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Applying Lean Startup Principles to Agile Projects
- 1. AT13 Concurrent Session 11/12/15 3:00pm “Applying Lean StartupPrinciples to Agile Projects” Presented by: Michael Hall Improving Enterprises Brought to you by: 340 Corporate Way, Suite 300, Orange Park, FL 32073 888-268-8770 · 904-278-0524 · info@techwell.com · www.techwell.com
- 2. Michael Hall Improving Enterprises AsPrincipal Consultant at Improving Enterprises, Michael Hall specializes in new product development using agile methods and Lean Startup principles. Michael has more than thirty years’ experience developing and delivering large-scale cloud-based systems, next-generation mobility solutions, mobile apps, embedded device systems, and wireless telecom systems. This deep technical experience gives Michael an excellent base of real-world product development knowledge and insightful understanding of the challenges a team is likely to face when transitioning from traditional to agile. An early adopter of agile methods, Michael has led several successful enterprise-wide transformations to agile.
- 3. 1 Applying Lean Startup Principlesto Agile Projects Mike Hall Improving Enterprises mike.hall@improving.com 972.757.9027 Lean Startup 2
- 4. 2 3 Applicability It’s for everyone:startups, new product development, new features, etc. 4 Mantra The goal of any project is to figure out the right thing to build. “What if we found ourselves building something that nobody wanted? In that case, what did it matter if we did it on time and on budget?” Look carefully at this picture – what is wrong?
- 5. 3 5 Thinking Question at thestart of a typical project: • Can we build a solution for that problem? Lean Startup questions at the start: • Do consumers recognize they have a problem? • If there was a solution, would they buy it? • Would they buy it from us? • Can we build a solution for that problem? • Can we build a sustainable business around this product/service? Ask “Should it be built?” instead of “Can it be built?” 6 Framework • Adaptable framework • Based on scientific methods • A journey of “discovery” - Subject the vision to constant hypothesis testing - React to customer feedback - Bypass work that does not lead to learning - Adapt to what the data is telling you “Successful entrepreneurs had the foresight, ability, and tools to discover which part of their plans were working brilliantly and which were misguided, and adapt their strategies accordingly.
- 6. 4 Lean Startup -Principles 7 8 Assumptions as Hypotheses • Identify your project/feature assumptions (continuously) • Reword them as hypotheses “The XYZ change will prove that customers want to ...” (value) “The ABC feature will increase new customers by at least 15%.” (growth) Avoid: • Acting as if assumptions are true and proceeding anyway! – Leaps of Faith • Taking statements for granted • Reports from anyone other than the customer
- 7. 5 9 Experiments • Think ofyour project as a set of small experiments • Break business plan down to its component parts – and test them • Define experiments to test each hypothesis • Results of the experiments guide decisions about product direction Experiments allow us to transition from guesses to knowledge. Case Study: Zappos 10 Exercise Handout • Think about a project you are working on • Name one big Assumption • Reword the assumption as a Hypothesis “The XYZ change will prove that customers want to ...” (value) “The ABC feature will increase new customers by at least 15%.” (growth) • List 2 – 3 Experiments (user story names, work items, tasks, etc.) that will help prove or disprove the Hypothesis
- 8. 6 11 Validated Learning • Theresult of the experiments • Can be positive or negative changes • Empirical data from the customer • “Learn lessons early” rather than “build features and fix bugs” • Faster and more accurate than market forecasting and classical business planning The measure of an effective team is how much validated learning did we achieve (as opposed to how much did we build). 12 A Tale of Two Teams • What to build? • Passionate debates • Suits decide • Implement several features at a time • Celebrates any positive perception • Clear baseline metric • Hypothesis on how to improve metric • Experiments to test hypothesis • Empirical data from customer usage • Celebrates learning
- 9. 7 13 Small Batch Size • Allows us to identify quality problems sooner • Pull – each step pulls the parts needed from the previous step, Toyota JIT production • As soon as we formulate a hypothesis, run the experiment as quickly as possible using the smallest batch size to get the job done! “Large batch sizes can create a death spiral of re-doing work.” 14 Build – Measure - Learn the smallest batch possible qualitative and quantitative Decision! Minimize time through loop from the data
- 10. 8 15 Minimum Viable Product 16 MinimumViable Product • The resultant output of successive Build – Measure – Learn loops • Remove/Avoid any effort that does not lead to learning • Goal of MVP – test your hypotheses, achieve validated learning • Decision after learning: pivot/persevere/quit • Iterate toward launchable product “The only way to win is to learn faster than anyone else.”
