Lenny's Podcast: Product | Career | Growth - Becoming evidence-guided | Itamar Gilad (Gmail, YouTube, Microsoft)

Episode Date: September 21, 2023

Brought to you by Ezra—The leading full-body cancer screening company | Vanta—Automate compliance. Simplify security | LinkedIn Ads—Reach professionals and drive results for your business—Itamar Gilad is a product coach, author, and speaker with over two decades of experience in senior product roles at Google, Microsoft, and various startups. He is also the author of Evidence-Guided: Creating High-Impact Products in the Face of Uncertainty and publishes a popular product management newsletter. In today’s episode, we discuss:• What it means to be “evidence-guided”• How to think about your KPIs as metric trees• How to prioritize ideas using the “confidence meter”• The GIST model for roadmapping• Common mistakes with ICE• Advice for using evidence to challenge gut-driven founders—Find the full transcript at: https://www.lennysnewsletter.com/p/becoming-evidence-guided-itamar-gilad—Where to find Itamar Gilad:• Twitter/X: https://twitter.com/ItamarGilad• LinkedIn: https://www.linkedin.com/in/itamargilad/• Website: https://itamargilad.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• Twitter/X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Itamar’s background(04:35) How his time working on Gmail shaped his philosophy of “opinion-based” development(08:35) Lessons from developing Gmail’s tabbed inbox (13:40) A brief overview of Itamar’s book, Evidence-Guided(14:30) Balancing founder creativity with an evidence-based approach(17:32) Advice on how to push back against founders(19:36) Signs you aren’t as evidence-guided as you may think(21:13) Itamar’s GIST model for becoming more evidence-guided(23:51) How to set overarching goals using his “value exchange loop”(28:45) North star metrics vs. KPIs(33:47) Using “ICE” to assess the value of ideas(37:39) Itamar’s confidence meter(44:28) Speed of delivery vs. speed of discovery(46:14) How to apply Itamar’s frameworks based on company type and stage(49:09) First steps in becoming more evidence-guided(50:21) Next steps in testing(55:41) The task layer in the GIST framework(1:02:54) Thoughts on roadmapping(1:04:56) How OKRs fit into the whole picture(1:07:11) Lightning round—Referenced:• Itamar’s presentation slides: https://itamargilad.com/wp-content/uploads/2023/09/Podcast-Slides.pdf• What differentiates the highest-performing product teams | John Cutler (Amplitude, The Beautiful Mess): https://www.lennyspodcast.com/what-differentiates-the-highest-performing-product-teams-john-cutler-amplitude-the-beautiful-mess/• Evidence-Guided: Creating High-Impact Products in the Face of Uncertainty: https://itamargilad.com/book-evidence-guided/• The co-founders of Google in Forbes: https://www.forbes.com/profile/larry-page-and-sergey-brin• Kanban: https://www.atlassian.com/agile/kanban• Jira: https://www.atlassian.com/software/jira• The ultimate guide to OKRs | Christina Wodtke (Stanford): https://www.lennyspodcast.com/the-ultimate-guide-to-okrs-christina-wodtke-stanford/• Amplitude: https://amplitude.com/• The ultimate guide to A/B testing | Ronny Kohavi (Airbnb, Microsoft, Amazon): https://www.lennyspodcast.com/the-ultimate-guide-to-ab-testing-ronny-kohavi-airbnb-microsoft-amazon/• ICE framework: https://growthmethod.com/ice-framework/• Sean Ellis on LinkedIn: https://www.linkedin.com/in/seanellis/• RICE scoring model: https://www.productplan.com/glossary/rice-scoring-model/• Idea Prioritization with ICE and the Confidence Meter: https://itamargilad.com/the-tool-that-will-help-you-choose-better-product-ideas/• Assumptions Mapping: https://designsprintkit.withgoogle.com/methodology/phase2-define/assumptions-mapping• What is Dog Fooding, Fish Fooding a Product?: https://matt-rickard.com/fishfooding-dogfooding-product• SVPG books: https://www.svpg.com/books/• The Lean series: https://theleanstartup.com/the-lean-series• Dreaming Spanish: https://www.youtube.com/c/DreamingSpanish• ElevenLabs: https://elevenlabs.io/• Lennybot: https://www.lennybot.com/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [e-mail].—Lenny may be an investor in the companies discussed. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.lennysnewsletter.com/subscribe

You fake door test. You do a fake door test. You do smoke tests, Wizard of Oz tests. We used a lot of those in the tabbed inbox, by the way. One of the first early versions was actually we showed the tabbed inbox working to people, but it wasn't really Gmail. It was just a facade of HTML. And behind the scenes, and according to the permissions that the users gave us, some of us moved just the subject and the sender into the right place. So initially, the interviewer kind of distracted them and then showed them their inbox. and in it the top 50 messages was sorted to the right place, more or less if we got it right. And people were like, wow, this is actually very cool.

But it gave us some evidence to go and say, hey, we should try and build this thing. Welcome to Lenny's podcast, where I interview world-class product leaders and growth experts to learn from their hard-win experiences building and growing today's most successful products. Today, my guest is Idamar Galad. Itimar is a product coach, author, speaker, and former longtime product manager at Google, where you worked on Gmail, identity, and YouTube. He also just published an awesome new book called Evidence Guided, creating high-impact products in the face of uncertainty.

Idemar has an important perspective on why and also how you can push your team and organization from an opinion-based decision-making process to a more evidence-guided approach. In our conversation, Idemar shares a number of very practical and handy frameworks to do just that, including the confidence meter, metrics, trees, GIST, and the GIST board, Plus, his take on how people often misuse ice for prioritizing ideas, also how you could make your OKRs more effective, and so much more. Enjoy this episode with Itamar Galad after a short word from our sponsors. This episode is brought to you by Azra, the leading full-body cancer screening company.

I actually used Ezra earlier this year, unrelated to this podcast, completely on my own dime, because my wife did one and loved it, and I was super curious to see if there's a anything that I should be paying attention to in my body as I get older. The way it works is you book an appointment, you come in, you put on some very cool silky pajamas that they give you that you get to keep afterwards, you go into an MRI machine for 30 to 45 minutes, and then about a week later you get this detailed report sharing what they found in your body. Luckily, I had what they called an unremarkable screening, which means they didn't find

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Get started today. Itmar, thank you so much for being here. Welcome to the podcast. It's a pleasure being here. Thank you for inviting me. It's my pleasure. I thought we'd start with the story of your work on Google Plus and Gmail and how those experiences formed your perspective on how to build successful product.

Can you share that? story? Google Plus was my first experience at Gmail. I joined Gmail in August 2011. And the first thing they asked me is let's connect Gmail with Google Plus. If you're hazy about the story, back then Facebook was massive. It's still massive, but then it was growing like mushrooms. People were spending hours that really freaked out Google. And the solution, the obvious solution, was to launch a social network of Google called Google Plus. And we all believe in this thing. it really caught on very well initially. We all used it.

