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Whether you’re a startup founder trying to disrupt an industry or an intrapreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and h Whether you’re a startup founder trying to disrupt an industry or an intrapreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you’ll know it’s time to move forward Apply Lean Analytics principles to large enterprises and established products


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Whether you’re a startup founder trying to disrupt an industry or an intrapreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and h Whether you’re a startup founder trying to disrupt an industry or an intrapreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you’ll know it’s time to move forward Apply Lean Analytics principles to large enterprises and established products

30 review for Lean Analytics: Use Data to Build a Better Startup Faster

  1. 4 out of 5

    Stephanie Sun

    This is a tough review to structure and write. First, I am not the ideal reader for this book. Although I am learning web design and care about accountability and embrace technology in my professional life, I am not trying to launch anything at scale and certainly not with VC money. But the single most striking thing about Eric Ries's book, original recipe Lean Startup (Ries was the Editor of Lean Analytics, but the publisher here is O'Reilly not Crown Business), was how readily applicable his co This is a tough review to structure and write. First, I am not the ideal reader for this book. Although I am learning web design and care about accountability and embrace technology in my professional life, I am not trying to launch anything at scale and certainly not with VC money. But the single most striking thing about Eric Ries's book, original recipe Lean Startup (Ries was the Editor of Lean Analytics, but the publisher here is O'Reilly not Crown Business), was how readily applicable his concepts were to so many professional ventures besides VC-backed startups. I didn't see that same hitting zeitgeist on the funny bone here. So many of the chapters, under the guise of offering specifics and a rigorous approach to startup management, do not offer specifics so much as undisciplined definitions of business jargon, unmemorable introductions to market research practices that have been around for decades (cohort analysis ftw!), anecdotes and case studies that rarely come to life (some are not even data or Lean related), and some very poorly designed and written charts. I do not think that the authors closed the sale on the One Metric That Matters. It's not clear that they even made a concerted effort to try. There is utility in their startup stages framework, but whereas Build, Measure, and Learn are three clear, active verbs in a similar vein, Empathy, Stickiness, Virality, Revenue, and Scale don't have much in common besides all being nouns evocative of the stages they represent. Empathy is a quality of the founder, while Stickiness (someone please, please invent a new term for whatever this is) and Virality are qualities of the product, and Revenue and Scale are qualities of the business. Do you see where my frustrations are stemming from? There is intellectual clarity and rigor lacking when even the five core concepts are not consistent with each other. I actually think The Signal and the Noise is a better book to read to spur creative thinking about milking the data beast in the age of the internet. You can read both if you want, but this book ain't cheap, and here are some examples of the Susan Miller-esque insights to be found within—Susan Miller-esque because if you want it to all make sense enough it will: "It's better to make big bets, swing for the fences, try more radical experiments, and build more disruptive things, particularly since you have fewer user expectations to contend with than you will later on." "A fundamental element of any pricing strategy is elasticity: when you charge more, you sell less; when you charge less, you sell more." "The reality is you'll quickly adjust the line in the sand to your particular market or product. That's fine. Just remember that you shouldn't move the line to your ability; rather, you need to move your ability to the line." - PREVIOUSLY: I love Lean Startup, but this is how it really goes sometimes, amirite?

  2. 4 out of 5

    Sebastian Gebski

    I'll start with 3 advices: 1.) Start with reading the subtitle 2.) Then read the subtitle 3.) And finally - read the subtitle This book is not about analytics in general or even analytics in lean production / development. It's just & only about analytics in terms of building & developing a Lean Startup. If you're fine with this assumption, you'll love it. Why? * TONS of examples, all of them under the actual names - and we're talking about really known brands here * VERY comprehensive approach to vari I'll start with 3 advices: 1.) Start with reading the subtitle 2.) Then read the subtitle 3.) And finally - read the subtitle This book is not about analytics in general or even analytics in lean production / development. It's just & only about analytics in terms of building & developing a Lean Startup. If you're fine with this assumption, you'll love it. Why? * TONS of examples, all of them under the actual names - and we're talking about really known brands here * VERY comprehensive approach to various start-up scenarios * practical approach you can easily utilize in your situation My two favourite bookmarks: * about growth hackers & the practical difference between correlation & causability * about the role of revenue stage in the LA It may sounds odd (well talking about the analytics book, right), but it was a very captivating read :) Recommended.

