495. Chris Pearcey

Email: bidemiologunde@gmail.com

In this episode, host Bidemi Ologunde sits down with Chris Pearcey, founder and CEO of decisio, to explore how personalization can reduce decision fatigue without manipulating people. Why do so many recommendation systems feel more tuned to ads and engagement than actual user intent? Can explicit preference signals beat passive behavior tracking? And what would it look like to build discovery tools that are ad-free, privacy-forward, and genuinely useful? Chris shares lessons from forecasting at Nike, the thinking behind decisio's four-way swipe system, and a practical playbook for designing technology that helps people choose with more clarity and less noise.

Transcript

SPEAKER_00 0:45

Okay. So thank you for joining me once again on another episode of the Bait Picture Podcast. I have a special guest from Oregon in the United States. Over to you, sir.

SPEAKER_01 0:57

Yeah, thanks for having me. I'm Chris Pearcy. I'm the CEO and founder of Decisio Media. We're an app that's uh that I we I created to help um eliminate decision fatigue. Right now we have movies and TV shows, and soon we're gonna have books, podcasts, tabletop games, video games, ultimately leading to the uh the North Star of having a social app uh that'll actually connect folks based on all their common interests, whether it would be to watch movies together, read books, or date. Wow. Wow.

SPEAKER_00 1:22

So take us into the moment where you basically hit this endless scroll wall. So you personally, you hit the endless scroll wall, and what did it cost you in time in terms of time, mood, choice quality, and so on?

unknown 1:42

Right.

SPEAKER_01 1:43

Yeah, so I I I the the specific moment I was laying on the couch looking for something to watch, like we all do. Um, and and so I'm gonna throw a couple stats at you while I talk about the story. So 32% of all people give up on searching for something to watch. This is a psychology number. This is not numbers made by the industries. This is psychology uh from Harvard, uh Harvard School of Psychology. 32% of people give up on something to watch. I fell on that trap that night. I gave up. I was frustrated. So I went to the office. I decided I wanted to hang out with Jim and Pam from the office. They're my go-to when I hit that wall. And then I grabbed my phone like we all do, and I started scrolling. But the good news is that night I found an article that was very interesting. I didn't do scroll, I found something good. It was an article about folks that had met their significant others on dating apps. And it asked the question what did you miss the most about the dating apps after you met your partner? 52% said they missed swiping, swiping for decisions. So at that moment, I was like, wait, why don't we have this for movies? Like if we're swiping the date, why aren't we swiping to find what we like in on other aspects? So that's when I came up with the idea. But initially I was thinking through and you know, very simple swipe, left and right didn't really work. Um, but then I started as I started thinking through this um with my because my background is analytics with databases. I was like, well, four-point is actually might be enough data points. So then I came up with the idea of a four-point swipe. Um left and right, I've seen it. That's my that's my taste. I liked it, didn't like it. But then this is the differentiator, the up and down. Up and down means I either want to see it or don't want to see it if I haven't, and that then uh that gauges my intent. So we were creating a new rating system that that that intersects rating and or sorry, taste and intent, which none of the other rating systems are doing out there. They're all based on what you thought of something after you watched it. Um, and even then, that's so, so um ambiguous. If you and I both rate a movie as a 10, what does that mean? It's first off. Like what like how do we determine a movie is a 10 or a 7 or a 6? But then if you and I both loved it, but then 27 other people had no interest in seeing it, what is that really saying about the content?

SPEAKER_00 3:48

Wow, wow. So when you say decision fatigue in entertainment, what are some drivers that actually factor into that? Um, high-level two to three drivers that matter most for decision fatigue.

SPEAKER_01 4:07

Yeah, so one right now is uh the fact that um there's 250,000 active titles on all the streaming providers. 250,000. When I was a child in the 80s, I had uh six options of what to watch at any given time, right? Not 250,000. So um so we're in an age right now where we have unlimited, um, unlimited opportunity to find things to to entertain us. So because of that, we get so overwhelmed. And Netflix actually does that by design. Um, that's actually part of their platform. Uh they have 80 uh uh 8,400 titles currently on Netflix. You wouldn't know it. Uh you would think they have about 100 titles because they recycle the same hundred titles to you with different covers. So they're so they're they're trying to make it a little easier, but they're still not because they're not giving you true choice. They're letting you choose choose from what makes them money. So again, decision fatigue, because if you you you you people are noticing this. So then they have to go outside and try to find it elsewhere. And then you then you start becoming uh the fatigue kicks in when you're trying to search on the internet for what to watch, because AI is not right half the time when it's telling you where to go. Um, there's so many choices, and no, none, none of these systems know you. So then you start to get frustrated. Uh so then we do so then everyone goes to you know goes to social media, and then you get really, really frustrated by what you're seeing on social media because today's climate has become so divisive on social media by design, because that's what sells. Uh divisiveness is what makes money you know for these social media platforms. So, you know, it's it's all it's all a vicious cycle by design to to help us um uh walk away from from the control we think we have and let them choose for us. And that's frustrating in itself because then we won't watch.

SPEAKER_00 5:43

Right, right. So invariably more options lead to less confidence. Am I correct?

SPEAKER_01 5:50

Yes, 100%, 100%.

SPEAKER_00 5:55

I guess there is some paradox named after that. I believe it's the choice paradox or something. No, I think yeah, you're spot on.

SPEAKER_01 6:03

Yeah, yeah, and and so all I'm trying to do is just create a little delightful fun experience, something that's gonna get to know you, who you are, what your tastes are, what your desires are, and help you decide what to watch. We we added a new feature this weekend called Battle Mode. So even then, when you have your your watch list and you're like, well, I don't want to get a specific recommendation, so go to my watch list. You hit battle mode, it's gonna take eight random movies, and they're gonna you can battle them out to a winner and then just watch whatever just whatever wins the battle mode. Yeah, trying to have fun with it, trying to make it fun again.

