Jordan Christensen at Analytics That Excite
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Jordan Christensen, Head of Data at Wattpad, discusses building effective data-driven organizations through a focus on culture, models, and science. Key strategies include fostering a strong data culture with daily performance emails and self-serve dashboards, setting targets using models, and using experiments to understand user behavior and improve retention. The presentation highlights the importance of continual learning and adaptation in leveraging data to enhance business performance.
Jordan Christensen at Analytics That Excite
- 4. Jordan Christensen Head of Data Wattpad
- 5. Jordan Christensen Head of Data Wattpad Canada, Eh?
- 6. Joined Wattpad in Sept 2014 Before that, founding team member at Kobo
- 7. Only real world-wide competitor to Amazon Kindle Built Product and Data Also consulted with other orgs on how to build their Data Orgs
- 11. ⃠
- 14. Warning!
- 15. ?
- 16. X
- 18. Also:
- 19. I made most of this up. From my experience.
- 20. Working with small to medium sized startups.
- 21. Lots of info out on the web. HBR, and a few books
- 23. Finally, this isn’t a prescription. Tools and Techniques
- 25. We let people discover and share stories about the things they love A new storytelling experience that is mobile and social
- 26. Last month 40 million people spent 13 billion minutes on Wattpad
- 27. More than 150 million stories have been shared on Wattpad
- 28. Wattpad stories are available in over 50 languages
- 29. After 3 39 Parts, Ongoing 2.6M READS 27.5K VOTES 4089 COMMENTS MOBILE Optimized for mobile consumption PERSONALIZED Via social & machine PARTICIPATORY Create story worlds of fan content on/off Wattpad SERIAL Maintain an ongoing connection with readers ACTIVE Elevate conversations and interactions in the story How Wattpad works for today’s readers
- 30. Desktop Android Wattpad continues to experience exponential growth Monthly Time Spent
- 31. Wattpad pushed the boundaries of social and mobile entertainment with over 1 billion chapter reads and 6 million comments
- 34. But Jordan, Aren’t all modern Organizations data driven?
- 35. Sort of….
- 36. Some Qualities of Data Driven Organizations
- 37. But we can do better
- 39. Okay Jordan, How do we build an Effective Data Driven Organization?
- 40. Simple! Focus on these THREE things
- 41. 1. Culture 2. Models 3. Science
- 42. Data Culture
- 43. Every Effective Data Driven Org has a strong Data Driven Culture
- 45. Start …
- 47. Start with Coffee Daily Coffee Email
- 48. At Wattpad, everyone gets an email in their Inbox (or on Slack) with the Daily Numbers
- 49. Arrives in time for Coffee (before 8am)
- 50. Not an overload Top Line Up or Down
- 51. Aside: Find your North Star
- 58. Starts discussions Why is iOS up so much today? Why is France slower than last week?
- 59. Most Importantly
- 61. Next simple thing after Coffee Emails…
- 62. Dashboards
- 63. * Not Real
- 64. Why do Dashboards matter? Public Transparent AND….
- 65. Self-Serve
- 66. Your Data team, if you have one, is small
- 67. You likely have more asks than you can possibly ever answer
- 68. Self-Serve is the only effective answer here Push queries to the edges of the org
- 70. Aside: Where is your data?
- 71. Aside: Where is your data? Many organizations start not owning their data Then they start collecting it
- 72. Now they have two problems
- 74. As your organization grows, you will outgrow hosted tools They will get expensive, and their capabilities will be limiting
- 75. Plan ahead - you’ll need to build your own ‘Data Lake’.
- 76. But Jordan, How do people know how to self serve?
- 77. Office Hours
- 78. To make Self-Serve work, people need to know how to do it Borrowed from Kickstarter
- 79. One Hour, Once a Week Drop In, ask questions, get trained Anyone
- 80. Get’s full? Take a number, come back next week But it’s always there
- 81. Data Models
- 83. Analytics sometimes feels like being a Weather person
- 84. People ask you about something (The Weather), and you tell them (It’s Cold*)
- 85. Ultimately, that isn’t that helpful.