We all believed in it. So our mission was to build this thing. And Google really cut no costs. It created a whole new division within Google. And it created a whole strategy around Google Plus. And we had to connect Gmail and YouTube and search to Google Plus to make them more personalized, in a sense, and more social. So that was the idea. And we went on and we loved.

a series of features in Gmail for a couple of years, honestly. And Google Plus itself became this massive project, very feature-rich and with a lot of it redesigns and iterations. And none of it worked. Turned out people actually didn't need another social network. People didn't love it. People didn't use it. Eventually in Gmail, we rolled back all the Google Plus integration a few years later, and

Google Plus itself was shut down in 2018. So putting aside all the tremendous waste that went into this, all the millions of person hours and person weeks, in hindsight, not only did Google bet on the wrong thing, it missed much easier opportunities. So just not far from Google's headquarters, there was WhatsApp, not very famous in the US, but they actually created massive impact. Hundreds of millions of people were using their stuff. and they became a threat to Facebook much more than Google. So Google missed the opportunity of social mobile apps like WhatsApp, like Snapchat, etc. And for me, this story kind of was the epitome of what I call today opinion-based development. We come up with an idea, we believe in it, all the indications show it's good,

maybe the early tests show it's good, then we just go all in and we try to implement it. And I made this very mistake many times at the product manager. I was the guy pushing for the ideas. So for me, this was kind of a turning point. I felt we need to adopt a different system. And just before you move on to the next story, how big was the team roughly, how many years was it spent on this area,

just to give people a sense of the waste, as you said? So there was a tremendous earthquake inside Google to create the Google Plus team. Teams and the entire divisions were kind of thrown apart and reformatted. And I think at its peak it was about a thousand people inside. Wow. It was a division, the size

of Android and docks and a really sizable thing. They're on their own buildings. It was taken from the playbook of Steve Jobs, you know, create this whole secretive project inside and just run like hell. Yeah.

I remember though Facebook was really scared. I remember they shut everything down. It was like a code DefCon one situation. too. So it really scared Facebook at the same time. Yeah, it's true. But at the end of the day, neither Google's advertising revenue was affected, neither was Facebook affected. So it turned out this idea was not that necessary after all. Yeah. Okay. So that's an example of something that didn't work because it was opinion-based software. I think the phrase used. And then there's a different experience with tabs, I think, with Gmail. That's right. So Google,

is a very successful company. It's not for me to criticize it or to, in hindsight, kind of say, you guys need to be better. And some of the people that were behind Google Plus were some of the smallest leaders, and I still think there are, despite this story, if you look back at the history of Google, how things started in the first decade or so,

Google was what I call an evidence-guided company. So essentially, it put a high premium on focusing on customers, on coming up with a lot of ideas, on looking at the data, looking at how these ideas actually worked out. They weren't shy about launching betas and things that were very rough and incomplete and learning from that. And then they expected people to take action based on the results. So fail fast is a very famous paradigm.

And so you had to kill your project or pivot it seriously if it didn't work out. And I think had we kept fail fast, it would really have helped Google Plus if we had this mentality. But for some reason, with Google Plus, Google put this playbook aside and used a different playbook, which I call plan and execute essentially. But I think inside Google, the DNA still existed. So inside Gmail, the next project after Google Plus was the tabbed inbox. So it started with the – it was kind of the reverse of Google Plus. We started at a very small idea that no one believed in. And we started looking what's behind the city, what's the goal, what's the problem actually

we're trying to solve. It turned out that a lot of people were receiving social notifications and promotions, etc. And most of them were very passive. They weren't clearing their inbox. They were just living in this world of clutter. And I came up with an idea how to fix this. I was sure it was great. I wanted to push it, you know, plan and execute.

But my colleagues were like, hold on, we actually tried this. We have a bunch of ideas to help people organize their inbox. They're not using it. Why is your idea good? So that sent us kind of, me and my team, into searching, into researching these users, into establishing a goal that was much more user-centric and then thinking of other ideas. And then we started testing them much more rigorously. And basically, we started testing on our own inboxes, and then we recruited other dog fooders.

other Googlers to test the same inbox. Then we put it outside for external testers. We did disability studies. We did data. We built a whole data mining team and a whole machine learning team to build the right categorization. And we ended up with a solution that turned out to be very successful for a lot of these passive users.

And this was a surprise to a lot of people because most of my colleagues and most of the people I talk with actually know how to manage their inbox. So for them, that solution makes complete nonsense. Like, splitting promotions and social to the side sounds like the stupidest idea.

But there's about 85% of the population, 85% to 88% that absolutely love it. And today Gmail has about 1.8 billion active users according to Gmail. Most of these users are using this feature. So it was a pretty high impact feature as well.

And the feature specifically, just in case people don't totally get it as the promotions folder in the social, I think. Yeah, there are a couple more that you can enable in settings if you like. I use it. I love it. Except it puts my newsletter in people's promotions folder. Who do I talk to about that?

Yeah, newsletters are very complicated scenario for the categorization engine. Yeah, we just need an exception for my newsletter and then we're good. Okay, but go on. So in hindsight, I was asking us to say, Why was this project so different? And I think the reason is that we didn't have that much confidence in our opinions.

We had opinions, we had ideas, but we didn't just go all in and just let's build it. We actually used an evidence-guided system. I think that's not unique just to Google. I think every successful product company out there that you look at, Amazon, Airbnb, anyone you will check, at least in their best periods, they found a way to balance human judgment with evidence. They didn't try to obliterate human judgment and opinion just to

supercharge them with evidence. And they came up with very different models. Apple is another example. But the principle still holds in all these companies. Awesome. So you took that experience and all the experience you've had from coaching, product leaders,

working with companies, and you wrote this book called Evidence Guided, which people on YouTube could see sitting there behind you. And so I want to talk through some of these stories and then some of these other lessons and frameworks that emerged. But maybe just to start, what's the elevator pitch for this book? So this is a book for people like us, product people, who want to bring evidence-guided thinking or modern product management, if you like, into their organizations. There's a lot of challenges. It's not simple.

We all read the books. We all know the theory. We all know some parts of the system. It tries to give you a system how to do that. It's a meta framework that kind of helps you lift your organization in the direction of evidence guidance, if that's what you want to do. So going back to the story briefly, before we get into the frameworks and lessons of the book, in the first example of Google Plus, basically came top down, hey, we need to build a social network, go build it. Obviously, that happens at a lot of companies. I don't know if there's an easy answer to this, but are there cases where it does make sense to approach it that way?

obviously Apple is a classic example of Steve Jobs is like, we need to build an iPhone. I don't know if that's exactly how went, but are there instances where it is worth just approaching new product ideas that way based on kind of the experience and creativity and insights of the founder? Or is your thinking it should always come from this evidence-based approach? I think the founders are very important, especially in the startup and scale-ups phase.