SPEAKER_00 6:33

I I want to assume you have a gaming background.

SPEAKER_01 6:37

I do, yes. Um, I actually have uh um I don't know if you can see it, yeah. The old Nintendo controller. I have every Nintendo console in my house too.

SPEAKER_00 6:46

Nice. Yeah, I grew up with um the Sega Genesis Sega Mega Drive. Yeah, yeah. Oh, yeah, the Mega Drive, yeah, yeah, yeah. So um you said there is a difference between personalization and manipulation. Where is that fine line between the two? If there is a fine line, yeah.

SPEAKER_01 7:09

I I don't know if there's a fine line. I think there's a lot of gray area there. I think there really is. I think the the industries are trying to not show it off. I think uh, you know, they they want to hide it because they want don't want us to realize that uh that they're they're taking that away from us, that that choice. Um, but I think Rotten Tomatoes, unfortunately, has made some mistakes in the last couple years, and people are starting to see it. Um, Rotten Tomatoes is the worst of the worst for this because they are now owned by Fandango, and Fandango is owned by Universal Pictures. Wow. Um yeah, so if you think about that, right? So the the the that documentary that recently came out about the First Lady, we won't talk about the politics of it because that's not what's important. But they but we've seen the uh the the score ratings. It is it is the currently the highest rated movie ever on Rotten Tomatoes, but it is one of the lowest rated movies ever on IMDB. But so where's the truth on that, right?

SPEAKER_00 7:59

I actually don't know anyone who say they've watched that movie.

SPEAKER_01 8:03

Well, come to find out, Rotten Tomatoes uh put a press release out about this, and they they were very specific about their verbiage. They said that every rating was by someone a confirmed ticket purchaser, not a ticket, the movie viewer, but a ticket purchaser. So now they just opened up a whole new world of bots buying tickets. Right so they so they can review them and inflate scores. So now the studios know they can use Fandango to buy tickets and inflate their own scores on properties. They just told the world this by by by admitting that.

SPEAKER_00 8:33

That's crazy.

SPEAKER_01 8:34

So now we'll never know on Rotten Tomatoes what we can trust ever again.

SPEAKER_00 8:38

Huh. Because I used to believe Rotten Tomatoes was the gold standard. If it says 91% or whatever their rating is, then it's a good movie. Apparently, it's been messed with.

SPEAKER_01 8:51

It's been monotonic monotonized. Oh, I can't say the word. Um, but money is money is the root of this now because Fandango is incentivized to sell more tickets because Fandango now sells over 90% of all tickets in the United States for movie theaters. They're they they they they have got exclusive contracts with all the major movie theaters, and they've forced all the major movie theaters to close their affiliate programs down so no one else can do affiliate programs now. So they have a 90% market share monopoly on ticket sales. So the more a blockbuster or or even a documentary in this case, the more money it makes, the more money they make. So so uh so Rotten Tomatoes is incentivized to inflate these scores on things that are gonna help make them money. Um, so again, that that that that line um is very blurred now because everyone used to trust Rotten Tomatoes, but now we you know, in some cases, you know, a movie that's you know a low-budget movie, independent movie, you're probably gonna be pretty accurate on the ratings. But anything that is of any substantial uh property now is not gonna be in my opinion, I wouldn't trust Rotten Tomatoes now with their ratings. Even the critics are you know, they the critics work for certain publications who are also incentivized to push certain properties. Wow, wow, yeah.

SPEAKER_00 10:04

So that means whenever I turn on Netflix and I see that top 10, and I'm thinking, well, this has to be what everyone is watching. Maybe not exactly, right?

SPEAKER_01 10:17

Well, they might be watching it, but they might be watching it while it's not in the background. Uh and here's the fun part. Think about this. How many times have you fallen asleep during a movie?

SPEAKER_00 10:24

All the time.

SPEAKER_01 10:26

Yeah, Netflix thinks you've loved it because you watched it all the way to the end.

SPEAKER_00 10:29

Interesting.

SPEAKER_01 10:30

Right? Think about that. Or the TV show, right? I'll ask you if you want to keep watching after five or six episodes, you fall asleep again. Yeah, now it thinks you're enjoying this series.

SPEAKER_04 10:40

Huh.

SPEAKER_01 10:41

Yeah, so again, and even if you look at Meta, Meta is worth$1.5 trillion. That's their current valuation. 1.2 trillion of that is their data. And their data is junk. And I I don't mind saying that out loud.

SPEAKER_02 10:54

Yeah, yeah.

SPEAKER_01 10:54

If if you're on if you see something on Facebook, you put your phone down and you walk away and come back and pick your phone back, and now thinks you love that content. Right? So it's, you know, the day everything out there is about is about data manipulation to try to sell you something. The Sizio is the exact opposite. We have no ads, we have no bias, we have no incentive to to prop anything up. What we want to do is we want you to come in and make some swipes. And and yes, that data of the swipes, we are going to eventually sell that data of the swiping, but we're not gonna sell your data. Right. We don't we don't care who you are. We we we'll make a persona of you and we'll sell the persona, but that persona, we don't know who it attaches to. We don't collect any personal data. We never will. Your email address is is for authentication only, and that's not attached to anything else. So we want to be very ethical and we want to make sure that your data is is very, very private. So the so if you swipe up on a movie saying you want to see this, you're not gonna see it on seven other apps an hour later, like you will on everything else. We're gonna protect you and we're gonna make sure that the choice is yours and all the recommendations are based on who you are and your persona.

