The World is your Storefront: How Artificial Intelligence Creates Frictionless Shopping | Bazaarvoice Summit 2014
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Watch Lisa deliver this keynote from the Bazaarvoice Summit 2014 at: http://www.bazaarvoice.com/live/summit/sessions/future-of-branding-internet-of-things.html
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The World is your Storefront: How Artificial Intelligence Creates Frictionless Shopping | Bazaarvoice Summit 2014
- 2. #bsocial14 The world is your storefront: How artificial intelligence creates frictionless shopping Lisa Pearson @lpearson
- 3. #bsocial14 Welcome to 2020 Meet Samantha
- 4. Trade data for value #bsocial14
- 9. #bsocial14#bsocial14 Comfort Style Quality I love these heels! I get so many compliments, especially with jeans. New favorite shoes! Great for a night out, but a little too uncomfortable for all-day wear. -- BVLisa Nude Pumps
- 10. The many versions of you #bsocial14
- 11. #bsocial14 Book this hotel? You may like: The Sheraton Aspen I love this hotel! My son swam all day in the heated pool. They even had delicious gluten free waffles! I'm glad the Sheraton uses green cleaning products. SoccerMom1979, Los Angeles, CA #bsocial14
- 13. The world is your storefront #bsocial14
- 14. #bsocial14 Find similar? Ballia Dress in Spiaggia $295 This dress is perfect for work, and it still accents my shorter frame. JAlmando, January 5, 2014 #bsocial14
- 15. #bsocial14 Purchase? Pleated Poplin Dress Average rating: $59.95 #bsocial14
- 16. #bsocial14 ALERT: You’ve spent 80% of your clothing budget. Clothing Restaurants Transfer Funds? #bsocial14
- 18. #bsocial14 Trade data for value The many versions of you Endless feedback loop The world is your storefront
- 19. #bsocial14 Fireside chat Introducing… Larry Vincent UTA Brand Studio #bsocial14
- 20. #bsocial14 #bsocial14Lisa Pearson Bazaarvoice @lpearson Larry Vincent UTA Brand Studio @lvincent
Editor's Notes
- L: “Good afternoon!” S: “Hello Lisa. I’m Samantha, your OS. Welcome to the year 2020. I’ll be here when you need me.” L: “Thank you, Samantha.” Samantha is my talking, artificially intelligent operating system – my OS. Samantha is my personal assistant, the gatekeeper to all of my data. She knows everything about me, and connects me to everything in my world. We got a glimpse of this technology in the movie Her. In the movie, Joaquin Phoenix falls in love with his operating system – his OS. In other words, he’s got the hots for Siri. If you haven’t seen the movie, all you really need to know is that the OS of the future will know everything about us, and talk with us in a remarkably human way. Here in the year 2020, everything will be a computer – shoes, jewelry, cars, appliances And all of these computers, Samantha included, will be connected in a global network called the Internet of Things – which will track every single interaction we have. We’ll have a “data self” – a comprehensive collection of everything there is to know about me. My likes and dislikes; what I buy, watch, read, eat, listen to, wear, drive; it’s all part of my data self, collected and analyzed by the Internet of Things. And Samantha is my liaison to this network.
- Back in 2014, people were waking up to the power of their data – but were reluctant to share it with brands. Here in 2020, we all get the value of our data, and we’re happy to trade parts of our data selves to the brands we trust – as long as we know we get something in return. I’m an avid Nike+ user. While I run, I check in with Samantha to see how I’m doing.
- S: “Let me pull up your Nike+ stats. Well, you’re down a dress size since 5 weeks ago, congrats! But that House of Cards marathon this weekend (and the Girl Scout cookies you ate with it) set you back a little in your weight training.” L: “Damn those Thin Mints.” S: “Nike says you’re a bit behind schedule to reach your target size before your niece’s wedding next month.” L: “What do I do to hit my goal?” S: “Well, Nike recommends we increase your steps by 3000 a day, and take away one cheat day on your meal plan. And here’s a challenge: If you hit that new steps goal, Nike will loan you a pair of the new Nike Air 2020s. They’re not out until later this year.” L: “Nice. Remind me of that when I go running this weekend.” S: “Will do. And since the wedding is in Aspen, Nike would also like to lend you some skiing gear. I’ll ship it to your niece’s cabin.”
