From good to great product ownership
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Product Owners plant the seeds for excellent agile delivery teams. Great POs know how to plant the best seeds, seeds that the team can swarm around and deliver quickly, that provide rapid feedback and learning, and that morph towards excellent customer experiences. In some situations we need a good PO, in others we need a great PO. The trick is to know the difference. Join me on a journey of discovery working with contemporary examples to find out how to be a great PO or a good PO, and why you might, at different times, want to be both. We look at two key dimensions that determine whether you need good PO or a great PO, and how to tell the difference. First, what problem is the PO trying to solve? Are you rolling out changes to a mature product or battling to enter an emerging field? Are you scaling rapidly or slowly? Second, how is the PO making decisions about their backlog. Give a PO a project requirements document and a timeline, and what’s a PO to do? Even the best and most experienced POs will struggle to deliver an exciting customer experience that captures the heart of the customer. Through the workshop, you will learn a simple model for identifying great POs based not on PO experience, but on how the PO makes decisions about their backlog. The best POs know how to combine data and stakeholder input to best effect.Finally, we consider the product problems you are trying to solve, the pace of change, and how this affects the PO - good to great - you want for your product.
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From good to great product ownership
- 1. www.agile42.com | All rights reserved. Copyright 2007-2018 From Good to Great Product Ownership or why experience is not enough Dave Sharrock, CST, CEC, CALE
- 2. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 The role of the product owner ● Clearly expressing Product Backlog items; ● Ordering the items in the Product Backlog to best achieve goals and missions; ● Optimizing the value of the work the Development Team performs; ● Ensuring that the Product Backlog is visible, transparent, and clear to all, and shows what the Scrum Team will work on next; and, ● Ensuring the Development Team understands items in the Product Backlog to the level needed. ©2018 Ken Schwaber and Jeff Sutherland, the Scrum Guide.
- 3. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 • Translate requirements into user stories • Focus on frequent delivery • Rank user stories based on value Good product ownership
- 4. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 • Understand value proposition of your product • Validate requirements and test hypotheses • Iterate rapidly with customer feedback • Keep long-term vision in mind, while changing near-term strategy Great product ownership
- 5. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Decisions & feedback Iterate forwards from release to release Based on expert POs and stakeholders Many, many small experiments Combines vision with data-driven decisions Told what to deliver. Project funding and defined requirements Decisions based on data, but feedback loop too long to inform next decisions QuickFeedback Loops SlowFeedback Loops Expert-driven Decisions Data-driven Decisions Good to great product ownership
- 6. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Slow feedback, expert-driven
- 7. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 In November 2007, economists — examining some 45,000 economic-data series — foresaw less than a 1-in-500 chance of an economic meltdown as severe as the one that would begin one month later Economists…
- 8. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 In 1940, the chance of an American being killed by lightning was about 1 in 400,000. Today, it’s 1 in 11 million. …vs weather forecasters http://www.nytimes.com/2012/09/09/maga zine/the-weatherman-is-not-a-moron.html
- 9. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Quick feedback, expert-driven
- 10. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Slow feedback, data-driven
- 11. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Quick feedback, data-driven
- 12. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018
- 13. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 The scientific method The first known description of an empirical scientific method is in an Egyptian medical text c. 1600 BCE Theory Prediction Experiment Observe Use the theory to make a prediction Design an experiment to test the prediction Run the experiment Modify or change your theory
- 14. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 The theory of general relativity The prediction that light bends in a gravitational field was observed 12 years after the prediction Einstein’s Theory of General Relativity Light bends in a gravitational field Solar Eclipse of 1919 Observations validated General Relativity Einstein’s prediction (1907) Wait for 1919 solar eclipse to observe whether or not light bends around the sun Arthur Eddington observed that Light did bend around the sun Gradual acceptance of General Relativity over Newtonian Mechanics http://thethoughtstash.wordpress.com/2011/01/03/how-eddington-demonstrated-that-einstein-was-right/
- 15. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Creating a hypothesis Start by describing your idea – what do you believe will happen if users do something specific?