SPEAKER_00 11:57

Right. Thank you for clarifying that. Because someone like me with a privacy mindset is saying, So if I go on your sites and your platform and I enter all this data, well, that means now you can tell when someone in Tampa, Florida is watching a stand-up comedy on a Saturday night, and then you can use that data to so none of that is happening. All you see is a persona that is de-anonymized from me is watching stand-up comedy on Saturday night.

SPEAKER_01 12:30

Correct, 100% nice.

SPEAKER_00 12:33

So you've led products and engineering at Nike and Amazon Web Services, AWS. What did that time period in your career teach you about incentives, metrics, and what good decisions look like on a large scale?

SPEAKER_01 12:54

So Nike specifically, we'll start there. Um, when I started at Nike, I worked there for five years, and I started when Nike was consolidating all of what they called at the time emerging markets. They renamed it to Asia Pacific Latin America. So that lets you know Mexico plus all of South America, plus all of Southeast Asia and Japan. Uh China was its own geography. But so all that the think of the Pacific, the entire Pacific rim minus uh the United States and Canada. Um that was Asia-Pacific Latin America. Uh, when I when I started there, um the forecast accuracy, I was in analytics for demand plan for some supply and demand planning. Um when I started there, the the the forecast accuracy was 65%. Now that means that Asia Pacific Latin America was losing a lot of money, a lot of waste. Um, so a lot of uh a lot of uh uh uh apparel was being given away because it's cheaper to give it away or incinerated, unfortunately, um at the time. So they consolidated everything, and I was hired on to actually create some planning-related analytic tools to help get that score up. So one of the first things I did is I wanted to create consumer profiles personas, you know. So you see that the connection now. And so I learned that you have to understand your consumer um to make informed decisions. Um, so when you look at Asia Pacific, Latin America, um no two countries are alike. Um believe it or not, in Malaysia, you still have a lot of people who are writing orders on pieces of paper and faxing them in on fax machines. So right, so you know, technology is not caught up in certain countries. Um, and it's very fascinating to see how they operate still. And so you have to understand all these nuances. So that was a big part of it. So we know we created, I created these tools, consumer profiles with the with the uh with uh machine learning cohort matching to help uh you know create 95% of these of these demand uh forecasts and then let the planners focus on that 5% nuance, right? So they could really dig in. Over the course of two years, we got it from 65% to 93% accuracy. That made Asia Pacific Latin America profitable for the first time um in Nike's history. Um and and here's the here's the interesting part you never want to go above 95%. It's a little fun fact. All the Nike factory stores, that's how they get their their inventory, is through that that that 5% overage. Um so they need the rental sell, right? Yeah, because you got to mix a little of the new stuff in with the stuff that are sp uhless specifically for the factory stores to get people in the door. Yeah. So it's so again, learning that, learning you know, you how you trick the system a little bit, you purposely inflate so you can get, you know, well, maybe one pair of LeBron James shoes in the factory store, so everyone comes to try to fight over it, then they leave with something else. Um, right? So um learning that too, you it's gaming the system to a certain degree. Um yeah, now I didn't have anything to do with that piece of uh but what I learned there though is you know, you have to understand the data, you have to understand the consumer, you have to break it down the right segments. So part of what I want to do too is when I sell this data back to the industries, I want them to understand the consumer. I want them to start understanding that we don't like what you're putting in front of us. I can tell you right now from the data point I have 200,000 data points already. You know, we're early, 200,000 data points, and I can tell you that 52% of the content out there people haven't seen and don't want to see. That's a waste. Um, you know, that's a waste of licensing by the streaming providers, that's a waste of of rack space, that's a waste in so many areas that they can they can they can start to trim down. Netflix doesn't need 4,800 titles or sorry, 8,400 titles. You know, Netflix could get away with two to 3,000 titles and be just fine. And they could probably let us start sharing our passwords again because they don't have to pay so much for licensing. Um, you know, and you know, fun little fact, um, did you know that Netflix has over a thousand Bollywood movies in the US on its platform?

SPEAKER_00 16:32

Those are the movies made in India.

SPEAKER_01 16:35

Correct.

SPEAKER_00 16:36

Huh.

SPEAKER_01 16:37

But you never notice them, you never come across them. Because they they don't make it.

SPEAKER_00 16:40

Does that mean they advertise it to Netflix users who they've identified to be of Indian origin?

SPEAKER_01 16:49

We'll see those, correct.

SPEAKER_00 16:50

Huh.

SPEAKER_01 16:51

But I'll tell you what, I've watched some really amazing Bollywood movies because they're becoming a little more Western, you know, style, Western civilization style. And they're there's they're great movies, but we're not being exposed to them. We're not. Again, because it doesn't make them the money.

SPEAKER_00 17:05

I noticed that whenever we visit friends who so I'm from Nigeria and we have friends also from Nigeria who live nearby. Whenever we visit them, and I'm always curious to see what's on other people's Netflix. And then I pull up my phone and I compare the top 10 to see if it's the same top 10. Most times it's this it's the same top 10. But then when you scroll past that top 10 banner, you start to see different recommendations. And because they watch quite a few Nigerian movies, I see the recommendations Nigerian movies. Our Netflix profile, my wife and I, she's into some TV shows about medical drama. I watch mostly stand-up comedy and maybe some documentaries about the military. And those are the kind of recommendations we see: medical drama, documentary, medical drama documentary. Our friends, depending on what they are into, Nigerian movies. Um, we see some shorter movies about, for example, Love is Blind and everything surrounding Love is Blind, um, selling sunsets and that genre of TV shows and so on and so forth. So I guess the algorithm really does know who you are, right?