They come up with many of the most important ideas. And it's super important that they have the space to express and to push the organization to look at those. However, it's not about shutting them down. It's about looking at them critically. You need to create the environment in your organization where the leader comes and says, you know what?

I talk to these three customers. I figured it out. Here's what we need to do in the next five years. You need to ask, where's your evidence? And by the way, the example you give, that's a classic example. Steve Jobs, he just brainstorm in his, I don't know, kitchen, the iPhone, and then just told it in to build it.

That's the story Steve Jobs told, but it's not the real story at all. Now we know what actually happened, and the iPhone has actually a story of discovery, of trial and there are multiple projects to do with multitash, with phones. Most of them failed. Steve Jobs was the architect. They kind of managed to connect the dots and eventually come up with this perfect device, but it wasn't actually the, the creator, it wasn't his brand child, he was actually against it for a while. But over time,

as he saw the evidence, as he saw what this thing can do, as he saw the demos, he was able to piece together something that was very useful. That's really important insight. People that are hearing this might feel like, okay, I like this idea of pushing back and encouraging the founders to make it more evidence-guided. In the case of, say, Google Plus was it even possible? Could have come to Larry and Sergey and be like, here's all this data I've gathered that tells us this is not going to work. Do you have any advice for how to push back and encourage the founders and execs to really take that the counterpoint seriously or really kind of vet their idea? So another nice thing about Google is that it's a very open culture.

And people are not shy to tell even Sergey and Larry that they are wrong. And they do this all the time in certain forms, right? it's not, you need to know the right channels. But there was a very big discussion about Google Plus and whether it's the right thing to create a clone of Facebook. There was a very public internal discussion. I think what I would change is not have this discussion based on opinions. Because when you have the discussion, you come with your own opinions.

Usually the most senior person's opinions will win. That's just the way it is. If we had come with data, hard data, we said, listen, things are not actually panning out the way you guys are all expecting. What can we do? Should we continue? Should we pivot this? I think the discussion would have done better. Now, I'm doing a huge disservice. I was not in all the discussions. I know probably in Google Plus, they were very serious discussions happening along these lines. but it just as a general trend, I find that evidence is very empowering

for us smaller people in the organization or mid-level managers to be empowered to challenge the opinions. Is there anything tactically found to be useful and effective in giving people say they don't work at Google? They work at companies where founders and bosses and execs are not as open to challenge.

Is there any tactically found about how to present a counter proposal or like, hey, I have this data that we should really pay attention to? I think if you come with data, if you run a secret experiment and you come back and you show them, you usually get one of two results. Either they get extremely mad at you

and they tell you to get back to work and to do what you were told. And in that case, probably you need to start polishing your resume and look for another place, either inside the organization or outside it, because that person is not being reasonable, to be honest. But the more common case is there,

they are pleasantly surprised. And that's what happened with Steve Jobs as well. He was against phones, but then people showed him all sorts of evidence that Apple can make a phone. It was against multi-touch initially, but then he changed his mind. There was a lot of, like, back and forth. So even Steve Jobs, given evidence, was willing to flip. And I say this in many organizations. So evidence is so powerful. That's why this is the principle I based the book on. you have this concept of being evidence guided. People listening may feel like, hey, we're evidence guided. We're on experiments.

We make decisions using data. Oftentimes they aren't actually. And so what are signs that maybe you're not actually that evidence guided, as evidence guided as you think you are? I think there's a few telltale signs that I look for. First, the goals are very unclear. Either there are many or they're very kind of obscure and vague or they're about output. there's misalignment.

So the goals part is not there. Usually this goes hand in end with metrics, missing metrics, or just using revenue and business metric, but there's no user-facing metrics. So that's another telltale sign. Then there is a lot of time and effort spend on planning, especially on road mapping, creating the perfect roadmap,

which really can consume a lot of time of the top management, and PNs, etc. Then as you go down, you see there's not a lot of experimentation. And if there is experimentation, there's not a lot of learning. And finally, another tell sign is that the team is disengaged. So the engineers are kind of getting the signal that what they need to do is deliver. They're focused on output. That's what they're measured on.

So they're kind of disengaged. They're disengaged from the users, from the business. they don't care that much. That's usually a sign of, it's usually something that you can fix by adopting a more evidence-guided system. Okay, so let's dive into your approach to becoming more evidence-guided.

In the book, you share this model that you call the just model, which is kind of this overarching approach to building product that almost forces you to be more evidence-guided. So let's just start with, what's the simplest way to understand this just model? With your permission, I can show a few slides.

Oh, let's do it. Maybe that will help. Yeah. And then, yeah, a good excuse to go check it. Check this out on YouTube. All right. You're seeing this. So this is the gist model, goals, ideas, steps, and tasks.

And essentially, it tries to break the change, which is a really big change for a lot of companies into four slightly more manageable parts. They're still big, but each one you can tackle on its own. And that's kind of the reason I kind of split it. And goals are about defining what we're trying to achieve. Ideas are hypothetical ways to achieve the goals. Steps are ways to implement the idea and validate it at the same time. So essentially build measure, learn loops.

And tasks are the things we manage, you know, in Kanban and Jira and all these good tools. These are the things that your development team is usually very focused on. And just listening to this, a lot of this will sound familiar to you because GIST is not, a brand new invention. It's a meta framework that puts in place a lot of existing methodologies. It's based on lean startup, on design thinking, product discovery, growth. There's a lot of all of these things here. It just tries to put them all into one framework or one model. So what's the simplest way to think about what this model is meant for? Is this how you think about your roadmap? Is this how you plan? What is this trying to tell

people to do differently in the way they build product broadly? I would say these are four areas that you need to look at and ask, are we doing the right thing in each? And each one you may need to change or even transform. And as I go and explain each one of those, I'll give you basically three things. In each chapter in the book, I try to touch on three things. The principles behind them, the frameworks or models that implement the principles, and then process. and the process honestly is the most brittle part and the one that you will need to change and adapt to your company

because not two companies are exactly the same and it's very tempting when you write a book not to give any process but that's the part that people actually want the most so it's included as well but just be aware that you will have to change this process awesome okay so we're going to talk about each of these four layers before we do that where do you like vision and strategy fit into this Do they bucket into one of these four layers? And how do you think about strategy and vision?

That's a great question. So there's this whole strategic context that is outside of just. Just is not trying to tackle that. It assumes it's in place. There's another huge blob, which is research. Just is not about research. It's more about discovery and delivery. But strategy is extremely important,

and you can use some of the tools we will talk about to develop your strategy as well. In many companies, the strategy is just a roadmap on steroids. It's more plan and execute just on a grand scale. And Google Plus, again, was a strategic choice, actually, if you think about it. So in the book, there is a chapter where I touch on strategy and I explain how the same evidence-guided methods are being used by companies to develop their strategy as well. Awesome. Maybe one last context question.

