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Prove It: Making the Case for Experimentation

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In the webinar, we’ll cover:
- Steps to build your own business case for experimentation
- Key considerations in estimating your ROI on future experiments
- Mistakes to avoid in building an experimentation program
- Exclusive insights from our research with Harvard and Duke on how experimentation positively affects innovation and success

Published in: Software
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Prove It: Making the Case for Experimentation

  1. 1. Experimentation Strategy & Value Hazjier Pourkhalkhali Global Director, Strategy & Value
  2. 2. 2 Optimizely is constantly researching the characteristics and impact of high performing experimentation programs ● Analysis of over 1,000 companies and more than 100,000 experiments ● Identification of best practices for experimentation Global Optimization Benchmark ● Analysis of 14,000 experiments to identify the best practices that make companies more successful in experimentation Experiment Design and Performance ● Analysis of 1,000’s of experiments to understand how risk-taking and innovation evolve over time ● Analysis of how risk-taking affects experimentation performance Experimentation and Innovation Experimentation and Firm Performance ● Analysis of how the scale of experimentation affects the financial performance of organizations
  3. 3. 3 12% 14% 32% 26% 10% 5% No change or unsure Increased revenues by 1-4% Increased revenues by 5-9% Increased revenues by 10-14% Increased revenues by 15-19% Increased revenues by 20%+ SOURCE: “How to Succeed in the Digital Experience Economy” (March 2019) Three quarters of companies surveyed say experimentation improved digital revenues by over 5% n = 808 companies, >500 employees, March 2019
  4. 4. 4 In August, professors at Harvard Business School and Duke Fuqua published the largest analysis of the value of Experimentation Experimentation and startup performance: Evidence from A/B testingú Rembrand Koning Harvard Business School rem@hbs.edu Sharique Hasan Duke Fuqua sh424@duke.edu Aaron Chatterji Duke Fuqua and NBER ronnie@duke.edu August 20, 2019 Abstract Recent work argues that experimentation is the appropriate framework for en- trepreneurial strategy. We investigate this proposition by exploiting the time-varying adoption of A/B testing technology, which has drastically reduced the cost of experi- mentally testing business ideas. This paper provides the first evidence of how digital experimentation a ects the performance of a large sample of high-technology startups using data that tracks their growth, technology use, and product launches. We find that, despite its prominence in the business press, relatively few firms have adopted A/B testing. However, among those that do, we find increased performance on sev- eral critical dimensions, including page views and new product features. Furthermore, A/B testing is positively related to tail outcomes, with younger ventures failing faster and older firms being more likely to scale. Firms with experienced managers also derive more benefits from A/B testing. Our results inform the emerging literature on entrepreneurial strategy and how digitization and data-driven decision-making are shaping strategy. ú Authors names are in reverse alphabetical order. All authors contributed equally to this project. We thank seminar participants at Harvard Business School, the Conference on Digital Experimentation, Duke, University of Maryland, Binghamton University, University of Minnesota, NYU, and Wharton for their feedback. We thank the Kau man Foundation for their generous support of this work. 1 Experimentation and Startup Performance: Evidence from A/B Testing Rembrand Koning Sharique Hasan Aaron Chatterji Working Paper 20-018 Working Paper 20-018 Copyright © 2019 by Rembrand Koning, Sharique Hasan, and Aaron Chatterji Experimentation and Startup Performance: Evidence from A/B Testing Rembrand Koning Harvard Business School Sharique Hasan Fuqua School of Business, Duke University Aaron Chatterji Fuqua School of Business, Duke University and NBER Experimentation and Startup Performance: Evidence from A/B Testing Rembrand Koning Sharique Hasan Aaron Chatterji Working Paper 20-018
