Last update: December 2021

4 mins to read - 2021/12/21

How to Boost Click-Through Rate With Personalized Push Notifications

It’s no secret that mobile device users are frustrated with push notifications. When executed poorly, notifications can be intrusive, spammy, and irritating. It’s no wonder that on iOS devices, where people are given the choice upfront, only about half of device users opt-in to receive push notifications. However, the bad blood between users and mobile engagement alerts is largely a result of lazy mobile marketing tactics. When you blast users with generic, irrelevant marketing messages at any which hour of the day, with no consideration as to whether the notification will benefit the user, they will understandably be fed up. Conversely, if you dial it back and send a few, highly personalized push notifications to different segments of users, you will get much better results.

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Personalized Push Notifications Outperform Generic Messages Every Time

Personalization is a must, and not only with mobile engagement. For any scenario where a brand is reaching out to communicate with consumers, taking the time to personalize your marketing content results in a more successful and meaningful interaction. In fact, 77% of consumers agree that they would choose, recommend, or pay more for a brand that provides a personalized UX. In this day and age where our apps and devices have so much insight on us in terms of behavioral data, consumers come to expect a certain level in which an app UX is customized towards their interests and convenience.

Nowadays, far from being a “perk,” personalization has come to be a basic expectation of consumers. Consequently, studies have shown that personalized calls to action achieve conversions at a 202% higher success rate than non-personalized. And while personalized emails and ads are common, push notifications have dropped the ball somewhat in this area. Perhaps this is because, with an industry click-through rate of around 2%, nobody expects too much from push notifications anyway. However, they could not be more wrong.

Personalized Perfect-Moment Timing and Adaptive Scheduling of Push Notifications

In a 2019 study from Yahoo! JAPAN, researchers from Keio University analyzed the effects of leveraging user data to determine when is the most convenient moment for users to receive push notifications. They found that, by sending push notifications at the right moment – for example, when the user is scrolling through their phone with nothing else going on – rather than at randomized times, mobile marketers can see an average increase in CTR of 23.3%, with a maximum increase of 60%.

What’s more, when adaptive scheduling techniques are combined with personalized contextual content, CTR of push notifications doubles. Most developers ignore perfect timing as an aspect of personalization for mobile engagement. However, this is because the industry standard of messaging software uses outdated technology that cannot guarantee reliable delivery.

OpenBack’s hybrid mobile engagement platform uses device-side data as opposed to the standard cloud messaging servers, APNS for iOS devices or Firebase for Android devices. This enables reliable delivery of notifications for the very first time in the industry. This then opens up a door into a whole new potential of use cases for adaptive scheduling in push campaigns.

To learn more, read our blog post: Reliability in Push Notifications Is About to Hit Its Stride mobile app push notification

Personalized Content and User Segmentation

Before you schedule your push campaign, it’s crucial to personalize notification content as well. This can be as simple as addressing your user by name, as consumers respond much more positively to a message with their name in it than to a generic alert.

Another fast and easy way to deliver a personalized experience to your user is to leverage geolocation data for localization of notifications. Essentially, whatever their device’s default language setting is, notifications should be translated into that language. This means investing in a quality translator, as well as a push notification platform that supports different languages.

The meat and potatoes of personalization lies in leveraging data insights to determine what content would be most beneficial to them, based on profiles compiled over time spent using your app. This could mean sending personalized purchase suggestions based on their previous purchases. For example, if your user bought the boxed set of a popular author’s bestselling series, you could send them a notification to alert them that the author’s latest book is available for pre-order.

If a user of your mobile game has just completed a level, you can invite them back into the game with a notification that saying, “Don’t give up now, Jeff… Level 32 awaits!” You can sweeten the deal by offering them a bundle of consumables that enhance gameplay, or tease some exclusive content or a cut scene that awaits them at the end of Level 32. Or you can send them notifications whenever friends from their contact list are online, to foment some friendly competition.

And, when it doubt, remember rich push notifications are far more attention-grabbing – and effective – than a plain block of text. Spice up your design with some emoticons, or even graphic or video content. (Remember to include alt text, for devices that don’t support visual content in notifications!)

OpenBack’s Device-Decisions Machine Learning Signal

OpenBack provides diverse features for segmentation and personalization of notifications, as well as 40+ data signals that developers can leverage to determine the right moment to send a notification. These include industry firsts, such as data points that can gauge the perfect moment which include device orientation, whether your lockscreen is locked or unlocked, whether the headphone jack is engaged, etc.

However, the signal with the most innovative potential use cases is OpenBack’s Device-Decisions Machine Learning Signal. The DD-ML signal combines adaptive scheduling with personalization in that it uses an algorithm to learn from both users’ past behavior and apply the pattern to real-time contextual factors to predict users’ actions. For example, this signal will map out a pattern of a user’s schedule based on their habitual device usage. Then if that pattern diverges on a particular night – say, a user goes home after work 99 nights out of 100, but then one Thursday night they go out for a friend’s birthday party – the DD-ML signal will take that into account and recalibrate your notifications delivery schedule accordingly.

To learn more about exciting ways to personalize your push notifications using OpenBack’s data signals, as well as tips on content writing and segmentation, get in touch with our team of experts.

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