  3. 4 out of 5

    Kelly Reid

    Another disjointed analysis with specifics about my company that I shouldn't tell people. Lean Analytics 10: Ratios are good for comparing factors that are opposed or have some kind of tension. A good metric changes behavior. 12: Types of Metrics - Qual / Quant, Exploratory / Reporting, Leading / Lagging, Correlated / Causal ( If you find a relationship between something you want and something you control, you can change the future ) 14: We care about active users, because it probably lead-indicates Another disjointed analysis with specifics about my company that I shouldn't tell people. Lean Analytics 10: Ratios are good for comparing factors that are opposed or have some kind of tension. A good metric changes behavior. 12: Types of Metrics - Qual / Quant, Exploratory / Reporting, Leading / Lagging, Correlated / Causal ( If you find a relationship between something you want and something you control, you can change the future ) 14: We care about active users, because it probably lead-indicates our churn rate. This requires a lot of different queries to diff DBs. (18) Analyze patterns of engagement and desirable behavior, find commonalities. 16: Knowledge Matrix - Known Knowns are facts that may be wrong and should be checked against data. Known Unknowns are questions we can answer with automated reporting. Unknown Knowns are intuitions we should quantify. Unknown Unknowns are located through exploratory reporting and where we can locate unfair advantages and new insights. 18: Pivot hard or go home, and be prepared to burn bridges. 19: Churn is a lagging indicator. Cohort analysis (comparing customer groups over time) is the road to leading indicators. 20: Are the metrics we track helping us make better decisions faster? 22: A company assumed "active" was 4x a week, when it turned out to be only 1x a week (to great success). Things they tried: Clarified signup flow, added more explanatory copy. Daily email notifications, transactional emails tied to actions on-site. 24: A segment is a group that shares a common characteristic. Segment visitors then compare segments to each other to understand differences in metrics. Look for disproportionate relationships. 26: Cohort analysis allows patters to emerge across customer lifecycles. 27: We can test anything, but focus on the critical steps and assumptions. 34: Venn Diagram: Expertise, Desire, Monetizable. E+D = Learn to M. E+M = Improve D. D+M = Learn to say no. All 3 = Victory! 52: Use Google Analytics multi-channel conversion visualizer to see which referral sources are combining to influence visitors 57: Find days where unsubscribe rate is high, then find out why. Need to tweak the unsubscribe process to get better resolution on this. Subs expire some time after cancellation, so a decrease in sub numbers on a given day is not necessarily indicative of anything. Find the action, not the result. 67: When you subscribe to QS, you're subscribing to a slice of our personality. 92: Backupify focuses on monthly recurring revenue. They watch churn, but are not going to focus on it until they hit the 10MM revenue level. 97: Properly calculating churn: Select a time period. Average the number of customers at the beginning and the end of the period. Divide the number of cancellations by this number. To increase data integrity, measure churn daily ( using the method in 57: ) 125: User Generated Content: Use the forums as a source of UGC and find ways to repackage it and syndicate it. 154: QS is in Stage 5, we have revenue and we are beginning to branch into new verticals. We need an ecosystem to help us cross the gap from niche site to industry staple. 159: Find out what's actually important to people. Get inside their head. Delve for this information aggressively. 211: Refresh the 3-year plan every 18 months. Align the entire company around the vision. 213: The best companies warehouse every possible data point about their site's interactions and use only the data they need. Rally ( a software co ) records everything from kernel-level performance to HTTP-based user gesture interactions between the browser and software. They can then correlate changes in site performance to user behavior and vice versa. 256: metrics for stage 5 (scale). Attention is a precious commodity. Don't waste the visitor's attention on stuff that doesn't matter. Internally, compare the metrics that matter across channels, regions, and other segments to find efficiencies and inefficiencies. 258: Get a better understanding of what new visitors really do. 260: Limit the company's vision to a 3-pronged strategy. 261: For each C-Level strategic assumption, what are the 3 line-level tactics that can be used to survey, test, prototype, then fill/kill quickly? 262: Enable (both emotionally and technically) anyone on staff to run a split test. Give the line level a wide range of flexibility. 263: Scale stage summary: Focus on the health of the ecosystem and ability to enter new markets. Pay attention to compensation, API traffic, channel relationships,and competitors. These are no longer frivolities or distractions. 274: Hypothesis to test: most people unsubscribe beacuse they don't need our service anymore, not because we're crappy. Limited data bears this out but we need to conduct FAR better exit surveys. 281: Product pricing has nothing to do with cost, and everything to do with what the customer pays and how they derive value from the product. 283: Try an intentionally absurdly priced package to anchor high prices, as well as to see if anyone actually bites. 286: We have no real way to measure virality. We can try to use the affiliate program but I suspect many will refer by word of mouth if given an easy way to do so. How many do they refer and how quickly? 287: Sharing by email accounts for 80% of social sharing, usually between small groups of people. Remember Emerson Spartz' theory about "bridge nodes". Which groups of people are likely to be conduits between other peer groups? (326) 288: 3pm is when people are most likely to open an email. If software permits, time newsletters on a per user level, based on signup time. 290: Site load time matters a great deal. Spend a lot of time to get this down. 302: Negative Churn is the long-tail of brand awareness. We might convert some "Long time listeners, first time callers" after a year, or longer. Focus on customers that have been on our mailing list for a long time but are not subscribers. 304: Top SaaS companies increase revenue per customer by 20% a year. Can we upsell the Buylist product enough to hit this target? Can our price increase for new subscribers help us hit this level? 324: What kind of content do different traffic sources expect? Twitter Time on Site is disproportionately high. Why is this? 325: Find outlier content and promote it more heavily. Unlocked Insider articles are great for this, as are "nexus pages" like our BOTG page. 326: Most sharing is intimate. Each share generates an average of 9 visitors, per the book's data. Can we find this number for our site? Book data; 5::1 Twitter, 36::1 on reddit. Sharing happens from a groundswell of small interactions between colleagues and friends rather than a one-to-many broadcast. See above (287) about bridge nodes. 334: Engagement rations: 90% lurk, 9% contribute sometimes, 1% engage heavily. Make participation easy, and a side-effect of site usage.

