What the Like Button Tells You About Your Customers with Bob Goodson, Inventor of the Like Button

y's design problem. We really only worked on it for a day and yeah, it kind of went from there. But the book is all about the fact that when new  things get developed, especially when there is successful, or something like the like button and now emotional reaction buttons.

It's never one person. It's never one company. It's always a, a long string of little tweaks and innovations that culminate in something that's very popular and successful. So it's really more of a process of evolution, which is what we're, pointing to and trying to bring light to in the book.

Richie Cotton: That is very, very cool that it was just, it was a one day project just solving a a little thing, and this is now a ubiquitous feature of just many, websites. So I'm always curious is whenever something is just everywhere you think, okay, well this is an obvious solution to a problem. But I guess at the time it was brand new.

It wasn't obvious at all. So did you try other things beyond just the idea of the thumbs up? Like what else was tried beyond what we now know as the light button?

Bob Goodson: I mean, even that day, you see from the notes we came up with various ideas for how to present this and how to interact with it really wasn't.  An obvious choice. The thumb wasn't an obvious choice at the time. Neither was the word. Like, which is why it's interesting that it was in the notes. And when we trace back the origins of, other sites, and and we talked to founders like, through this book, we talked to founders of, of YouTube and LinkedIn and Twitter and recently interviewed the founder of Pinterest.

And we talked to so many people and around that time, in the sort of early two thousands. So many things were being used, like the first icon on YouTube was actually a heart symbol, which wasn't used much at all at the time. But when I spoke to Steve Chen, one of the co-founders, he was saying that they started with their heart, partly because YouTube started out as a dating site, actually only for a two or three weeks.

Most people don't remember that era, but they thought it would be a good place to. Had videos for dating purposes but it didn't last very long. And they quickly realized there was a bigger, need and opportunity, but instead of any kind of thumb or like, they actually had hearts on those videos that quickly got changed.

Friend feed, which later got acquired by  Facebook actually had just little smiley face, even though they, they were the first to put the word like. On a social media newsfeed, which has now become a natural part of all social networks, but at the time it wasn't.

even for them, it, with the word, like it wasn't obvious, the thumb would go next to it. They had a smiley face so many different icons were being tried. And it, it seems like the thumb has some magical properties to it that made it a very accepted icon in the west.

Richie Cotton: Absolutely. That's fascinating. The way the idea just went from one company to the next. I also have no idea about YouTube originally being a dating site. That's quite a pivot, really. So, it's interesting, so the thumbs kind of, when it, I guess you occasionally see things like hearts as well.

Do you wanna tell me a bit more about like, how the progression went then beyond that? Like how did it take over the world?

Bob Goodson: It's been interesting for me to watch because on that day, we put these buttons on the content and none of us. Thought that something important was happening. everyone we spoke to by the way, that made important  contributions to development of a light button today have said the same thing that they were solving that day's design challenge.

And, and the thing evolves, right? And now there've been many thousands of people that have worked on this feature and many different companies. And so. Suppose the, the thing that I've, I noticed over the years is that, first of all, we didn't think too much about it. Within a year or so, I saw it start to spread to other sites.

I remember thinking really every few years, for the last 20 years, I've. I witnessed, 'cause I have a background in user interface design. I'm interested in these things and all the apps I use, I'm curious like how they're evolving and, and I would see this feature kind of gradually spread and become more and more popular.

It was about three years after we did that at Yelp, that Facebook added the like button to its site. So it even took several years before it reached Facebook, which most people think of as sort of the, the iconic implementation of that feature. And yeah, and I suppose I just kept thinking every few years someone's gonna write a book about this or someone's gonna write like a, an amazing wired  article about this feature and it, I would look, I would search Google every few years and look for that, that thing.

And no one's, no one had written it, which was really surprising. So I ended up chatting to a good friend of mine, Martin Reeves, and we've written some things before, a few years ago. And I mentioned this fact to him and he was so curious about the origin of the button. He asked questions like you have, like, where did it come from?

Or who really did invent it? And I couldn't answer these questions, so we started pulling the thread and as we researched it we just found it a really interesting keyhole when you asked this question. Who invented the light button? Where did it come from? How has it changed the way we interact online?

