Back To The Future (With Notes)

Have you noticed that every app and idea you encounter today seems like a rehashed, recycled, redone version of something else? As we approach the year of Marty McFly's hoverboard, we are going to talk about the current state of mobile and IoT, where the leaders will be in the next 5 years, and how we can start building the next generation of experiences for our users today. You will walk away from this talk with 4 new ideas, and a passion to be the first to solve them.

Transcript

2. I work for Microsoft. I run the Stir Trek conference here in town. (May 1, Avengers 2) I love things that can be taken apart, reassembled, and added to. That’s why I drive a Jeep Wrangler, build things with Lego, and LOVE writing apps. 2
3. I’m on the Twitter. You can get me at @jeffblankenburg. For today, make sure you add the tag #m3conf so that you can participate in the conversation. 3
4. So I want to take you on a quick journey through the promises that were made to us in Back to the Future II. The day is October 21, 2015 (That’s a Wednesday.) That’s only 334 days from today, or exactly 11 months. 4
5. We were told that cars will run on garbage in small nuclear fusion reactors. 5
6. Shoes will tie themselves. 6
7. Cars would fly! (They would also apparently time travel.) 7
8. Boards will hover! 8
9. As it turns out, they just might. Hendo Hoverboards are a real thing, and they even have developer kits you can buy to play with the technology yourself. 9
10. We were promised Jaws 19 and the realistic 3D hologram shark. 10
12. First, we’re more likely at this point to see Sharknado 19 next year than we are to see Jaws 19. 12
13. Second, 3D holograms have started allowing musicians to perform beyond their time. Freddie Mercury and Tupac Shakur are notable examples of this. 13
14. Hovercams. This seemed mind-boggling in 1985, but we’ve got this problem solved easily today. 14
15. Drones are only going to become more and more self-sufficient as time progresses. 15
16. Here’s a few more examples of how Back to the Future thought our future would look. Voice activation and wearables are both prominently featured. 16
17. You know what WASN’T a part of Back to the Future? MOBILE COMPUTING. They still had phone booths, actually. But mobile computing isn’t interesting because of the form factor. There’s lots of potential for new form factors evolving in the future.. 17
18. Phones that become headphones. 18
19. Phones that are as thin as a credit card. And transparent? 19
20. Phones that can change their shape to fit your lifestyle. This is a real Philips concept. 20
21. This was one of the first concept drawings I saw for the Apple Watch. It’s a little creepy, but I can see things like this existing the not-too-distant future. 21
22. Form factors aren’t the interesting part of mobile computing. At least not to me. It’s because they’re always on, always present, and packed with sensors that can generate tons of interesting data. It’s the data, not the device, that makes so many scenarios possible. 22
23. Form factors aren’t the interesting part of mobile computing. At least not to me. It’s because they’re always on, always present, and packed with sensors that can generate tons of interesting data. It’s the data, not the device, that makes so many scenarios possible. 23
24. Form factors aren’t the interesting part of mobile computing. At least not to me. It’s because they’re always on, always present, and packed with sensors that can generate tons of interesting data. It’s the data, not the device, that makes so many scenarios possible. 24
25. Let’s think about a situation for a moment. You’re home, alone, getting ready to go to sleep. You hear a loud noise downstairs. Your brain starts running through the possible scenarios in your head. 1) Should I get ready to fight? 2) Should I call the police? 3) Was that just the cat knocking something into the sink again? 4) Was it my wife coming home? 5) Was it the bowling alley I live above? The key to all of this is context. Your brain is incredible at this, and with some data, your phone just might be as well. 25
27. GPS started to scratch the surface of contextual content for us. It spawned things like FourSquare and Yelp. 27
28. True contextual content can only be achieved after we master four very specific data sources. 28
29. 1: SOCIAL DATA This is specifically about showing how you are connected to others. 29
30. There are lots of people trying to solve this problem, but nobody is doing it terribly well. 30
31. I was just looking for a new job at a specific company, and went to LinkedIn to see if I know anyone that works there. (I don’t.) Then, I took a couple of the names of people that seemed relevant, and I dropped them into Facebook. Only by looking through a few photos did I discover that one of these people is the sister (with a married last name) of a friend of mine. Instant connection? No, this required stalking of the third degree. 31
32. This is the Kevin Bacon game, right? Who am I connected to, and how? 32
33. 2. PERSONAL DATA This is the data that makes you unique in the world, where social data is really more about what makes us similar. Social data doesn’t mean much unless you can understand the individual, and stereotypes aren’t good enough for this kind of data. 33