- 16. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Lean Experience Canvas™ has been created by agile42 as an extension to the Business Model Canvas of Alex Osterwald and is licensed using Creative Common 3.0 with attribution (by), non commercial usage (nc) and share alike (sa) options. You can reuse and modify the template, but you will always have to leave the logo on it 1. Opportunity 2. Customer Segments What is the problem to be solved? What type of customers & users will benefit from this solution? How is the customer solving the problem right now? 5. Business Readiness What steps are required from the business side to be able to use this capability? 4. Benefits What are the benefits for the customers? What are the benefits for internal stakeholders? 6. Measuring Success What metrics will be best measure the success of the feature? 3. Possible Solution What are the key points of a possible solution to the presented problem? 7. Cost of Delay Which profile better represent the cost of delay (CoD)? 8. Costs Structure How does the cost structure look like for such a feature? One time, ongoing costs, contractors expenses, development costs? 9. Value to Customer and Business What are the expected incremental revenue for selling this feature, and what are the strategic and tactical benefit? What are the intangible values (usability, performance, customer knowledge obtained...) Using the experience canvas
- 17. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Or a dedicated hypothesis test 4. Success Criteria How do we know that the test is successful? At what point do we declare the test over, and move on to the next test. 5. Failure Criteria 1. Hypothesis Describe in one or two sentences what the hypothesis is. What you believe is true, what action you expect to see, and why. 2. Assumptions Explain a little about your thinking behind the hypothesis. What assumptions are you making? What theories is the test based on? 3. Audience 6. Sample Size How do we know that the test has failed? Failure criteria are the numbers below which it isn’t worth making the change. How many test cases do you need to run to be sure? How long will you need to wait to get a result? Consider who should take part in the test. Is it internal staff? Beta customers? How do you make sure you select the right group.
- 18. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 What are you testing for? Don’t test what you already know, and don’t confuse A/B testing with feature hypothesis testing
- 19. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Let’s create a hypothesis or two 1. In small groups discuss the case study described in your handouts. 2. Create either an experiment canvas or a hypothesis test (check the back of the handout). You can choose an HR practice or a product feature or experience. 3. Share your completed canvas or test with another team (preferably working on a different problem).
- 20. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Three types of MVPs Thank you Ben Hall Research
- 21. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 One test is worth a thousand expert opinions. Wernher Von Braun netflix
- 22. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Three types of MVPs Thank you Ben Hall Pitch Research
- 23. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 buyabeercompany An early attempt at crowd-funding to purchase Pabst Blue Ribbon ended with an SEC probe
- 24. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Three types of MVPs Thank you Ben Hall Research Concierge Pitch
- 25. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 zappos Zappos famously launched without stock, selling shoes from their local high street shoe store
- 26. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Iterate forwards from release to release Decisions based on expert POs Seek options on defined requirements Many, many small experiments Combines vision with data-driven decisions MVPs are small and targeted Project funding Defined requirements Often major change (e.g. system replacement) Single release or multiple major releases Decisions based on data not intuition Enables breakdown of major rollouts into value-based increments QuickFeedback Loops SlowFeedback Loops Expert-driven Decisions Data-driven Decisions Good to great product ownership 1 What types of work are common
- 27. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Simplify releases to gather feedback on single feature sets Release frequently and gather data Challenge project funding models and defined requirements Create and test many small hypotheses Feature toggles and product configuration Deep understanding and curiosity of personas and customer behaviours Be releasable as fast as possible Define small, releasable chunks Run systems in parallel to learn and provide value Use data to make trade-off decisions Come from a scarcity mindset (small MVP) Early talk about what will be dropped QuickFeedback Loops SlowFeedback Loops Expert-driven Decisions Data-driven Decisions Good to great product ownership 1 2 What types of work are common What do good & great POs do?