You actually uncover all sorts of new and deeper narratives that have lessons, broadly when it comes to understanding tech, how companies can innovate and what it means for, future waves of technology as well.

Richie Cotton: Lots of great questions, so hoping we're gonna get some insight onto some of those in the rest of this episode. Alright, so I guess just from a technical point of view, it's like, okay, as a  user of a website, you think, like, it's not immediately obvious what happens next. So when you click the button what goes on behind the scenes.

Bob Goodson: Those of you interested in data and analytics will enjoy thinking about and hearing about some of this. First of all, before we get into, some of. The interesting data that flows from likes. I would point out that liking is, and reacting to content is what I describe as the atomic unit of user-generated content.

So Web 2.0 was all about the era of user-generated content. Essentially, the web went from being something that around 10% of. People on the web would contribute or create content, and about 90% of users would read the content. And we can tell this from anyone that's developing apps in the early two thousands would look at their analytics log and they would see, they've got all these visitors coming in, consuming content, and very few people actually creating the content.

 'cause the barrier to create was quite high. There wa wasn't really the level of like trust in creating content that there is. Now. I know there are other types of trust issues, but at the time it was almost like a niche activity to create content and almost everyone was reading it. What Web 2.0 did is that it effectively brought 90% of people that used the web into content creator roles, and now that number's probably much higher.

what companies were doing at the time is trying to figure out. Ways to bring more people in, ways to engage people into content. And of course, photos and uploading photos is part of that. Uploading videos commenting, sharing forums. All of these are things that brought down the barrier, but nothing then or now.

Is a simpler way to generate content than simply clicking on something to give your reaction. And most people don't think of that act as an act of content creation or an act of data contribution. They're merely trying to signal to someone else that they appreciate or like, they, they're expressing something to another person.

 But of course. When someone clicks, like, then they are putting their name to that content, a face to that content, and they're also contributing to the underlying graph. And much has been said about the social graph, which of course was very powerful , and is behind a company like Meta. Ultimately because of the social graph that Facebook mapped and created, meaning we know that you and friends, because we're connected on a, on a platform, for example, so it draws a line between.

and the network has more information that it can use. And I would argue that the second wave of graphing was actually from like data. We can think of that as the like graph, it's the fact that you and I liked or reacted to a piece of content. The third wave that's come since we can think of as the attention graph.

You and I like looked at the same piece of content or watched the same video, that too can form a graph or part of a graph. So the latest wave of social media is, is mostly driven by that with the rise of TikTok,  which is really the main graph they care about. The only graph they, they seem to care about is the attention and what that tells them about what other people would want to see.

Richie Cotton: Yeah, I hadn't really thought of the like, button being around the same time as all these other web 2.0 companies. So you had Wikipedia started out about, I guess a similar time where it's like, okay, you can write it's like Wikipedia articles. You've got things like WordPress maybe a little bit later and well write many blogging platforms where you can write your own blogs.

But yeah, just being able to click a button and yeah, a very simple form of creating content. So I definitely wanna get into the, the like graph and the attention graph shortly, but maybe's back up and talk about why you should care about like, buttons. So, can you spell it out like, what is the benefit to the user of clicking like on something.

Bob Goodson: Yeah, that's a good. A question because we have to ask ourselves with something that's now clicked, we estimate more than 7 billion times a day. we estimate there are more than 7 billion reactions to web content on apps and on on, you know, in the  browser. Plus a tremendous amount of activity. It might be that this feature is the most successful user interface element of all time.

And so that leads us to ask like, why is it so successful? What is it? Telling us about, ourselves and about our needs to make it so successful. 'cause there was never a manual, no one ever had to read how to react to a piece of content, None of none of you have checked the instructions for how to use.

This is incredibly intuitive and it's something that is, is so widely used. there were a couple of elements. we talk about this at length in the book. We interview a number of prominent neuroscientists and psychologists as well to inform our perspective on this and two main things we took away is that it is a powerful way to with the dopamine response. there is a sort of anticipation element of, will I get likes for this? And then there's a reward element that you do get likes.  Similarly when you, give likes, there's, a hit involved in that as well.

