1. INFO 344 Web Tools And Development CK Wang University of Washington Spring 2014

2. Telemetry Instrument & measure usage data Very important in web services

3. Data = Power Web companies track a lot of data Not just user data but usage data For most web companies (esp free ones) – data == product – data == competitive edge – data == business moat (Warren Buffett)

4. Facebook Usage data as product – Newsfeed Usage data as competitive edge – Facebook login/share in mobile apps – Sponsored mobile apps ads = high CTR Usage data as business moat – Facebook network effect, the more people use it on a daily basis = switching cost = higher

5. Google Usage data as product – Query suggestions Usage data as competitive edge – Spelling correction & Ranking Usage data as business moat – The more traffic, the better the query suggestion/spelling/ranking systems are, the harder it is for other services to compete

6. Amazon Usage data as product – Product recommendations Usage data as competitive edge – User reviews Usage data as business moat – More buyers => more products => more reviews => more buyers

7. Usage data is SUPER important Let’s try playing with some user data

8. Tracking Track events (specific example later) – Event category/name – Event date – User id – Other common parameters Goal = measure user experience, understand what the user is experiencing – When do users feel frustrated?

9. A/B Testing Improve success rate/metrics Measure sign up rate for these 2 homepages

10. Growth funnel framework Overall = Improve product Acquisition – Ex: Goal = Increase signup rate by 25% – From Inbound Marketing (SEO/Blog/BR, non-paid organic) – From Outbound Marketing (Ads, SEM, paid marketing) Activation – Ex: Goal = Facebook 7 friends in 10 days – Get users to understand the value & start using the product Retention – Ex: Goal = DAU/MAU 25%, Day 7 Retention up by 10% – Deliver core value as early as possible & as often as possible Referral – Ex: Goal = 5 invites per user – Leverage network of user Resurrection – Ex: Goal = Re-engage users who have not logged in for > 10 days – Emails? Notifications?

11. Recommendations All based on usage data – Amazon buy recommendations – Google query recommendations – Netflix movie recommendations Collaborative filtering – http://en.wikipedia.org/wiki/Collab orative_filtering http://en.wikipedia.org/wiki/Collab orative_filtering – Matrix, user to product, find product to product correlation Correlations: Tv & controller => strong + Tv & books => strong - Tv & photos => weak correlation

12. Track & Analyze Data

13. Lab, submit on Canvas (3pt) Groups of 2 Download usage data from: http://uwinfo344.chunkaiw.com/files/ltuuserdata.zip http://uwinfo344.chunkaiw.com/files/ltuuserdata.zip Load data into localhost mysql (load wamp, phpmyadmin) Answer these questions about the data! – How many total users do we have in this game? – How many different devices are used to play this game? – How many times are each of the first 5 levels played? – How many levels does each person play on average? – What % of users get past the 3 rd level? How about the 10 th level? – Which of the first 20 levels has the highest dropoff rate? % of people who stop playing and never come back – What is a possible explanation for #users on L22 >> L21? – Which levels are the hardest? (highest fail vs. success ratio)

14. Hints IAP = in app purchase Google ‘adcolony’ and ‘vungle’ to see what they are Other event category/name should be self explanatory This is how to get the unique event categories/names

15. Questions?