make the LTV CAC CAP ratios much more, nicer.
[00:15:26] Greg Kihlström: Yeah, yeah. Well, a- a- another thing you mentioned earlier, a- and, and we talked about a little bit, is just the sustainability aspect of this too. And, you know, a- admittedly, sustainability can mean a lot of things, you know, whether it’s within different industries or, or things like that, and, and can often maybe feel a bit vague to a consumer. How do you look at measuring sustainability and impact a- as well as the, the impact on, on your customers and, and how they feel about it?
[00:15:58] Anya Cheng: Yeah. we measure the impact based on how, h- how the industry have, say, how much, less new clothes people buy. And research also show when people renting clothes or buying secondhand, they are less likely to buy new stuff. Because you probably might know today, actually, fashion industry generate 20% of polluted water in the world, and making clothes is take a lot of energy, a lot of water. So what we have found out was that people, we, we, we look at industry research and people renting and people buy secondhand, so we encourage people to buy, but only after they really love it. and on the other hand, we don’t think solving the problem is about having more people to buy secondhand because if you have f- that much junk, today 40% of clothes in the world goes unsold.
[00:16:48] Anya Cheng: 40%.
[00:16:49] Greg Kihlström: Wow.
[00:16:49] Anya Cheng: Four zero, and 30% of them goes to landfill. So if the fashion brand continue to produce junk, the world is not going to be better. But why they produce junk? They don’t mean to, because they are designing something or something two years down the road. And when they design, the only informations, you talk about number, mo- only number they use, mostly use, was last year, what are things that sold?
[00:17:15] Anya Cheng: But funny enough, in the fashion industry, what was sold last year often is really bad prediction of what’s going to sell next year. Usually it’s opposite, right? You want- skinny jeans, so definitely not skinny jeans anymore.
[00:17:27] Greg Kihlström: Right.
[00:17:27] Anya Cheng: And that’s where Taelor come in. We realize that because we offer styling service, so customer tell us their context. “I’m going on date night. This is my high-end way. And this is my occupation. I’m going to conference, and I’m a consultant. I come into a company. Can you pick me something? I don’t want to stand out. I wanna fit in. I want those engineer tell me what happened so I can fix their problem. I want those marketer tell me the problem so that I can help them out.” And so we know the context from styling service. We also, because we offer rental subscription, so we know the quality of a garment and with real feedback-of the garment.
[00:18:04] Anya Cheng: So combining the two, we know the feedback after people wearing the clothes. It’s almost like in marketings How can I run a campaign without the performance from the meta ads? I cannot optimize it. I don’t know until I write, I, I see- I run it and see the impact. The same thing in the, in fashion. We believe the rental feedback after wearing the clothes from real customer, and we know who are them, in what context when they wear the clothes, they provide feedback, is valuable.
[00:18:36] Anya Cheng: So what we are doing now is making this AI agent and model, and help fashion brand, and help retail buyer to predict what’s going to sell.
[00:18:45] Greg Kihlström: Yeah. Yeah. And so, you know, certainly there, there’s a lot of implications, or there’s a lot of things that other fashion companies and, and brands can, can take a look at here. But I, I also wonder from a broader perspective, you know, there, there’s something to be said about the idea of consumer ownership versus rental, and the sustainability conversation a- and things like that. Do you, do you see this being applied in the future to other things, even well outside of fashion? Like, is, are, are there, are there things that can be applied in, in other industries, in other words?
[00:19:22] Anya Cheng: Yeah, for sure. I think the share economy is everywhere. We already know no one buys DVD anymore.
[00:19:29] Anya Cheng: We all listen, watch the Netflix show. We used to buy the CD. We don’t own any CD. We listen to Spotify. We share that to people. Airbnb, Ubers, and in many countries now Uber now have, like, Uber Bicycle, Uber, Scooter, Uber, Schooler, and also there’s many other, like renting the, the Di- Dysons and vacuum machines, and renting the sport gears, and many more. so I think share economy is everywhere. Sometimes people say, “Oh, wear someone’s clothes?” But if you think of that, you go to restaurant, the spoon you put in your mouth-
[00:20:08] Greg Kihlström: Right [laughs]
[00:20:08] Anya Cheng: … was eaten by another 3,300 people, right? So [laughs]
[00:20:12] Anya Cheng: It’s everywhere. You, even you go to supermarket, the bag were recycle, sometimes even smells. It were recycled from junk, right? You-
[00:20:20] Greg Kihlström: Right
[00:20:20] Anya Cheng: … you go to the hotel, you were naked, and you slept on the bed which was 300 people slept there before.
[00:20:27] Anya Cheng: And it’s just where we are. The, the world is all share, assets versus ownership. We are moving into the era where people are living more, like, lazy economy, or we call it pamper economy or gig economy. Even though the Uber, the restaurant is just a five-minute drive, you still open your app and order the Uber Eats, right? So-
[00:20:47] Greg Kihlström: Right
[00:20:47] Anya Cheng: … it’s just people were so care about their time, efficiency, and wanna getting things done. For people, for example, our customer who live in New York, oh, my God, laundry is a half day. It’s three hours going downstairs [laughs]
[00:21:03] Anya Cheng: … and doing the stuff, right? Your weekend just lose 20% by doing laundry. For people who do, ha- not enjoying shopping, if you’re browsing through the site, spend two hours, that’s two hours that you can have for your kids. Um-
[00:21:16] Anya Cheng: … so what we have found was these are the trend, and we believe that’s where the co- the co- the world is going.
[00:21:21] Greg Kihlström: Yeah. Love it. Well, Anya, thanks so much for joining today. I got a couple last questions as, as we wrap up here. first one, if we were having this interview one year from today, what is one thing that we would definitely be talking about?
[00:21:35] Anya Cheng: Huh. I, I think we would talk a lot more on how we utilize data to predict the future. we are sig- expanding the area. We realize we can help fashion brand and retailer do more. we also doing a lot more partnerships. We just partner with Yankees. so I think so we are a lot of marketer knowing that partnerships is a way that… We partner with dating sites, so any dating site grow means that we grow. We partner with, PLG1 community, so anyone lose weight, they need new outfit. So as long as the community grow, then we grow. so that’s the area where, we are doing more. And, so if you are also in those area, if you’re a marketer, BD person, we love to partner with you. We also partner with Coffee Meets Bagel, Carters, Rent the Runway. And so if you wanna target busy man who have money but don’t have time, and that’s a great area where we can partner together.
[00:22:30] Greg Kihlström: Love it. Love it. And then last question for you, what do you do to stay agile in your role, and how do you find a way to do it consistently?
[00:22:37] Anya Cheng: Yeah. I think agile, we all know the, analogies. Instead of building a car, you wanna buy a bicycle, build a bicycle, because then it’s not because you will be faster. Some people say, “Oh, I just hired an agile coach. My team will go much faster.” That’s just not true, right? But what it means that you will get customer feedback sooner. So for example, you build a bicycle. You realize nobody need you, the car, because they are working from home. Then you don’t have to build a car. You get the feedback early, so then you build something that people truly wanted. for being a startup founder, that’s what we do every day, try to find the minimum viable product to testing the hypothesis. Like, Rent the Runway started, they don’t have a site. They just send an email to people, and screenshot, and sending them, like, “Would you like to rent clothes?” And those people who go to party say yes, then they validate the hypothesis that people rent it. And we use that the same thing every day, and nowaday, AI make it easy to have prototype. You can testing with your customer, get real feedback right away before spending your engineering effort. So that’s what we do all the time.