So much has been said about dopamine and social media in general, and certainly liking ties into that. The other is around social, the fact that humans are fundamentally wired for social learning. We learn from those around us. We wanna surround ourselves with people we can learn from. and we have a, a craving for sort of mild hierarchy as humans.

we don't have such attraction to sort of stark hierarchy. we like a certain amount of freedom and space, but we are drawn to a, what we describe as sort of mild hierarchy. We want to be around people that we think are slightly cooler than us and slightly smarter than us, or slightly more attractive than us.

so we have that interest so we need ways to kind of level up with those around us. And social media provides an effective way to do that. And liking is a sort of currency of interaction in that type of what  we describe as sort of learning sociality. so those are a couple of things that humans are very, very hardwired for, that people hardwired for. 

and the like button seems to tap into both of those in a pretty smooth and, and elemental way.

Richie Cotton: Okay. Yeah, I suppose it makes sense that something that's gonna be successful has to kind of align with people's psyches in general, or, or cultural preferences. And I suppose there's a, politeness angle here as well. Like someone creates some great content and just clicking like is a very simple way to say thank you.

Bob Goodson: Yeah, exactly. There's, there's certainly a a reciprocation or gratitude, mechanism at work as well.

Richie Cotton: So, that's kind of, the one of the mechanism for like, people creating content and enjoying the implications of like buttons then, and also for people who are consuming content. I'm curious from a business point of view, like why would you want a, a like button feature in your product?

Like, how are you gonna benefit from that?

Bob Goodson: One of the things that we realized as we researched the book is that the. Customer experience industry, which has been very successful for businesses over the  past 20 years and successful for consumers as well. Customer experience has, led to no doubt, a rise in the experience that we have interacting With brands and with services however it gets measured. There are many studies on this that measure over time how satisfied consumers are cause it, certainly seems to be the case that the customer experience industry has been a huge contributor to the success and rise of that.

If you think about one of the cornerstones that customer experience is built upon is on feedback. And customer experience has, has made it much easier for any consumer to give feedback to a point where it's, almost overwhelming these days, right? You go for a coffee and you get an email from the cafe afterwards saying, did you give us a rating?

'cause we, comment on our staff, you're served by this person. How did they do? it's kind of getting silly how much of this kind of interaction we.

Richie Cotton: I don't.

Bob Goodson: I see your review.

But if you look at the timing when this rise happened,  the popularity of the like button slightly preceded the success and popularity of these features. And we argue that the like button paved the way and conditioned people to get used to reacting to content. And that made it much more likely that we would react to an email or a text or a call.

these ways that companies can now reach out and say, how was that experience? And so if we get used to and conditioned to, reacting to content to our friends, we're much more likely to be willing to do that with companies. And so we don't think it's been observed anywhere before the book.

But we, we suspect that this feature is one of the things that made the success of the customer experience industry possible in the last, 10 or 20 years.

Richie Cotton: That's really interesting. From speaking to. Product managers, they're always desperate for feedback on how good is their product, what is changing, all that kind of stuff. And it's pretty difficult to just get people to give you useful information. So that's interesting that you're trying to condition people to give feedback on your product, and therefore you can make your products better. 

Bob Goodson: And I don't think anyone planned that, by the way. I, I just think that's just how it panned out. Again, it's, the customer experience probably. Reacting to the fact that people are more prone and willing to, to react, and so why not gather that data and find ways to make it simpler for customers to. 

Richie Cotton: Or what's the easiest way of analyzing like data?

Bob Goodson: I, I founded a company called Quid, which is analyzing social media data, media data, and other forms of public content. Without personally identifiable information, we, we don't manage it at an individual level. We're, we're looking, we help brands and companies find. Trends that are important that they can then act on. 

for the type of analysis that we do and that others in our industry do that, we look at a combination of the content itself. So we use natural language  processing and now forms of large language models that allow us to read the content because we're dealing with things at scale, right? So if someone reacts to a trend like flared jeans are, are hot at the moment, I'm told.

so, companies, whether they're retailers or in a. Fashion brands are, are, are reacting to that, right? And trying to fulfill that customer need and interest in flad jeans. So what they need to know is, some demographics, but they also need to know like why, what is it about the Flad jeans?