  4. 5 out of 5

    Heather Aislinn

    Needed to read this for the Growth Tribe academy. It's very insightful and gives a lot of examples. However, there's a load of different kinds of information, therefore I have a feeling I didn't remember everything. Needed to read this for the Growth Tribe academy. It's very insightful and gives a lot of examples. However, there's a load of different kinds of information, therefore I have a feeling I didn't remember everything.

  5. 4 out of 5

    Ahmad hosseini

    Lean startups core concept is build-> measure-> learn. Learn analytics focuses on measure stage. The authors believes that there are six business model e-commerce, SaaS, mobile apps, media site, user-generated content, and marketplace. They describe these models in details and introduce the important metrics for each one. I think, in these business models media site is simplest and marketplace is most complex model. After introducing business models book introduces Lean analytics stage that includ Lean startups core concept is build-> measure-> learn. Learn analytics focuses on measure stage. The authors believes that there are six business model e-commerce, SaaS, mobile apps, media site, user-generated content, and marketplace. They describe these models in details and introduce the important metrics for each one. I think, in these business models media site is simplest and marketplace is most complex model. After introducing business models book introduces Lean analytics stage that include 1. Empathy 2.Stickiness 3.Virality 4.Revenue 5.Scale. For each stage, there are certain rules and tasks that must be implemented. There are many good case studies, advices and guidelines in the book and it is useful for entrepreneurs, web developers, and data scientist. Also, I recommend this book to anyone who directs the business. But I believe this quote: “In theory, theory and practice are the same. In practice, they are not.” ― Anonymous

  6. 5 out of 5

    Hamide meraj

    It was a really useful book for me because I've worked in a two-sided marketplace and I really need some information about the jargon that we use in our business. I think it is a must_hae book for every entrepreneur and everyone who wants to start a new business. This book shows you how to validate your idea in every stage of your business, find the right customers, decide what to build, how to monetize your business. Packed with more than thirty case studies and insights from over a hundred busi It was a really useful book for me because I've worked in a two-sided marketplace and I really need some information about the jargon that we use in our business. I think it is a must_hae book for every entrepreneur and everyone who wants to start a new business. This book shows you how to validate your idea in every stage of your business, find the right customers, decide what to build, how to monetize your business. Packed with more than thirty case studies and insights from over a hundred business experts. it is necessary for me to read it again to understand it better

  7. 4 out of 5

    Abhilashkedlaya

    Great book to understand how to use data to drive decision making, particularly in startups. Most of the insights/approaches are also transferable to specific areas like product development in larger companies. The book has chapters devoted to different verticals and can help technical data analysis audiences in developing a more business oriented outlook

  8. 4 out of 5

    Ramil

    Must read book for new business starters. Talks about different business models and metrics we should keep our eyes on to track our progress, gives some useful hints and attempts to tackle in detail the stages each startup goes through from its launch