- 11. 9 17 Case Study: Dropbox • Very popular web-based file-sharing service • Initial MVP: a YouTube video • Targeted to early adopters • Beta waiting list went from 5,000 to 75,000 overnight • Company now worth over $1B 18 MVP Patterns • Concierge MVP – personalized service as a learning activity • Wizard of Oz MVP – behind the scenes humans doing the work • Case Study: Aardvark • Low-quality MVP • Case Study: Craigslist • Case Study: IMVU avatar teleportation • Smoke test - marketing materials • UI mockups • Etc.
- 12. 10 19 Pivot, Persevere, orQuit • Based on the validated learnings of an MVP, decide! • Pivot – structured course correction designed to test new hypotheses • Persevere – continue on with next set of hypotheses • Quit – cancel the project and move on to the next one “There is no bigger destroyer of creative potential than the misguided decision to persevere.” 20 Case Study: Potbelly Sandwiches • Started out as an antique store • Began selling sandwiches to drive traffic to the stores in the hopes of selling more antiques • Lines formed out the door • Pivoted to a sandwich store • Today over 280 sandwich stores nationwide
- 13. 11 21 Pivot Types • Zoom-inpivot – refocus product on what was previously considered one feature • Zoom-out pivot – single feature is inadequate, so add features • Customer segment pivot • Customer need pivot (Potbelly) • Platform pivot • Business architecture pivot • Value capture pivot • Engine of growth pivot • Channel pivot • Technology pivot Pivots take courage! 22 Others • Innovation Accounting • Engine of Growth • Adaptive Organization
- 14. 12 Application to Agile Projects 23 24 CaseStudy: DCAPI • Goal is to accurately measure the user’s video play time • Measurement messages are from video players - Start, Stop - Playhead position - Ads - Etc. • Original product was a downloadable SDK integrated into apps - Logistical issues when software changes - Different SDK for each OS - High certification costs • New product: web service to receive measurement messages - Defined Restful API called DCAPI
- 15. 13 25 Get Started: Hypotheses-DrivenVision • Develop a classic vision board • List assumptions • Continue to identify assumptions as you go • Translate implicit assumptions into explicit testable hypotheses • List hypotheses 26 DCAPI Vision Board Target Group Needs Product Value For clients who have a need for capturing census-based usage analytics on their connected devices/applications, Data Collection API (DCAPI) is a cloud-based service that provides a simple easy-to-understand way of reporting measurements. Unlike classic embedded SDK approaches, the DCAPI will provide a direct reporting experience based on web service calls. Digital customers • CBS Interactive • MobiTV • A&E Apps • Crown Media • Univision Apps • Pandora • Viacom • Fox News • DirecTV • NBCU Apps • AT&T • JW Player • Yelp • Roku • Xbox • Connected TVs • PlayStation • Ease of measurement reporting • Use of familiar programmatic approach • Less software development • No need to download/integrate SDK • One solution for all digital • Cloud-based • Transparent evolution • Linear scaling as demand grows • Fault tolerant • Increase revenues • Satisfy pent-up demand • Increase digital footprint • 1-stop-shop Assumptions Hypotheses • Customers will prefer DCAPI over the embedded SDK • DCAPI will make it easier to certify apps • DCAPI can handle a large amount of users • DCAPI will need a super-fast DB • An early release to friendly customers will provide good feedback • > 80% of all customers will prefer DCAPI • DCAPI can be self-certified by customers • DCAPI can handle 50K simultaneous sessions • Redis is the best DB for DCAPI • An initial release can be built with limited (but valuable) functionality for early adopters Vision Statement
- 16. 14 27 Get Empathetic: KnowledgeBroker Personas • A special form of persona • But emphasizes the knowledge sharing that each can bring • Customer Archetype – humanizes the proposed target user Knowledge Sharing • Will consider the new API approach • Can explain advantages of Restful API over SDK • Can provide feedback on error handling • Can give strong opinion on Offline message handling • Can share the CBS-I deployment schedules • Etc. Kyle Fisher – Senior Software Developer at CBS Interactive Personal Profile Kyle is a 42 year-old mobile software developer. He is familiar with video streaming and codecs such as MP3, Vorbis, and AAC. He understands transport protocols such as MMS, RTP, HLS, and Adobe’s HDS. His platform