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Lean Analytics: Use Data to Build a Better Startup Faster
Marc Andreesen once said that "markets that don't exist don't care how smart you are." Whether you're a startup founder trying to disrupt an industry, or an intrapreneur trying to provoke change from within, your biggest risk is building something nobody wants.
Lean Analytics can help. By measuring and analyzing as you grow, you can validate whether a problem is real, find the right customers, and decide what to build, how to monetize it, and how to spread the word. Focusing on the One Metric That Matters to your business right now gives you the focus you need to move ahead--and the discipline to know when to change course.
Written by Alistair Croll (Coradiant, CloudOps, Startupfest) and Ben Yoskovitz (Year One Labs, GoInstant), the book lays out practical, proven steps to take your startup from initial idea to product/market fit and beyond. Packed with over 30 case studies, and based on a year of interviews with over a hundred founders and investors, the book is an invaluable, practical guide for Lean Startup practitioners everywhere.
Lean Analytics can help. By measuring and analyzing as you grow, you can validate whether a problem is real, find the right customers, and decide what to build, how to monetize it, and how to spread the word. Focusing on the One Metric That Matters to your business right now gives you the focus you need to move ahead--and the discipline to know when to change course.
Written by Alistair Croll (Coradiant, CloudOps, Startupfest) and Ben Yoskovitz (Year One Labs, GoInstant), the book lays out practical, proven steps to take your startup from initial idea to product/market fit and beyond. Packed with over 30 case studies, and based on a year of interviews with over a hundred founders and investors, the book is an invaluable, practical guide for Lean Startup practitioners everywhere.
440 pages, Paperback
First published March 8, 2013
1,592 people are currently reading
19.7k people want to read
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December 7, 2014
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:
-
PREVIOUSLY:
I love Lean Startup, but this is how it really goes sometimes, amirite?
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?
March 20, 2015
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.
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.
February 19, 2014
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.
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.
September 6, 2017
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
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
August 15, 2021
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
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
October 3, 2017
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.
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.
November 29, 2018
Cá nhân mình thấy cuốn này khá hữu ích vì nó giúp mình hiểu hơn về công ty và công việc hiện tại. Sách được viết dễ hiểu kèm nhiều case study + guideline thực tiễn.
- Phần 1
Giới thiệu về mô hình lean startup: xây dựng - đo lường - học hỏi và cuốn lean analytics này tập trung vào phần đo lường. Tóm tắt một vài điểm chính:
• Tầm quan trọng của việc phân tích và tìm được những chỉ số tốt (có khả năng so sánh, dễ hiểu, dạng tỉ số/tỉ lệ)
• Các công ty trong một thời điểm chỉ nên tập trung vào tối ưu 1 chỉ số (OMTM) trước khi chuyển sang chỉ số khác.
• Giới thiệu và giải nghĩa về các cặp chỉ số: định lượng vs định tính, chỉ số ảo vs chỉ số tác động được, chỉ số giải thích vs chỉ số báo cáo, chỉ số dẫn dắt vs chỉ số thể hiện kết quả, chỉ số tương quan vs nguyên nhân
• Cảnh báo người đọc về các chỉ số ảo có thể làm ảnh hưởng đến việc ra quyết định
• Giới thiệu về các cạm bẫy dữ liệu
- Phần 2,
Tác giả chia các lĩnh vực kinh doanh thành 6 mô hình chính gồm: (1) e-commerce, _(2) saas, (3) mobile app, (4) media site, (5) nội dung người dùng sáng lập và (6) thị trường lưỡng diện. Sau đó tác giả phân tích chi tiết và đưa ra các chỉ số liên quan đến từng mô hình.
Sau khi đã xác định những chỉ số liên quan đến mô hình kinh doanh của mình, tác giả đưa ra mô hình lean analytics gồm 5 giai đoạn: (1) thấu hiểu, (2) bám trụ, (3) lan truyền, _(4) doanh thu, (5) mở rộng. Ở mỗi giai đoạn sẽ có các chỉ số, cách thực hiện và bài học để phát triển công ty đến giai đoạn tiếp theo.