What other ideas are being related? What other trends are connected to that? Who are the influencers in that trend? And all of that is, gonna come from analysis of the text in the posts themselves in social media posts involving fled genes. What the like data does is it allows an understanding of the magnitude and level of interest in.

In indies, whereas other types of data is not visible, the attention, for example, is only gonna be known by the company that's providing that, that page.  Whereas liking is actually a, form of public content that can be utilized by companies such as quid to understand and, quantify these trends.

Richie Cotton: Oh wow. So it is basically likely to, can then feed into, I guess like your, your supply chain, like working out, like how much stock you're creating. So as suppose, yeah, this is going to forecast planning and yeah. Are there any other sort of related business use cases here?

Bob Goodson: Certainly, as you mentioned, some retailers are able and, and, and do utilize social media content as a way of informing demand. And as algorithms become more sophisticated and more predictive, the window in which they can look ahead becomes a. Longer. So there was a period where we could tell you kind of what's happening right now through the analytics.

And this isn't just light data, this is number of posts, we call it post volume, but number of posts on the topic, which platforms how people are sharing, interacting. There's many metrics that can be used, but it is interesting for those you're interested in data analysis that There was a time when you could kind of look backwards at trends, and then we got  to a point where you could look at what's happening now, like real time analysis. This is what's happening now. We're now moved into a territory thanks to large language models, where we can look ahead and that means that, several months before the summer, we can start to provide an understanding of which trends are gonna be hot in fashion.

For example, this summer. We look ahead to big events like Easter. You might be wondering like, which children toys are gonna be big this Easter? And if you know, two or three months in advance, that is enough time if you're a reseller to manage your stocks and inventory and even set up campaigns and other assets to be able to serve customers interest in that trend and give yourself a competitive advantage.

Richie Cotton: Absolutely the sounds incredibly useful, especially if you've got physical products that take a bit of time to create. And ship and all that kind of stuff. Okay. So you mentioned the idea of a light graph as well, so this seems like it's maybe the next level up in terms of analytics difficulty.

So just talk us through how is the light graph generated and what can you use it for?

Bob Goodson: So any company that has  this data has the opportunity. Some, some use it, some don't. I. If you're not a social media platform, then you probably wouldn't get so much value from creating a graph out of it. But if you have a platform where users are communicating with each other, and you can.

Understand that two people like the same content and you can draw a line between them. And ultimately a graph gets built based on interests and that interest data can then be used to inform what to show a user. For example, if enough people have liked the same piece of content, you, like.

There's a good chance you'll like the thing that they've liked, that you haven't seen yet. So that can be applied in shopping environments. It can be applied in brands to show which other items within a catalog that you might like. and then of course, it is used within social media platforms already to help guide what content to show that person next.

One of the things that we realized, this came out in an interview I conducted with  Max Lev Chin, one of the PayPal co-founders, is he pointed out that Meta has an incredible, and he didn't say this because he's involved with meta, but rather just as an outsider looking in. He made the observation that meta must have this incredible wealth of like data and this massive graph of like data between content and reactions to content.

And in a generative AI world, this could be a real advantage for meta. That is proprietary data that can't easily be collected or scraped from the outside, like as text data can and Indeed has been by other AI companies. Meta has this graph of how people have reacted to text and content and yeah, max suggested the fact that this might be one of the most valuable assets that meta in fact has in the race for more effective generator ai and chat applications.

Richie Cotton: So this is incredible. So it sounds like actually based on the, this  light graph, there are gonna be all these other I guess, Richie clones or people with the same interest as me. Like the same things, but I just don't know about them at all. is that kind of the idea?

Bob Goodson: Yeah. Absolutely. And that's how, that's how you, you're provided content. If you were to join TikTok right now and start swiping. It, will quickly tune into the fact that you, you left your attention on that item for longer. so it knows, okay, that's the signal, right? It's almost like a thumbs up.