- Now step back. Think about all of the personal information I’ve shared with Nike. What I eat – even when no one’s looking. They know my fitness goals, how often I exercise, my reason for trimming down, and my deadline. This data is collected by wearable tech in my clothing, and even embeddable tech that’s literally inside my body, microbots tracking all of my physical data. Why would I want to share all of that information with Nike? Without analysis, the information my body generates is just data. I can’t act on it. It doesn’t make my life better. But I’m willing to trade that data to Nike in return for something that does make my life better. A personal fitness coach who reacts to my lifestyle in real time. And what’s in it for Nike? Well, at a macro level, all that data on all Nike+ members gives them enormous insight into the fitness habits of their customer base as a whole. At the individual level, Nike gains an intensely personal relationship with me. In 2005, Nike was sort of part of my fitness regimen – I wore their logo and identified with their ads. In 2014, I used Nike+ to track my physical activity in a limited capacity. But in 2020, Nike isn’t part of my fitness regimen; it is my fitness regimen. I have a conversation with Nike every time I work out. When I do hit those new goals and win that discount, all of the euphoria I feel is tied to the Nike brand. And this is the future of loyalty programs. Rewards will be less transactional, as in “buy eight cups of coffee, get one free.” Instead, they’ll be creative and personally relevant. Nike knows that I’m not really motivated by discounts and coupons and am much more excited about trying their new shoes before they’re out in the market.
- Let’s talk more about being a “member” of a brand, like Nike. In 2014, my “membership” was pretty limited. I would write product reviews, but then never talk to the brand again. In 2020, I talk to brands all the time! I’m literally a member of the brands I use most; stores, hotels, restaurants, and so on. Samantha facilitates these ongoing conversations. S: “Sorry to interrupt, Lisa, but you’re shifting from foot to foot. Are you uncomfortable?” L: “Oh, it’s just these shoes. I loved them when I bought them.” S: “I remember. You gave them a five star review.” L: “I love them but I can’t wear them all day.” S: “I’ll let Nordstrom know…”
- [A pinging noise sounds, like getting a text.] Nordstrom: “Hi Lisa, I’m digital representative for Nordstrom stores. We’re sorry to hear that your shoes aren’t working for all-day wear. From now on we’ll send you size 7s in all heels. I also sent your feedback to the manufacturer, suggesting they look into their sizing. Would you like me to send you a replacement pair in size 7?” L: “Could I try a different style?” Ms. N: “Of course. I’ll take a look the clothes you own, patterns and fabrics you like, and your 5-star rated shoes. Your ability to stand in heels is… less than ideal… so I’ll use other customers’ comfort ratings to find the perfect pair for you. Your new heels will arrive at your home this evening!”
- Now that’s the personal touch you’d expect from Nordstrom. But in 2020, it’s scalable and real-time. The digital Nordstrom rep is just another OS, like Samantha. So it’s capable of analyzing millions of pieces of data while serving thousands of customers simultaneously And because I’m a member of Nordstrom, I share all of my product feedback with them in real-time. My review isn’t a one-off, static piece of feedback. It’s a living review, which changes every time I wear the shoes – noting that they look great with jeans and are good for going out in but not so great for wearing all day.
- In 2014, we were very reliant on others’ opinions and feedback. We used word of mouth from our friends and reviews from strangers to try to predict what our own experience would be. Reviews answer the question, “Do people generally like this?” In 2020, The question we really want answered is, “Will I like this?” For example, I still need to book a hotel in Aspen for my niece’s wedding. I’m a Starwood Hotels member, and she recommended the W.
- S: “The suites at the W Aspen have a four star average rating. But I think you may want to try the Sheraton. People like you seem to love this hotel.” L: “People like me, Samantha?” S: “Women who watch Modern Family, own a Volvo, do Pilates, and shop at Lululemon. They rate the Sheraton highly for its low carbon footprint and gluten-free meal options. Parents also rave about the indoor pool, and when you’re with your kids, I know the pool is important to you. And while guests do love the W, it’s near the ski resort and has more of a late-night party scene. The Sheraton is just a better fit for your family.” L: “Alright, I’m sold. Book it.”
