Kathryn Hume: Chapter 3: Feedback Loops

M-A-C-H-I-N-E-Y-E-S-R-X-I-S-G.

It's Machine Yearning from Assist. It's a podcast where we think and dream about the future of AI, the talking internet, and how we're reshaping our culture. This chapter is an excerpt from our longer conversation with Kathryn Hume. Kathryn Hume came to us via Robin Sloan, a great friend of the pod. In this chapter, Shane and Catherine dig into feedback loops and how they help brands finally build real genuine relationships with a community of customers. Catherine works at Integrate AI now and has done product marketing, investment, and mentored startups. We loved our time with her and we think you will too. Enjoy.

Take capitalism and business and how it impacts communicating with your customer like we're communicating as two people who have found meaning. How do you find meaning with your customers?

It's a great question. I actually do believe that sort of the big shift that's rendered possible with AI is that customers now give feedback and can give feedback and can be listened to even by their actions and transactions in a way that really wasn't as focused on in technology systems that just sort of push stuff out. What's neat about new products, like I love Spotify, is that they have this very simple interface where we can say plus, thumbs up, thumbs down, and shape the set of choices that will be presented, like the experience that we have in the future.

So that's fascinating. That's a great example of AI being used very well for good.

Oh, for sure.

My weekly playlist on Spotify is amazing.

Somebody asked me recently in terms of how AI is going to impact sort of the human experience, they were like, is it gonna take away self-determination? I was like, oh, actually they also said A, self-determination and B, serendipity. So like, you know, the experience, sort of a nostalgic experience where you walk into a bookshop and you think you're going in to buy Oscar Wilde's Portrait of Dorian Gray and you sort of browse around and find something that you never would've discovered otherwise. And that's great. Like I'm all for that. But I think Spotify does that because of the Discover Weekly where, you know, we don't have a lot of time go on and search for new music unless you're really a diehard and you're reading Pitchfork every Monday or every Tuesday.

So if I'm a brand in the world, how should I think about language? Or how should I change my language? Or what do I need to do?

I mean, I think it's about that feedback loop. So you need to open up the ability to listen to customers as individuals. And here it's like— It's funny I say that, but my mind immediately goes, like, this doesn't mean that it's become creepy and collect every single solitary piece of data about somebody in every aspect of their life so that you know them better than themselves and can, like, do creepy stuff with data. But rather, understand that, like, you might be underserving certain populations if you go in with your expectation that, like, everybody works this way or sort of these ossified approximative stereotypes on what people might interested in, if you can get some vehicle, it has to be easy because no one wants to fill out a survey. Nobody wants to do extra work. They just want to be delighted. And also what I love about design thinking and applying this with AI is that when you think about offering a product, don't view it as this is the product, but rather what does it mean to experience this product? So Warby Parker is actually really great at this where they don't sell glasses. They sell the end-to-end experience of what it means to fix your vision and include in the customer experience everywhere from being able to try on a couple of pairs to figure out what works best to— I think they have some— I actually don't— I'm not a Warby Parker customer myself because my eyes are very close together and standard eyeglasses look horrible on me.

Interesting.

So I have to go to kids' glasses shops, which sucks, but it is what it is. But they just contextualize in the full experience. So I think that there's— really this magical opportunity with AI. We tend to think of it as like it's gonna, you know, there's fear of job automation and like everything becoming like sort of the realm of the robots. But if we reconceptualize the tech as what I consider to be the connective tissue between humans who want to love one another as individuals, or like what it means for a company to really love a customer, and loving them means, empathizing with their experience and different people's experiences. I think it's like a wonderful way to view these tools.

And brands are driven by attention.

For sure.

Capitalism is. So when they can deploy AI, and if attention is the game and it's focused on that, is it going to get bad?

I mean, for me, this goes to long-term versus short-term. So if the brand is optimized to get short-term revenue wins and attention and hacking attention is the hook there, then they're going to optimize for that. If, on the other hand, there starts to be sort of a— consumers don't want that anymore and the brands really need to focus on their long-term loyalty—

Relationship.

—and relationships with people, then for me, somehow it has to make sense. And of course, I don't want to be too overly idealistic. There needs to be the right balance between— P&L holders and businesses that have near quarterly revenue targets and fiduciary duty back to shareholders. And this is how the system is all structured. But we're starting to see things fray a little bit at the seams. What are some examples? So gosh, even in the company that I'm working with right now, there's a notion in sort of thinking about working with data and consumer enterprises of lifetime value. So you want to maximize the lifetime value from a customer. So it might be that somebody comes in and they spend like $700 on day 1. And you're like, ooh, interesting. Let's focus on this person. But then you notice that they spend you spend once and then they're just gone. So their lifetime value is exhausted from that first transaction. And others where it starts off kind of slow, but it builds over time. And you got that, it's almost like a one-night stand versus a long-term enduring relationship. Awesome. Yeah, for sure. Amazon Prime actually is, it's a brilliant mechanism to ensure customer loyalty. And what I've heard, and this is a little bit of just, it might be apocryphal, but All of the features like Amazon Video that you get as a Prime subscriber, those are tricks to get people on Prime. It's not like a service in itself. It's an additional benefit because what they really care about is having that subscription because that leads to the correlation between that being a leading indicator of stickiness with the customer is so tight that they're just like—

The best business is subscription. For sure.