- Phần 3
Một số ranh giới chuẩn của các chỉ số để có cái nhìn toàn cảnh và xác định vị trí của doanh nghiệp. Tóm tắt 1 vài bài học:
• Ta rất dễ bị mắc kẹt trong một chỉ số trong có vẻ xấu và đầu tư quá nhiều thời gian và tiền bạc để cải thiện trong khi không biết mình đang đứng đâu so với đối thủ vạnh tranh và trung bình ngành. Biết được ranh giới giúp quyết định có nên chuyển sang chỉ số khác không
• Cung cấp một vài chỉ số hiệu quả với ví dụ và case study từ nhiều công ty khác nhau.
Ví dụ: Chỉ số hiệu quả khi dành dưới 1/3 doanh thu khách hàng để thu hút khách hàng mới hay tỉ lệ churn của các công ty Saas hàng đầu thường dưới 5%/tháng (khoảng 1,5-3% là lí tưởng)
• Có một case study về ảnh hưởng của paywall (bức tường trả phí) để thử một sản phẩm Saas. Với số liệu phân tích dù tỉ lệ chuyển đổi những người đăng ký cao hơn nhưng do số người thử quá ít so với khi không có thẻ tín dụng (nghĩa là khi người dùng thấy phải điền thông tin thẻ sẽ rời bỏ trước khi thử sản phẩm). Điều này khiến công ty mất đi những khách hàng còn phân vân.
Cách tiếp cận với trường hợp này là phân nhóm khách: người quan tâm, người bình thường (gồm người đang phân vân) và người tò mò (những người nhiều khả năng không mua sản phẩm). Bằng phân tích dữ liệu sử dụng và tập trung nguồn lực bán hàng, marketing lên mỗi nhóm khác nhau => tối ưu được doanh thu mà không tốn thời gian vào những người không quan tâm.
- Phần cuối, áp dụng cách thực hiện lean analytics vào môi trường nội bộ và cách áp dụng đối với doanh nghiệp B2B.
- Phần 1
Giới thiệu về mô hình lean startup: xây dựng - đo lường - học hỏi và cuốn lean analytics này tập trung vào phần đo lường. Tóm tắt một vài điểm chính:
• Tầm quan trọng của việc phân tích và tìm được những chỉ số tốt (có khả năng so sánh, dễ hiểu, dạng tỉ số/tỉ lệ)
• Các công ty trong một thời điểm chỉ nên tập trung vào tối ưu 1 chỉ số (OMTM) trước khi chuyển sang chỉ số khác.
• Giới thiệu và giải nghĩa về các cặp chỉ số: định lượng vs định tính, chỉ số ảo vs chỉ số tác động được, chỉ số giải thích vs chỉ số báo cáo, chỉ số dẫn dắt vs chỉ số thể hiện kết quả, chỉ số tương quan vs nguyên nhân
• Cảnh báo người đọc về các chỉ số ảo có thể làm ảnh hưởng đến việc ra quyết định
• Giới thiệu về các cạm bẫy dữ liệu
- Phần 2,
Tác giả chia các lĩnh vực kinh doanh thành 6 mô hình chính gồm: (1) e-commerce, _(2) saas, (3) mobile app, (4) media site, (5) nội dung người dùng sáng lập và (6) thị trường lưỡng diện. Sau đó tác giả phân tích chi tiết và đưa ra các chỉ số liên quan đến từng mô hình.
Sau khi đã xác định những chỉ số liên quan đến mô hình kinh doanh của mình, tác giả đưa ra mô hình lean analytics gồm 5 giai đoạn: (1) thấu hiểu, (2) bám trụ, (3) lan truyền, _(4) doanh thu, (5) mở rộng. Ở mỗi giai đoạn sẽ có các chỉ số, cách thực hiện và bài học để phát triển công ty đến giai đoạn tiếp theo.
- Phần 3
Một số ranh giới chuẩn của các chỉ số để có cái nhìn toàn cảnh và xác định vị trí của doanh nghiệp. Tóm tắt 1 vài bài học:
• Ta rất dễ bị mắc kẹt trong một chỉ số trong có vẻ xấu và đầu tư quá nhiều thời gian và tiền bạc để cải thiện trong khi không biết mình đang đứng đâu so với đối thủ vạnh tranh và trung bình ngành. Biết được ranh giới giúp quyết định có nên chuyển sang chỉ số khác không
• Cung cấp một vài chỉ số hiệu quả với ví dụ và case study từ nhiều công ty khác nhau.
