README.md

ADVERTISING PUSH NOTIFICATIONS PERFORMANCE

Ironhack Data Analytics Bootcamp - Final Project - Real problem Statement and dataset from OutPush.

1. Introduction to outpush
2. Problem statement
3. Conclussions
4. Presentation

1.Introduction to

• Push-notifications ads network
• Launched in december 2020
• Middleman between advertisers and publishers

How does it work?

• Clients install a module
• Outpush send personalised ads to the client users
• 1 click = money for the client

2. Problem statement

Subscribers receive same notifications multiple times: is it usefull?

Challenges of this project:

• Understand how do repeated impressions affect users ‘Click Through Rate’ over time
• Perform statistical analysis

*‘Click Through Rate’ = Number of clicks on Ads / Number of impressions of the Ads

3. Insights and recommendations

Boost the notification push during the time periods with higher subscribers activity:

• Wednesdays, the higher 'Click Through Rate' of the week
• Saturdays, the lower 'Click Through Rate' of the week
• Between 09:00 AM and 11:00 AM the higher 'Click Through Rate' of the day

Subscribers lose interest on the ads embeded in the noticitions over the time:

• A subscriber “interest retention” plan could help to boost the Ads 'Opened Rate' for longer time subscribed users.

  

The optimal number of Ads repetition should be in average 4:

• CTR decreases after every repetition of the Ads.

  • 1st impression: 17.9%
  • 4th repetition: 7.1%
  

• The probability of opening the Ads decrease after every repetition

  • Probability on the 1st impression: 0.8%
  • Probability on the 4th repetition: 0.3%
  

• The conditional probability of clicking the add, given it was not opened before, drops from 0.79% in the first impression to 0.38% in the 4th repetition.

  

4. Presentation

To see the presentation, click in the below picture.