So people might be seeing this and thinking, okay, cool, I have goals. I have ideas, steps, I have tasks. I'm already doing this. What is this kind of a counter or reaction to? What are people probably missing when they're seeing this? And they're like, oh, I see. This is like what we're not doing. And this is the most important.

This is something we should probably change. And I know we'll go through these in detail too. I think talking about each one will help. Let's do it. Let's do it. But we can talk about in each level what's actually being done. So when people say, I have goals, usually they take the goals layer and use it as a planning session. They talk about what shall we build by when, what are the resources, and that's

actually not goals at all. That's planning work. Cool. Let's talk about goals. And I know part of this is OCR related too, so I'm excited to hear you're taking OCARs. Oh, that's a whole different discussion. You had Christina, you had a real expert over there, so I doubt I can add more to that. But it's true. OCR is all part of it. But let's start with goals. What are goals supposed to be. Goals are supposed to paint the end state to define where we want to end up. And the evidence will not guide you unless you know where you want to go. And in many companies, what you have is goals at the top for revenue, market share, whatever it is. And then a bunch of siloed goals for each department. There's engineering goals. There's design goals. There's marketing goals, etc.

And that actually pushes people into different vectors and it's really hard to decide. And I would that in evidence-guided companies, and he worked for a few, so probably you've seen this, they use models in order to construct overrouting goals for the entire organization. One of the models I show in the chapter about goals is the value exchange loop, where basically the organization is trying to deliver as much value as it can to the market and to capture as much value back. And by creating a feedback look between these two, you are actually able to grow very fast. Now, I would argue that you want to measure both of this and to put a metric on each.

And the metric we usually use to measure value delivered is called the North Star metric. I know you wrote an article, a very good article about it. Thank you. And in it you listed dozens and dozens of companies like leading companies and what they consider the North Star metric. Super interesting. I would argue that what they told you is what is the most important metrics we measure. what is the number one metric for us.

But it's not what I call the Northstone metric. The Northam metric measures how much value we create for the market. For example, let's take WhatsApp. WhatsApp for a very long time measured messages sent. Because every message sent is a little increment of value for the sender, the receiver is free, it's rich media. You can send it for anywhere in the world. Compared to SMS, that's huge value.

So in year one, we have a billion messages being sent. and the year to 2 billion, probably we doubled the amount of value. In Airbnb, I think one of your key metrics, or the real Nostal metric was Knights Booked. I don't know if it was still the case while you were there. Yeah, absolutely. And there are examples like this in amplitude, for example, they measure active learning users or weekly active learning users,

which are users that found in the tool some insight that was so important and they shared it with at least two other users and they consume it. So it's a very powerful thing to point at this metric, this is the most important metric, combined with the value metric that we want to capture, revenue, market share, whatever it is. Once you have these two, you can further break them down into what I call metrics trees.

So there's a metric tree for the Nostal metric, and there's the metric tree for the top KPI, the top business metric, which you see here on the left side in blue. And usually they overlap. So you might find in the middle some metrics that are super, super important because moving them actually moves the needle on everything else.

Can you clarify again the difference between what you call this top KPI versus North Star metric? So the North Star metric is measuring how much value we're creating for the user, the core value that they're getting. In this case, this is some productivity, suite. So this is number of documents created per month, for example, because we think that every document created, maybe it's a small document, I don't know, AI is in fashion now, is a little incremental value. So that's the number we're trying to grow. The top KPI is what we expect to get. It should

be revenue or profit. I see. This is the value exchange. I see. One is what users are getting, one is what you're getting back from them. Basically, what the business is, how the business is benefiting. Awesome. I think this is a really important concept of metric tree. I think a lot of people think they have something like this in mind where they're just like, cool, here's our North's our metric. Here's the levers and things that we can work on to move that. But I think actually mapping it out the way you have it here where it kind of goes layers and layers deep to all of the different variables that impact this metric, not only is a way to think about impact and goals and things like that, but also helps you estimate the impact of the experiment you're potentially

thinking about running. So if you're going to work on something at the bottom here, like activation rate, like say you move that 10%, how much is that going to impact this global metric? It's probably a very small amount. This is a very important one, and we will talk about impact assessment shortly. This helps with it. It also helps with alignment because the entire organization is trying to move these two metrics. It's the two sides of our mission, essentially. We have the mission that's the top objective of the company. And these are the two top most key results, if you like, most things. So when you go and work with another team and you say, hey, why don't you work on

my project? They might say, you know, this project, this idea, actually might move the Nostal metric more than your idea. And that helps you guys align. And I've seen cases where Team B put aside their own ideas to jump on the ideas of Team A because of this model. It also creates an opportunity to give some sub-metrics to teams to own on an ongoing basis. So it creates a little sense of ownership as well and mission within the tree. It also helps you figure out what teams you should have, which teams have the biggest potential to impact the metric. Another thing that happens in a lot of organization, the team topology reflects the structure of the software or some hierarchical model where we want to organize the organization a particular way. But if you start with a matrix tree,

you can try to arrange the topology around goals. And sometimes you need to need to be a very important. And Sometimes you need to readjust. It's not a constant reorg, but from time to time, you will realize the goals of change, and we need to reorganize. So the tree helps visualize that as well. I think for people that are listening to this and thinking about this, I think the simplest way to even think about this is basically there's a formula. There's like a math formula that equals your North Star metric or your revenue or whatever

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And they may come from, as you said, the founders, the managers, the stakeholders, from the team, from research, from competitors, from, we're flooded with ideas. And what usually happens inside the organization is some sort of battle of opinions or some sort of politics sometimes or highest paid person's opinion. Hippo, you had Roni Cahvio invented this term in your show. What doesn't happen is very rational, logical decisions. These are the best ideas. Because it's really, really hard to predict, honestly. There is so much uncertainty in the needs of the users, in the changes in the market, in our

technology, in our product, in our own organization, it's almost impossible to say this idea is going to be the best. But we do say this because we have cognitive biases that kind of convince us that this idea is far superior to anything else, and it's definitely the right choice. In order to avoid this, what we want to do is to evaluate ideas in a much more objective and consistent and transparent way. In the book, I suggest using ICE, impact confidence and ease. I think I have a slide coming on this. So impact confidence in ease, which is basically a way to assign three values to each idea. The impact tries to assess how much impact it will have on the goals. And that's why it's so important that we have

very clear goals and not many, how we're measuring the ideas on the Nostal metric, on the top business KPI, on a local metric of the team. Whatever it is, let's be clear about it. And then let's evaluate the ideas against this thing. Is is basically the opposite of effort, how easy or hard it's going to be. But both of those are guesstimates. Both of those are things we need to estimate. I would argue that just by breaking the question to these two questions, we usually have a slightly better discussion than just my idea is better than yours.

