Push Notifications – Signal Vs Noise
On an average, there are about 20 apps that a user has installed on their mobile. These apps vie for the user’s attention, which is becoming harder and harder to get. The apps that standout, focus on establishing relationships with the users through personally relevant messaging and app experiences.
The key point is that the app should serve a function, that is useful and this includes messaging as well. Especially in case of news apps, it is very important to walk the fine line between the urge to push out everything and limiting it to have an impact. Too many notifications and you can ruin the experience, and too little can have your subscribers feeling left out.
Mobile, no doubt, is a more personal medium, than any other delivery mechanism, and it is highly responsive too. According to some studies the open rates of mobile notifications can be around 50% and most of the notifications are opened within a couple of minutes of sending. No other messaging mechanism matches the speed and performance.
So, it makes all the more sense to deliver an experience which relevant and adds value.
There are multiple concepts that come together in piStats that helps you achieve the perfect balance by personalizing the messaging:
First, find out what are your user groups:
- Basic user groups: target based on geography, technology, e.g. Android vs iOS or a combination of user attributes like “All users from Delhi who use Android phones”
- Advanced: based on the actions taken by users, e.g. if a user has read one story, send another related story. This can be mixed and matched to create some very specific user cohorts which can then be used to send very specific notifications.
Then pick the content that is best for each of the user groups:
- Basic Business rules e.g. you can say, send out most trending stories at a given interval or story by a specific author.
- Machine Learning piStats has sophisticated algorithms to find out the content that can be sent out to specific users. This can be based on each user’s historical usage combined with latest trends and recency of the content. With powerful machine learning algorithms driving the messaging, we can possibly have different messages personalized for each individual user.
All of these capabilities are out of the box available for you and can be tweaked to best suit your needs.
This blog has been written by Mr. Gaurav Batra, one of our core Leadership Team’s Member
Gaurav Batra, Head, Products
Gaurav is a technology enthusiast.