of choice is iOS, but can work in Android when needed. Kyle has previously used our downloadable SDK for the CBS-I video app. Background • 42 year-old Caucasian male • Father of two • Enjoys hockey • Loves anything mobile app related Attributes • Upper middle class • Technically savvy • Carries latest iPhone • Has iPad at home • Enjoys social media “I want an easy-to-use well defined Restful API for my mobile apps to report usage analytics.” Kyle’s Product-Content Needs • Simple API • Parameters are readily available • Inline https: invocations • Uncomplicated state diagram • Guidance on how to handle offline scenarios • Succinct API specification 28 Get Organized: Learning Maps • Create a story map on a wall • But organize and prioritize it by Hypothesis from left to right • Which will deliver the most learning? • Which learnings are most crucial? • Which learnings reduce risk? • Which are most crucial in answering “Are we building the right product?” • For each hypothesis, name the user stories and/or work items • Prioritize the user stories top to bottom
- 17. 15 29 DCAPI Learning Map Hypotheses Experiments: Stories,Work Items 30 Tee It Up: Experiment Backlogs • Similar to Scrum product backlog • But is learning-based prioritization • List of all experiments 1..n • Stories, work items, research, etc. • Tagged with Hypothesis name/description
- 18. 16 31 DCAPI Experiment Backlog CloudAPI DCAPI-72 Minimum Product Epic Open Unresolved 3/13/2015 19:05 3/23/2015 11:15 Hypothesis: An incomplete DCAPI can be built that is "good enough" for luminary clients. Cloud API DCAPI-4 Start session, collection switch enabled Story Open Unresolved 3/13/2015 14:17 3/23/2015 9:00 As an application, I want to start a session, so that I can begin reporting metrics to the downsteam systems. • When I request a session, I expect that my session is started by DCAPI. • When I request a session, I expect to receive a successful return code from DCAPI. • When I request a session, I expect to receive a unique session id that I can use in subsequent DCAPI calls. I also expect to receive an opt-out URL that I can display in my Privacy page. • When I request a session and DCAPI is unable to start my session, I expect to receive an error code that indicates the reason. • When I request a session and my device/application is opted-out, I expect to receive an error code that indicates opted-out as the reason. • When I request a session, I expect DCAPI to read in my config file from the Config system. The Config file contains variable name mappings that allow me to use my own defined variables instead of the Nielsen defaults. • When I enable the collection switch after it was previously disabled, I expect measurement collection to begin again. • When I enable the collection switch after it was previously enabled, I expect measurement collection to continue as previously. Cloud API DCAPI-5 Start play Story Open Unresolved 3/13/2015 14:17 3/19/2015 16:09 As an application, I want to start play, so that I can report the exact timestamp when media has started playing. Acceptance Criteria • When I start play, I expect to receive a successful return code from DCAPI • When there is an error in the data transmission to DCAPI, I expect to receive an error response code. Note: Start play is sent when media content begins playing. This occurs after the app requests the content to play (request start play) and the (potential) buffering time occurs Cloud API DCAPI-8 Pause/stop play Story Open Unresolved 3/13/2015 14:18 3/23/2015 8:59 As an application, I want to report when I pause play, so that I can send metrics to DCAPI • When I am sending pause for live content, I expect DCAPI to accept the data as defined in the API such as event, UTC time, and type. • When I am sending pause for VOD content, I expect DCAPI to accept the data as defined in the API such as event, offset time, and type. • When I am sending pause, I expect DCAPI to be able to accept my data every 10 seconds. • When I send pause, I expect an OK response code. • When there is an error in the data transmission to DCAPI, I expect an error response code. • When I send pause to DCAPI, I expect DCAPI to send a ping to Census based on the applied business logic. 32 Get Focused: Minimum Learning Product (MLP) • Similar to MVP, but much smaller • Learning-based, not viable product- based • Smallest chunk of the Learning Map that can be developed to learn something important • Real or mock form • Goal is to get just enough learnings - Then pivot, persevere, or quit • Choosing the MLP replaces classic Scrum sprint planning - Break into tasks if it helps
