Personalization can be a powerful engagement tool. The ability to personalize your product can unlock many opportunities for engagement, customer loyalty, and attracting new customers. It sounds so simple to offer every user exactly what they're seeking, but behind the scenes, it involves a great grasp of how recommender systems operate to serve highly relevant content to your users.
Copywriting is everything
If you want to personalize your marketing, the bare minimum you can do is to work closely with professional copywriters. Copy can be an effective tool to make a product more appealing. Whether it’s a simple “Good morning, Alex!” or special offers on a customer's birthday.
Creativity is key
You can make your product feel more personalized by using images and icons. Consider using seasonal images and adding small details to your icons.
Knowing your users
There are countless user signals that you might think of, but let's start with the most frequently encountered:
- Location: It’s possible to get an estimated location, from less exact to precise results: currency, language, sim card details, IP address, wifi, phone location.
- Device types and browsers: Knowing whether your users are using your desktop web app or your mobile app can be really useful in figuring out what they want. Browsers can also tell a lot about a user, such as a user on Google Chrome may be more inclined to sign-up through Google.
- User activity: Whether it's liking an Instagram post, watching YouTube ads, swiping down on Tiktok, or perusing a popular Amazon book. You need to find within your product these user actions, which can tell you a great deal about what your users are interested in.
Content-based filtering refers to the idea of bucketing your offerings and products according to their content. For example, Spotify offers a "Disco" category, Netflix provides a "Horror" category, and Amazon includes a "Books" category. Spotify scrapes large quantities of data written in blog posts, articles, and discussions about specific artists using natural language processing. It tracks the sentiment of people about that artist, what other musicians have written, and other songs that might be mentioned simultaneously. The system identifies and associates descriptive terms, phrases and nouns, with a particular song or artist. It is designed so that your user will be able to easily access offers related to what they are interested in if they engage within one of these categories. Despite their ease of implementation, these systems don't allow for much personalization and may feel a little static to your end users.
The most popular method of filtering in the tech industry is collaborative filtering. With Netflix, its purpose is to predict which movies a user might like based on what other Netflix users watched along with the movie they watched. The idea is to highlight content from users with similar tastes. It is recommended to partner with a data scientist at this point in order to build recommendations based on collaborative filtering. Such a powerful method can be applied in a variety of ways. Amazon uses it as a way to boost basket sales by displaying its products with "Customers also purchased" or "Frequently purchased together" sections.
It is highly recommended to involve your legal department in all of these discussions, as well as to pay attention to being GDPR compliant. Users should be able to comment on your recommendations, from thumbs up/down voting for any recommendation to removing any suggestion. In this way, your team will be able to understand what can be improved and where.