- 28. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Major retail banking backbone rebuild Insurance release process (CI/CD) Fashion retailer shoe configuration eCommerce giant merchandising team ERP contacts DB replacement Insurance web replacement project Factory goods configuration tool Fashion retailer data model migration QuickFeedback Loops SlowFeedback Loops Expert-driven Decisions Data-driven Decisions Good to great product ownership 3 1 2 What types of work are common What do good & great POs do? Examples of real products
- 29. agile42 | the agile coaching company www.agile42.com | All rights reserved. Copyright 2007-2018 Shorten the feedback loop, then capitalize on data-driven decision-making Understand how your organizational constraints (e.g. funding model, business proximity, long feedback loops) impair product ownership Conclusion Thank you! @davesharrock dave.sharrock@agile42.com
Editor's Notes
- The Product Owner is responsible for maximizing the value of the product resulting from work of the Development Team. How this is done may vary widely across organizations, Scrum Teams, and individuals. The Product Owner is the sole person responsible for managing the Product Backlog. Product Backlog management includes: Clearly expressing Product Backlog items; Ordering the items in the Product Backlog to best achieve goals and missions; Optimizing the value of the work the Development Team performs; Ensuring that the Product Backlog is visible, transparent, and clear to all, and shows what the Scrum Team will work on next; and, Ensuring the Development Team understands items in the Product Backlog to the level needed. The Product Owner may do the above work, or have the Development Team do it. However, the Product Owner remains accountable. The Product Owner is one person, not a committee. The Product Owner may represent the desires of a committee in the Product Backlog, but those wanting to change a Product Backlog item’s priority must address the Product Owner. For the Product Owner to succeed, the entire organization must respect his or her decisions. The Product Owner’s decisions are visible in the content and ordering of the Product Backlog. No one can force the Development Team to work from a different set of requirements.
- Probably not from the business Focused on meeting time and scope Less time in front of customers
- https://www.amazon.com/Navigation-Navigator-Distance-Engraving-American/dp/B078BW1XKP Frequently in front of customers Understands business Focused on product usage
- Hippos - peter principle... Project funding Defined requirements Often major change (e.g. system replacement) Often one single release, or multiple major releases Good POs will Define small, releasable chunks - be releasable as fast as possible Run systems in parallel to learn and provide customer benefit e,g, SAP contacts data store replacement BCAA web replacement project
- Iterate forwards from release to release Decisions based on expert POs Seek options on defined requirements Good POs will Simplify releases to gather feedback on single feature sets Release frequently with sufficient time to gather data Gather macro data to validate expert opinions Use Experience Canvases at product/project level Challenge project funding models and defined requirements E.g. be2 releases HSBC backbone rebuild BCAA release process (CI/CD)
- Decisions based on data Recommends go/no-go decisions based on data not intuition Feedback loop often too long to inform next decisions Enables breakdown of major rollouts into value-based increments Great POs Use data to make trade-off decisions Come from a scarcity mindset (minimum viable products are small) Talk about what will be dropped early and often E.g. Brady configuration tool Nike data model move VCH patient journeys
- Car - we are all driving Many, many small experiments Combines vision with data-driven decisions MVPs are small and targeted Great POs Create and test many small hypotheses A/B testing is the norm Feature toggles and product configuration are the norm Deep understanding and curiosity of personas and customer behaviours E.g. Nike shoe configuration BestBuy Canada merchandising
- Blockbuster CEO : John Antioco (Jim Keyes, Carl Icahn) Netflix CEO : Reed Hastings What can you do as PO? What information do you need next? I’ve been following Netflix since 2005, when I first visited its headquarters in Silicon Valley and interviewed Reed Hastings, its founder and CEO. I don’t think I’ve learned more about strategy, technology, and culture from any other company I’ve studied. It’s a stretch to claim that everything I know about business I learned from watching Netflix, but there’s no doubt that many leaders can see glimpses of the future of competition and innovation by looking at how the company does business. Despite this week’s news that the company had added fewer new subscribers than expected, if there were an Academy Awards show for business performance, Netflix would still sweep this year’s categories — the corporate equivalent of “Titanic” or “Lord of the Rings.” Wealth creation? The