UX Analytics for Data-driven Product Development
5 likes2,731 views
- UX analytics can help companies turn their user data into real products by discovering user interests in real-time. - Mobile analytics is important because mobile devices are becoming the dominant way users access the web, and big data and analytics are major trends. - Core KPIs for mobile analytics include users, sessions, events, and other metrics to understand user behavior and how to engage app users.
UX Analytics for Data-driven Product Development
- 1. UX Analytics for Data- driven Product Development ● Turn your data into real products ● Discover user interests in real-time way Trieu Nguyen - http://blog.trieu.xyz or @tantrieuf31 Lead Engineer at Ad Platform ( http://adsplay.vn ) at FPT Telecom
- 2. If you like Big Data Analytic Intern Jobs, submit your CV to me: trieunt@fpt. com.vn More at http://engineering.adsplay.net
- 3. Just little introduction ● 2007 I did my first Graph Analytics on Yahoo 360 friend' blogs (use Web Crawler) ● 2008 Java Developer, develop Social Trading Network for a startup (Yopco) ● 2011 joined FPT Online, engineer at social network platform, develop first API for VnExpress Mobile App ● 2012 Join Greengar Studios to learn more about mobile ● 2013 at FPT Online, back-end engineer for http://eClick.vn ● 2015 at FPT Telecom, lead engineer for http://itvad.vn
- 4. Contents for this talk ● Trends of Now and the Future ● Why analytics for mobile development ● Core KPIs ● How to implement, case study and demo ● Lessons ● Questions & Answers
- 5. Trends of Now and the Future ● Mobile ● Big Data ● Analytics
- 6. In 2013, mobile devices will pass PCs to be most common Web access tools. By 2015, over 80% of handsets in mature markets will be smart phones. Source:http://www.forbes.com/sites/ericsavitz/2012/10/23/gartner-top-10-strategic- technology-trends-for-2013/
- 8. We are in the age of Internet of Things with connected handheld devices
- 10. Why analytics for mobile development ?
- 11. Turn your data to actionable things ?
- 12. Measure UX using quantitative research ?
- 13. Mobile Apps => Backend APIs => Statistics => Find the Trends & Insights?
- 15. Connecting the dots ? Users are active dots. and ... “We Belong When We Connect with Each Other” http://tinybuddha.com/blog/we-belong-when-we-connect-with-each-other/
- 16. How could we see "user interest graph" in our user's database ?
- 17. ● Social Graph => Keep the connection ● Interest Graph => Make new connection => recommendation platform Source: http://en.wikipedia.org/wiki/Interest_graph
- 20. Why do analytics for your business ? => read these Behavioral Economics Books http://www.goodreads.com/shelf/show/behavioral-economics
- 21. Core KPIs for Data Analytics
- 22. Web vs Mobile App Web Visitors Visits Pageviews Events Mobile App Users Sessions Events
- 23. How we build KPIs for mobile analytics ? ● Keep it simple as possible, but no simpler ● Identity => Tracking => Data Mashup (Social API) ● Leverage the "small" data in real-time
- 24. Metrics: Causes and Effects ● Screen Size => App Design, UI/UX, Usability ● App version => Deployment, Marketing ● Connectivity => Code, User Experience ● Location => Marketing, User Behaviour ● OS => Marketing, Cost, Development ● Memory => User Experience ● Feature Session => How to engage app users
- 25. Big Data on Small Devices: Data Science goes Mobile http://strataconf.com/strata2013/public/schedule/detail/27605
- 26. Keep it simple: Just log them all ! How to implement, case study and demo
- 27. And your databases could be overloaded ?
- 29. We can't solve problems by using the same kind of thinking we used when we created them. Albert Einstein
- 31. “lambda architecture” proposed by @nathanmarz I have applied this architecture at FPT since 2012
- 32. My “lambda architecture” technology stack ● Kafka (http://kafka.apache.org) ● RFX ( https://github.com/rfxlab ) ● Redis ( http://redis.io ) ● MEAN stack for reporting ● Hadoop (HBase, HDFS) ● Spark ecosystem https://spark.apache.org ● D3 - http://d3js.org
- 33. Too theory. I want "Seeing is believing". Examples from my experience
- 35. Case Study (from my freelance project) Problem: ● Build the app to promote advertising information in real time way ● Measure everything ● Report useful information ● Mashup and data integration with Facebook API for social data analytics Context: ● PhongCachMobile - Smartphone Retail Store https://play.google.com/store/apps/details?id=com.mc2ads.browser4x
- 36. Simple architecture ● App <=> PHP API <=> JVM Data Analytics API ● User tap on an item, tracking it. ● User shares/likes an item with Facebook ID, tracking these events, crawling data using Graph API for Statistics.
- 39. Data Collector
- 44. Lessons What I have learned so far
- 45. What I have learned ● Keep it as simple as possible, but no simpler ! ● Choose right KPI, right questions => Profit ● Design an architecture for your data products ● Implement it! Just right tools for right jobs. ● Turn your data into the things everyone can "look & feel"
- 46. Stay focused, keep innovating
- 47. “Logic will get you from A to Z; imagination will get you everywhere.” - Albert Einstein Use your imaginationwith data analytics, notjust logic