  9. 5 out of 5

    Caroline Gordon

    The one metric that matters (OMTM) is the key measurement you need to be be making of your current endeavour. My endeavour to learn all about building products and the business that build products seems to be measured right now by the number of books I'm reading on the topic, so +1 to that! Lean Analytics builds on the Lean Startup and presents some quite prescriptive ways to views various types of internet businesses. It's an easy quick read but I don't think it will stand the test of time, it's The one metric that matters (OMTM) is the key measurement you need to be be making of your current endeavour. My endeavour to learn all about building products and the business that build products seems to be measured right now by the number of books I'm reading on the topic, so +1 to that! Lean Analytics builds on the Lean Startup and presents some quite prescriptive ways to views various types of internet businesses. It's an easy quick read but I don't think it will stand the test of time, it's too rooted in today's view of the importance of mobile apps and SAAS businesses, which is sure to be transient. But, as a tool for today it's a great read. Thoroughly recommend it. It has a great section on intrepreneurs as well. One of my favourite parts is the rule of 3's which goes something like this: - at a certain size you will likely have 3 levels of management - board, executive and operational - you likely have 3 big assumptions in your current business model - be clear what they are (pick the top 3) - these kind of assumptions will be ones paying your pay role (or internally keeping your stakeholders at bay) - they are fairly static (ie not changing month by month) - they are agreed at the board level and communicated to the entire company - at the executive level you agree the 3 main tactics to make your assumptions reality - ie what actions are you taking to move those metrics in the right direction? (new features, new marketing campaign ..), this is like your sprint goals in agile - on a daily basis everyone in the company is performing individual tasks related to the tactical actions, these are experiments you run to attempt to move the metric, each is a small hypothesis, experiment and learn cycle, for each of these tests, what 3 experiments are you running? Three assumptions (monthly at board level ) -> three tactics (weekly at exec level) -> 3 experiments (daily at staff level)

  10. 4 out of 5

    kartik narayanan

    Read the full review at my site Digital Amrit Measuring something makes you accountable. You’re forced to confront inconvenient truths. And you don’t spend your life and your money building something nobody wants What is the book about? Lean Analytics: Use Data to Build a Startup Faster is written by Alistair Croll and Benjamin Yoskowitz. It is part of the ‘Lean Startup’ series started by Eric Ries in his seminal book ‘The Lean Startup’. In a nutshell, Lean Analytics focuses on the ‘measure’ portio Read the full review at my site Digital Amrit Measuring something makes you accountable. You’re forced to confront inconvenient truths. And you don’t spend your life and your money building something nobody wants What is the book about? Lean Analytics: Use Data to Build a Startup Faster is written by Alistair Croll and Benjamin Yoskowitz. It is part of the ‘Lean Startup’ series started by Eric Ries in his seminal book ‘The Lean Startup’. In a nutshell, Lean Analytics focuses on the ‘measure’ portion of the Build-Measure-Learn cycle. I had an opportunity to present on this topic (whose content I borrowed almost wholly from this book). You can see the recorded video on this topic here or download the PDF here. If you are new to the Lean Startup, I would recommend reading that book first before picking this one up. What does this book cover? Lean Analytics is arranged in a sequential fashion. The topics covered are as follows - The need for metrics - The concept of the One Metric That Matters - 6 business models and how current analytics applies to them --E-commerce --SaaS --Free Mobile App --Media Site --User-generated Content --Two-sided Marketplaces -The Lean Analytics Framework -How does the Lean Analytics Framework apply to the 6 business models -What are the baselines for these business models -Putting the framework into action What did I like? Lean Analytics helps plug in the missing gap in the Build-Measure-Learn cycle. While there is a lot of literature on how to build and what to build, there isn’t enough on figuring out whether you are meeting expectations or not. Read the full review at my site Digital Amrit

  11. 4 out of 5

    Maciek Wilczyński

    Amazing. Period. Full of "meat", contentful and actionable from the next business day. If you're into start-ups or at least anything connected to cutting-edge digital marketing you need to read it. It's one of these books like: "Lean Start-up", "Rework", "Business Models Generation" and "Startup Manual". It's a must-read and I'm surprised that it took me so long to get to it. Even though I knew about 90% metrics, it was still useful to put them into the right context. Especially, adding the case s Amazing. Period. Full of "meat", contentful and actionable from the next business day. If you're into start-ups or at least anything connected to cutting-edge digital marketing you need to read it. It's one of these books like: "Lean Start-up", "Rework", "Business Models Generation" and "Startup Manual". It's a must-read and I'm surprised that it took me so long to get to it. Even though I knew about 90% metrics, it was still useful to put them into the right context. Especially, adding the case studies. The book is not the one you would read before going to bed. You need to be focused to get 100% value of it. Initially, I had it borrowed from the library, but decided to buy my own copy to be able to make my own notes. 5/5