SPEAKER_01 18:22

They do. They they they know who you are, but they don't know your taste. That's the difference, right? Um, they they know that you like stand-up comedy, but they may not know that like that stand-up comedy may actually lead to other things that you may like, right? They're not exposing you to the things that are connected to stand-up comedy. Um, a lot of these stand-up comedians have amazing movies they've been in. They're not exposing you to those movies either, right? Um, because if you like, you know, Chris Kevin Hart's a great example. Kevin Hart, love him or hate him, he has a lot of movies, right? And I'm a fan, I'm a fan of Kevin Hart. Kevin, if you're listening, you know, you can invest. Um, you know, I'm a fan of Kevin Hart. He he isn't he, I think he's a talented actor too. I prefer his acting over his stand-up. Um, and so like for me, I watch a lot of stand-up as well, but I never get the movies that these these comedians are in. I never do. And that seems like a miss. Again, I don't think they understand, you know, no, they're not letting they're not exposing us to the other things to even see if we're interested. And that's what's interesting to me. Um, and then you look at, and there's a poster behind me. This is a I'm a huge zombie fan. So that's from an independent movie called ZombieCon Volume One. Um, they reached out to me, so that's why the poster, they were one of the first fans of Decisio. And what we're also trying to do is we're trying to help these little studios also be able to get traction and get seen. Um, you know, they'll have the same access to the data everyone else has. At the same time, when you're swiping through our platform, we don't care how big your budget it is. We don't care who your start was. We just care that this is something that the the the algorithm thinks you might like. So right now I can tell you that this movie is rated or has been recommended quite a bit for folks, where on no other platform I don't think it would recommend them for them. Wow, wow.

SPEAKER_00 19:58

I did a a monologue about the video game industry. Um, that was back in actually a few weeks ago, episode 452, I believe. So basically, I talked about disruptions in the video game industry. I compared the triple A titles to the indie studios and so on. So yeah, yeah, I've I've been doing some digging into that industry, and it's interesting to see how AI is disrupting everything. And you just mentioned all the different recommendation engines and how it affects small studios who try to develop something in the game industry, and on and on and on. Wow, wow.

SPEAKER_01 20:39

And so we are gonna tackle uh so we have books and podcasts coming out late April, and then we have tabletop and video games coming out this summer. Nice, so we're gonna cover them all. We're gonna cover them all. Um, tabletop games is actually the fastest growing industry in the United States. It's gonna get it's gonna double in size by 2029. Um, and then video games, as we know, just always international. But that's that's the thing. Look at with Steam now, how many games are out there now because of it? Again, another, you know, everyone plays Fortnite, everyone plays Call of Duty, everyone plays these main titles, but there's so many good indie titles out there that aren't people aren't being exposed to. And I want I want to help these folks as well. I want to even the playing field. I don't care how big your budget is. I want every everyone's content to have the same opportunity to bring someone's delight. Nice, nice.

SPEAKER_00 21:50

So, in that light, um, what is surprisingly similar between demand forecasting and recommendation engines?

SPEAKER_01 21:59

Yeah, that's a great question. Um, I mean, you're having to predict, you're having to guess. There's a lot of guesswork, right? There's a lot of us, there's you know, we're not making assumptions. That's the good news because we are doing matching, we're cohort matching, we're we're we're using data to give it to make a foundation. Um so the very similarity there is we're making we're we're matching data, we're creating profiles, and we're we're trying to um uh predict what you're going to, you know, in the case of demand forecasting, what you're gonna what you're gonna like. And then in the case of there, it's always 18 months in the future because demand forecasting takes a while because you have to produce. In our case, we're trying to predict what you're in the mood for right now. Um, so we are trying to predict um someone's habits. It's about habit prediction. Um and believe it or not, we're all creatures of habit, and it's not that difficult if you do it the right way. It's not difficult to predict a habit if you have enough data to understand the persona of the person. Right, right.

SPEAKER_00 22:54

And most recommendation systems uh Actually built around either ad revenue or engagement. So how does that shape what gets shown to the viewers or to the audience, basically?

SPEAKER_01 23:09

Yeah. So when you think of like the mainstream recommendation engines, instead of selecting from 250,000 titles, they're selecting from a couple hundred titles that are more that are that that were paid to be there. So those recommendation engines are biased off the bat because it's a smaller set that people paid to have there. It's reducing the opportunity and and selling it as choice. So the bigger recommendation engines out there do not have the robust uh back uh um inventory that it's trying to pull from. Now at the same time, it's also hard to understand someone's habits if you don't analyze the entire inventory, right? If you're also analyzing a small inventory, because as we know, all these blockbusters, majority of the people have seen them, right? Right. So we know the intent is high there. Um so it's easy to predict on a blockbuster if someone's gonna see it. But some of these indie films is harder because they don't get enough exposure to understand the intent. Again, taste is easy enough to predict, but intent is a lot harder because no one no one predicts intent.

SPEAKER_04 24:14

Right.

SPEAKER_01 24:14

Um, when it especially on a smaller scale. Um when they do these these these these projections for uh a blockbuster movie theater or blockbuster movie in the summer, they're like, we're predicting this is gonna make$300 million. They are predicting intent there, but only for a handful of movies because they can look at last year's blockbusters, see a similar movie, and say, this movie made 304 million last year with inflation, it'll make 312 million this year. So it's not a hard uh math equation to solve. Uh, but again, you're they're only doing it for a select few, they're not doing it for the entire inventory. Right. Um, I I think intent is missed from the mainstream recommendation engines by far. Right.

SPEAKER_00 24:51

So, in that in that sense, why do passive signals outperform intent? So, passive signals, I mean watch time, scrolling, clicks, and so on. Why do they outperform explicit intent? And why does that still feel like drag to users?

