What is app personalization ?
App Personalization is the process of building a mobile application to meet the needs of specific audiences. Similar to other forms of personalization, app personalization aims to present users with experiences customized to their specific needs, rather than a broad, one size fits all experience for all users. Mobile apps that influence users by providing personalized experience tends to get higher level of engagement and retention rate.
Implementation of personalization in apps –
Let’s take a look at ways to personalize app for users:
Know your users and what they want :
With the right mobile engagement solution and analytics in place, you can glean a lot of valuable information about your users. In order to personalize something you need to get familiar with your user’s persona.
Real time responses :
Ideally, mobile marketers should respond differently to each user’s unique feedback. Responsiveness of the app is also one of its key deciding factors.
Test, Experiment & Optimize :
Constant testing and optimization is the heart and soul of marketing. You can test subject lines, content, visuals, layouts, and more. You can experiment with different flows, such as nurturing order, at the same time you can constantly optimize your content and scoring system.
Why Personalization ?
> Better user experience
> Improving with use (retention)
> Improved engagement and delight
> Helps the business to grow
Why Netflix embraces personalization?
In past years, the main aim of Netflix’s personalized recommendation system is to provide right titles to each of their members at right time. But this is not their only job! Why should the members care about the recommendations from Netflix? How do they convince the users that a title is worth watching?
One avenue to address this challenge is to consider the artwork or imagery they use to portray the titles. If the artwork representing a title captures something compelling to the users, then it acts as a gateway into that title giving them a visual “evidence”, for why the title might be a right choice for them. This artwork is hunted using the multi-armed bandit algorithms.
An example of personalization based on user activities
The above snapshot depicts how the shows are recommended based on previous history. As it says “Because you watched Narcos”, hence the titles of similar genre has been recommended to the user. Being more specific, other recommendation here is “Pablo Escobar -Countdown to Death’’, as Narcos is based on the character of Pablo Escobar. Another recommendation is “Drug Lords” because similar to Narcos it portrays narcotic world. This is how Netflix recommends different titles uniquely to its members depending on watch history.
Behind the scenes, Netflix is leveraging powerful machine learning to determine which will be recommended to you specifically. The Netflix system is largely driven by Collaborative filtering, which means it will not only look at what we like, but also what “similar” users liked and watched. In this context, similarity is based on parameters like User ID, Movie ID, Rating and Timing. Holding these parameters together, the algorithm works. Netflix says more than 80% of shows watched over the last two years come through its own recommendations, as opposed to someone searching for a particular show and watching it.
These recommendations get fine tuned as the members use Netflix. Personalized genres are generated using myriad number of movies and TV show characteristics including actors, story line,characters and time period.
Another example could be the Content-Based Filtering used by Amazon.
App Personalization is important to create an overall engaging experience. Hence, giants like Amazon and Netflix, are leveraging personalization to a larger extent for their personalized customer experiences.