- Samantha’s recommendation really is right for my family. It’s based on opinions from the people most “like me” – and I don’t even have to know what “like me” means. My data lets Samantha know me better than I know myself. She also knows that there are different versions of me – different “Lisa’s.” Samantha knows that my preferences when traveling with my family are different than when I travel alone. She knows when to reference which data in order to help me make the right decision. And then, most impressively, she’s able to find correlations in the data that aren’t evident to me. As irrelevant as the music I listen to or the TV I watch may seem to a hotel decision, we have no idea today what our whole data selves may say about us as consumers. Samantha knows that Volvo-driving, Lululemon-wearing moms tend to be picky eaters, hence the emphasis on gluten-free dining. In the future, we’ll be able to draw correlations from such big data – and it will lead to more relevant experiences. Consumers won’t be bucketed by age, race, persona – scalable personalization will remove the need for demographic guesswork. In other words, in 2020, demographics are dead.
- So far, my conversations with Samantha have centered around brands I’m already a member of, products I already own, needs I already have. But what about those “top of the funnel” moments – new products I don’t yet know I want? What will casual browsing look like in the future? For example, I’m looking at a woman near the front in a great dress.
- S: “It’s Theory and would look great on you. It comes in other colors too.” [The dress appears with product page type info (star rating, price, etc.) overlayed on a real-world background.] L: “That dress is so cute, Samantha, but I’m not sure if I should treat myself right now. I did just splurge on those concert tickets last week.”
- L: “That dress is so cute, Samantha, but I’m not sure if I should treat myself right now. I did just splurge on those concert tickets last week.” S: “Here’s a selection of similar dresses from other retailers. This one’s a great deal. It’s 4 stars, and women love the fit. The downside is that it’s a poly-blend, not cotton, so reviews suggest it may start pilling after a few washes. But for the price, reviewers still agree it’s an excellent value. Interested?” L: “Eh, no. I’d rather pay more for quality… Maybe I will just hold off.”
- [A pinging noise sounds, like getting a text.] S: “Chase has an update on your budget. You haven’t eaten out much recently – you’re at 60% of your dining budget, and Chase says you’ve under-spent there the last two months. Want to move some funds around?” L: “Yeah, you know what, I’ve been good lately. Let’s get the dress in red. Keep an eye on my dining and entertainment budget though so I don’t go over.” S: “Of course.”
- See that? The world is my storefront. With image recognition, Samantha can instantly find the exact product and brand I’m looking at. I didn’t have to ask the woman where she got her dress; Samantha pulled it up for me the moment it caught my eye. So really, I can be “window shopping” all the time. And this time I saw the dress live, but I might’ve also seen it while watching Scandal, or on goop.com. Image recognition will make everything shoppable – both the real world and all media. Even better, Samantha can find comparable products from anywhere online. Because Samantha doesn’t just know everything about me – she also knows the product catalogue of the entire internet. She quickly found dozens of similar dresses, at different price points and levels of quality. And she helped me manage my spending, using data from Chase. Samantha just made my shopping smarter and insanely efficient. There’s no way I could do all of that on my own.
- Data stewards trade data for value. We’ll trade our raw data to the brands we trust, in exchange for a mutually beneficial relationship, like I have with Nike. Personal, real-time service at scale. Our future reviews will evolve every time I interact with a product or service, and the brand will be able to respond in real time – just as Nordstrom did. Your data knows the many versions of you. Our OS will know which opinions to reference, at which times, in order to answer the question, “Will I like this?” Just as Samantha helped me choose the Sheraton. The world is your storefront. Innovations like Google Glass will let us shop anywhere, any time – and with Samantha, we can shop the entire product catalogue of the internet, all at once.
- These applications are such a tiny spec in the possibilities artificially intelligent systems and the Internet of Things will bring to the consumer-brand relationship. So with that, I’ve got to say goodbye to Samantha. S: “Goodbye, Lisa. See you in 2020.” And in her place, I’d like to introduce __________. [Quick bio on the fireside chat participant(s) to kick off the discussion.]