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Lean startup concepts

Lean startup concepts. “Existing companies execute a business model; startups search for one.”. What a startup is. Why not a business plan?. Business plans are full of untested assumptions and rarely survive first contact with customers

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Lean startup concepts

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  1. Lean startup concepts

  2. “Existing companies execute a business model; startups search for one.” What a startup is

  3. Why not a business plan? • Business plans are full of untested assumptions and rarely survive first contact with customers • Nobody, aside from venture capitalists and the former Soviet Union, requires five-year plans to forecast a series of unknowns. • Startups are not smaller versions of large companies. Successful startups go quickly from failure to failure while adapting, testing new iterations, and improving ideas with continual feedback from customers

  4. The lean launch pad approach

  5. Key principles • Instead of engaging in months of research, entrepreneurs recognize that all they have is a series of untested hypotheses – good guesses • Lean startups “get out of the building” to test their hypotheses and collect evidence about whether they are true or false – this is customer development • Lean startups practice agile development by working iteratively and incrementally with the customer – this process leads to the minimum viable product

  6. (a) Favorite quote "My goal in advocating a scientific approach to the creation of startups is to channel human creativity into its most productive forms, and there is no bigger destroyer of creative potential than the misguided decision to persevere.“ - Eric Ries, The Lean Startup (2011: p 149)

  7. Motivation and intent (long version) • Startups can shorten their product development cycles by adopting a combination of business-hypothesis-driven experimentation, iterative product releases, and "validated learning". • If startups invest their time into iteratively building products or services to meet the needs of early customers, they can reduce the market risks and sidestep the need for large amounts of initial project funding and expensive product launches and failures.

  8. Motivation and intent (shorter version) • Build – Test – Learn • Build products (services) iteratively to meet the needs of early customers – reduce market risks and funding needed to find successes and failures. • Test hypotheses about customer behavior • Learn from their reactions to your product (service)

  9. Terms from Lean Startup • Innovation accounting • Minimum viable product • Continuous deployment • Cohort analysis • Split testing • Actionable metrics • Pivot

  10. Minimum viable product • "version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort." • Goal of an MVP is to test fundamental business hypotheses (or leap-of-faith assumptions) and to jump start learning process as quickly as possible.

  11. Continuous deployment • A process “whereby all code that is written for an application is immediately deployed into production,” which results in a reduction of cycle times. • Ries states that some of the companies he’s worked with deploy new code into production as often as 50 times a day.

  12. Cohort analysis • A split or A/B test is an experiment in which "different versions of a product are offered to customers at the same time.“ • Goal is to observe differences in behavior between the two groups and to measure the impact of each version on an actionable metric. • A/B can also be performed in serial fashion where a group of users one week may see one version of the product while the next week users see another

  13. Actionable metrics • Measures that can lead to informed business decisions and subsequent action. • IMVU used “funnel metrics” that were critical to company growth: • Customer registration • Download of application • Trial, repeat usage, purchase • Contrast to 'vanity metrics' - measurements that give “the rosiest picture possible” but do not accurately reflect the key drivers of a business. • For example, a company specializing in creating web based dashboards for financial markets might view the number of web page views per person as a vanity metric as their revenue is not based on number of page views.

  14. Actionable metrics

  15. Actionable metrics

  16. Pivot • a “structured course correction designed to test a new fundamental hypothesis about the product, strategy, and engine of growth.” • E.g., When Groupon first started, it was an online activism platform called The Point. • After receiving almost no traction, the founders opened a WordPress blog and launched their first coupon promotion for a pizzeria located in their building lobby

  17. Lean Analytics • Use data to build abetter business faster.

  18. Some background on Lean, analytics, metrics, and segmentation.

  19. Most startups don’t know what they’ll be when they grow up. Mitelwas a lawnmower company Freshbookswas invoicing for a web design firm Paypalfirst built for Palmpilots Wikipediawas to be written by experts only Flickrwas going to be an MMO Autodeskmade desktop automation Twitterwas a podcasting company Hotmailwas a database company