  5. 5. 5 They analysed over 35,000 startups over a period of 3 years Experimentation and startup performance: Evidence from A/B testingú Rembrand Koning Harvard Business School rem@hbs.edu Sharique Hasan Duke Fuqua sh424@duke.edu Aaron Chatterji Duke Fuqua and NBER ronnie@duke.edu August 20, 2019 Abstract Recent work argues that experimentation is the appropriate framework for en- trepreneurial strategy. We investigate this proposition by exploiting the time-varying adoption of A/B testing technology, which has drastically reduced the cost of experi- mentally testing business ideas. This paper provides the first evidence of how digital experimentation a ects the performance of a large sample of high-technology startups using data that tracks their growth, technology use, and product launches. We find that, despite its prominence in the business press, relatively few firms have adopted A/B testing. However, among those that do, we find increased performance on sev- eral critical dimensions, including page views and new product features. Furthermore, A/B testing is positively related to tail outcomes, with younger ventures failing faster and older firms being more likely to scale. Firms with experienced managers also derive more benefits from A/B testing. Our results inform the emerging literature on entrepreneurial strategy and how digitization and data-driven decision-making are shaping strategy. ú Authors names are in reverse alphabetical order. All authors contributed equally to this project. We thank seminar participants at Harvard Business School, the Conference on Digital Experimentation, Duke, University of Maryland, Binghamton University, University of Minnesota, NYU, and Wharton for their feedback. We thank the Kau man Foundation for their generous support of this work. 1 Experimentation and Startup Performance: Evidence from A/B Testing Rembrand Koning Sharique Hasan Aaron Chatterji Working Paper 20-018 Working Paper 20-018 Copyright © 2019 by Rembrand Koning, Sharique Hasan, and Aaron Chatterji Experimentation and Startup Performance: Evidence from A/B Testing Rembrand Koning Harvard Business School Sharique Hasan Fuqua School of Business, Duke University Aaron Chatterji Fuqua School of Business, Duke University and NBER Experimentation and Startup Performance: Evidence from A/B Testing Rembrand Koning Sharique Hasan Aaron Chatterji Working Paper 20-018 § Websites with and without experimentation snippets § Date snippets are added / removed § Pageviews, time on site § Venture Capital funding § Weeks with new products or features announced on key marketing websites n = 35,913 startups, 2015 – 2018
  6. 6. 6 Benefits of one year of experimentation for startups n = 35,913 startups, 2015 – 2018 PAGEVIEWS TIME ON SITE PRODUCTS LAUNCHED VC FUNDS RAISED +12% +4% +9-18% +10% >99.9% >99% >99% >95% Significance SOURCE: “Experimentation and Startup Performance” (Koning, Hassan, Chatterji 2019)
  7. 7. 7 Without clear business cases, even high performing programs face constant risks Organizational inertia halts growth or collaboration Executive inattention creates perpetual risk of backsliding Lack of resources and risk of losing resources to other projects Employees leave due to lack of recognition or career growth
  8. 8. 8 Without a clear business case Organizational inertia halts growth or collaboration You can generate urgency and momentum Executive inattention creates perpetual risk of backsliding You ensure executive focus Lack of resources and risk of losing resources to other projects You can better advocate for and protect resources Employees leave due to lack of recognition or career growth You can better recognize and reward performance With a clear business case…
  9. 9. 9 Estimating returns from future experiments Average Test Impact Annual Experiments Win Rate Conservative Factor Average Uplift How many revenue driving experiments will you run over a year? What is the improvement to your financial metrics per experiment? Example: If 10% of experiments win on revenue, and the average winning uplift is 3%, then the test impact is 10% x 3% = 0.30% How much will we discount the total result in order to be conservative in our projections and give margin for error? What percentage of your digital revenue is affected by the average experiment? Revenue Scope Digital Revenue What is the digital revenue this property generates per year?