company, which is barely 20 years old, has a stock-market value of nearly $165 billion, more than Disney. Cultural sway? Netflix recently got 112 Emmy nominations, the most of any network or streaming service, toppling HBO, which had received the most nominations for 17 years. Management cred? Its reputation is so strong that a simple PowerPoint slideshow about its culture and HR policies has been viewed more than 18 million times. Here are three lessons from the rise of Netflix that apply to every company: Big data is powerful, but big data plus big ideas is transformational. Netflix is a technology juggernaut whose analytics, algorithms, and digital-streaming innovations have changed how customers watch movies and TV shows. But this technology has always been in service of a unique point of view — building a platform that shapes what customers watch, not just how they watch. The company has vast amounts of data on the viewing habits of its 125 million subscribers, from which movies and TV shows they liked or disliked to how long they watched an individual episode or how much they binged a new series. This powerful data system creates a rich social system that influences the movies and shows members see, based in part on which shows they’ve liked in the past what other subscribers see and like. Here’s how Reed Hastings explained it in 2005, when the company had just 3.5 million subscribers. “It’s possible to totally misunderstand Netflix,” he told me. “The real problem we’re trying to solve is, How do you transform selection so that consumers can find a steady stream of [entertainment] they love? We give everyone a platform to broaden their tastes.” This point of view has driven Netflix from the beginning, and it underscores the power of original ideas in business success. The core takeaway: Technology matters most when it is in the service of a compelling strategy. If you aim to disrupt an industry, you must be willing to disrupt yourself. Netflix could be the dictionary definition of a Silicon Valley disruptor, a new entrant that reshaped the logic of an entire industry. Yet what’s truly remarkable about the company’s trajectory over the last two decades is how dramatically it has disrupted itself in service of its mission. Netflix began, of course, with a pretty simple innovation — crushing Blockbuster by shipping DVDs by mail and abolishing late fees. It then transitioned from mailing content to streaming movies and TV shows digitally. Today, Netflix is most noteworthy as a creator of content; it will spend a staggering $12 billion this year alone on programming. Here again, Netflix is entering an industry by challenging its conventions. As a recent cover story in New York magazine noted, the company’s approach to programming “has upended so many norms of the TV business,” from eliminating pilot episodes to inventing “the idea of binge-watching” to replacing “demographics with what it calls ‘taste clusters’ — an approach to niche programming fueled by technology. At every step, Netflix’s dramatic strategic moves invited external skepticism and required deep internal rethinking of what had worked before. The key lesson: For companies and leaders alike, you can’t let what you know, all your past success, limit what you can imagine going forward. Strategy is culture, culture is strategy. Most analysis of the rise and reinvention of Netflix emphasize its strategy and technology (as I have thus far). But what struck me about Reed Hastings from the first time I met him is that he and his colleagues think just as rigorously about people and culture as they do about digital streaming and content. When it comes to who it hires and what it promises them, how it makes decisions and shares information, even what it does about vacations, Netflix has invented (and reinvented) a range of practices that are designed explicitly to connect what the company aims to achieve in the marketplace to how it organizes the workplace. Last year, the company updated its manifesto on Netflix Culture, a detailed statement of its principles, policies, and practices with respect to the human factor in business. What’s unusual about the manifesto is how sharp the language is; there is no hint of HR boilerplate. “Many companies have value statements,” it begins, “but often these written values are vague and ignored. The real values of a firm are shown by who gets rewarded or let go.” So what kind of people get rewarded at Netflix? “You say what you think, when it’s in the best interest of Netflix, even if it is uncomfortable,” the manifesto says. “You are willing to be critical of the status quo” and “You make tough decisions without agonizing.” Moreover, “You are able to be vulnerable, in search of truth.” The essential point: Great companies understand that they have to work as distinctively as they hope to compete. It’s always dangerous to try to learn too much from the performance of a single organization — even the most successful companies are bound to experience setbacks and disappointments. (It wasn’t all that long ago, after all, that GE was considered