Improving sticker keyboard with data analysis

Naho Tanigawa
LINE Data Science Team2 Data Scientist
https://linedevday.linecorp.com/2020/ja/sessions/9540
https://linedevday.linecorp.com/2020/en/sessions/9540

Transcript

2. Self Introduction › Joined LINE Corporation as a Data Scientist in May 2019 › Working on data analysis for LINE Sticker Service › Naho Tanigawa
3. Agenda › Sticker Keyboard Project › Challenging Task 1 : Definition of Session › Challenging Task 2 : Identification of KPI › Case Studies of Analysis
4. Sticker Keyboard Project < Goal > Increase the frequency of sending stickers < Approach > Reduce time to search and send stickers Sticker Sticker Keyboard
5. Data-Driven Improvement User Activity logs Analysis Collecting Data feedback
6. Data-Driven Improvement User Activity logs Analysis Collecting Data feedback We have no data of sent contents nor user identity.
7. Sticker Keyboard Update new functions released in June 2020
8. Sticker Keyboard Update new functions released in June 2020 New panel for searching stickers Notification of time-limited sticker’s expiration
9. 2 Challenging Tasks Definition of Session Identification of KPI
10. Definition of Session
11. Problem Logs of 1 LINE App. Launch = sending stickers E Need to group sequences for understanding user behavior 1 LINE App launch: Too long 1 action on Sticker keyboard: Too short
12. 1 Session on sending a sticker Problem Logs of 1 LINE App. Launch = sending stickers E E 1st session 2nd session 3rd session E E Need to group sequences for understanding user behavior 1 LINE App launch: Too long 1 action on Sticker keyboard: Too short
13. Solution Define session by splitting log based on rules S Action to start session Action to end session (Sending stickers) E Click E S 1st session 2nd session 3rd session Logs of 1 LINE App. Launch E S Click
    We have no data of sent contents nor user identity.
14. Solution User Activity logs Randomly sample the logs of 1% of all users Splitting logs by rule-based Estimated Statistics By × 100 Action to start session Action to end session (sending stickers) E Pipeline of processing session logs Session Logs S 1st session 2nd session 3rd session Logs of 1 LINE App. Launch E E S S 10 billion in a day
15. Identification of KPI
16. Problem Identify KPI to represent both Goal & Approach < Goal > Increase the frequency of sending stickers < Approach > Reduce time to search and send stickers
17. Solution # of Sending Sticker Count # of Unique Sending Users Average Sending Sticker Count per Users Goal
18. Solution # of Sending Sticker Count # of Unique Sending Users Average Sending Sticker Count per Users Average Sending Sticker Count in 1 App Launch per Users Average App Launch Count per Users Goal
19. Solution Sending Sticker Count in 1 App Launch per Users
    2 # of Sending Sticker Count = sending stickers E # of Unique Sending Users Average Sending Sticker Count per Users Average Sending Sticker Count in 1 App Launch per Users Average App Launch Count per Users Goal Approach E S 1st session 2nd session 3rd session Logs of 1 LINE App. Launch E S
20. Solution ≒ Elapsed Time of 1 App Launch Elapsed Time of Sending 1 sticker Sending Sticker Count in 1 App Launch per Users
    2 # of Sending Sticker Count = sending stickers E # of Unique Sending Users Average Sending Sticker Count per Users Average Sending Sticker Count in 1 App Launch per Users Average App Launch Count per Users Goal Approach E S 1st session 2nd session 3rd session Logs of 1 LINE App. Launch E S
21. Case Studies of Analysis
22. Case 1 : UI of New sticker panel Reduce time to send stickers by allowing users to do the most common way TO-BE Input method in new sticker panel: double-click a sticker … AS-IS Most frequent sending pattern: double-click a sticker 26%
23. Case 2 : Notification Timing of time- limited sticker’s expiration expiration Download date Reduce time to search stickers by highlighting stickers near expiration AS-IS Last use date of time-limited stickers: Increase in 10 days before expiration TO-BE Notification timing: 10 days before expiration
24. Data-Driven Improvement User Activity logs Analysis Collecting Data feedback
25. Thank you