  10. 10. 10 12% 14% 32% 26% 10% 5% No change or unsure Increased revenues by 1-4% Increased revenues by 5-9% Increased revenues by 10-14% Increased revenues by 15-19% Increased revenues by 20%+ SOURCE: “How to Succeed in the Digital Experience Economy” (March 2019) Three quarters of companies surveyed say experimentation improved digital revenues by over 5% n = 808 companies, >500 employees, March 2019
  11. 11. 11 2.1X Development resources are crucial to long-term success 8% 10% 11% 13% 15% 1 – 5 6 – 10 11 – 20 21 – 50 51 – 100 17%>100 Lines of Code / Variant Significant Uplift on Primary Metric
  12. 12. 12 You need to ask yourself two big questions: How willing are you to be confronted every day by how wrong you are? And how much autonomy are you willing to give to the people who work for you? And if the answer is that you don’t like to be proven wrong and don’t want employees decide the future of your products, it’s not going to work. – David Vismans Chief Product Officer, Booking.com “ ”
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  15. 15. 2 Variations 3 Variations 4 Variations >5 Variations 77% 14% 5% 3% Experiments run by number of variations
  16. 16. 2 Variations 3 Variations 4 Variations >5 Variations Significant uplift Significant reduction Inconclusive 77% 14% 5% 3%
  17. 17. +75% +48% +32% 25% 33% 37% 44% +75% 2 Variations 3 Variations 4 Variations >5 Variations Significant uplift Significant reduction Inconclusive Teams with the freedom to test more variations are far more successful
  18. 18. — Peter Gray VP of Product Optimization Wall Street Journal “For a vast digital product like the Journal, applying data-driven experimentation was like discovering plutonium; it’s the most powerful product development tool on the face of the planet.” Product, marketing, engineering, editorial teams, and more are testing with Optimizely across every step of the customer journey to drive engagement and subscription revenue. WSJ fuels full-funnel improvements with Optimizely 64% Increase in Subscriptions
  19. 19. “Our goal is to increase digital revenue from $400m to $800m between now and 2020. Our existing digital subscription business is powered by an internal, legacy framework. Over the course of 2016, we expect to replace our internal framework with Optimizely -- entirely.” NYT is using Optimizely to make decisions across the two most important pillars of their business: content, and subscriptions. NYT Optimizes Over 1 Billion Experiences Every Month 5000+ Experiments per year 46% YoY growth in digital subscription revenue — Clay Fisher SVP, Consumer Marketing New York Times
  20. 20. “Missguided has an entrepreneurial approach and isn’t afraid to experiment with new ideas and offerings to drive the business forward. Working with Optimizely gives us enormous insights into our customers’ needs, desires and behaviours and allows us to adapt and evolve our approach fast to reap the commercial rewards..” Missguided uses Optimizely to experiment, personalize, and recommend products to its users Missguided is heavily experimenting and personalizing 177% Conversion uplift for next-day deliveries 33% Revenue increase — Mark Leach Head of e-Commerce Missguided
  21. 21. — Erin O’Leary VP of Marketing Rocksbox “Without the ability to experiment, we may have not tested some of the ideas that resulted in our most significant wins because we either did not think it would make a difference, or we thought it was too risky.” Product, engineering and marketing teams are testing with Optimizely across every step of the customer journey to improve revenue and retention. Rocksbox optimizes their customer journey 99% Conversion rate uplift
  22. 22. — Conor Coughlan Senior Marketing Manager Metromile “I think this paints a great story. An important part of our journey was learning from our negative tests, which helped us understand what things do and don't work..” Customer acquisition costs drastically lowered through experimentation. Investments into a more conversational UI increased conversion rate and helped generate more sign-ups. Improving Customer Experience through Experimentation 20% Increase in Conversion Rate 250% Increase in Velocity
  23. 23. — Ben Murphy Digital Director NS&I “By evolving our company culture and using experimentation, we’ve increased customer satisfaction, lowered costs-to-serve and shifted users from paper to digital. In just a few months our.” Product helpfulness increased by 45%, deflecting offline support requests and reducing cost to serve. Meanwhile, 39% fewer users opted to print a PDF and mail by post and instead used digital journeys, saving considerable time and effort. NS&I revamp digital touchpoints with experimentation 39% Shift in applications from off-line to digital $1M+ Cost savings in first quarter

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