But then there's the third element, which is confidence, which tries to assess how sure are we or should we be. about our first guesstimates, about the impact and the ease. It's interesting to use the word ease because I think it's usually effort. Is there you kind of make it positive? Is that an intentional tweak you make? I'm using the definitions of Sean Alice. Sean invented ice.

You know, Sean. I don't know if you had him yet, but he's... I haven't had him on yet. Yeah, for the people who don't know him, Sean is amazing. It's like one of the fathers of the growth. He coined the term growth hacking. And he very popularized the concept of product market fit. He created ice.

He created a bunch of things that we use in product and we don't even know. I didn't know he came up with ice. Okay, cool. So the original version of ice is ease instead of effort. Exactly, yeah. Fun fact. A lot of your viewers are wondering where is the all, because there's another variant of this called rice, where it is rich as well.

I prefer ice because I prefer to fold the rich. reach into the eye for various reasons, but both are valid, both are equivalent in a sense. I'm in your boat. That's exactly how I think about it. I think people over complicate this stuff and try to get so many math formulas involved with estimating impact. And I feel like these are just simple heuristics to kind of bubble the best ideas to the top. It doesn't have to be a perfect estimate of impact and confidence and all those things. So I think the simpler is better. It always ends up being a spreadsheet. People always have these tools to estimate these things, but like a spreadsheet, Google Sheets.

So, yeah, you're actually leading me to my next point. So when you come to estimate impact, you will realize it's the hardest part. So sometimes it's just a gut feeling and it's a guess. And sometimes it's based on some spreadsheet or some analysis and back of the envelope calculation you've done. And I think that's legitimate. Sometimes these things do show you some things you didn't think of. And sometimes the best case, it's based on tests.

You actually tested it. You interviewed 12 customers. you show them the thing and out of those only one actually liked it. You should reduce your impact based on that usually or you do other types of tests. We will talk about tests in a second. What happens is that people tend to just go with gut instinct and then give themselves a high confidence. They say it's an eight and I'm pretty convinced so it's eight for confidence. And I found this a bit disturbing because it kind of subverts the whole system.

So I wanted to help people realize when they have strong evidence in support over their guesses and when it's weak evidence. How to calculate confidence in a sense. And for that, I created a tool called the confidence meter, which you can see here, this colorful thing. Should I go and explain it? Yeah, let's do it. And then again, if you're just listening to this, you can check this out on YouTube and you can see the actual slide. All right. Awesome.

So basically I constructed it a bit like a thermometer. It goes from very low confidence, which is the blue area or the upper right, all the way to high confidence, which is the red area. And you can see the numbers going from zero to 10, where zero is very low confidence. We don't know basically anything. We're just guessing in the dark. And 10 is full confidence. You know for sure. This thing is a success.

No doubt about it. And across the circle, I put various classes of evidence you might find along the way. So, for example, starting at the top right, all of this blue area is about opinions. It could be your own self-confidence in the idea, your self-conviction. You feel that it's a great idea. Guess what? Behind every terrible idea that was ever, someone thought it was great. That gives you 0.01 out of 10.

Maybe you created a shiny pitch deck or a six-page document that explains in detail why this is a great idea. Slightly harder to do, but still very low confidence. Maybe you connected it to some theme. You know, it's about the blockchain. Well, sorry, the blockchain is out of fashion. What's hot right now? AI. Exactly.

AI. It's about AI. That makes it a good idea? Absolutely not. Or the strategy of the company, that's another thematic support. Thousands and thousands of terrible ideas are being implemented. right now as we speak based on these themes. So all these things combined can give you a maximum 0.1 out of 10,

according to the tool if you follow it. Then we move into slightly harder tests. One is reviewing it with your colleagues, your managers, your stakeholders, the idea. They don't know it either. They don't have a crystal ball. They're usually not the users. They cannot predict. But they can evaluate it in a slightly more objective way and maybe find flaws

in your idea. On the other hand, groups tend to have biases too, politics, group think. So groups can actually arrive sometimes at worst decisions than individuals. There are some research to that. Next are estimates and plans, so you may do some sort of back-off and envelope calculation, or your colleagues might go out and try to evaluate the ease a little bit better. That gives you a little bit more confident, but still, we're at a level of guesswork at this point. Next, we're moving to data. And data could be anecdotal, so you find a few data points dotted across your data, or you talk to a handful of customers,

or maybe one competitor has that same idea. In many companies I meet, if the leading competitor has this feature and we think it's a good idea, validation is done. Let's launch it. That's it's a great idea. We need to do it. Never works, honestly. You should not assume that your competitor actually.

knows what they're doing any more than you do. Data could be also what I call market data that comes from surveys, from assessing a lot of your data by doing a deep competitive analysis, and there are other methods where you create a larger data set and you contrast your idea against it. Finally, to gain medium and high confidence, you really need to build your idea and test it. And that's where the red area is.

So there's various forms of tests. We will talk about them if we have time. And they give you various levels of confidence. Awesome. This is a very cool visual. We'll link to an image of this in the show notes too if people want to check it out. I think what's awesome about this is you could just use this as a little tool on your team of just like, where are we on long this spectrum?

Like we think the impact of this is very high, but we're probably in this like blue area of confidence. And so let's just make sure we understand that. and it's really clear language to help people understand. I see if we had this, it'd be a lot more confident. So you can also tie your investment into the idea based on the level of confidence you had found, essentially. So early on, you want to do the cheap stuff just to get more confidence and then you can go and invest more.

If it's a really cheap idea, you can jump to a high confidence idea, a test, you can do an AB experiment, early adopter program, whatever it is, and then launch it. Some ideas you don't need to test. Sometimes expert opinion is enough. If you're just changing the order of the settings, no one sees this or no one will be impacted, the risk is low.

You can launch it without testing. So part of the trick is also knowing when to stop, not just trying to force your way all the way up when you don't have to. That's a really important point. The other important point here is just a big part of a PM's job is to say no and to stop stupid shit from happening. And this is an awesome tool to help you do that, to be like, okay, here's this idea you have. Just like, let's just be real.

How confident are we this? And, okay, it's going to take us three months to do this. Maybe we should think about some different. Maybe we should work up the confidence meter before we actually come into this. Yeah, this is a real world usage that I hear about a lot. People use this to kind of do an objective way to say no and gently or to say, we will think about it, but look at these other ideas we have and how they're even. impact and ease and confidence take up.

Classic PM move, just like, that was a great idea, but what about this better idea? Coming back to something that we talked a bit about at the beginning, say you, say you've a founder who's like actually very smart and experienced, say even at a startup where you don't really have the time to build tons of evidence for ideas, you have a different perspective on how much time to spend building confidence in ideas versus just like, well, they're, they actually have really good ideas. let's just see what happens. So there's always like a trade-off between speed of delivery and speed of discovery.