Ví dụ: Chỉ số hiệu quả khi dành dưới 1/3 doanh thu khách hàng để thu hút khách hàng mới hay tỉ lệ churn của các công ty Saas hàng đầu thường dưới 5%/tháng (khoảng 1,5-3% là lí tưởng)
• Có một case study về ảnh hưởng của paywall (bức tường trả phí) để thử một sản phẩm Saas. Với số liệu phân tích dù tỉ lệ chuyển đổi những người đăng ký cao hơn nhưng do số người thử quá ít so với khi không có thẻ tín dụng (nghĩa là khi người dùng thấy phải điền thông tin thẻ sẽ rời bỏ trước khi thử sản phẩm). Điều này khiến công ty mất đi những khách hàng còn phân vân.
Cách tiếp cận với trường hợp này là phân nhóm khách: người quan tâm, người bình thường (gồm người đang phân vân) và người tò mò (những người nhiều khả năng không mua sản phẩm). Bằng phân tích dữ liệu sử dụng và tập trung nguồn lực bán hàng, marketing lên mỗi nhóm khác nhau => tối ưu được doanh thu mà không tốn thời gian vào những người không quan tâm.
- Phần cuối, áp dụng cách thực hiện lean analytics vào môi trường nội bộ và cách áp dụng đối với doanh nghiệp B2B.
This entire review has been hidden because of spoilers.
November 26, 2019
عالی. این کتاب مسیر زندگی حرفهای من رو تغییر داد
September 26, 2023
Took me a long time to get through this one. Not because it is particularly difficult or dense, but mostly because I have run into this content, piece-meal, across many other books and articles over a period of two years or so. After a while, it reads a little like an encyclopedia of product analytics, which isn't bad, but it might also not be what you'd call "gripping".
I think it would have been received with a lot more enthusiasm at a much earlier time (or later time, I'll explain) in my journey into PM/Analytics/DS. And that's sort of the issue, as my inexperienced self sees it: you either need this book very early in the "product discovery phase" of your startup/product journey, or later on when you already have already built some of the analytics frameworks that allow you to take advantage of the knowledge found herein.
If you are somewhere in the middle and still trying to get the product to market after having done the legwork and figuring out what you need to build in order to secure your first couple of users, then perhaps it will be a bit like getting ahead of yourself.
Either way, I would totally recommend this to buddying founders / PMs / Data Scientists.
I think it would have been received with a lot more enthusiasm at a much earlier time (or later time, I'll explain) in my journey into PM/Analytics/DS. And that's sort of the issue, as my inexperienced self sees it: you either need this book very early in the "product discovery phase" of your startup/product journey, or later on when you already have already built some of the analytics frameworks that allow you to take advantage of the knowledge found herein.
If you are somewhere in the middle and still trying to get the product to market after having done the legwork and figuring out what you need to build in order to secure your first couple of users, then perhaps it will be a bit like getting ahead of yourself.
Either way, I would totally recommend this to buddying founders / PMs / Data Scientists.
September 14, 2020
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
January 2, 2021
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
November 18, 2017
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
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
December 19, 2022
Re-read after ~5 years. Still one of the most practical and useful books I've ever read when it comes to tech and business. It's hard to believe 10 years have passed since its original publication, given its content is still fresh. As a subsequent thought, it's frankly depressing to realize how little the digital economy has changed in these past 10 years from 2013 to 2023 (the authors mention Amazon, FB/IG, Quora, Twitter, Pinterest, Reddit, etc. - basically, most of the names in the book are still dominating their relative niches a decade later; also, the analyzed most popular business models for startups have remained exactly the same), compared to the massive changes that happened in the previous 10 years from 2003 to 2013.
The book's content is organized in roughly six different macro-sections: Analytics strategy, Business models (a selected list of models getting analyzed), Customer development, Metrics, B2B startups, Intrapreneurship. The fist two are especially remarkable (5-star content).
The book's content is organized in roughly six different macro-sections: Analytics strategy, Business models (a selected list of models getting analyzed), Customer development, Metrics, B2B startups, Intrapreneurship. The fist two are especially remarkable (5-star content).