SPEAKER_01 25:12

Yeah, because I well, that's I I think we're designed to stay on our phones. I think that's the thing. I think um everything is designed to keep us on our phones as long as possible. So I think that that that drag is by design, but we can't we can't stop it. We're addicted to it. I think the addiction has has has made it more um has made it more per made it more intentful because we're all addicted to it. They've addicted, they've got us addicted. So now they know they can control us. They know that they know we're all gonna stay hooked on our screens. Even if we just give up on something to watch, like in the case of mine, I gave up on something to watch, I was still on my screen, right? I went somewhere else. Um so that's why I think the signals are stronger in that way because we're addicted. What I'm trying to do is I'm trying to actually help you make the decision quicker and put your phone away. I'm trying to break the cycle and get you off your phone and enjoy your movie, your book, your game, right? Um, you know, everything else. Uh so I'm trying to break the cycle of that, of that passive, uh, that passive method that is uh that is very strong, but it's it's that dopamine uh in our brain, the dopamines in our brain that are keeping us up. Right, right.

SPEAKER_00 26:16

So the the trust has basically collapsed because platforms want to keep people on them as long as possible, from Instagram to Facebook. Even um the AI chatbot. Now you put something on Chat GPT, it gives you the answer, and then it asks you a question saying, Do you want me to turn this into a slide deck for you? And then you say yes, and then it do you want me to turn it into a Word document for you? And then it just keeps going because it wants you to stay on as long as possible. And then surprise, surprise, now they're bringing ads on Chat GPT because, well, guess what? They've run out of excuses to keep you on the on the app for as long as possible.

SPEAKER_01 26:58

Yeah, yeah, it's silly, right? It's it's silly. It's and the thing is we because we are so addicted to our phones, we we don't even notice it. We don't even notice it. Um, and that's and that's the thing is I'm hoping there's more people becoming vocal about this. I think that I'm hoping, I'm hoping that um over the next couple years, Gen Z in particular, who's starting to notice it of all the generations, um, hopefully we you know we can we can start to fight back and get away from it because I think it's unhealthy for the long term. And again, I I want to enjoy life. I want to enjoy my pleasures I enjoy. I don't want to be stuck at a little eight-inch screen.

SPEAKER_00 27:32

Yep, yep. So um Decisio, I know we've talked about it since we started um recording. Um, could you walk us through the four-way swipe and what each direction means? And why did you even come up with four directions? Why does each direction matter? And so on.

SPEAKER_01 27:52

Yeah. So the so the four the the four-way swipe. So um when you think of uh uh you know, Tinder, Bumble, or these other dating apps, you know, I want to start with something familiar. So the left and right, like, dislike, right? That's that's that's the known. Um in this case, it's it's I've seen it in like or dislike. So and I kept the same method, you know, uh swipe right is like, swipe left is dislike. So basic familiarity. I didn't want to reinvent something that's gonna confuse folks. But when I when I sat back and I looked at that, two data points is not near enough. Again, that's just your detaste. That's what you're feeling about something you've already seen. Um, but that's when I wanted to create the intent. The intent to me is the key differentiator between us and everybody else. So the up and down. Up means I haven't seen it, but I'm interested in seeing it. Down is I haven't seen it, but I have no interest in seeing it. Now, will I see it? It may maybe or you know, maybe not with the swipe up, but I have interest. It's it's something that interested me, something I think looks appealing. So that's that's a signal. Um, so I I've created a one through four. So one, one point is if you swipe left, seen it dislike. Two points is I swipe down, I haven't seen it, no interest. I didn't want to give it a one because you know you you haven't seen it, so it's hard to really truly say so. The intent two swiping up is a three, swiping right is a four. But we will never we will never produce a number rating. Never. We were gonna produce words rating. Uh whether it be people like this, people are interested in it, mixed or low reviews, or people have no interest, or people just people actively dislike this. Those four, four segments, that's it. Um, and right now, looking at uh the data, the early data we have on some highly swiped uh titles, um, in my opinion, based on it, um it's it's very accurate. Um, because it's it's and again, it's it's it's it's a it's creating a uh uh it's it's a basic method to create a complex persona.

SPEAKER_00 29:39

A basic method to create a complex persona. And yeah, listening to you describe it, and just this one-line tag you just dropped allows me to then say it looks like there is transparency built into your system driven by expressed preference. So, in terms of products, um in in product terms, what does transparency really mean when you go one layer down as you just described it to us?

SPEAKER_01 30:43

Yeah, so transparency means that uh we we actually care about what you like. We actually care about who you are. Um again, we don't know who you are, but we care about who you are. I know that's a paradox to say it that way. Again, right, we we we care about the person, but we don't know who the person is. But we're gonna create a persona so we can actually give you what you want, what you desire, what you intend to watch. Um, so that's where the the the what you know we're we're trying to be as ethical as possible, or trying to be transparent as possible is we are not going to influence, make this biased. We're gonna truly only react to what you want. Wow, wow.

SPEAKER_00 31:21

And you actually don't force people to write reviews or rates anything on any artificial scale or rating system. What do most platforms get wrong about asking for feedback in quote?