But you didn't have to even click anything. You just spent more time on that video. And then the application is capturing that like, and or rather that attention and it's feeding it into a model that says, well, what did people that like this also like? Where did they spend time? So from the moment you open the application, the moment you first pause and spend time looking at video, it's already building an understanding of your interests, but based on, hundreds of millions of people who are on those applications  viewing content.

And this graph is just in real time getting built and informing. And, and of course this can all be very simply ab tested in terms of the effectiveness of those models. And those models just get stronger and stronger, which is why. Attention is such a powerful way of building these graphs.

And this is ultimately how these graphs are are used. I just saw in the news that open AI may be building their own social network, and the speculation is that if they do so, there will be using their models to create the most compelling content possible. And then my assumption would be that.

The algorithms in a similar way that TikTok has, which is based on the tension because now there, there'd be the only two things you need to build a social network. Originally, you know, in Web 2.0, you had to build a graph of people, people had to invite each other to the app to know that they're related and connected and, Facebook was built out that way.

But what TikTok has proven out.  Is that provided you have a great source of content and you know you can get eyeballs on it and people are willing to then share that content with others, then it's quite feasible that you could build a huge social network. But what would be your content advantage like right now?

like pre LMS, if you wanted to build another Instagram. How would you do it? They're really hard to displace. Once that network is there and the attention is there, it's very, very hard to create another Instagram. Which is why there's so much attention from like a, an antitrust standpoint on these companies because.

Like, why is it so hard to display something like Instagram? Well, the content is naturally gonna go there, if you put out content, you put out a snippet about this podcast, you are not gonna take your time to put it somewhere where, there aren't eyeballs. You will naturally put that content, that snippet from this podcast in one of the platforms where, there are eyeballs.

And so if you're a competitor, how do you ever attract that content in the first place? But LLMs can create content. they can bypass that  altogether. They can just take all the content ever created. They can create new compelling content. They can hone that model based on the interactions with that content.

So the model gets even better at creating compelling content. And if content's compelling, you wanna share it with people. Well, then you've got the virality coming from sharing. So, anyone with a, with a powerful and distinctive large language model right now that's good at handling images, has the potential to create new social networks.

So we'll see, we'll see if open AI do this. We'll see if they succeed at it, but it's certainly a very interesting time as we see this intersection between the, the power and value of social networks and large languish models, which Elon Musk is clearly demonstrated in, the acquisition of x.

I mean, Twitter's just been acquired twice in a matter of a few years, right? It was acquired by, by Elon and from being a public company to being, owned by and controlled by Elon And now it's, been acquired by Xai, an AI company. Well, why, why  is there such synergy between social media and social networks and, large language models?

It's because of the data and it's because they provide a rich environment for people to interact. LinkedIn is another amazing asset which Microsoft have, and.

Peace in the chess game between these companies too, because it's, a place that people are interacting, connecting around content and a particularly refined sort of content as well.

Richie Cotton: Wow. Lost back there. So, I think first of all, the idea of this attention graph seems like from a user I point, user doesn't at all, and still providing. Like signal to the companies on What, what customers like or not in terms of the, the social media open ai creating a, social network.

so I just assumed it was because a lot of the sort of. The data that is needed to create a large lounge model. It comes from from eggs, from Reddit, from all these social networks. And actually that's kind of the secret source that like  meta and with the LAA model and X ai with grok have and open AI just wanted to sort of hedge against that.

But the idea that they can just create a load of content, attract people, do it, and you not even having to get new kind of tweets or images from users. Is using the light data from that, that's also very fascinating. Like a very interesting take there. Okay. Alright, so it seems like this stuff is incredibly valuable and I almost feel like this sort of network analysis, like graph data is one of those underappreciated areas of data science.

Can you talk me through like more generally like, who needs to care about network data? Why do you want this?

Bob Goodson: I'll pull back a level and talk about why relational information is so important and valuable or perhaps illustrate it for people. , so if you look at the last three major waves of value creation in tech, but certainly in the internet, you have search. Exemplified by Google. You have  social media exemplified by Facebook and you have large language models exemplified by chat GT and OpenAI.