March 19, 2018
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
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
June 12, 2020
Zakladateľovi start-upu bez znalosti základov podnikovej ekonomiky a financií môžu niektoré časti pripadať vzdialené, pre skúsenejších start-upistov však kniha prináša veľké množstvo príkladov rôznych metrík, ktoré sú vhodné v rôznych fázach životného cyklu. Konkrétne best practices uvádzajú obdobné riešenia iných technologických spoločností. Veľmi prínosné je škálovanie príkladov podľa rôznych typov start-upov, aj keď niektoré sa hodia skôr pre skúsenejších IT špecialistov. 4*, lebo podľa mňa lean analytika ešte aj o niečom inom.
December 25, 2014
Super awesome for anyone wanting lots of ready made gyan and benchmarks on analytics
Highly recommend for product managers!!
Highly recommend for product managers!!
May 1, 2018
"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.
The abundance of examples makes Lean Analitycs helpful for each job which requires entrepreneurial gene because the book shows how decisions should be made.
March 20, 2017
La lectura de esta semana, lastimosamente, es para NO recomendar. Este libro trata uno de los temas más importantes en una empresa, grande o pequeña: la medición, las métricas, los indicadores (KPI). Si existe algo con lo que me he ido obsesionando en los últimos años es precisamente este; entender en detalle lo que ocurre tras bambalinas, extraer la sustancia de lo que no vemos y suponemos que anda bien. Este libro, bastante largo, se salva por 2 o 3 capítulos que mencionan los puntos claves sobre la construcción de indicadores. Creo que es más rápido aprender sobre este tema con algunos artículos o cursos en internet. Se considera la segunda parte del libro Lean Startup de Eric Ries, bastante exitoso y que también recomendé en mis redes, pero como siempre, las segundas partes.... son segundas #libroNoRecomendado
February 23, 2019
Những lần đọc thêm những quy��n mới là lại thấy mình ngu muội và làm những điều thật là sai trái. Data driven decision là gì?
The one metric that matters (OMTM) là gì?
Why we need analytics, tatics?
Phải nói thật đây là một trong những quyển cứu rỗi cuộc đời mình qua cơn mê làm product khi phải đối mặt với một thứ mang tính sống còn là churn rate.
The one metric that matters (OMTM) là gì?
Why we need analytics, tatics?
Phải nói thật đây là một trong những quyển cứu rỗi cuộc đời mình qua cơn mê làm product khi phải đối mặt với một thứ mang tính sống còn là churn rate.
March 10, 2022
As for data analyst this book wasn’t very helpful, but it gave some insights. Also, really loved the concept and structure of the book!
I truly recommend this read for anyone wanting to start a company or getting into finding answers based on data. However, I would say this is just a start for real actions.
I truly recommend this read for anyone wanting to start a company or getting into finding answers based on data. However, I would say this is just a start for real actions.
October 16, 2017
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.
February 21, 2019
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.
April 26, 2022
Painfully useful book! Took ages to read because I took so many notes - and still it feels like I did not get it all! I think I just expected metrics that are useful but instead I got several workable tutorials on how to make a company and which metrics to track while your business is in different stages of growth. Will use my notes vigorously on this one!
November 17, 2022
4,5 stars - very actionable, loads of references, structured examples. Only thing I was missing was more in-depth / data driven case studies. The ones provided were good, but could be made even more tangible IMHO.
November 20, 2022
As a data analyst, I’m always tempted to dive into trying to understand and explore more technical skills and complex tools. Of course, this doesn’t always bring the most value to businesses.
The book highlights the importance of choosing the right Lean metrics for business wishing to identify risk areas/potential growth areas. Having read this without owning any businesses myself (yet) or interest in a particular chapter, I’ve also learnt a lot about different business models.
In terms of content, it gives you *just about* enough info before moving on. It’s also surprisingly calm - I thought startup writing would be more chaotic.
The book highlights the importance of choosing the right Lean metrics for business wishing to identify risk areas/potential growth areas. Having read this without owning any businesses myself (yet) or interest in a particular chapter, I’ve also learnt a lot about different business models.
In terms of content, it gives you *just about* enough info before moving on. It’s also surprisingly calm - I thought startup writing would be more chaotic.
January 23, 2020
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.
April 24, 2020
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.
July 27, 2020
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!
June 4, 2019
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.
February 25, 2018
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!
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!
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