And that actually leads to the next layer of how do we combine the two? Because people tend to think it's an either-or either we are building very fast or we're learning and then we're building very slow. But I think we're using the wrong metric. the metric is not how fast can we get the bits into production. When there's a lot of uncertainty, and we all face uncertainty, and startupists especially, it's not about getting the bits to production. It's about getting the right bits to production.

It's about creating the outcomes that you need, the impact. And so it's about time to outcomes. And I would argue that the evidence-guided method is far more impactful, is far faster, is far more resource-efficient. than the opinion-based method. Because opinion-based methods tend to waste a lot more of your resources building the wrong things

or discovering, learning too late. Well, evidence-guided helps you learn earlier. Plus, it is a fallacy that if you learn, you don't build. Good teams know how to do both at the same time. And that's actually what the steps layer is meant to teach you or to help you do. Awesome. So maybe just to close off that loop, say someone listening is at a big car company, say Netflix versus a series A, series B or startup.

Is there something you'd recommend about them approaching this differently? Any kind of guidance there of just how to take what you're sharing differently if you're a different source of companies like that? Absolutely. I think the concept we talked about of the no-style metric, the value created versus the value captured is very important in every company. building your entire metric streets, maybe overkill, doing heavyweighted OKRs, maybe overkill for early stage. Early stage companies even don't know how they create value. So they need to iterate. And their goal is really to find product market fit.

Beyond that, what happens is that you need to start building your business model. So that's your goal and you iterate towards that. And you need to put metrics on that. And then when you move into scale, you need to try to create all. Because when you scale up, and all of this is covered in the book, there's a special chapter just about these questions. When you scale up, you get a lot of people and a lot of money and everything is happening at the same time.

So there you need the order of evaluating ideas in a very systematic way. In a company like Netflix, by the way, I don't know if they need this specific method. They're very... Yeah, maybe that was a bad example. They're probably doing things pretty well. One thing I discovered, by the way, there's two types of companies that's really. really benefit from this technique. One is those companies that are kind of emerging into modern product development. They have product teams, they have product managers, they have OQRs,

they're starting to do agile, but they're starting to do experimentation, but they're struggling to put it all together. Every CPO is building their own little framework. And the other type is those companies that used to be evidence guided and they regressed. And that happens way too often, change of management, change of culture, and then all of a sudden they need to rediscover, to rekindle that spirit that was lost la Google Plus. So some of the people that actually respond to the strongest are actually surprisingly in these companies.

What I love about your frameworks and kind of all these things we're talking about is these are just kind of a, you can almost think of them as a grab bag set of tools to make you more evidence guided as a company. you could start with thinking about the confidence meter. You could start using ice more. You could start using the metrics tree. And all these things just push you closer and closer

to being more evidence-guided. You don't have to adopt this whole thing all at once. Absolutely. I would recommend that you don't try because if the transformation is way too big, you will get fatigued and you will just create a lot of process for a lot of people and you will not see the results. And after a quarter, you would give up.

So exactly what you suggested is the right approach. What would be the first thing you'd suggest if people were trying to move closer to being less opinion-oriented and more of it in space, which of these frameworks or models would you recommend first? I recommend that they discuss internally where is the biggest problem that they're facing. If the goals are unclear, there's misalignment, we keep chasing the wrong things, start at the goals layer, try to establish your Nostal metric, your top business metric, your metrics trees, start assigning teams with their own area of responsibility.

If you're spending a lot of time in debates and you're constantly fighting and changing your mind, start with the ideas layer and establish impact, ease, confidence, or whatever prioritization model you like, but involve evidence in it. I think the confidence meter is a good tool to use irrespective. If you're building too much and you're not learning enough, start adopting the steps layer, which we haven't seen yet. And if your team is very disengaged, you have one of these teams where the developers are very into agile, very into quality, very into launching things, start working on the tasks layer. Awesome. Okay, let's keep going. All right, so steps.

Steps are about kind of helping us learn and build at the same time as we said. And one of the patterns I see is that organizations don't know that they can actually learn at a much lower cost. They believe they need to build this elaborate MVP, which is not minimal in any way, and then launch it, and then they will discover. And basically, it's what we used to call beta 20 years ago, but just with a different. What I'm trying to do here, the steps layer, is to help companies realize there's a gamut of ways to validate your ideas, or more specifically, to validate the assumptions in your idea. And I created a little model for this. It's called after assessment, fact-finding, tests, experiments, and release results. But again, it's just putting together things that much smarter people invented.

So in assessment, you have very easy things, things that don't require. a lot of work, you check if it aligns with the goals, this idea that you have in your hand, you do maybe some business modeling, you do ice analysis, you do assumption mapping, which is a great tool by Davy J. Bland, or you talk to your stakeholders one-on-one just to see if there are any risks, et cetera. These are usually not expensive things, and they can teach you an awful lot about the impact and the ease of your idea. The next step is to dig data, and usually that goes hand in hand with this, so you can find data and your data analysis, through surveys, through competitive analysis, through user interviews, and through field research, observing your

users. Obviously, these last two are pretty expensive. So it's often good not to wait until you have the idea and then start doing your research. It's best to keep doing your research ongoing, and then you have some sort of data to lie on and to compare your idea against. But until now, we didn't build anything. Now you're ready to start testing, building versions of the product and putting them in front of users and measuring the results. But initially, you don't build anything.

You fake door test. You do a fake door test. You do smoke tests, wizard of all's tests, concierge test, usability tests. We used a lot of those in the tabbed inbox, by the way. One of the first early versions was actually we showed the tabbed inbox working to people, but it wasn't really jimmy. It was just a facade of HTML. And behind the scenes, and according to the permissions that the users gave us, some of us moved just the subject and the sender into the right place.

So initially the interviewer kind of distracted them and then showed them their inbox. And in it the top 50 messages were sorted to the right place, more or less if we got it right. And people were like, wow, this is actually very cool. And that gave us a lot of evidence. That's an awesome story. So that was in the user research. It wasn't like rolled out to people. It was a manual individual.

There wasn't a single line of code that's written. This was just cooked up by our researchers. That's awesome. Our designers. Yeah. But it gave us some evidence to go and say, hey, we should try and build this thing.

Love that. So initially you fake it. Mid-level tests are about building a rough version of it. It's not complete. It's not polished. It's not scalable. But it's good enough to give to users to start using. So those are early adopter programs,

alphas, multitudinal user studies and fish food. Fish food is testing on your own team. Fish food. I haven't heard that term before. So it's dog fooding, but more local to your team. I think it's a Google leaf thing, but some people told me that they use fish food as well in their company, the name.

So I'm using it. I don't know if there's a better name for it. I wonder why it's called fish food because it's like little, it's like a little gentle, little flakes. It could be, yeah. I don't know. Wow. Okay.