a model of world-class management.) Still, as more and more of us turn to Netflix for entertainment, the company bears watching as a source of insights about the future of business and work. 6 Strategies Netflix Can Teach Us For Dominating Our Market2 You are here: Home / 6 Strategies Netflix Can Teach Us For Dominating Our Market In May, Netflix saw its stock price jump a massive 30%. Pretty darn impressive… One of the key drivers behind the rise was Netflix’s decision to increase its video subscription fee by $1.00. One teeny-weenie little dollar. When they surveyed their customers about the increase, 73% said that they were either “not at all” or only “slightly likely” to cancel. (Source) Here’s why the Netflix business model is succeeding – even when competitors like Blockbuster have failed. #1 – Fill the Gap Netflix founder Reed Hastings explains the origins of Netflix in terms many of us can relate to… Hastings had a large amount of videos overdue and was facing a big fat fine – a fine so large, he was embarrassed to tell his wife about it! He couldn’t believe that this was the best way for people to borrow movies. His solution was to come up with a business model that let people keep the videos as long as they liked – as long as they paid a flat fee every month. Virtually every successful business is based on satisfying some currently unmet need in the market. #2 – Think Strategic Partnerships Strategic partnerships can be a win-win for both sides… For Netflix, a partnership with Apple is one such example. Netflix allowed the owners of the Apple TV set-top box to sign up for Netflix directly. They could even pay for the service through their iTunes accounts. For Netflix, it was an opportunity to access Apple’s large customer base. For Apple, it was a chance to provide their customers with more content in a convenient way. A win/win/win approach… #3 – Be Prepared Much has been made of Netflix’s decision to offer streaming. What is less well known, however, is how long it has taken consumers to catch up with the company’s vision. As Hastings notes: “In 1997, we said that 50% of the business would be from streaming by 2002. It was zero. In 2002, we said that 50% of the business would be from streaming by 2007. It was zero… Now streaming has exploded… We were waiting for all these years. Then we were in the right place at the right time.” As Netflix shows, being in the right place at the right time has as much to do with preparation as it does to do with luck. #4 – Think Recurring Income One of Reed’s inspirations for Netflix came from the gym he belonged to… The gym only had to attract a customer once, but it could continue to charge customer’s every month. As long as a business is giving the customer something they want, subscription-based buyers will stay on board. The fruits of this business model were shown recently when Netflix was able to raise their prices by just a $1 a month, but have an outsized impact on their profits. Because Netflix was offering a great product that customers wanted, people were willing to pay a little extra each month. #5 – Learn From Others One of the principles I live by is the idea of modeling the successes – and learning from the failures- of other business leaders. Experience is a great teacher,butyou can shorten the learning curve by learning from other people’s failures. Reed Hastings learned from AOL, who was slow to adapt to the development of broadband – and suffered as a result. It was a mistake he was committed to not repeating with Netflix. Learning from the mistakes of others, Netflix has always carefully observed changes in its industry and adapted as needed. #6 – Add Value For The Customer One of the reasons that Netflix has proven to be so popular is, well, they are so good at giving the customer exactly what they want! Hastings has noted that the real secret to Netflix’s success is that it adapts to its user. The Netflix recommendation engine is very good at predicting what types of movies people are likely to want to watch… As a result, 60% of the movies that are added to subscribers’ queues come from recommendations! Netflix is more than just a movie rental service… It’s a place where you can find the right movie for you to watch. With hit shows “Orange Is The New Black” and “House Of Cards,” Netflix has even become an innovative creator of new content. It’s a development that few would have predicted when Netflix was simply the “DVD by mail” service… Netflix has long practiced the maxim of going where the ball is going to be, not where it has been… And it will be exciting to see where that adaptive mindset takes the company in the future. In 2000, Reed Hastings, the founder of a fledgling company called Netflix, flew to Dallas to propose a partnership to Blockbuster CEO John Antioco and his team. The idea was that Netflix would run Blockbuster’s brand online and Antioco’s firm would promote Netflix in its stores. Hastings got laughed out of the room. We all know what happened next. Blockbuster went bankrupt in 2010 and Netflix is now a $28 billion dollar company, about