Appboy analyzed 3.5 billion sessions from users in the U.S. on November 8th, 2016, comparing the day to the four previous weekdays for a look at smartphone activity by state, and by time of day.
Presented @ Bigdata Singapore Meetup. Good Enough Analytics is a methodology I am working on to achieve decent analytical results at a reasonable cost. Warning: For the consumption of Data Nerds Only. For 99% of normal humans, these slides are snooze inducing =P.
DSD-INT 2016 Urban water modelling - MeijerDeltares
Presentation by Didrik Meijer, Deltares, The Netherlands, at the Delft3D - User Days (Day 1: Hydrodynamics), during Delft Software Days 2016. Tuesday 1 November 2016, Delft.
Presented @ Bigdata Singapore Meetup. Good Enough Analytics is a methodology I am working on to achieve decent analytical results at a reasonable cost. Warning: For the consumption of Data Nerds Only. For 99% of normal humans, these slides are snooze inducing =P.
DSD-INT 2016 Urban water modelling - MeijerDeltares
Presentation by Didrik Meijer, Deltares, The Netherlands, at the Delft3D - User Days (Day 1: Hydrodynamics), during Delft Software Days 2016. Tuesday 1 November 2016, Delft.
Your App Metrics Are Telling You How To Make Them Better: The Digits Data ReportBraze (formerly Appboy)
Digits, Appboy's new mobile marketing benchmarks report, draws on data from more than 500 apps and 6.5 billion push notifications to provide a truly comprehensive look at today's mobile marketing landscape.
This session of Relate Live discussed current app engagement metrics benchmarks, and what marketers can do to improve their results. In this presentation, you'll learn:
- App engagement and push messaging benchmarks
- Tips you can implement today to improve crucial KPIs, based on how your metrics measure up to benchmarks
A user's first session determines the rest of their journey with your brand. If your onboarding process fails to help new users become more familiar with your app and understand your app's value and core features, you may lose them faster than you acquire them.
Here are the essential components of an effective onboarding campaign and tips and examples on how to introduce new users to your app and increase engagement and retention.
the foodpanda group is a global mobile food delivery marketplace headquartered in Berlin, Germany, and operating in 40 countries and territories, including India, Pakistan, Russia, Brazil, Mexico and Singapore. The service allows users to select from local restaurants and place orders via the mobile application as well as the website. The company has partnered with over 58,000 restaurants.
Foodpanda is a small team of problem solvers, designers, thinkers and tinkers, working around the clock to make foodpanda the most powerful online tool for food delivery in the universe. We believe that ordering food should be easy, fast and definitely fun! We wanted something simpler, so we made it.
Tableau for statistical graphic and data visualizationBAINIDA
Tableau for statistical graphic and data visualization Somkiat Kraikriangsri – Enterprise Sales
Marut Veerawatyotin – Sales Consultant
THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE
In the spring of this year I have mapped the vast majority (>95%) of shops and retail areas in Nottingham, the 8th largest retail centre in the UK, serving a market of around 1 million people. In addition I have repeated the exercise for a Maidenhead a town of 70,000 which is only a local shopping centre.
Comprehensive mapping of retail outlets allows a more rigorous analysis of OSM data, with significant implications for how we should collect and map data for shops. These include: better support from mapping tools (particularly for mapping change), defining a usable classification for retail areas, and improving consistency in tagging.
These need to be cross-checked for different countries, so I hope this provokes extensive contributions from other participants.
Geneve Monitoring event - InfluxDb and Loud ML presentationSebastien Leger
The presentation that was delivered at Seedspace for Geneve Monitoring Meetup on March 8, 2018. This talk was an introduction to Loud ML machine learning API and its integration with InfluxDb and Chronograf.
Your App Metrics Are Telling You How To Make Them Better: The Digits Data ReportBraze (formerly Appboy)
Digits, Appboy's new mobile marketing benchmarks report, draws on data from more than 500 apps and 6.5 billion push notifications to provide a truly comprehensive look at today's mobile marketing landscape.
This session of Relate Live discussed current app engagement metrics benchmarks, and what marketers can do to improve their results. In this presentation, you'll learn:
- App engagement and push messaging benchmarks
- Tips you can implement today to improve crucial KPIs, based on how your metrics measure up to benchmarks
A user's first session determines the rest of their journey with your brand. If your onboarding process fails to help new users become more familiar with your app and understand your app's value and core features, you may lose them faster than you acquire them.