34. Here’s a good example. If I were using an app to plan recipes, if it had a way to know that I generally hate vegetables, it could tailor its recommendations accordingly. Or when my friends try to decide on a place to meet for lunch, it could do its best to recommend nearby restaurants that aren’t specifically vegetarian. 34
35. Now let’s add your personal beliefs. Are you religious? Are you not? Where do you stand on scientific discoveries? 35
36. Now let’s take your political leanings, and quantify them. Not just democrat or republican. I mean let’s dive into every single issue. Where do you stand? 36
37. Finally, let’s add your core values. What is most important to you in life? What drives your decisions? Now let’s put them all up, available for the public to consume. 37
38. This is exactly how I’d expect you to react to this one, and it’s going to be a VERY long time before this truly becomes part of our data. 38
39. Transcendance came out in April, and it’s an illustration of how far we could take things with this personality data. 39
40. 3. INTEREST DATA. YOUR PREFERENCES. 40
41. This is definitely the one being most attacked. Netflix, Facebook, Amazon, Twitter, and everyone else are tracking this data all the time. 41
42. They translate star ratings, likes, and favorites into analytics. These analytics are supposed to bring you better insights, offers, and recommendations. 42
43. Amazon has a decent recommendation engine, but it is really just a re-hash of things you’ve viewed, or variations on a similar theme. “Oh, you looked at a cell phone? Here’s some screen protectors, cases, and chargers that work with that phone you didn’t buy!” 43
44. Netflix, despite their great service, has really never done a good job of cracking this recommendation nut very well. For one, you don’t have a way to remove something you watched from your history. This is a huge miss, because I’m still getting recommendations based on stuff my kids watched two years ago. Two, they seem to be doing the obvious kinds of matching, like “if you liked Tommy Boy, surely you will love every movie that starred David Spade.” Nope. These kinds of recommendations need more data than just “what I watched.” 44
45. GoodReads is specialized enough that it does an effective job. You read these ten books? Here’s a book that other people liked that read those same 10 books. The problem is that you have to be religious about adding your books to their site. There’s not an effective way to trigger a data entry when you finish a printed book from the library. 45
46. Even if they do have a good engine for recommendations based on your preferences, it’s not a good predictor of much off of the website. The books you’ve read aren’t a very good predictor of what you’ll order at an Applebee’s. Or even if you would GO to an Applebee’s. 46
47. In addition, self-reporting is FAR from accurate. 47
48. Yeah, sure I did. 48
49. This is a better illustration of what I probably did that day. 49
50. Of course I’ve read Oliver Twist, David Copperfield, and Jane Eyre. 50
51. This is absolutely all I had to eat yesterday. 51
52. I definitely didn’t have 12 pieces of my kids’ Halloween candy. 52
53. 4. BEHAVIOR DATA This is actual, verifiable data that you produce. Things like steps taken, heart rate monitored, places visited. But very specifically, this is not data that you can “enter.” It’s data that has been generated by actual events. 53
54. Just because you LIKE Pulp Fiction doesn’t mean you’ve actually seen it. But if Netflix has a record that you watched it, that counts. 54
55. We have apps that track our runs. This is actual, generated data. How fast, how far, what the incline was, where you were, etc. 55
58. So the real power of contextual content comes when you put them together. Here’s a quick example: 58
59. Your phone notices that it is noon, and you haven’t even made plans for lunch yet. You have some friends (social) that have lunch planned at a location that is near your own (behavioral). As it turns out, you have already marked several of the meals at Barley’s as your favorites, and you select the White Truffle Mac-n-Cheese (preferences). Since you’re now running a few minutes behind, it orders it for you, but of course, tells the restaurant to hold the tomatoes, because you hate tomatoes (personal). 59
60. Our next section is mobile moments, or instant gratification. We spend so much of our time going here, doing that, that sometimes we don’t get to do the thing we wanted to do in the first place. 60
61. Here’s a good example: going to a baseball game. You bought a ticket to watch the game, and the game is good right now. But you’re thirsty. It’s a hot day, and a cold beer sounds really good. If only you could just tell the “beer guy” you want a beer at your seat location. Maybe you could even pay for it on your phone, instead of having to lug a bunch of cash out with you. 61
62. The San Francisco 49ers just built a new stadium, and a new app that solves this exact problem. You can order food, check lines for a long wait, save your tickets, pay for parking, even explore what parts of the stadium you’d want to visit before you ever get out of your seat. 62
63. I’m a little concerned about their NiNerds program. Are regular people only comfortable with getting technical help from people that have thick rimmed glasses, bowties, and pocket protectors? 63
64. It’s the same concern I have for Best Buy’s Geek Squad. 64