SPEAKER_01 31:36

Yeah, so so feedback is different to everybody. Um, well, first off, we know that we there's a rule. Um, and I worked at my first tech job was at GoDaddy years ago. So you know, I've been around a while. Um, and and when I worked there, I was I was a I was a trainer in the call center. And there was a survey system, and I learned something very early in my career. And the the goal was to have a 95% score, not 100% 95. And I was curious, I was like, why 95%? And then someone someone, you know, one of my my leader at the time, great, great person, uh Lori, uh, she said, because 5% of all people will complain regardless. We want to make this realistic. So, again, to your point, um, regardless of you know of what happens, 5% are gonna give it a bad review, whether it was bad or not. That's just the way it is. Uh, so that there's first, so that's a biased. Uh second, um uh Zach Snyder's a great example, great example of uh of fanboys. Um, when uh the new Superman came out, the Zach, the Zack Snyder fanboys immediately started trashing the new Superman unseen. So, again, there's another bias, right? We we know, right? Now don't get me, I love both Superman. I love them both. I enjoy both, right? Or I shouldn't say I enjoyed both. I would watch both again. The Man of Steel versus the New Superman. Great, you know, they're enjoyable. But again, you haven't seen the movie and you're gonna bash a movie because it's a re it's a it's it's a uh a re a reimagination of a character that was made by someone you love. Silly. So again, they're getting that wrong, right? They're allowing that to happen. And then to the point of i i if you're a film aficiano and I'm just an average fan and we both give it an eight, what does that mean? I don't know what that means. If you know, an eight out of how do you say a movie is an eight out of a 10? Like what does that mean? It's gonna mean something different to everybody, right? So when you look at a score, an aggregate of a score there, even then, if a movie is like a you know, because Rotten Tomatoes takes an eight and transforms like 89 or whatever, you know, they do their 100%. What does an 88 really mean? I don't know what that means. And again, these are the people that saw that actively saw the movie or had or in or intend to see it or with Rotten Tomatoes, bought a ticket and never showed up and just wanted to give it a high score. Um, unfortunately. That's you know, but uh but again, if if 3,000 people reviewed it and saw it and gave it high reviews, but 300,000 have no interest in seeing it, they're missing that component. They're missing the component of people. In my opinion, if Rotten Tomatoes wanted to actually add a button and say no interest in seeing, and that impacted the score, that would be brilliant. I hope they don't rehear this and do that. But but you know what I mean? That that would add a that would add a comp uh uh complexity to their system that would, I think, inform a things a little better. Right, right. Because again, rating something on how well on how good it is is arbitrary and doesn't actually tell you signal if if it you know if the masses want to see it, have interest in it. Got it. So I think they're getting all of it wrong. I think they're getting it all wrong at this point. Um, I think I think I think arbitrary scores are just that. They're arbitrary and they mean nothing. They're just they're just people, people's opinion. And and also once a certain property has a certain like threshold of scores, people tend to vote with the masses because there's a really great book I read a few years ago called Everybody Lies. It's about data, and it's about how people start to to vote or start to uh answer things the way they think they should answer it versus how they really feel once they start to feel like they want to be part of that community. Right. So that's the other big piece of it. Right.

SPEAKER_00 35:05

I was just describing that to someone just a few hours ago, preference falsification. Yes, to fit into to fit in into a group, people would alter their preferences so that they can be accepted or approved by a group of people, which is I find that crazy, but what do I know?

SPEAKER_01 35:25

Yeah, but it but it's it's it it's it's massive. Um, yeah, and this book went over a lot of different segments of the of types of cohorts, and it was pretty impressive that it happens across all these cohorts too, not just in the more popular or or with the young kids, it's across the board. Um, that people you know answer the way they feel that people think they want them to.

SPEAKER_00 35:48

So you've you've mentioned how there is no ads, no paid placements, no sponsored rankings. What are those three things unlock in the experience for the users of your system?

SPEAKER_01 36:00

So the first thing it unlocks is delight. And I use that word a lot. I use that every time I do an interview. It's delightful. I want to make this a delightful experience. I want people to come in and not feel overwhelmed, influenced. I want people to come in and just enjoy the app and have some fun with it. That's why we created the battle mode, and we're doing more things like that. The other thing it does is it also gives a sense of trust. We're trying to build trust. We want people to trust our platform, we want people to use our platform. We have a lot of early adopters right now. We're making great traction, and we've had a handful of people say they're they're our number one fan already because they trust us already, because they don't feel like they're being they you know, one person said every single recommendation I've got I've never heard of, which is great because that means that we're not just throwing things at we're throwing the big titles at them. Um, and the third thing it does is it also uh allows anyone to use this app at any given time. Here's another fun number. The average person right now is spending$130 a month on streaming services if they have streaming services.$130 a month. That's the same as cable now, plus internet. The whole point of streaming was to cut the cord, right? That was the point. But now uh we have all these things. Now we're getting we're we're we're we're we're creating value. We once you get a thousand swipes, and that's by design, that's because you we need enough data points. We're actually gonna tell you which streaming providers you can cancel. We're giving you a letter grade on your streaming services based on what you like. So we're also giving you value. We're gonna show you how to save money on your streaming services. Um, you know, in my case, I only need three. I mean, I'm using all of them, obviously, because I for the platform I want to make sure the platform works. I want to keep getting recommendations, but according to the Cisio, I only need three of them. Three of the ten I'm paying for. Wow. And so again, we're getting value as well, we're providing value. Nice, nice.

SPEAKER_00 37:41

So you just so many insights, and I'm thinking of what direction to go next. Um, okay, you've positioned the Sizio as a social tool, um, watch lists, tastemakers. I believe I read group decisions on one article.

SPEAKER_01 38:05

Yes. So so you can actually do like a movie night, a book club. So uh our best ad campaign is actually the watch uh the what we call the the the the uh relationship saver, um, where where our partners can actually use the app and choose something together.

SPEAKER_00 38:18

Argue on what to watch.

SPEAKER_01 38:20

Exactly, exactly. Um, someone recoined it as the third wheel. Uh this they actually said on the review, they left the review said the sigio is not the third uh wheel on all of our movie nights.

SPEAKER_00 38:30

Yeah, that's a good I thought that was clever. That's a good problem to have.

SPEAKER_04 38:34

Yeah, yeah, exactly.

SPEAKER_00 38:36

So what's what's your take on um shared taste versus my feed, a personalized feed versus a shared taste?

