Opticon 2017 From CRO to Experience
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I. Clearhead was acquired by Accenture Interactive on July 12, 2017. The document discusses Matty Wishnow, CEO of Clearhead, and his experience optimizing and personalizing customer experiences over 300,000 hours from CRO to full-stack optimization. II. It discusses the challenges of experience optimization and why more experiments sometimes do not lead to better results. It also discusses how conversion optimization differs from experience optimization. III. The document presents frameworks and approaches for solving problems through experiments, including Problem Solution Mapping and mapping the full customer experience across stages and channels to increase optimization success.
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Opticon 2017 From CRO to Experience
- 1. opticon2017 From CRO to Experience Optimization in 300,000 Hours Matty Wishnow CEO, Clearhead
- 2. Experiment # 123456781552194612100630134120580174929814 OPTIMIZING AND PERSONALIZING CUSTOMER EXPERIENCES Clearhead was acquired by Accenture Interactive on July 12, 2017 TOGETHER
- 3. OPTIMIZING AND PERSONALIZING CUSTOMER EXPERIENCES Clearhead was acquired by Accenture Interactive on July 12, 2017 TOGETHER
- 4. opticon2017 +3.82% +1% +1.21% +1.24% +3.65% -1.06% +3.81% +11.18 +18.65% +28%
- 5. opticon2017 Why do we not win bigger with more experiments? & Why is experience optimization still stuck in first gear?
- 6. opticon2017 5 Lessons from Expedia’s test-and-learn culture - CMO.com Digital marketing incubation: how to develop a test & learn culture - Econsultancy A Step-by-Step Guide to Smart Business Experiments - HBR Personalization is a Big Challenge for Digital Marketers - Adweek Personalization is a Big Challenge for Digital Marketers - Forbes Continuous optimization is the future of making consumers happy, but we’re stuck in the past - VentureBeat
- 7. opticon2017 Optimization Technology Investment Optimization Resource Investment Enterprise Optimization Adoption 2010 2017 Clearhead’s Birth Personalize Full Stack Test and Learn Optimizely’s Birth Lean Startup Published Artificial Intelligence
- 11. opticon2017 ProblemGoal Hypothesis Problem Long drives without a lot of entertainment are boring for kids. Goal Get from Austin to Taos as fast as possible. Hypothesis Ask Waze for the fastest route possible to minimize pain for everyone.
- 13. opticon2017 Problem Long drives without a lot of entertainment are boring for kids. Goal Get from Austin to Taos as fast as possible. Hypothesis Ask Waze for the fastest route possible to minimize pain for everyone. Goal Get to Taos within 2 days with "thumbs up" from all family members. Problem There aren't many fun places to stop between Austin and Taos. Hypothesis I believe that if we select a route from Austin to Taos based on two destinations the kids choose and two the adults choose, we will have a happier road trip.
- 15. opticon2017 ProblemGoal Hypothesis Problem Our Product filters and facets are not well designed. Goal Make it easier for our customers to shop online. Hypothesis We should redesign our Product filters and facets. Goal Increase new customer order conversion by 20% in the next twelve months. Problem Our new customers require more guidance than our filters and facets for selecting the right blender for them. Hypothesis I believe we should design a guided recommendation flow based on factors like price, size, color and recipe to make it easier for new customers to match a blender to their needs.
- 17. +5% lift in product detail page views +5% lift in add-to-carts +13% lift in “Find a Retailer” usage +3.5% lift in overall site order conversion rates for all site visitors +47% lift in order conversion rates for Blender Recommender users against those who demonstrated household shopping intent
- 18. 1 2 3 4 1 2 3 4 product roadmap design containment capabilities hypotheses prioritized by data experiments validated by data outcomes
- 19. PS Ps pS ps problemsgoals 1 2 3 1 2 3 4 1 2 3 4 product roadmap design containment capabilities hypotheses prioritized by data experiments validated by data outcomes
- 20. Problem Solution Mapping (PSM) A unifying framework for optimizing user experience around a common set of goals, problems, and solution hypotheses — researched and validated with data. PS Ps pS ps problemsgoals 1 2 3 1 2 3 4 1 2 3 4 product roadmap design containment capabilities hypotheses prioritized by data experiments validated by data outcomes
- 21. opticon2017 Goals + Problems + Solutions Constrained Optimization = What you’re allowed to test ÷ What’s easy to test ÷ Resources skilled ÷ Resources available
- 22. opticon2017 Conversion Optimization ≠ Experience Optimization
- 23. opticon2017 Web Conversion Brick and Mortar Conversion Net Margin NPS
- 25. Goals Stages Channel 1 Channel 2 Channel 3 Channel 4 Customer Business Problems Hypotheses Channels Reach Engage Activate Nurture 1 2 3 1 2 3 1 2 3 1 2 3 1 2 1 2 1 2 1 2 1 2 3 4 5
- 26. Goals Stages Element 1 Element 2 Element 3 Element 4 Customer Business Problems Hypotheses Elements Reach Engage Activate Nurture 1 2 3 4 5 1 2 3 1 2 3 1 2 3 1 2 3 1 2 1 2 1 2 1 2
- 29. Centralized
- 33. Channel 3 1 _______________________ 2 _______________________ 1 _______________________ 1 _______________________ 2 _______________________ 1 _______________________ 2 _______________________ 1 _______________________ 1 _______________________ 2 _______________________
- 34. 1 _______________________ 1 _______________________ 2 _______________________ 1 _______________________ 2 _______________________ Element 3 1 _______________________ 1 _______________________ 2 _______________________ 1 _______________________ 2 _______________________
- 35. opticon2017 I. Experiments fail when they don’t solve problems II. Conversion optimization does not equal experience optimization III. The prescribed narrative for optimization maturity is flawed
- 36. opticon2017 I. Experiments fail when they don’t solve problems Problem Solution Mapping (PSM)
- 37. opticon2017 II. Conversion optimization does not equal experience optimization Map the Customer Experience Experiment on the Biggest Problems Measure for Incrementality
- 38. opticon2017 III. The prescribed narrative for optimization maturity is flawed Widen the aperture from test manager to experience conductor. Develop a common process, tech stack and data layer so experimentation can be democratized.
- 40. opticon2017 Q&A
- 42. opticon2017