65. This is taken directly from their brand guidelines at Best Buy. 65
66. Uber is another great example of this. I need a ride NOW. Boom. Done. 66
67. Has anyone ever done this with Chipotle? You walk up to the restaurant, notice a line out the door, and just order it from your car? I’ve skipped that line plenty of times thanks to their mobile website and app. 67
68. Another company that really gets this is Disney. From their app (while you wait in line), you can make dining reservations, schedule FastPasses, find characters, see wait times for rides, even locate your family and friends. 68
70. October 20, 2014 Mobile payments aren't any better because of Apple Pay. Paying at a terminal is something we've been doing for decades. Find a way for me to pay a stranger, on the street, and you've got yourself a deal. Now find a way for me to pay the sherpa that helped me climb Everest, while we're at the top. No signal. Just devices. 70
71. September 19, 2011 71
72. June 20, 2012 72
73. I want to turn this wallet… 73
74. Into this wallet. Not just so I don’t have to carry a wallet, either. It solves a TON of practical problems. But we can’t do it yet. 74
75. Find a way for my phone to act as my identification. I went to an AT&T store recently, and wanted to swap out my SIM card for a different size since I had purchased a new phone. COULD. NOT. DO. IT. But I called their Customer Service line on my way home, and they had no problem. 75
76. Insurance cards. You need to carry proof of auto insurance in Ohio, and you probably want to carry your health insurance information with you too, just in case. Why do I need a physical card for this? GEICO has the right idea. 76
77. Save all of my rewards cards, so I don’t have to carry them around on a big metal hoop. Why does everyone want me to have their card? Why is it still easier/cheaper/etc. in 2014 to design, manufacture, program, distribute, and swipe physical cards than to just have an app? 77
78. KeyRing and CardStar each do a great job of this. 78
79. Create a way to make it so that I don’t have to carry cash or change ever again. 79
80. ESPECIALLY in Europe. Man, those folks LOVE their coins. 80
81. Give me a way to give a bellboy a tip without exchanging information of any kind. I don’t want his phone number, his name, his email address, or anything else. Tap, select a tip amount, and go. We’re done. 81
82. Same goes for tip jars, charity collections, and the dozens of other places that just popped into your head. Payments need to be done everywhere. Not just the terminals we already have credit cards for. 82
83. Now let’s take it one step further. Let me do all of this when I don’t even have a connection. 83
85. Ah, the things. We all of a sudden care about watches again… 85
87. We can now change the temperature of our house from anywhere. 87
88. We have lightbulbs that not only connect via wifi, but that can be color-changed to match any mood, any beat, at any time. 88
89. We’ve even got systems that help us manage all of these devices from one place. 89
90. This is a map that was created to reflect every internet connected device on the planet. But the internet of things, sadly, isn’t even about the internet. It’s about more, and more, and more sensors. Everywhere. Detecting everything. 90
91. Imagine having moisture sensors in your lawn. They can tell your sprinklers when to turn on. But only before checking the weather forecast and historical weather data. 91
92. New pets can be hacked too. Embed a sensor in your dog’s collar (or maybe just in your dog?) that alerts you if they leave the yard. Cleaning up the backyard? Retrieve the last two days worth of “pauses” your dog took while outside. 92
93. While you’re at the store, your refrigerator notices your children drank the last of the milk. Instant alerts. 93
94. While you’re shopping, your cart is calculating your total, comparing it to your shopping list (that was dynamically generated based on the sensors in your fridge and pantry), and even checking you out. No lines. 94
95. There’s stuff for workers, too. For those of you that live in Columbus, you’ve seen this when the weatherperson mentions snow. Smart shelves could solve several problems: 1) Alert the stock team that the shelves need to be restocked. 2) Modify prices on water, bread, and other necessities as the demand rises. 3) When customers that have an item on their shopping list walk by, the shelf could beam them a coupon for a specific brand of that product. 95
96. Thankfully, this talk is about the future. We don’t know what it will bring, and it’s surprisingly hard to predict. 96
97. Bill Gates has an interesting perspective on this… 97
98. “There’s a tendency to overestimate how much things will change in two years and underestimate how much change will occur over 10 years.” 98
99. So we find ourselves at this interesting fork in the road. If we take the road on the left, we can just continue to follow the patterns that have become so familiar. Gather data, show reports on that data. Status quo. If we take the road on the right, we start thinking about what makes our users’ lives better. More data, more insights. More experiences. But there’s lots of risk. We’re probably better off taking the advice of another brilliant man, Dr. Emmett Brown. 99
100. Roads? Where we’re going we don’t need roads. 100