And once you've already invested that sunk cost, And you get all of the benefits of it's going to arrive in 2 days, the amenities that go along with being a Prime subscriber. Of course. Now, does that mean that it's really in the customer's best interest? Good question. It certainly is the case that as you think about why Amazon is the business that it is, that they invest in long term. They're thinking about when they may take an action today, it's not oriented towards short-term profitability and horizons. And this is a lot of the sort of companies that— large Silicon Valley companies where if you look at their balance sheet, it's so much startling that the valuations are so high, but they're sort of playing a different game than most other companies are governed by.

If I'm a brand, and even where we in our past got taught is to have the answer and raise our hand and get an A and not really be cool with saying I don't know. In this space, how is a brand who's probably not cool with a bunch of errors, cool with errors.

They struggle with it. You know, it's—

That's why people struggle with how to even understand to implement AI or bots or whatever.

From my take, it's like the biggest cultural challenge is getting comfortable with making mistakes, learning, having the output of a system be a probabilistic guess that may or may not be right and being OK with that.

Especially because software actually was built on the internet in a way where it worked or it didn't.

Yeah, for sure. People are used to also even in agile product development being able to know how long it takes to build X or Y widget. When you move into AI and you're like, we're going to do some experiments. We start with this hypothesis. We get some data. It's like, nope, it didn't work. There's basically re— it's almost being more like an investor where you spread your bets across multiple companies knowing that 9 out of 10 are going to fail. But there could be the opportunity for one to be really great.

Focusing on long-term opportunity, knowing that errors and failure is a big part of the new world.

It's part of it, and it's the only way to learn. Also, even as humans, you learn a lot more from the mistakes you make. And it's helpful when the end of the story is positive. If you're just always failing, it's not really the best recipe for well-being, mental health and well-being. But— Go ahead. Yeah, but if there's no— If you don't make mistakes, you learn the most when you try something and it's not as great. You can sort of refine over time.

What are the 2 or 3 words that you think, as if I was a company, long term I should focus on to get me started today to walk into this space?

So I think a lot of it is going to depend upon data. So imagining what could be possible and then ensuring that you're designing interfaces to collect really great and interesting data to help solve that problem. What do you see as the best interface? So I just did a podcast interview on my podcast with my friend Eric Colson, who is the chief algorithms officer, which is such an interesting title, for Stitch Fix. And so Stitch Fix is this— are you familiar with it? It's like this— Explain it, though. Yeah, it's an outsourced personal shopper company where it's an e-commerce play. And you can come in as a user, and you just sort of input information into 60 questions. And then that's it. They send you clothing. And you can get 5 items per month, 5 items per week, however many you'd like. What's really fascinating about what they've done is they've actually— they get a lot more data about people. So they've designed this interface where it's not just their transactions, like what you bought, but they've got like people measurements, size, taste, right? So it's a much richer collection that does require that upfront effort from a person to come in and sort of fill out the initial information. But that effort is then offset by the fact that going forward, they don't have to shop anymore. You spend 15 minutes to get rid of the shopping entirely. And in the process, they're getting a totally different rich set of information about people and their preferences and what they're interested in that a typical e-commerce play doesn't have. Fascinating.

So because of the relationship channel that's one-to-one, it's not a bad idea to make me work up front, as long as you deliver on the promise. It's an interesting trade-off.

Because anytime there's friction and there's a little bit of work up front, it's like, it's going to be you're going to lose some customers because they're going to get to that interface and be like, screw this, I'm not doing it.

But then your value is in the relationship because that's the only thing that keeps people coming back. Yeah, for sure. Yeah. Interesting. It's even thinking about a hotel. I hate when I have to rebook again and re-sign it. Everything you have to do again, it's almost you got to get me to never have to do anything.

Oh, for sure. I mean, they know you well enough to know a little bit about your preferences. And you've set this thing. And each time you stay, it should become easier to stay the next time.

That's a good motto. Every time the customer interacts with you, it should be easier and more delightful. That's right.

Yeah. I think that's a good way to think about it. And then that flip side of getting there is knowing that there's going to be some error. It's not going to be perfect every time. But in the aggregate, overall, every time the customer interacts with you, it's easier and more delightful.

How do you interact with the customer if it is an error? Do I just tell them, hey— is it more like what we're trying to do as humans, like more direct, honest feedback? We're really sorry, we just don't know that answer yet, but we'll let you know when we do.

I think that's the way. And then you say, well, how do we get there as a culture, right? Where, what do we have to do in terms of educating everybody about how this technology works so that we can have more honest conversations via, like, AI interfaces to be like, we made a mistake, we're really sorry, you know, here's why, what can we do differently next time?

We need to reprogram ourselves to be cool with making mistakes.

And that goes not only at the company level, but even at a— think about when— I mean, I'm in sales meetings all the time. And I've always found that it's a great way to gain the prospect or the customer's trust by saying, I don't know the answer to that. I'm going to go home and do my homework. And it almost legitimizes then everything else you've said because then it's like, everything you've heard here to date, it's right. I'm not faking. Going back to the beginning of our conversation, How do you know if this is sarcastic or real? It's like, one way is to express vulnerability and be upfront with areas where you can't, you don't have the right answer. It's the key to trust. Yeah. And I think trust is a huge thing right now between consumers and brands. Thank you very much. Thanks for having me. It's fun. All right.

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