So what has led to the creation of $3 trillion opportunities? I mean, I remember in the early two. The holy Grail was to create a company worth a billion dollars, or something, that level of value is what people aspired to. Well, in these three cases, Google, Facebook, and I think soon to be open ai are at.

Trillion dollar levels of of value creation. That's staggering. And there's one simple thing that connects all three of them, and that is they each pioneered a new way to relate information. They each found a new way to create a graph and relate information. So with Google, it was page rank, which at the time was.

It's become, more sophisticated as  other layers of metrics got added onto it. But for the cornerstone of it's still and was at the time, to look at which webpages are pointing at each other through links or hyperlinks, and if a page has 20 hyperlinks pointing at it and it has content about a certain subject, well we can weight that, more strongly than a page that only has one link or has no links pointing at it.

And so it was a very simple way of just. Graphing the, the content of the early web and being able to rank content so when you search, you don't have to troll through a million search results. The most important ones were at the top, and so that was a simple innovation that ultimately unlocked search and and creation of a trillion dollar opportunity.

With Facebook, the graph is the social network. It was knowing that, it was, knowing who knew who, that was. It, it was just that data was always out there in the world, but no one had ever captured it before. And Facebook built a mechanism and scaled a mechanism to effectively capture the  relationships between hundreds of millions of people.

and then with large language models, the underlying innovation is just a way of relating content. And by putting content into these models, we can now see how they're related. And this doesn't just apply to text, it applies to images or indeed any kind of information. So it's incredibly scalable across, all types of information, which is why it's so important scientifically in another way.

So I encourage people who are interested in data to think a lot about relational information because it's clearly a key unlock in each massive wave of value creation. And that just goes to illustrate. How much power there is in relating information, understanding how it's related is, critical to making the. 

Richie Cotton: It's gonna be of business now sitting ons. 

Nothing's happening with it. Do you have any advice for like, any businesses, like what should you be doing  to like even discover that this exists or.

Bob Goodson: Yeah, any, any data scientist is gonna be all over it in terms of, what models and approaches could be used. So if you're listening to this and you're, you're on the business. Side and you're thinking about the data that your business is accumulating, then yeah, highly recommend reaching out to, your friendly data scientist if you have one in, in the company or indeed, bringing in a consultant and, asking them to explore your data because, often hidden business models that are even available for some companies, if they are, attracting and acquiring information in unique ways.

Then there can be new business opportunities to serve customers. You may not have even, thought.

Richie Cotton: Yeah, certainly. It seems Social network data of like maybe existing customers, that might be a good place to start. Or even just like, yeah, if you've got like buttons built into your product, you probably want to be looking at what's going on there. And it should bring, bring you something useful.

Alright, so, I'd love to kind of go back to where  we started about the like button as a, user.

So you got things like as well, just thumbs up. You got like the, the idea of a thumbs down button as well. You've got things like review stars. Can you talk me through like how do those other user interface ideas relate back to.

Bob Goodson: It makes me, when you mentioned five Stars, it makes me think about the early days of Yelp because we had to choose how, what the scale would be to rate a business. And again, that wasn't obvious. And Five Stars has become kind of ubiquitous on the web. And certainly there were sites out there that had already used five stars, but there was nowhere near as much consensus as there is now around that convention.

you'd see Three stars, you see 10 stars, you see, various kinds of, pluses, minuses, you can plus one on something, you can plus two on something, in some environment. So there were a significant number of ways of, of rating content.

And Five Stars has sort of emerged to be one of the  most popular ways of doing that. And Yelp, that's what we went with from the. Though, but we had to consider lots of different options. And it was this combination. I think it was attractive to Yelp at the time. It was mainly our, CEO and founder. I seem to remember who was sort of, leading the way in, thinking through, what Yelp should use.

But I remember it was this, what was discussed is this combination of simplicity, but with enough granularity, Like three stars. Quite enough granularity to distinguish between say, excellent and good. In that scenario, two stars is mediocre. Three stars could be excellent or it could be just good.

