The document discusses analyzing user activity to personalize their experience on a platform and reduce bounce rates. It aims to provide contextual recommendations for the best time to post insights or send push notifications to users based on their previous session behavior and login patterns. The recommendation system's accuracy improves as the number of a user's login sessions increases. It works by counting logins in hourly intervals, identifying peak times, and updating the data with each new session in Coordinated Universal Time. Future goals include analyzing which dashboard sections are accessed most and comparing access times across different parts of the platform.
Anomaly detection and data imputation within time series
User activity analysis
1. User Activity Analysis Document
Solution Brief
This aims to personalize user experience and ensuring we get the least bounce rate on our
dashboard also when an insight is posted on “My Hub” on the KPISOFT platform.
The recommendation should be contextual taking into consideration previous session
behaviour of the user. It aims to provide the best time of the day when we should schedule
the a) insight posting b) push notification to a user in any tenant.
The time recommendation system should be effective despite the user have less login
sessions but works better if the login session increases which improves the accuracy.
Solution Approach
Our solution approach comprises the number of distinct components:
1. Count of logins in all hour brackets
2. Maximum count considering all the hour slots
3. Gets updated every time a user enters a new session
4. Time zone will be Coordinated Universal Time (UTC) – 24-hour clock
Future Goals
1. Include which section of the dashboard page is accessed the most and least.
2. Dashboard access time vs Hub access time vs EPMS access time (In words with Ritesh
for this data access if that’s possible)
Sample Output:
