Posted by Danielle Goodman
In our Designing for Place series, we discussed that precise location data can provide richer insights into your users’ behavior and interest. Those insights can help you decide the best places to build experiences that reduce friction between the user opening the app and getting value. We call this "appticipation." Having contextual data on where and when your users are accessing your app and its functionalities can help you to design your app to become vital to the daily life of your users. The end goal is delivering personalized app experiences to each user.
The app feature usage analysis we outlined in previous posts can give you a better understanding of your app usage and functionality. Now, we'll take those findings and see how music apps can apply appticipation to better understand what functionality users favor and use in the places they are in.
Many smart music apps allow users to create entire stations or playlists around a single song or artist. Users can also follow pre-made playlists, categorized by genre of music or artist. In the free models, users might have to listen to advertisements every few songs, or are prompted to do a sponsored activity, and in some instances with paid profiles, users can skip ads altogether.
Today, music apps leverage user Registration Data and information about their devices to provide service, including serving them advertising based on gender, age and zip code.
Now let’s walk through some examples of how music apps can better leverage the location and context of its users:
We are proposing that music apps take user experience to another level based on the current location, historical location, and demographics of the user. Music apps app presents further opportunities to learn about user with Skyhook’s Context Accelerator. It can build Personas on user’s via location samples collected through background monitoring.
Music apps like Pandora and Spotify can use location to add context to their app in order to anticipate the next moves of their users while simultaneously making their lives easier and their app more vital.
Music apps with a personalized environment, engaged audience and targeted ad solutions deliver results. Whether your goal is higher engagement rates or driving more in-store traffic, the Skyhook platform is the perfect complimentary piece to these core value propositions to advertisers - in fact, we help with all of them. We add a new element to help personalize experiences, we use location as a new way to drive engagement for them and we complete the targeting story - as well as create new targeting opportunities.
If a music app was able to see where users eat, shop and travel to, advertisers would be able to send live offers as they move through these places and listen to their music. Giving advertisers the opportunity to serve their message to music app listeners based on their location could not only increase ad engagement, but the advertising could be useful and timely for users.
For example, if a female user is shopping in a Nordstroms and an ad for clothing that is sold there was served with a this-hour-only, “touch to redeem” coupon, a user might take actually advantage of it because of the app’s ability to interpret their context. The music app can further optimize advertising recommendations with offline user intelligence, and pass along these rich datasets to advertising partners, who can align clicks to certain Personas or places.
Skyhook can give music apps an added element of engagement by using offline location for moment-in-time experiences. This level of insight completes the customer story, using location to influence engagement.
From a user’s perspective, they are always looking for apps to add more value to their everyday lives. In addition to being a platform that allows users to discover new music they may like based on their taste, a music app could also notify users if an artist or band they “Liked” will be playing in a venue close to their location. Or, they could advertise that information during commercials breaks in a user’s station. Having contextual knowledge of what’s popular in a certain city could also fuel new recommendations to users, such as “Users in Your City Are Listening to…”
With Skyhook’s Context Accelerator, music apps can build Personas to learn more about their users via location samples collected through background monitoring. This information goes a level deeper than the static demographics information users provided when they first download / sign up. We also added active filters that users could flip between to further add context - through their location (My City), their social profiles (My Friends), or what they feel like listening to (My Mood).
Using dynamic Personas - like Luxury Shoppers, or Coffee Lovers - that evolve with user behavior, music apps can more accurately serve their users based on who they are and what they might be interested in. For example, geofencing can be deployed to understand what songs other Luxury Shopper personas listen to most often while shopping at different store locations. These songs or stations could also be recommended to other Luxury Shopper Personas, such as in a pop-up menu that says “Users similar to you also like…”
This context is perfect for different types of users. Categorizing users into Personas and serving them recommended stations based on what other users in the same persona are listening to can help them to discover more easily. For example - College Student Personas may be listening to different music than Business Travellers.
Music apps could conduct a background analysis of each of their users’ behaviors to discover if there is a correlation between where users like or skip certain music to serve up automatically. Then, they could deploy different “Modes” that cater to each of these locations to better serve their users when they want to hear different types of music.
For example, certain playlists could be served up when users are on the train, at the gym, at work or shopping. Some music apps already have playlists or stations like “Workout” and “Studying” for users to choose from, but what if they were able to appticipate when users would want to listen to each type of music? This could work when a user walks into a gym with a pop-up menu that prompts “Go to Gym Mode?” and starts playing the music or station the user loves to listen to while they are working out. If the music app already knows a user’s Persona, the “Recommendations for Me” feature could also be turned on to serve up fresh new suggestions based on their device behavior.
Many music apps have already developed a dynamic app that allows for the evolution of their users’ playlists and station preferences. Enhancing user intelligence with a layer of context could not only open new and more precise advertising opportunities, but also delight users in giving them relevant experiences. These relevant experiences can be both within their musical preferences and in the context of their everyday lives - where in some cases, music is a constant.