  12. 5 out of 5

    Jacek Bartczak

    "Instincts are experiments. Data is proof." - it is a good summary of Lean Analytics, the book about data-driven decision-making. The book describes 2 areas: stages of a startup development and a couple of scalable business models. Each part is full of case studies and business insights. If you already read Lean Startup or The Startup Owner's Manual probably the first part would be less interesting. The abundance of examples makes Lean Analitycs helpful for each job which requires entrepreneuria "Instincts are experiments. Data is proof." - it is a good summary of Lean Analytics, the book about data-driven decision-making. The book describes 2 areas: stages of a startup development and a couple of scalable business models. Each part is full of case studies and business insights. If you already read Lean Startup or The Startup Owner's Manual probably the first part would be less interesting. The abundance of examples makes Lean Analitycs helpful for each job which requires entrepreneurial gene because the book shows how decisions should be made.

  13. 5 out of 5

    Chris

    Pretty dry at times, but definitely worth the read for any project manager, product manager, scrum master, or product owner. The insights here - when put to good use - are probably worth their weight in gold.

  14. 5 out of 5

    Felipe Gonçalves Marques

    The book floats between principles and recipes for lean Analytics very well. It provides nicer use cases, segmentations of business model and examples. On the other hand, the examples could be more concrete , showing in a more didactic manner how the data was used and the decision was made.

  15. 5 out of 5

    Soumik Ray

    Loved how the book is structured. This book works brings a great clarity on which metrics to track depending on the type and the stage of your business.

  16. 4 out of 5

    Surbs

    i thought this was a pretty practical and helpful book. it helped me gain a better perspective on analytics as a whole for different types of businesses. it was also pretty fun to read.

  17. 5 out of 5

    Aditya Kulkarni

    A succinct and apt collection & summary operational wisdom of the start-up ecosystem. Must read for someone who's aiming to work for a VC / an accelerator. Good read! A succinct and apt collection & summary operational wisdom of the start-up ecosystem. Must read for someone who's aiming to work for a VC / an accelerator. Good read!

  18. 5 out of 5

    Matias Koskinen

    The first six chapters provide an exceptional introduction to the world of data-driven decision-making. Most of the content cover analytics frameworks for startups of varying kinds and at different lifecycle stages. These you might find useful or not.

  19. 5 out of 5

    Tom Stofmeel

    The book is an interesting read especially for somebody working in the startup scene. It introduces a lot of key concepts and throughout reading the book ideas about implementing these in the business you are working for are constantly brought to your mind. You are sparked to think about how the concepts in this book relate to the business you are working in. However, the book keeps its information really generic and the examples included are more open doors than eye openers. Maybe because the bo The book is an interesting read especially for somebody working in the startup scene. It introduces a lot of key concepts and throughout reading the book ideas about implementing these in the business you are working for are constantly brought to your mind. You are sparked to think about how the concepts in this book relate to the business you are working in. However, the book keeps its information really generic and the examples included are more open doors than eye openers. Maybe because the book tries to reach all (different) types of technological startups in one book it is missing the depth you would expect. This is a pity and I hope to one day read a similar book that delivers a more precise description of the do and don’ts of a specific type of technological startup. Still worth reading though!

  20. 5 out of 5

    Louis

    Lean Analytics bills itself as how data can be used as a startup. But it really is how you use data to make and check business decisions. And it takes the discussion of key performance indicators and puts them into a context where 1. data is readily available and 2. analyzing the data is relatively easy if you knew what data to look for and what and why you are analyzing it. The first part is fairly standard fare for metrics oriented organizations. A discussion of what data is, how to choose meas Lean Analytics bills itself as how data can be used as a startup. But it really is how you use data to make and check business decisions. And it takes the discussion of key performance indicators and puts them into a context where 1. data is readily available and 2. analyzing the data is relatively easy if you knew what data to look for and what and why you are analyzing it. The first part is fairly standard fare for metrics oriented organizations. A discussion of what data is, how to choose measures, recognizing that data is never clean. But the rest of the book starts through a range of scenarios. Different types of businesses. Different stages of business development. Different competitive environments. With the variety presented, the point is not to find the chapter that matches your situation or to pick at a description and find ways that it does not apply, the point here is that data-informed decisions have a place in a wide range of contexts. The goal with metrics is not to decide that because it is not perfect it cannot be used, the goal is to use data in a way that complements experience, instinct, and intuition in making better decisions. For that, Lean Analytics is good to read for anyone thinking about how data can be made to work, not just in internet based startups. Disclaimer: I received an free electronic book edition of Lean Analytics through the O'Reilly Press bloggers program.