Whereas five gives you like, five is exceptional, four is very good, three is okay. So it, it still allows for that kind of um. Sort of segregation, I suppose. And then stars were also attractive to us because it was a convention that was popular popularized by Michelin guides, So you have a, a one star Michelin restaurant and a two star Michelin restaurant and so on. so  the idea of a being attached to a really quality business. Had the way had been paved by the Michelin guides and by their restaurant rating system. And so I credit the Michelin guides a little bit to giving a little prestige to stars.

And I actually saw an academic paper that came out on this topic a few weeks, and it was fascinating. I dunno why I ended up reading this, but I think it was in nature. But it was paper that had, where they had studied, they were assessing different ways to rate employees and seeing whether that brought racial bias into the employee rating process.

So that was their question of the study is that.

Versus, a one to 10 rating scale. they were the two key ways. They were comparing  five stars rating system versus one to 10 scale. What they found is that in an HR context, if you rate people from one to 10, it actually brings in significantly more racial bias than if you use five stars.

Richie Cotton: It's not very intuitive either because you think, well, equivalently, these are just like arbitrary scales from like lowest to highest and they should give the same effect. But the fact that one system introduces virtual bias, the other one doesn't, that's very unexpected.

Bob Goodson: Yeah, and it's a fascinating study and it just goes to show that different rating systems, condition users to to behave differently. if you are interested in user interface, you have to get very interested in the mechanism you're using to collect responses because it turns out it makes a big difference.

This paper's just one example. that fact and in a, in a different context, in an HR context. But also if you listen to this and you're in HR  and you're into data, please look up that study and please make sure you use five star rating systems on employees and not one to 10.

Richie Cotton: Yeah. This is very interesting and even the idea of the Michelin stars, even though it the star system was very different from like consumer review stars because. One star means absolutely amazing. And then three stars unheard of best in the world, whereas the want to buy stars, it's like, well, four stars just means average.

So, yeah. Out of like subtle changes in the university interface, it's gonna have very, very different implications. okay. So for anyone who's interested in user interface design, do you have any advice on how you make sure that you're doing it well and all these kind of subtle cultural issues are being taken care of?

Bob Goodson: For rating systems. I mean, I suppose. the number one thing in UI is, and anyone that does UI knows this and has probably been trained in it, but it really is this simple. It's use the convention that is most familiar to your  users. That's it. never want to try and invent new elements or design new things.

I know that's ironic coming from me because I have done, created a few new user interface conventions over the years, including, company, quid was the first to put network exploration visualizations into the web browser. So there were applications in chemistry and physics that allowed you to map information.

Protein graphs, for example. There was an application called Cyto Scape which I dunno if it's still around, but back in the day, that's what was used by scientists and, but it was a desktop application. you could visualize, a thousand nodes on a graph and navigate them visually, but it was a desktop.

ever been able to make those kind of graphics work in a browser. And quid, we were the first to do that, and we made it work with 3000 nodes to interact in a browser. So, there have been some conventions that I've, I've pushed on. But, even when  creating new things like an element, interactive element that you, that has never existed before, you always have to start with what are the users already using that is as close to this.

it exists. So you look to the applications that are most popular, that are most popular with your users, and you go and you look around the interface and you say, how is that happening? So Excel is an example of an extremely popular data interface. So before you do anything in terms of data.

You kind of wanna look at Excel and say, what do their dropdowns look like? What do.

And wherever possible you want to sort of, mirror those because that's what people are used to. So when they look at your interface, you just do, you wanna make it as intuitive possible. the mantra is, don't make me think. If a user has to pause and think, what will it do if I click that?

Then you've messed it up. you just want to take all that friction out of it for  people. And in Web 2.0, that's what everyone was obsessing about. there was a book. Call, don't make me think, which became a sort of, bible for user interface designers on the web, but that was the simple idea underneath it.

Like, always look for the closest possible preexisting paradigm, and then use that if you can.

Richie Cotton: Yeah, don't make me think is is a great. I remember that earlier in my career. But also I, I have to say I have used cyto scape back in an earlier phase of my career. I

Bob Goodson: serious props.