SPEAKER_01 38:45

Yeah, um, well, I I don't think we're I I think there's still gonna potentially be issues there. You know, no one's uh as we know, the larger the group, someone's gonna be unhappy. But if we can be if we can have a majority of the folks happy, we've won, right? We're not I know we're not gonna make everyone happy. Um, but you know, when it comes to two people, it's a lot easier to find common uh takes in that case. Uh but yeah, I I you know book clubs. Um I've heard the horror stories of guns being pulled out in book clubs when they didn't get the book they wanted to be read. Yeah, like book clubs can get very vicious. Book clubs, very vicious. Book clubs, yeah. Yes, yes. Um, so we're hoping when the book comes out, we can actually we can actually potentially save some aggravation there uh by letting the the the app choose the books for them instead.

SPEAKER_00 39:28

So when I picture book club, I picture grandmas in like walking chairs.

SPEAKER_01 39:34

Uh but then there's a lot of young groups too. There's a lot of young interesting. Um I go so the gym I go to has a book club as well, and they're all in their you know 20s and 30s. Oh yeah.

SPEAKER_00 39:43

Oh, okay, okay.

SPEAKER_01 39:44

Yeah.

unknown 39:45

Yeah.

SPEAKER_01 39:45

Book clubs, they they they will get serious about a book they want to they want them everyone else to read.

SPEAKER_00 39:49

I need to change the kind of book clubs I go to. Wow.

SPEAKER_01 39:54

And the gun getting pulled is a true story. I read that article a few years ago about that.

SPEAKER_00 39:58

That is crazy. Yeah. I'm trying to imagine what could have led to that point where I want to read fill in the blank, you want to read fill in the blank, and then we pull our gun. Really?

SPEAKER_01 40:15

I mean, that is you know, fortunate the world we're in, but yeah.

SPEAKER_00 40:19

So, how do you keep your business model aligned with um the user when the product is ad-free? That is still like blowing my mind a loop.

SPEAKER_01 40:31

Yeah. So so we have five potential revenue streams here. One of them is we're gonna do an affiliate program with books, video games, and tabletop games. So, right now in the United States, every day, two million print books are sold. I was blown away by that number. Uh 900,000 ebooks are sold, and that's expected to go over a million by 2028. Um, we can get 8% on print books, we can get 14% on ebooks. Um, if we can infiltrate just half percent of those markets, we can get about 14 million a year in revenue. Um and and we're not, you know, all we're doing is we're it's gonna be a buy now option. We're gonna have the major real re uh retailers on there, including Barnes and Noble, so you can go pick it up the same day. Um so people will, you know, we when it when it gives you a recommendation, you click buy now, you go buy it. Um again, we're not pushing you to that book. That's the you know, it's an honest recommendation. I honestly think you're gonna start to see a lot of smaller authors call us out for saying, Thank you. I'm starting to see increased sales now. That's what that's my hope, right? Um, so that's that's one one revenue uh revenue is with the you know, video games, tabletop games, and uh, and books. Video games affiliates are actually a lot higher because they're all digital now. Um, so there's you know, there's no uh copies of things, so the the affiliates are a little higher. Um and there's actually more video game sales worldwide than there are books, which is interesting. So yeah, there's so there's opportunity there um for that. The second is um uh we're gonna let uh uh we're gonna we're uh we're gonna let um if a company wants to get immediate feedback on a on a on a title across the board, we're gonna let them do a priority swipe. Again, not pushing on them, but the next time you log in, it'll be the first thing you swipe on. So they can get immediate feedback on how that's doing. Um so that's that's one way that the companies can pay for the priority swiping to get a little more feedback. Because if Netflix wants to understand a title that they may want to license, um, and they're like, we want to get we want 100,000 data points before the end of the week. They can pay for that. But again, we're not promoting it, we're not pushing it. It's just we're we're just pushing that to the front of the line for the swipe only. Right. Um, and then we're also gonna do that with coming soon titles uh before they come out because that's gonna give a good that's gonna accomplish two things. That's gonna let folks know what's coming out, and they can determine if they want to see it. If they swipe up or down, they swipe down, no big deal. They swipe up. We'll remind them when it comes out so they know they said they want to see it. Um, but again, so studios can gauge this. I call that the Megan 2.0 uh phenomenon. Uh we could have helped, we could have saved Bloomhouse about$63 million had we been the scale at the time to let them know they shouldn't have released the movie when it was. Right. Yeah. Wow. Um, so so there's value there, right? There's value on both ends. There's value for the users who are swiping to be exposed to this new content coming out, and then there's value to the studios to understand. Um, the third is we eventually want to create our own LLMs because right now, I will tell you, the LLMs on the with all for all the AIs, they get out of date so fast with this with this data. I was getting a 40% false positive when I was trying when I was trying to initially do some analysis here of titles to the streaming providers because of how fast they change. So that's another revenue stream. Uh, the fourth is we're gonna eventually sell the data once we get enough data. Again, we're not selling, we're we're selling at that higher level. So we know the data is not gonna be worth as much, and that's okay. We're not gonna be uh uh Facebook with our data value. We're gonna be a lower range, but we're still gonna be just fine with that. Again, ethical data. So we're, you know, I'm happy. Um, and then the fifth way is when we go to the the social matching where we're gonna match people, that's gonna be a subscription based. That's gonna be a separate app that will be subscription based. Uh, but it but I will say we're not gonna be like Bumble, we're not gonna be like Tinder, we're not gonna charge you$40 a month, right? We're gonna charge you a small, uh, a small monthly fee because that's gonna be a lot more data intensive, that's gonna cost us money uh to produce. Um and that's about actually meeting people in relationships. So we got to put a lot more safety features into that, but we're still gonna be at a price point when that comes out that you know you can use it for dating initially if you want, or you can use it for friends. But if you use it for dating, you can then convert it to friends. And then you can start to find people you can find couples. I'm gonna make it so couples can find couples to hang out with. You're new to town, you want to find your you know, someone to go watch movies with, you want to find your book club. That's what I'm gonna be uh doing is finding those kind of matches with the data. But the first thing is with the dating piece of it, when you log in, you're gonna have a compatibility score based on all these swipes you've done. So you're actually gonna have meaningful connections. We're gonna get rid of the swiping, uh superficial swiping. You're just gonna see people with your your scores. So if you're looking for a movie partner, it's gonna show you uh scores of people who are also looking for movie partners.