  21. 4 out of 5

    Eleonora Pogorelova

    This review has been hidden because it contains spoilers. To view it, click here. Lean Analytics is a book, which covers primarily the main aspects a startup should consider before going forward. The authors Alistair Croll and Benjamin Yoskovitz make is simple for the startup businesses to understand what they need to concentrate on and what steps need to be taken before moving forward. Basically, the authors are stressing on the fact that there is no need to reinvent the wheel, when it comes to starting a new business. A startup is an organization formed to search for a scal Lean Analytics is a book, which covers primarily the main aspects a startup should consider before going forward. The authors Alistair Croll and Benjamin Yoskovitz make is simple for the startup businesses to understand what they need to concentrate on and what steps need to be taken before moving forward. Basically, the authors are stressing on the fact that there is no need to reinvent the wheel, when it comes to starting a new business. A startup is an organization formed to search for a scalable and repeatable business model. The startups should incorporate the following approach : build - measure - learn. It is said that if you can’t measure it, you can’t manage it. At the beginning, the startup should find a good metric, which should be comparative, understandable, a ratio or rate and changes the way you behave. The authors recommend finding the right metrics and understand the differences between: qualitative vs quantitative metrics, vanity vs actionable metrics, exploratory vs reporting metrics, leading vs lagging metrics, correlated vs actual metrics. Quantitative data adhors emotion, while qualitative data marinates in it. If you have a piece of data on which you cannot, it is a vanity metric. Finding a correlation between 2 metrics is a good thing. Correlations can help you predict what will happen. But finding the cause of something means that you can change it. Lean analytics is about finding a meaningful metric, then running experiments to improve it until that metric is good enough for you to move to the next problem of the next stage of your business. Lean Analytics introduces Lean Canvas, which is an adaptation of Business Model Canvas by Alexander Osterwalder which Ash Maurya created in the Lean Startup spirit (Fast, Concise and Effective startup). Lean Canvas promises an actionable and entrepreneur-focused business plan. It focuses on problems, solutions, key metrics and competitive advantages. Before starting a business, the book suggests answering the following questions: What can you work on? Can I do this thing I am hoping to do, well? Do you like doing this thing? Can you make money doing it? Humans do inspiration, machines do validation. 10 common pitfalls that entrepreneurs should avoid: Assuming the data is clean; Not normalizing; Excluding outliers; Including outliers; Ignoring seasonality; Ignoring size when reporting growth; Data vomit; Metrics that cry wolf; Focusing on noise. There are multiple ways of presenting the stages of a startup available in the book. However, the book concentrates on the most common ones: Empathy - will anyone care? Finding the problem and the solution; Stickiness - will the dogs eat the dog fool; Virality - word of mouth etc; Revenue - will people open pocketbooks; Scale - move from nice player to a large company. Each stage is described with many details, however, I would not like to concentrate on them taking into account that Lean Analytics is the book, which seems like the one, which requires quite a lot of attention after being read. Still, I wanted to draw your attention to the fact that the book mentions that the users will always complain and that it’s important to understand the Minimum Viable Vision (MVV). At each stage of the startup development, the book suggests concentrating on One Metric That Matters (OMTM). Basically, the startup should pick a signe metric that’s incredibly important for the step you’re currently working through the startup. It is recommended to capture everything, but to focus on what’s important. The OMTM is recommended for the following reasons: It answers the most important question you have; It enforces you to draw a line in the sand and have clear goals; It focuses on the entire company; It inspires a culture of experimentation. The authors review various business models, such as ecommerce businesses, SaaS, media, user-generated content and two-sided marketplaces and highlight on the fact that business model is more important than business plan. Considering the fact that IO is a SaaS business working with media customers, I would like to address both of them. SaaS KPIs include: Attention - how effectively the business attracts visitors; Enrollment; Stickiness - how well the users utilize the product; Conversion; Revenue per customer; Customer acquisition cost; Virality (invite others); Upselling; uptime and reliability (complaints); Churn; Lifetime value. The ultimate metric for engagement is daily use. The media sites KPIs include: Audience and churn; Ad inventory - the number of impressions that can be monetized; Ad rates - how much a site can make from impressions based on the content it covers and the people who visit (cost per engagement); CTR - how many of the impressions actually turn into money; Content / ad balance - the balance of ad inventory rates and content that maximizes overall performance. Wrinkles: Hidden affiliate models; Background noise; Ad blockers; Paywalls. Model and stage drives the metric you track. Media site line in the sand include the CTR; session to click ratio referrers; referrers; engaged time / time on page.SaaS line in the sand includes: paid enrollment; freemium vs paid; upselliing and growing revenue; churn (2% per month). Metrics for startups working with enterprise businesses include: Ease of customer engagement and feedback; Pipeling for initial releases, betas and POCs trials; Stickiness and usability; Integration costs; User engagement; Disentanglement; Support costs; User groups and feedback; Pitch success; Barriers to exit. In order to instill a culture of data in your company, the book suggests: Start small, pick one thing and show value; Make sure goals are clearly understood; Get executive buy-in; Make things simple to digest; Ensure transparency; Don’t eliminate your gut; Ask good questions. Overall, the book provides the hints on how to start a company understanding the main business models available on the market and weighing the stage of the business. In order to engage the reader, the book provides a number of success stories to understand what worked and what didn’t. I would recommend reading it to all the newcomers to the fintech industry as well as the PMs that are building solutions for other companies.