Richie Cotton: yeah, I was uh, looking at protein networks for that and yeah. That's one of the challenges with the, visualizing graphs is like when you've got a small graph, looks brilliant, as soon as you start hundreds or thousands of nodes is a total mess and visualizing it becomes a real challenge.

Yeah. So, yeah. Using, uh.

Bob Goodson: if you've ever listened to, if you've ever used Side escape, put it in the comments. Tag me and Richie because we want to hear from you. Not a widely used application.

Richie Cotton: two, maybe three other people have used it, but uh, yeah.

Bob Goodson: If you're listening, you know, you know it's, you we're talking to, so. 

Richie Cotton: Wonderful. Alright, so, do you have any final advice for people who are interested in working on, like data and analyzing it?

Bob Goodson: There are a couple of paths to it, right? If you have some within your organization because you work at, a retailer, then you've got a, like, feature within a retail environment. I think that would be very interesting to explore and to see if you can then use that to inform what shows up next or what related products you can give to, to the users.

If you are in a social media environment, you're probably already making very good use of it. And if you're just a, a business that's trying to figure out, how can we make better use of social media data in our day-to-day operations? We see a lot of companies right now trying to be more in tune with what's trend, whether you're a brand, whether you're a service, whether you're a.

Social media is only becoming more and more important. You can see that from the growth stats, from the usage stats. And it's the place where people are going there to search for new ideas, new products, and, it's a huge  source of influence, but it's also an amazing source of data to understand what people want, what they need, what they think, and feel about the world around them.

By quid we call that customer context. 'cause it it's about understanding what's the context that your customers are operating in and companies that can more quickly adjust and operationalize around these trends do have a competitive advantage. So you can learn more about quid or there are obviously other companies in our space that you can read more about and connect with to, to yeah, sort of, adjust to the trends as they happen.

Richie Cotton: Yeah, I think like. Everyone's aware that social media is everywhere, and I'm sure every company has a strategy for posting on social media and they're maybe doing bits of things, but I feel like not very many companies are tapping into the full potential of what social data can do to help them with their own business.

Bob Goodson: Definitely, and I'll just add to that, that even those of you that have sort of explored social data, maybe 10 years  ago, five years ago, what was possible. Then is completely different to what's possible in a world of large language models because the insights that you can now draw from social media content and media content and other forms of public content are far deeper, far more meaningful and far more cost efficient.

Access than they were before. So if it's something you looked at in the past and maybe got bored of it or thought there wasn't enough value, highly recommend people get interested in this topic again, because in the era of LLMs, a whole new sway of things are possible and can be set up and automated, for the business.

So yeah, it's a pretty exciting time to be in, in data and data analysis in general. and certainly there's, there's a lot of new potential with social media data.

Richie Cotton: Certainly exciting times. Right there with you on that. Alright, finally, so I always want new follow recommendations. So can you tell me who work are you most excited about?

Bob Goodson: there's a book that I'm reading right  now that I absolutely love. It did come out a few years ago, but I only recently was recommended. It, it's called Range and the SubT is why generalists thrive in a world of specialists, and I love this book because it does deliver on what it says in a title.

it is explaining and advocating for why a diverse set of interests and following your passion and curiosity across your lifetime creates meaningful advantage over time. But it's more than that. it actually offers a kind of framework for great ways to think and great skills to develop.

In a world of, of ai, it's not trying to do that. It's true. It was trying to do something else. But I think it's a very important book to read when it comes to sort of plussing your career development. Many people right now are thinking about what does AI mean for me? What does it mean for my job, my industry?

and rightly so. I think, all thinking about that and range is, is a perhaps a slightly lesser known book for  contributing to your thinking about that and thinking about. Thought patterns and things you can develop that will be more robust and more long lasting than some more specialized skills.

Richie Cotton: That's very interesting and just from your. Description of it, it sounds like I can now justify my complete lack of career direction going all over the place. And it's like, oh, I've got more range. That's wonderful. maybe that has benefited me. So yeah, maybe don't overplan your career read range and go where the wind blows with your career.

Wonderful. Alright. Thank you so much for your time, Bob.

Bob Goodson: Thanks having me.