Super cool. I'm learning a lot here. So the next stage is to actually build a very, a kind of more complete version of this, and then you can dog food it. Then you can give this to your users internally. When I joined Microsoft many years ago, the first thing I noticed was that Outlook was very buggy. Ask people what's going on, and they told me,

we are all dogfooding the next version of Outlook that hasn't come out yet. And that's a very common practice in Silicon Valley. You can do previews, you can do beta, it can do labs. So those are tests. Now, there's a special class of tests which are experiments because they have a control element, so AB tests, multivariate tests, those are all experiments. I'm using the word experiment the way data scientists use it, although people tend to call experiments to everything that you see here.

And finally, even the release, you can do stage release, you can do percent launches, you can do holdbacks. All of these things help you further validate your assumptions. Sometimes you need to roll back and change things. but it's another opportunity to learn. So the key point is you don't have to start at the right-hand side, which is expensive. You can start early on, and that leads to pulking a lot of ideas very quickly. You realize they are not as good as you thought, and then you can invest more effort into the good ideas.

If they generate positive evidence, you can go further and further until that point where you feel you're ready for delivery. Okay, so we've talked about goals, we've talked about ideas, we're talking about steps here. Is there anything else along steps? And then next I know comes tasks. No, this is it for steps. There's a lot more, but we will not go into all of it. Okay, that sounds good. Let's talk about tasks and what you mean there.

All right, awesome. So in many organizations, there's these two worlds, there's the planning world where basically you have the managers, the stakeholders, there are some of the PMs, really sit and think about what we need to launch. And that's where we create the strategies and the roadmaps and the projects. But guess who is not invited to the party? The people who are actually doing the work. They live in Agile World. They're very focused on moving tickets to the Dunn State, on completing burning story points, you know, pushing stuff into production. And there's a big

gap between these two worlds. They don't understand each other. They don't see eye to eye. There's a lot of mistrust being built sometimes against the plans or the managers feels that the teams are just not being very effective. We've seen all this. And the solution, kind of the stopgap, is to put a PM in the middle. The PM is supposed to make all of this work, deliver on the roadmap, like a project manager, feed the agile machine with perfectly prioritized product backlogs and stories. And it just doesn't work, honestly. And the PMs I meet are very tired, and they have to spend so much time in planifications and roadmap discussions.

And they're very busy. They don't have time to do research or to test ideas. So I suggest changing this and bringing the developers a little bit out of their kind of agile cage, if you like, and no disrespect to agile. It's a great thing, but let's let them do more than just develop. Let's let them discover as well. And one of the tools I suggest, and again, this is a process, is what I call the GIST board. So it's basically the top three layers of GIST.

The goals are on the right. This are just the key results. And usually per team, I suggest. not more than four. So you create a G-sbot per team. Then the ideas we're working on right now, sometimes with our eye scores, and then the next few steps that we might want to pursue

in order to validate these ideas. And this is a very dynamic thing. It changes all the time. The team leads need to update it, and the team needs to meet around it, at least once every other week, to think, to talk about what's going on. Are we still following the right ideas?

how are we doing on the goals, what are the next steps, what's blocking us from completing the most important steps. And this is a discussion that is not happening today, because most of the discussion happens at the roadmap level, and then there's a lot of discussion at the task level, but this middle layer of what actually are you trying to achieve and how well are we doing on it does exist. If you do have this, you create a lot more context in the minds of your team, and then they need to ask you fewer questions.

You need to tell them less what to do. They know what's success, and they are able to actually do a lot more on their own. Is the way to think about the just board as the way you should be road mapping, or is this more of a strategy framework to think about why you should be prioritizing broadly? The way I say this is at the beginning of the quarter, the team defines its goals. The leads of the team define the goals, but they review it with the team. they review it with the managers, of course, with the stakeholders. Everyone is in agreement.

These are the maximum four key results and the one or two objectives you guys need to work on. Teams cannot deliver on more than that. You copy these key results into the GIFT board. Then you start looking at your idea bank or you start generating ideas and say, how can we achieve these key results? And to clarify the thing you copy is the key result as the goal. Yes, exactly. You can write the objectives alongside that, to remind people,

what are we trying to achieve, but the key results are the thing we show here. Then you pick some ideas, the ones that look most promising, and as unintuitive as it sounds, or counterintuitive as these sounds, I would recommend that you let the team pick these ideas. The manager, the stakeholders can propose the ideas, everyone can propose, but the team should use the ice process to kind of, and especially the product manager is very important here, to choose which ideas to test first. And then the team together needs to develop which steps should we run. How can we validate this?

Some of the steps will be done by the PM, some by the data analysts, some by user researcher. But some will involve the team. There will be some coding. There will be some running of experiments. And so there's some ownership around the steps. A sub-team owns each one of these steps. And we will change the board very actively. So if an idea turns out to be bad, we will take it off the board and put another idea in this place.

Or maybe we achieve the goal. We don't need to work on this anymore. We can focus something else. So it's a project management tool, in a sense. Awesome. And so I'm looking at it. And I think maybe the most important piece of this is that steps aren't just like a project, like launch a better onboarding or add the step to onboarding.

It's you want to emphasize the steps that you're getting to. take to get to more and more confidence, essentially, and more evidence-guided thinking versus just where let's figure out how to launch this feature idea. Exactly. It's not an engineering milestone or a design milestone. It's a learning milestone. So we build something. And along the way, we actually grow the scope of what we build.

We are building the product in the process, and we learn. So the two have to come hand in hand. And just to give for folks that aren't watching this on YouTube, just to walk through example, do it real quick. So one of your goals here is average onboarding time. You want your goal to be the average onboarding time, less than two days, currently five and a half days. An idea there is an onboarding wizard, and then the steps are a usability test with mockups, and then a usability test as a prototype, and then an AB test. Yeah, basically. And you can alter this as you go along. Sometimes you can run multiple steps in parallel. It's not always sequential. But that's basically

the process, yeah. Awesome. So again, what you're trying to emphasize here as a team is just, we're not just going to launch this onboarding wizard, and we're not going to figure it out later. It's like, let's be upfront about the steps we're going to take to build more and more confidence. This is something we should keep investing more and more in, which is really interesting. Yeah. And something, another interesting thing that happens, every time you run a step, if it's successful, you have evidence. And you can go back to the managers and tell them and share and say, you know, with this idea, we thought it was great, but we got this result. What do you think that means?

And sometimes that manager that proposed it would say, you know, I think the test failed, let's rerun it, or sometimes I would say, you know, maybe it's not as strong as I thought. The discussion just becomes that much more nuanced and objective, if you like. Maybe just to close out this framework, how does this relate to a roadmap that they may have in a spreadsheet or in Jira or in a sauna or something like that? Does it sit on top of that? Is it replacing a roadmap somewhere else? I would say that release roadmaps where you are just saying, by Q3, we want to launch this or by October, we have to launch that.