  22. 5 out of 5

    Konstantin Valiotti

    The idea of focusing on "One Metric That Matters" (which is different for different stages of the product) is pretty good by itself. Author provides certain ideas of what those metrics should be, and how could you integrate that into working on your product. But while the idea is good, most of the book is filled with vague estimations. I don't see any reason to include chapters, which are concerned with certain benchmarks for different metrics. This might simply be misleading. The numbers make th The idea of focusing on "One Metric That Matters" (which is different for different stages of the product) is pretty good by itself. Author provides certain ideas of what those metrics should be, and how could you integrate that into working on your product. But while the idea is good, most of the book is filled with vague estimations. I don't see any reason to include chapters, which are concerned with certain benchmarks for different metrics. This might simply be misleading. The numbers make the book feel 'scientific', but it is far from it. The framework offered (or, better, a mindset) is useful, but the numbers are not.

  23. 4 out of 5

    Jose Restrepo

    Comprehensive book on analytics you should be looking for in your business. Since it covers a vast array of business models it can get a little tiring at the end, however, if you're reading for your business you can skip around to the parts that make sense for you. Very clear and didactic with formulas, examples (different examples, not the same ones used over and over). Will definitely be referencing it over time. Not more of the same. I recommend it! Comprehensive book on analytics you should be looking for in your business. Since it covers a vast array of business models it can get a little tiring at the end, however, if you're reading for your business you can skip around to the parts that make sense for you. Very clear and didactic with formulas, examples (different examples, not the same ones used over and over). Will definitely be referencing it over time. Not more of the same. I recommend it!

  24. 4 out of 5

    Pankaj Ghanshani

    Super awesome for anyone wanting lots of ready made gyan and benchmarks on analytics Highly recommend for product managers!!

  25. 5 out of 5

    Quan Mt

    This books helped me a lot to understand how to measure stuff going on with my startup

  26. 5 out of 5

    Juanmi

    - Re-readability: have on the shelf and check random passages often - Note: 5/5 - On Instagram: https://www.instagram.com/p/BDmAXapTQ... Lean Analytics book - Re-readability: have on the shelf and check random passages often - Note: 5/5 - On Instagram: https://www.instagram.com/p/BDmAXapTQ... Lean Analytics book

  27. 4 out of 5

    Tom Purdom

    One Metric that Matters - what a simple, yet powerful concept that can drive change across an organization department by department.

