In my latest post for @programmableweb, I review Quettra. Quettra is backed by $2.9 million in seed investment, is a mobile intelligence solution that focuses on personalizing mobile experiences through understanding users' interactions with their phones, on a deeper operating system-level view. That is, Quettra attempts to drive up organic retention through the creation of personas, using its User Interest API to provide the ability to build a portrait or persona of a user.
Quettra gives developers a holistic view of user interests, brand affinities, mobile personas and inferred demographics, where available.
Quettra’s value proposition is simple: The first experience an app provides to users defines whether they will understand the app’s value proposition. According to Quettra, up to 77% of people stop using the average app within the first three days of installing it (when observed using statistical modeling such as cohorts), which comes down to apps in most cases providing a "cold-welcome" to their new users.
Quettra gives developers the tools for retaining users and providing a personalized narrative and experience when they first install the app. This is called building the user’s persona.
Building your users’ personas consists of the following categories:
- Language, location, device, phone carrier
- Age, gender
- Personal interests (fitness, technology, fine arts)
- Brand affinities (Nike, Apple, Macy’s)
- Personas (a book lover, new parent, dog owner)
In fact, you can take a look at Quettra’s complete taxonomy to get an idea of the gamut of personas and demographic groups that the SDK uses to categorize each of your users.
When a user first launches an app, the developer would generally provide some onboarding to introduce the app to the user. This is an excellent opportunity for developers to tailor the app to individual users, providing content for non-signed-in users, with suggestive content based on their personas.
A news app (think Flipboard) would therefore already have content that is of interest to the user, matching his or her persona. You can either preselect the topics that match the data you suggested from the SDK, or you can let the user decide from the predictive list.
Another use case is to integrate Quettra Portrait with your mobile advertising solutions. With quick and early insights into users, developers are able to fine-tine ads to create more accurate and targeted marketing campaigns based on real interests.
To read the rest of the rest of my article, including how to get yourself setup with Quettra, visit programmableweb.