They're kind of competing with this. If you're doing that, and people know that the goal is to launch that thing by October, forget about learning. Forget about evidence guided. I recommend using outcome roadmaps saying, By October, we want to achieve this outcome. By Q4, we want to launch in other three countries, or we want to grow our usage in India by that much.

By this time, we need to tackle the problem of churn. And how we achieve this, sometimes we know. We have a concrete idea that is high confidence, that we already tested, we switch into delivery. Then we can put it on the roadmap and say, yeah, we're going to build this thing, and we'll aim for October. But otherwise, you want to keep it open.

And the roadmaps can kind of suffocate this process if you decide up front with low confidence that this particular idea must be launched. Okay, so you're proposing people switch the road mapping practice to this, which is very ambitious. I love it. Well, this is not a roadmap. This is just a tool for the team to manage the project. But I have a proposal for outcome roadmaps inside.

in the book. Okay, awesome. Okay, so I was going to ask if people wanted to try this approach, the book is the best way to fully understand the framework and not implement it. That's one way. I have articles. I have resources on my site, but I try to condense much of what we just discussed and much more, a lot more nuance in the book.

So if you were interested in that, I would give it a go. Awesome. Maybe just on the topic of Okyars real quick. how do OKRS connect to all this? It sounds like broadly, you kind of assume people will keep working on. Here's our metric or key results or objectives, and then that plugs into this kind of gist framework.

So the metrics tree is plus your mission, plus the individual missions of the teams, give you most of what you need to populate your OKRs. There's, of course, a process of alignment top down, bottom up, side to side, which I talk a little bit about as well. OKRs is a very rich topic. But those things are usually the core.

There's usually some other OKRs that are about the health of the company, the health of the product, etc. Those are called supplementary OKRs. I talk about those as well. So yeah, I think OKRs are a helpful tool, if you like them. Just zooming out again, basically you don't need to take all of these ideas and lump them all together and change the way you work as a business. you can start with picking some of these ideas and starting to become more and more evidence guided. It sounds like this just board isn't where you probably want to start,

but maybe it's once you have more and more experience using some of these tools, or you tell me, do you sometimes go straight to this way of thinking about the roadmap and the plan? So it might not be the full board because you're missing some of the pieces, maybe your goals are not as good or your idea of prioritization isn't as good. but if your team is very, very delivery focused, and sometimes it's also the opposite. The managers are telling them what to build, and you want to break this kind of dynamic,

you want to create a step backlog. So instead of a product backlog, let's create a backlog of steps, which are just validation steps, betas and previews, et cetera, and that changes the dynamic pretty strongly. So by the time this podcast comes out, the book will be out. What is the best place to find the book?

Hopefully on Amazon, you can search for it. You can go to my site. Itomaragila.com, and it will be presented prominently there. And there's also the book landing page where you'll find everything you need to know about the book, Evidenceguided.com. Well, with that, we've reached our very exciting lightning round. Are you ready? Yes, let's go. What are two or three books you've recommended most to other people? So I'm going to cheat. I'm going to recommend a series of books, so two series. series.

Cheaping is allowed. All right, cool. And those are obvious one. One is the series published by SVPG, Silicon Valley Product Group. So inspired, empowered. Now I think transformed has come out. I haven't read it yet, but I'm sure it's amazing. So this is Maltikagan and his colleagues.

They write some tremendous books and every product manager should read them. The other series, a bit older. This is the Linz series, the Linhstall. up, Lean Enterprise, Lean Analytics. There's gold in all these books, LinuX, really, really important books. And I think they're not as appreciated as they should. Running Lin, that's another example. What is a favorite recent movie or TV show? I'm not really a big TV or movie buff. I just put on whatever it comes up. I'm discovering that YouTube is actually becoming one of my sources of information and entertainment.

I'm learning a lot of Spanish recently, so I discovered this channel called Dreaming Spanish, which is, if you're learning Spanish, it's incredible. So that's my recommendation. That's a unique choice. I love it. Favorite interview question you like to ask candidates? I like to ask them to design something for a niche audience, so a navigation system for elderly people or some sort of laptop for people with vision impairment, etc. So those are good questions to see their customer empathy, their creativity, their ability to evaluate multiple ideas, the ability to find flaws in their own ideas. So there's a lot of room to dig in there and kind of see how this person is thinking as a product person.

What is a favorite product you recently discovered that you love? It's a cliche, but it's AI. There's a company called 11 Labs that do voices, like the best voices, synthetic voices you heard, but they can also replicate your own voice. So you can create a voice signature. If you're American, you can use their kind of default free version or cheap version to replicate your own voice. And that could be pretty useful if you need to, I don't know, narrate an audiobook or do some online course. So I'm finding this service very interesting.

This is all part of my big retirement plan. Find all these components together that can replace me eventually. You got AI generating content. We'll have this voice thing. I love it. Yeah, there's an AI version of you, right? I can ask you questions now without... Oh, there is.

Lennybot.com. Right. It's all part of the plan. Okay, what is a favorite life motto that you repeat most to yourself, that you share with others? That's a big one. Albert Einstein, I think, said, strive not to be a success, but to be of value. And I think that's a great motto for people and for companies.

It's something that kind of guides me. And this whole concept of the value exchange, et cetera, is kind of loosely connected to that. I love that. That's such a important point for people putting out content online. So many people are just like, I just want to be successful. Get followers here. all these things I'm tweeting and showing. And the thing that actually works is deliver value,

create valuable stuff that people really value and want. And I find the signal for that is, do you find it interesting and valuable? Like if you're like, oh, wow, that's really interesting, oftentimes other people are going to find interesting. So I love that. Great choice. I'm going to look at that one out.

Two more questions. What's the most valuable lesson you learn from your mom or your dad? I think both of them, in the own way, They had relatively modest jobs, you know, teaching or doing other things. But they always strive, again, to be the best they can, to deliver the most value they can. So it's very connected somehow. Maybe I'm seeing the world through this lens. But they kind of taught me to strive to be the best I can at what I do.

Final question. You're Israeli for folks that can't tell. What is your favorite Israeli food that people should definitely check out? I try to get whenever they can. When I arrive in Israel, I usually go for shawarma, which is like donor kebab. If you know, it's just better. So if you're in Israel, if you go visit Haifa, which is the city where I grew up, definitely check up the shawarma.

Awesome. It's Amar. I hope people got the gist of your book from our conversation. What's the best way to find it? What's the best way to learn about you and reach out if they want to ask you any questions? And then also, how can listeners be useful to you? To find it, you can go to Itamarogila.com or to evidenceguided.com and you'll find a book and you'll find me. Best value to me. Try it out. Just take some of these ideas, bring them back to your office, talk with your colleagues, say, what do you think we should do about this?

Just give it a go and reach back to me. Tell me, I'm easy to find in my website. Tell me what happened. I'm really interested. Amazing. Isamara, thank you again so much for being here. Thank you. Bye, everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lenniespodcast.com.

See you in the next episode.