ten times what Blockbuster was worth. Today, Hastings is widely hailed as a genius and Antioco is considered a fool. Yet that is far too facile an explanation. Antioco was, in fact, a very competent executive—many considered him a retail genius—with a long history of success. Yet for all his operational acumen, he failed to see that networks of unseen connections would bring about his downfall. Over the past 15 years, scientists have learned much about how these networks function and how his fate could have been avoided. A Social Epidemic When Hastings flew to Dallas and proposed his deal in 2000, Blockbuster sat atop the video rental industry. With thousands of retail locations, millions of customers, massive marketing budgets and efficient operations, it dominated the competition. So it’s not surprising that Antioco and his team balked at simply handing over the brand they had worked hard to build. Yet Blockbuster’s model had a weakness that wasn’t clear at the time. It earned an enormous amount of money by charging its customers late fees, which had become an important part of Blockbuster’s revenue model. The ugly truth—and the company’s achilles heel—was that the company’s profits were highly dependent on penalizing its patrons. At the same time, Netflix had certain advantages. By eschewing retail locations, it lowered costs and could afford to offer its customers far greater variety. Instead of charging to rent videos, it offered subscriptions, which made annoying late fees unnecessary. Customers could watch a video for as long as they wanted or return it and get a new one. A Look Back At Why Blockbuster Really Failed And Why It Didn't Have To Greg Satell Contributor In 2000, Reed Hastings, the founder of a fledgling company called Netflix, flew to Dallas to propose a partnership to Blockbuster CEO John Antioco and his team. The idea was that Netflix would run Blockbuster’s brand online and Antioco’s firm would promote Netflix in its stores. Hastings got laughed out of the room. We all know what happened next. Blockbuster went bankrupt in 2010 and Netflix is now a $28 billion dollar company, about ten times what Blockbuster was worth. Today, Hastings is widely hailed as a genius and Antioco is considered a fool. Yet that is far too facile an explanation. Antioco was, in fact, a very competent executive—many considered him a retail genius—with a long history of success. Yet for all his operational acumen, he failed to see that networks of unseen connections would bring about his downfall. Over the past 15 years, scientists have learned much about how these networks function and how his fate could have been avoided. A Social Epidemic When Hastings flew to Dallas and proposed his deal in 2000, Blockbuster sat atop the video rental industry. With thousands of retail locations, millions of customers, massive marketing budgets and efficient operations, it dominated the competition. So it’s not surprising that Antioco and his team balked at simply handing over the brand they had worked hard to build. Yet Blockbuster’s model had a weakness that wasn’t clear at the time. It earned an enormous amount of money by charging its customers late fees, which had become an important part of Blockbuster’s revenue model. The ugly truth—and the company’s achilles heel—was that the company’s profits were highly dependent on penalizing its patrons. At the same time, Netflix had certain advantages. By eschewing retail locations, it lowered costs and could afford to offer its customers far greater variety. Instead of charging to rent videos, it offered subscriptions, which made annoying late fees unnecessary. Customers could watch a video for as long as they wanted or return it and get a new one. Netflix proved to be a very disruptive innovation, because Blockbuster would have to alter its business model—and damage its profitability—in order to compete with the startup. Despite being a small, niche service at the time, it had the potential to upend Blockbuster’s well oiled machine. The Threshold Model While Netflix’s model clearly had some compelling aspects, it also had some obvious disadvantages. Without retail locations, it was hard for people to find it. Moreover, because its customers received their videos by mail, the service was somewhat slow and cumbersome. People couldn’t just pick up a movie for the night on their way home. Still, customers loved the service and told their friends. Some were reluctant at first, they actually liked being able to browse movies at the store and pick one up at a moments notice, but others jumped right in. And as more of their friends raved about Netflix, the laggards tried it too, fell in love with it and convinced people they knew to give it a shot. Network scientists call this the threshold model of collective behavior. For any given idea, there are going to be people with varying levels of resistance. As those who are more willing begin to adopt the new concept, the more resistant ones become more likely to join in. Under the right conditions, a viral cascade can ensue. The best way to understand thresholds is