- 19. 17 33 DCAPI Minimum LearningProduct MLP 34 Experiment Test Iteration • Experiment Test Iteration (ETI) • Similar to Scrum sprint but variable time length - Depends on size of experiment - Get through Build/Measure/Learn as quickly as possible! Scrum: fixed iteration length ETI: variable iteration length ETI 1 3 days ETI 2 5 days ETI 3 9 days ETI 4 17 days ETI 5 6 days ETI 6 7 days
- 20. 18 35 Build It: ETI • Build out the MLP • Measure progress based on validated learning • Use modified storyboard showing Validated column Story To Do In Work Done Validated 36 Get More Data: Learning Results Period • Obtaining results from knowledge brokers • Sometimes the validation takes longer than the end of the ETI • Run this in parallel with the next ETI - Defer pivot/persevere/quit decision until this data is in ETI 1 3 days ETI 2 5 days ETI 3 9 days ETI 4 17 days ETI 5 6 days ETI 6 7 days ETI3 Learning Results Period Pivot/Persevere/Quit
- 21. 19 37 Demo It: ETIReview • Dev team demos their progress • Discuss learnings obtained from the Learning Results Period • Experiment findings are discussed with stakeholders • Decision: pivot, persevere, or quit 38 Think About It: ETI Retrospective • Team discusses - What went well, what did not go well - How to get better • A spirit of “continuous improvement” • Plus: - How is the team feeling about the assumptions? - Are there any not identified previously?
- 22. 20 39 Rinse & Repeat:MLPs • Build series of MLPs to reach final launchable product • Use innovation accounting to “tune the growth engine” • Be courageous in pivot/persevere/quit decisions 40 Team Dynamics • Scrum team becomes a small “innovation factory” - Responsible for code and/or artifacts that prove/disprove a hypothesis - Continuous innovation • Practicing the art of “genchi genbutsu” - “Go and see” - The only way to truly understand the requirements is to get out of the office and spend time with the customer - Gemba – the real place - Don’t rely on information from other sources
- 23. 21 41 Gemba Walk • GembaWalk - Go see the actual process - Purposeful attempt to learn what is really going on - Direct customer interaction - Ask questions - Show respect - Learn 42 Team Dynamics (cont) • ScrumMaster becomes “shusa” • Chief engineer responsible for guiding the product to success • Guides team on experiments and MLPs to product
- 24. 22 Conclusion 43 44 Conclusion • Lean Startupprinciples can and should be used in Agile projects - To help insure we build the right thing • Approximately 10 techniques presented, but there are probably even more • This could be the next major evolution of Agile! “If we stopped wasting people’s time, what would they do with it? We have no real concept of what is possible.”
- 25. 23 45 Introducing: Gemba • Gemba:a validated learning Agile method Gemba Scrum Lean Startup Lean 46 Gemba Manifesto We value • Validated learning over reasonable assumptions • Data-driven decisions over plausible-sounding arguments • Building minimum learning products over additional features • The courage to build the right thing over something that might work
- 26. 24 THE END Mike Hall ImprovingEnterprises mike.hall@improving.com 972.757.9027 Backups 48
- 27. 25 49 Innovation Accounting • Measurethe progress of innovation towards validated learning – instead of burn rate or $ • Three steps • Use MVP to establish real data • Tune the engine from baseline towards ideal • Pivot, persevere, or quit • Use actionable metrics – clear cause&effect • Split-test of a feature caused 20% increase in sales • Per-customer metrics • Cohort metrics – groups of customers • Avoid vanity metrics • Number of hits to a website • Action to take is not obvious “If you are building the wrong thing, optimizing the product or its marketing will not yield significant results.” 50 Engine of Growth • Use a small set of actionable metrics • Customer acquisition cost • Activations • Retention • Revenue • Referrals • Consider viral coefficient • How many friends will each customer bring? • Case Study: Hotmail • “Tune” the engine every time learning occurs
- 28. 26 51 Adaptive Organization • Auto-adjustprocess and performance based on current learnings • Andon cord – anyone can stop the production line! • Slow down – invest in preventing issues • Ask “Why?” 5 times to get to root cause Avoid • Handoffs and approvals • Making decisions on plausible-sounding arguments • Low quality products • Defects