  20. Product/market hypothesis Product/market hypothesis Product/market hypothesis You are here Trial startup Trial startup Trial startup Trial startup PIVOT Product/market hypothesis Possible problem space

  21. Kevin Costner is a lousy entrepreneur. • Don’t sell what you can make.Make what you can sell.

  22. 5 things you need to know about metrics Qualitative or Quantitative Exploratory or Reporting Vanity or Actionable Correlated or Causal Leading or Lagging

  23. Qualitative Quantitative Unstructured, anecdotal, revealing, hard to aggregate. Numbers and stats; hard facts but less insight. Warm and fuzzy. Cold and hard. http://www.flickr.com/photos/zooboing/8388257248/ http://www.flickr.com/photos/x1brett/4665645157/

  24. Exploratory Reporting Speculative, trying to find unexpected or interesting insights. Predictable, keeping you abreast of normal, managerial operations. http://www.flickr.com/photos/50755773@N06/5415295449/ http://www.flickr.com/photos/elwillo/4737933662/

  25. Donald Rumsfeld on analytics Are facts which may be wrong and should be checked against data. we know know Are questions we can answer by reporting, which we should baseline & automate. we don’tknow Things we Are intuition which we should quantify and teach to improve effectiveness, efficiency. we know don’tknow Are exploration which is where unfair advantage and interesting epiphanies live. we don’tknow (Or rather, Avinash Kaushik channeling Rumsfeld)

  26. Vanity Actionable Picks a direction. Makes you feel good, but doesn’t change how you’ll act. http://www.flickr.com/photos/lostseouls/807253220/ http://www.flickr.com/photos/aussiegall/6382775153/

  27. A metric from the early, foolish days of the Web. Count people instead. Marginally better than hits. Unless you’re displaying ad inventory, count people. Is this one person visiting a hundred times, or are a hundred people visiting once? Fail. This tells you nothing about what they did, why they stuck around, or if they left. Count actions instead. Find out how many followers will do your bidding. Poor version of engagement. Lots of time spent on support pages is actually a bad sign. How many recipients will act on what’s in them? Outside app stores, downloads alone don’t lead to lifetime value. Measure activations/active accounts. Time on site, or pages/visit Emails collected Number of downloads Hits Page views Visits Unique visitors Followers/friends/likes

  28. Correlated Causal Two variables that change in similar ways , perhaps because they’re linked to something else. An independent factor that directly impacts a dependent one. Summer Causal Causal Drowning Correlated Ice creamconsumption

  29. Causality is a superpower, because it lets you change the future. Correlation lets you predict the future Causality lets you change the future “I will have 420 engaged users and 75 paying customers next month.” “If I can make more first-time visitors stay on for 17 minutes I will increase sales in 90 days.” Find correlation Test causality Optimize the causal factor

  30. Leading Lagging Number today that shows metric tomorrow—makes the news. Historical metric that shows how you’re doing—reports the news.

  31. A leading indicator for e-commerce How many of your customers buy a second time in 90 days? Then you are in this mode Your customers will buy from you You are just like Focus on 1-15% Acquisition Once 70%of retailers Low CAC, high checkout 15-30% Hybrid 2-2.5per year 20%of retailers Increasing returns >30% Loyalty >2.5per year 10%of retailers Loyalty, inventory expansion (Thanks to Kevin Hilstrom for this.)

  32. ☀ Multivariateanalysis Changing several things at once to see which correlates with a result. A/B test: Changing one thing (i.e. color) and measuring the result (i.e. revenue.) ☀ Cohort: Comparison of similar groups along a timeline. ☁ ☁ ☀ ☁ Segment: Cross-sectional comparison of all people divided by some attribute (age, gender, etc.) Segments, cohorts, A/B, and multivariates

  33. Why use cohorts? Here’s an example. Is this company growing or stagnating? How about now?

  34. Why use cohorts? Here’s an example. Look at the same data in cohorts

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