Here are the essential components of an effective onboarding campaign and tips and examples on how to introduce new users to your app and increase engagement and retention.
the foodpanda group is a global mobile food delivery marketplace headquartered in Berlin, Germany, and operating in 40 countries and territories, including India, Pakistan, Russia, Brazil, Mexico and Singapore. The service allows users to select from local restaurants and place orders via the mobile application as well as the website. The company has partnered with over 58,000 restaurants.
Foodpanda is a small team of problem solvers, designers, thinkers and tinkers, working around the clock to make foodpanda the most powerful online tool for food delivery in the universe. We believe that ordering food should be easy, fast and definitely fun! We wanted something simpler, so we made it.
Tableau for statistical graphic and data visualizationBAINIDA
Tableau for statistical graphic and data visualization Somkiat Kraikriangsri – Enterprise Sales
Marut Veerawatyotin – Sales Consultant
THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE
In the spring of this year I have mapped the vast majority (>95%) of shops and retail areas in Nottingham, the 8th largest retail centre in the UK, serving a market of around 1 million people. In addition I have repeated the exercise for a Maidenhead a town of 70,000 which is only a local shopping centre.
Comprehensive mapping of retail outlets allows a more rigorous analysis of OSM data, with significant implications for how we should collect and map data for shops. These include: better support from mapping tools (particularly for mapping change), defining a usable classification for retail areas, and improving consistency in tagging.
These need to be cross-checked for different countries, so I hope this provokes extensive contributions from other participants.
Geneve Monitoring event - InfluxDb and Loud ML presentationSebastien Leger
The presentation that was delivered at Seedspace for Geneve Monitoring Meetup on March 8, 2018. This talk was an introduction to Loud ML machine learning API and its integration with InfluxDb and Chronograf.
Jena Donlin, Product Marketing Manager at Appboy (soon to be Braze), discusses what Apple and Google's mobile OS updates mean for the future of mobile engagement
Find out how Dots used smart segmentation together with in-app messages to support a better user experience and drive in-app purchases, boosting LTV per user by 25% and total revenue by 33%.
During Grow.co's MAU Las Vegas 2016, Appboy VP of Customer Success, Spencer Burke, shared best practices for mobile acquisition, retention, and monetization alongside Urban Outfitter's Senior Director of Global CRM and Customer Analytics, Andrew Rauch.
Today’s marketers must embrace a new engagement strategy to adapt, anticipate, and deliver in mobile moments. Placing mobile bets solely on ads or one-off purchases isn’t enough to secure revenue and engagement—marketers have to embrace continual cycles of real-time, two-way consumer engagement.
This webinar will show marketers how to create and deliver these experiences and campaigns.
SXSW is one of the largest tech and innovation events of the year and one of the most important events for apps looking to create a standout mobile experience. Here's a rundown from one of our Appboy team members of several mobile app experiences during SXSW 2016.
Learn from author and former ESPN Magazine editor Gary Belsky about how our various unconscious biases drive decision making, and what that means for marketers.
Learn about how SoundCloud is building a best-in-class marketing tech stack to significantly grow and engage their active user base across mobile and other digital channels.
Customer engagement is the process of actively building, nurturing, and managing relationships with customers. Customer engagement can unlock exponential growth for your company. Learn more about how it's done.
2. ELECTION DAY MOBILE ACTIVITY
Activity Differences vs Typical Weekday
ActivityvsBaseline
-50%
-25%
0%
25%
50%
Eastern Time
12:00 AM 3:00 AM 6:00 AM 9:00 AM 12:00 PM 3:00 PM 6:00 PM 9:00 PM
Election day starts off with mobile activity
13% lower than a typical weekday
1
2
3
4
5
Mobile activity peaks when polls open,
with activity 34% higher
2
During the workday, mobile activity dips
46% lower
3
Mobile activity picks up again after the
workday and is 13% higher
4
Election day closes with mobile activity
down 19%
5
1
Typical Weekday
November 8, 2016
Baseline calculated as average activity in the 4 weekdays prior to election day. 0% represents no difference from the baseline. Analysis includes 3.5 billion session start events in the US.