- 86. Weather people need to tell you what the Weather will be like tomorrow.
- 87. Or Why is it going to be Cold tomorrow?
- 88. Or what can they do to change The Weather?* * May be limited to Super Villains
- 89. We’ve been predicting the Weather* for centuries
- 90. How do we do it?
- 91. Models
- 92. But Jordan, Why do I need these complicated models?
- 93. First, I didn’t say they had to be complicated
- 95. Second, so we know where we are going
- 96. Targets
- 97. Effective Data Driven Organizations use Models to set their Targets
- 98. 1. Quarterly Goals 2. Yearly Goals 3. Multi-Year Goals Targets
- 99. Without Models, Targets have another name
- 100. Guesses!
- 101. Aside on Targets: You’ll get them wrong and you’ll miss them (or exceed them)*
- 102. Learning how to model your business and set targets takes time
- 103. But get started, track variance and be Open and Transparent about them
- 104. Maybe put them in the Coffee Email, everyday, so people know how they are doing
- 105. Targets are in some way aspirational. They are where you WANT to be.
- 106. If we want to know where we think we’ll be, we need…
- 107. Forecasts
- 108. Just like the Weather, shorter term these are, the more accurate
- 109. Daily - Did it rain yesterday? It’ll probably rain tomorrow!
- 110. Weekly - Did it rain yesterday? Who knows what next week is like
- 111. Short Term barometer Is this week above or below the Forecast? Best indication of short term performance.
- 112. Proxies
- 113. Data Science lives here Finding Short Term correlations for Longer Term metrics
- 114. Example: MAU is SLOW 30 Day Moving average, would take about twice that long to see any real change
- 115. How do we know if MAU is going to get better or worse? We need a Proxy.
- 116. At Wattpad, we looked at what our users were doing and found something magical…
- 117. People who Read More, Stay Longer
- 119. If we increase Reading Time, we increase Retention, and that leads to more MAU
- 120. These Proxies do two things for us: 1. They let us see results quickly 2. They point us towards things we can effect
- 121. Finding Proxies is great, but how do you change things, and how do you know you’ve made things better?
- 122. “Do” Science
- 123. You’ve probably seen this book. Random Walk experiments
- 124. Experts can’t tell the difference between randomness and causation
- 125. Looking at Charts (in, say, GA) is the same thing
- 126. Human’s have this natural need to find order in noise
- 127. It has served us well for thousands of years, how do we change?
- 128. A/B Test
- 129. Used to be hard, how we have great tools.
- 130. Sometimes you need to go deeper. You might need to build this, or there are other toolkits to leverage
- 131. We built this at Wattpad, most larger orgs do as it needs to be very close to your data.
- 132. Wattpad is about 120 staff, but we’re running > 20 experiments at any time
- 133. Causation
- 134. Learn what you can change, and why it works, or why it doesn’t.
- 135. At Wattpad, we were changing our on boarding experience.
- 136. Nothing Happened.
- 137. Tried again.
- 138. Nothing Happened.
- 139. Turned it off all together
- 141. Turns out our On Boarding didn’t do anything since it didn’t change the experience
- 142. Connected to the experience, we increased our retention by 10%
- 143. Council
- 144. Experiments are hard. You need a council.
- 146. Borrowed from a Google White Paper on how they run their process
- 147. Provide Governance, Best Practices, and share results
- 148. Learnings
- 149. Best part of Science
- 150. You Learn!
- 151. You learn what your users want, what they don’t, what makes them happy, what makes them sad
- 152. And you can tell the rest of your company. Science is a process by which you learn and improve your business
- 153. Ultimately, that’s why we are here - to use Data to improve our businesses
- 154. And you’re done!
- 155. In Summary
- 156. 1. Culture 2. Models 3. Science
- 157. 1. Email 2. Dashboard 3. Self Serve 4. Office Hours Culture
- 158. 1. Targets 2. Forecasts 3. Proxies Models
- 159. 1. A/B Test 2. Causation 3. Council 4. Learnings Science
- 160. * Still Continuing on our Journey at Wattpad Working every day to make Data more of our day- to-day decision process