  28. 4 out of 5

    Hatem

    Confined and bored, decided to give a this book a read. Lean Analytics is one of the Lean books, a series curated by Eric Ries, founder of the Lean movement and author of The Lean Startup. “Data is the antidote to delusion.” - Alistair Croll & Benjamin Yoskovitz This book is mainly tailored to companies at the startup or the founding phase. It gives a couple of lessons on how to leverage data to be on you side and scale up. It mainly goes through what are the most metrics for different business mod Confined and bored, decided to give a this book a read. Lean Analytics is one of the Lean books, a series curated by Eric Ries, founder of the Lean movement and author of The Lean Startup. “Data is the antidote to delusion.” - Alistair Croll & Benjamin Yoskovitz This book is mainly tailored to companies at the startup or the founding phase. It gives a couple of lessons on how to leverage data to be on you side and scale up. It mainly goes through what are the most metrics for different business models throughout different stages. Not all statistics is useful. Be Data-informed, not data-driven. This is one of the hardest lessons for anybody working with data professionally. The book explains why data should be the passenger not the driver in your decisions starting a new business. Choosing the right metric and even defining what a “metric” is may sound simple, but it has lots into it. Choosing the right metric is also highly correlated to your business type. Is it SaaS? e-Commerce, Mobile App, Two-Sided Marketplaces, User-generated Content, or a Media site. The book explains what are the right metrics and their baseline for each of these models. Focus: Pick one metric and make it actionable. Although this may sound unproductive but the authors stress on the fact that you should focus only on one, key metric at a time. Stages: Which data at what time? The book describes the lifecycle of each company in phases and explains how its data model should transform at each stage. The main phases: 1. Empathy: This is where you connect with other people to determine a real problem that they have, which you can solve. 2. Stickiness: Where you figure out how to efficiently solve that problem in a way that people would be happy to pay for. 3. Virality: Here you build product features that keep people coming back and referring friends to make the product itself better. 4. Revenue: That’s when you start growing, expanding, and making sure you’re profitable. Scale. When your company tries to enter new markets and starts hiring a lot, you know you’re scaling up. Personally I think this book is highly valuable if you are starting an online business. The agility of measuring, creating, and switching metrics is a luxury only Internet business posses. Honestly, while all of the advice in the book can be extremely helpful, they render themself impractical in a corporate or enterprise environment.

  29. 4 out of 5

    Yu Takk

    A good, practical book for entrepreneurs, product owners, marketers etc. Not so "Lean"... not even focused on "analytics". It was not what I expected (I expected something more about data analysis, not about how to build a startup), but it might be useful when I actually work for a startup or make a startup. [Takeaways] - what makes a good metric? 1. Comparative, 2. understandable, 3. a ratio or a rate, 4. that changes the way you behave - Avoid "vanity" metrics such as page views, number of follower A good, practical book for entrepreneurs, product owners, marketers etc. Not so "Lean"... not even focused on "analytics". It was not what I expected (I expected something more about data analysis, not about how to build a startup), but it might be useful when I actually work for a startup or make a startup. [Takeaways] - what makes a good metric? 1. Comparative, 2. understandable, 3. a ratio or a rate, 4. that changes the way you behave - Avoid "vanity" metrics such as page views, number of followers, email addresses collected, number of downloads etc. They don't help you to decide what to do next. - Problem > Solution Hypothesis > Metrics - Three important types of analysis 1. Segmentation : cross-sectional 2. Court analysis: longitudinal 3. A/B testing (you could use multivariable analysis to save time) - One Metrics That Matters (OMTM) What’s most important right now? (it changes as you proceed) Don’t puke metrics. You can’t focus on multiple metrics at the same time. - Segment your customers. Focus on good customers. - Five "mores" for business growth...which one? 1. More stuff 2. More people 3. More often 4. More money 5. More efficiency for example... Selling physical things > more efficiently High viral coefficient > more people loyal returning customers > more often - Use "Business Model Canvas" and "Business Model Flipbook" to clarify your business model. - Business model is much more important for start-up than business plan - Five stages of startup growth: focus 1 thing at a time. 1. Empathy: qualitative research(a day in the life etc.), questionnaires 2. Stickiness: engagement metrics(time spent, revisit rate, etc.), using cohort analysis 3. Virality: viral coefficient (invitation accepted/existing customer number) 4. Revenue: return on acquisition, "penny machine"metric (revenue increased / marketing expense), churn 5. Scale: compensation, API traffic, channel relationships, competitors... - Be careful about the timing you move from one state to another. Premature growth burns money and time and will quickly kill your startup. - Start small.

  30. 5 out of 5

    Marcell Nimführ

    Maybe I am just at the right junction to get the most out of this book - a few months before launching my app, that is. But I can say with conviction that this is the best digital marketing / startup book I have ever read. Most other books are written by authors who had success and re-engineered the experience into a blue-print. These books have ton of advice that might or might not fit in the readers case. Most often they are not transferrable. Lean Analytics is not even short on actionable adv Maybe I am just at the right junction to get the most out of this book - a few months before launching my app, that is. But I can say with conviction that this is the best digital marketing / startup book I have ever read. Most other books are written by authors who had success and re-engineered the experience into a blue-print. These books have ton of advice that might or might not fit in the readers case. Most often they are not transferrable. Lean Analytics is not even short on actionable advice but the focus is on making us understand the frameworks, the context and the how to make informed decisions. This, after all, is what entrepreneurship is all about. There is always less time and money then there is opportunity. I don't need the details of a particular path but rather to understand the consequences of each. The benchmark part at the end is great, but the book is from 2012, so it's ageing. Nevertheless, this is the one must-read book in this field. All else is just there to fill in the details.

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