3. ELECTION DAY 00:00 - 01:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
4. ELECTION DAY 01:00 - 02:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
5. ELECTION DAY 02:00 - 03:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
6. ELECTION DAY 03:00 - 04:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
7. ELECTION DAY 04:00 - 05:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
8. ELECTION DAY 05:00 - 06:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
9. ELECTION DAY 06:00 - 07:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
10. ELECTION DAY 07:00 - 08:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
11. ELECTION DAY 08:00 - 09:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
12. ELECTION DAY 09:00 - 10:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
13. ELECTION DAY 10:00 - 11:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
14. ELECTION DAY 11:00 - 12:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
15. ELECTION DAY 12:00 - 13:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
16. ELECTION DAY 13:00 - 14:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
17. ELECTION DAY 14:00 - 15:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
18. ELECTION DAY 15:00 - 16:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
19. ELECTION DAY 16:00 - 17:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
20. ELECTION DAY 17:00 - 18:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
21. ELECTION DAY 18:00 - 19:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
22. ELECTION DAY 19:00 - 20:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
23. ELECTION DAY 20:00 - 21:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
24. ELECTION DAY 21:00 - 22:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
25. ELECTION DAY 22:00 - 23:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
26. ELECTION DAY 23:00 - 23:59 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
27. DAY AFTER ELECTIONS MOBILE ACTIVITY
Activity Differences vs Typical Weekday
ActivityvsBaseline
-50%
-25%
0%
25%
50%
Eastern Time
12:00 AM 3:00 AM 6:00 AM 9:00 AM 12:00 PM 3:00 PM 6:00 PM 9:00 PM
Mobile activity was 21% lower when the
election was called
1
2
3
4
The day begins with mobile activity up
17%
2
During Hillary’s speech, mobile activity was
down 41%
3
Mobile activity for the rest of the day was
volatile and depressed, with the day
ending at 39% lower
4
1
Typical Weekday
November 9, 2016
Baseline calculated as average activity in the 4 weekdays prior to election day. 0% represents no difference from the baseline. Analysis includes 3.5 billion session start events in the US.
28. ELECTION DAY VS THE DAY AFTER MOBILE ACTIVITY
Activity Differences vs Typical Weekday
ActivityvsBaseline
-50%
-25%
0%
25%
50%
Eastern Time
12:00 AM 2:00 AM 4:00 AM 6:00 AM 8:00 AM 10:00 AM 12:00 PM 2:00 PM 4:00 PM 6:00 PM 8:00 PM 10:00 PM
Baseline calculated as average activity in the 4 weekdays prior to election day. 0% represents no difference from the baseline. Analysis includes 3.5 billion session start events in the US.
Typical Weekday
November 8, 2016
November 9, 2016
29. NOVEMBER 9TH 00:00 - 01:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
30. NOVEMBER 9TH 01:00 - 02:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
31. NOVEMBER 9TH 02:00 - 03:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
32. NOVEMBER 9TH 03:00 - 04:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
33. NOVEMBER 9TH 04:00 - 05:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
34. NOVEMBER 9TH 05:00 - 06:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
35. NOVEMBER 9TH 06:00 - 07:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
36. NOVEMBER 9TH 07:00 - 08:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
37. NOVEMBER 9TH 08:00 - 09:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
38. NOVEMBER 9TH 09:00 - 10:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
39. NOVEMBER 9TH 10:00 - 11:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
40. NOVEMBER 9TH 11:00 - 12:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
41. NOVEMBER 9TH 12:00 - 13:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
42. NOVEMBER 9TH 13:00 - 14:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
43. NOVEMBER 9TH 14:00 - 15:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
44. NOVEMBER 9TH 15:00 - 16:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
45. NOVEMBER 9TH 16:00 - 17:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
46. NOVEMBER 9TH 17:00 - 18:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
47. NOVEMBER 9TH 18:00 - 19:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
48. NOVEMBER 9TH 19:00 - 20:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
49. NOVEMBER 9TH 20:00 - 21:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
50. NOVEMBER 9TH 21:00 - 22:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
51. NOVEMBER 9TH 22:00 - 23:00 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.
52. NOVEMBER 9TH 23:00 - 23:59 EST
More activity
relative to baseline
Less activity
relative to baseline
Baseline calculated as average activity in the 4 weekdays prior to election day. Analysis includes 3.5 billion session start events in the US.