to look at the diffusion of ideas model formulated by Everett Rogers in the 1960’s. While ideas usually take hold in small niches of innovators, they can often spread to early adopters, who are only slightly more resistant to join in. Once they’re on board, those in the early majority begin to feel comfortable giving it a try. As each threshold is past, the next group becomes more likely to adopt the new idea. That’s how disruption happens. Unfortunately, this effect is devilishly hard to quantify. Duncan Watts, a pioneer in network theory, is quick to point out that social dynamics tend to be idiosyncratic and it’s not always clear exactly where thresholds exist. Still, you can use conventional marketing analysis to evaluate whether an idea is spreading to new groups or just growing within a niche. It is not clear whether Antioco’s team did such an analysis or not, but by 2004—six years before the company went bankrupt—he sensed that Netflix had become a significant threat and sought to change his firm’s policies. Yet how he went about doing that sealed his, and ultimately Blockbuster’s, fate. A Different Network Altogether Once John Antioco became convinced that Netflix, and to a lesser extent Redbox, was a threat, he used his authority as CEO—as well as the credibility he had earned by nearly doubling Blockbuster’s revenues during his tenure—to discontinue the late fees that annoyed customers and invest heavily into a digital platform to ensure the brand’s future. Antioco’s article in Harvard Business Review describes what happened next. While he convinced the board to back his plan, one of his lieutenants, Jim Keyes, led a rear guard action. He pointed out that the costs of Antioco’s changes — about $200 million to drop late fees and another $200 million to launch Blockbuster Online—were damaging profitability. Eventually, an activist investor, Carl Icahn, began to question Antioco’s leadership. Antioco lost the board’s confidence and was fired over a compensation dispute in 2005. Keyes was named CEO and immediately reversed Antioco’s changes in order to increase profitability. Blockbuster went bankrupt five years later. Icahn would later write: Keyes felt the company couldn’t afford to keep losing so much money, so we pulled the plug. To this day I don’t know what would have happened if we’d avoided the big blowup over Antioco’s bonus and he’d continued growing Total Access. Things might have turned out differently. So the inability to understand the networks that would determine his fate struck John Antioco twice. First, he failed to realize how quickly a niche idea could snowball into a viral cascade. Second, he failed to construct a network that could carry his ideas of change throughout his own organization. Strategy In A Networked World For all the excitement surrounding online social platforms such as Facebook and Twitter, we really haven’t scratched the surface on the networks we encounter in real life: The networks of consumers that make up our brands and industries as well as the organizational networks that determine how things get done—or don’t get done—in our enterprises. And it’s imperative that we start thinking about them more seriously. We need to stop acting as if there is a recipe for business—like a cake or a casserole—and start thinking in terms of how factors are connected. The structure of those unseen connections, their context and how they relate to our objectives increasingly makes the difference between success and failure. Unfortunately, there are no definitive answers. As Duncan Watts told me, “You have to test and learn as you’re going along, but if you understand how networks work and are willing to invest resources into researching the ones that affect your business, you can significantly improve decision making.” Watts points to recent research done at Facebook as an example how a well designed study can reveal much about how influence spreads through networks. He also notes that that digital trails left by emails and electronic calendars can be very useful for mapping organizational networks. Fortunately, we have far more tools today than Antioco did then. The irony is that Blockbuster failed because its leadership had built a well-oiled operational machine. It was a very tight network that could execute with extreme efficiency, but poorly suited to let in new information. Antioco’s fatal flaw wasn’t one of intelligence or capability, but a failure to understand the networks that would determine his fate.
- Einstein's prediction (1907): Light bends in a gravitational field Einstein's theory of General Relativity makes several specific predictions about the observable structure of space-time, such as that light bends in a gravitational field, and that the amount of bending depends in a precise way on the strength of that gravitational field. Arthur Eddington's observations made during a 1919 solar eclipse supported General Relativity rather than Newtonian gravitation.[62]
- Start Has to include failure conditions as well as success conditions How do you avoid false positives (or false negatives)