The document summarizes the findings of an internship analyzing user data from the Bobble app. Some key findings include: - The average Bobble user has 17 apps installed taking up 14MB of space, excluding default Google apps. - Peak app usage occurs between 8-10am each day, with Wednesdays seeing the lowest traffic. - Newer devices from 2012 or later showed higher retention rates, while older devices were more likely to uninstall. - Getting users to create 5 or more personalized avatars significantly increased chances of continued app use. - Social media insights found peaks in Facebook likes in April, June, and October while Instagram interactions remained steady after July.

1. Winter Internship Report
-Anuj Gopal
23, Green Avenue
Vasant Kunj, New Delhi

2.  The objective was to find relevant differences between the types of app installed in active users and those by
uninstalled users
 Although no significant differences were observed, most prominent categories for an avg. bobble user were found
out to be communication, tools, productivity, shopping and photography.
User installed Apps Analysis

3.  Avg. size of app a Bobble user has installed on their phone is 14MB.
 Avg. number of app a Bobble user has installed on their phone is 17. This excludes Google default apps but includes apps
which are pre-bundled with their device.

4. Daily Usage

5. Prime Time
 A comparison was made for the prime time for different days and following barchart was obtained using the data.
 Peak was observed between 8-10 on each days with slight variations, Wednesdays having the minimum traffic.

6.  The objective was to analyse retention rate and the uninstall rate of our users based on the information of their devices.
 The analysis was made on 3 basic features of a device: Brand Name, Year of Class and Root Status
 Table below shows top 10 brands our users have. Red ones are where uninstall rate is higher and green ones are where
retention rate is higher.
Device info

7.  Device YearClass (Specs similar to the top-in-line devices made in that
year) wise distribution. Devices with YearClass >= 2012 are showing
positive trend while uninstall rate is higher in devices with YearClass <
2012.
 For example, the Galaxy Duos S was released in 2012, but its specs are
very similar to the Galaxy S that was released in 2010 as a then top-of-
the-line phone, so it is a 2010 device.
Device info

8. Head frequency
 The magic number is #5
 If we can get our users to create more than 5 heads their chances of keeping the app and using it every week
increases significantly. Obviously, provided we maintain the hygiene of not pissing off these users with bandwidth,
storage, memory and wrong product features.

9.  Percentage of clicks on heads increased from 11.49% to
22.61% as was expected from the 3.4.1 home screen UI. This
is the primary reason for increase in percentage of people
who go back to edit the head and not what we assumed.
 Stickers, Stories and canvas contribution decreased
marginally, again because of significant increase in heads
contribution.
 Menu open is now contributes almost the half and cloud
storage percentage also decreased. It was not expected from
the 3.4.1 UI.
Screenwise Event Distribution

10.  Removing the unnecessary noisy & lesser use case
features from eraser screen certainly helped.

11.  People selecting the bobble and moving to next step has
increased significantly from 75.4% to 94.5% given the UI
placements have changed.

12. Social Media Insights
 The graph shows variation of Net, Organic, Paid FaceBook Likes and Unlikes from March to November. Unlike showed
no clear variation, whereas likes had peaks and minimas.
 Likes dropped significantly in the month of August, with organic likes as low as 186.
 Peaks were found in the month of April, June and October.

13. Instagram Insights
 Although number of new followers shows variations through out the year, the total Instagram Interactions remained
almost constant after the month of July.
 We started with 2315 Instagram interactions in the month of April, while the average Instagram interaction from July to
November is greater than 14000, as shown in the next slide.

15. Twitter Insights
 Twitter Profile visits has peaks in the month of March, May and September, while troughs in the moth of April and
August.
 The

16. Competition Insights
 A comparison between different chat apps competitors is shown in the graph below [Whatsapp excluded].
 Hike progressed intensively in the month of September to November, while downloads of Jio chat decreased in the
month of November.
 Downloads of Bobble, Snapchat, Wechat, Viber and Line remained almost constant within this period.

17.  In the consumer app section, Instagram dominated through out the year, although Dubsmash witnessed peak
downloads between May to June.
 The downloads of Dubsmash decreased exponentially after July, while others had maintained their quota of downloads
through out the year, with Bobble app slightly behind Dubsmash and Bitmoji.

18.  Finally, a Master Table was prepared which was based on several parameters observed by the team
 The table is useful for comparative analysis of users based on their device info, gender, connection, root status as well as
the corresponding campaign
 For example, 54.72% of the Total users are purely cellular while only 18.37% of the users targeted through 9apps APK
are wifi users. This way we can clearly identify areas where we are excelling and the areas where we need to focus
Master Table