SPEAKER_04 44:56

Nice.

SPEAKER_01 44:57

So you know that you have common taste, so you know this is somebody you're gonna go to a lot of movies with. Nice, nice or you can even start a movie club out of it, right? Things like that.

SPEAKER_00 45:05

Wow, that's actually a brilliant idea, and good luck with everything coming coming up for you and so on. Um out of curiosity, have you considered launching into podcasts, just asking for a friend?

SPEAKER_01 45:21

Oh, sorry, I totally forgot. Uh podcast is coming out with books. Nice. Sorry, pod so so podcast is gonna come out with books. We We have no monetization monetization options for podcasts, and that's okay. Uh, there's no good podcast engine out there. Yeah, and I totally forgot to mention this, but we want to be because we know we'll get users from it. So we're gonna we're gonna provide podcasts knowing that we'll probably end up losing money on the podcast version because it'll bring users in it and help grow the other aspects. But at the same time, I want to I want to be true to my word and I want to help people find the things they love. And podcast right now is a black hole. It's either recommendations, or you hope Spotify tells you something good, or Apple, you know, but it's it's very dicey, right? And and here's the fun part here's the fun part about it. You can use your books or your movie or your video game uh data swipes that also influence the podcast uh recommendation engine. So if you're a big movie buff and you want something related to the kind of movies you like, it's gonna show you podcasts based on that. Nice, nice. Yeah.

SPEAKER_00 46:18

Because I've been doing this for five years and I listen to a lot of podcasts, and of course, obviously, I also have my own podcast. It's I always wonder how the algorithm presents because I don't even subscribe to podcasts anymore. I just go by feed and I listen to the top episodes in this topic and the top, I just add them to my queue. I have subscriptions to like five or six podcasts that I have to follow them. But other than that, my weekly podcast listens all come from recommendation algorithms, recommendation engines from the Apple Podcast platform I use from the Spotify and from YouTube. And I'm guessing that's how a lot of people consume podcasts as well, and which is why I post my podcast episodes and I make sure to put the links to where they can get it, because less than 30% of people that listen actually subscribe to my podcast. Less than 30%. And I'm thinking I've tried everything to get them to subscribe, but it's just not subscribing. Well, they are listening. Well, I will take listening to subscribing.

SPEAKER_01 47:33

So yeah, and and with the podcast, one of the things we'll do is it'll be a button to take you to your pod whatever podcast service you're using for that podcast. Right. Um, so that way you can you if you want to an easy subscribe button, we're gonna add that for the podcast as well. Um but yeah, no, podcasts definitely need help with with Discovery Engine because Apple, you know, all these other services, they do okay, but um, they don't still they still don't know your taste and your intent, your desire, right? They don't know any of that. So we're hoping to really curate that as well. And podcasts, thankfully, it's not as gonna be as hard to get out. Um, that's why we're gonna we're gonna roll it out together with books at the same time. And it's all gonna be on the same app. You're gonna be able to toggle between the experiences. So you don't have to have five apps. You can have you can have the the Sizio app, and then we'll have the social matching app. That's it.

SPEAKER_00 48:21

Nice. So to start wrapping off here on this very insightful podcast, um, looking into the future two to three years down the line, what can you foresee would be the main uh changes to put it that way, that would happen in your sector of the entertainment industry 2027, 2028, knowing fully well that well, there's AI now, there's agentic AI, there is all these other tech related stuff happening every week at this point. So, what what do you see in the future?

SPEAKER_01 49:01

I I think AI is gonna you know be more prominent, it's gonna be more influential on our life, but I think things like decision making like this, I think people still want control over it. So I think folks are still gonna want mechanisms to have choice um over over AI telling them. Um and I think Gen Z is a big pusher of that. The youngest generation, I think, does not want to be told what to do. I think us older generations are like, ah, we're tired, tell us what to do, right? But I think uh I still think there's gonna be a separation. I think from the entertainment industry standpoint, though, I think with all these mergers, I think we're only gonna have two to three streaming providers. Many are gonna merge, many are gonna fold. And then I think having two to three or you know, hand just a handful of streaming providers left, I think you're gonna be even over more overwhelmed with choice because they're gonna have 30,000, 40,000 titles instead of you know eight to ten thousand titles. So I think an app like the Sizio in three to four years is gonna be even more prevalent. And I think you're gonna even see things like where we're gonna be in your smart TVs and we're gonna be your TV guide for the movies and shows. So you open up Decisio, you go to your watch list, and then you when you click on the movie, it opens up the app to the streaming provider and starts playing. So I also want to position myself in three to four years that we're we're the modern day TV guide for helping people find what to watch on their TV. Um, so I think you're gonna see that. I think the Sizio is gonna be a mainstay on all TVs in three to four years.

SPEAKER_00 50:22

Nice, nice. Wow. Thank you so much for all you do, and this has been a very fun conversation. I would definitely want to keep in touch with all the cool stuff you do and even keep in touch with you personally because it's it's one of the things that it's a fast-moving industry and you actually know what you're doing. So maybe sometime in the second half of the year we can have this conversation again and talk about all the cool new stuff that has happened since today when we spoke last. I would love that. I would love that. That would be great. Thank you so much, Sarah. Talk to you later. Thanks, guys.