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Stage 2: Investigation
User Research Methods, Justifications and Reflections
Interviews
Our first IDEO method was interviewing individuals. Our open-ended question format, personalized engagement with the interviewee, and selection of questions allowed us to better understand people’s music exploration preferences and their thoughts on existing music platforms. Learning why people prefer different platforms, how they categorize and find music, and what they would change about existing platforms was particularly valuable. As each question allowed for a fair amount of elaboration and follow-up, the interview format was ideal to flush out answers and get useful and actionable information. Additionally, recording the participants’ responses in a Google Form enabled us to easily compile and compare answers to find common trends. Nothing went poorly, however some questions may have been redundant or did not provide particularly helpful data. For example, we found that 60% of people do not consider cover art when seeking new music, though with a small sample size, this statistic is not that informative or convincing. If we could, we would perform more interviews because our sample size was quite small and may not accurately represent our userbase.
Secondary Research
Our second IDEO method was conducting secondary research. Each team member read and summarized two research papers that described unique systems for visual music exploration. This allowed us to investigate how these systems were programmed and what considerations and attributes the developers found were important for such exploration tools. The summarization of articles enabled us to quickly compare them and pick out the important components in each. This knowledge could be incorporated into our solution to provide useful functionality and address issues in popular consumer music platforms. This helped us understand the variety of options not currently available to the public, their configurations, and the extent of their functionalities as well as consideration to take away from them. We noted elements that were valuable and could be used in our solution and ones that we disliked that should be altered or discarded. Similarly to the interviews, nothing went especially poorly, though we were not able to thoroughly search the existing literature so there may have been useful studies that we missed. If we could, we would review more articles concerning music exploration systems and further discover existing platforms to assess.
Task Descriptions
Micheal is a university student. He’s studying and wants to find music that will help him focus. He knows that he wants relaxed music, so he starts by looking through his music library for a chill song. He finds John Lennon’s Imagine, which is ideal, and sets the system to keep playing songs with a similar tempo. After 45 minutes, he finds the playlist has become too slow, but earlier songs were perfect. He looks at his music history and finds that a previous song was Billy Joel’s Piano Man. He then begins playing songs with similar themes and tempo to Piano Man. As he’s finishing studying, he finds himself listening to a new song he really enjoys and adds it to his library before closing the app.
Brittany is a 25 year old banker who wants to kick back and relax. She normally likes listening to pop music, but today wants something different, so she puts a random song on. She likes the beat, but finds the style too reggae. She wants a song with a similar feel, but closer to music she’s used to. She follows one of the branching paths generated by the system and listens to Margaritaville by Jimmy Buffet. She wants more songs by Jimmy Buffet so she gets the platform to play his collection randomly shuffled.
Sona has recently gotten into Japanese punk, but she cannot read Japanese, so it’s hard for her to find new songs in the genre. She likes Omega Rhythm by Uplift Spice and wants similar songs to add to her library. She flips through songs in the genre until she finds ヒステリックナイトガール feat. Such by PSYQUI, which she enjoys. Sona wants to add it to her library, but fears it will get lost in her other Japanese songs, so she attaches a note to the song indicating it has a “catchy beat”. She then finds other songs with similar rhythms and categorizes them so they can all be found through their mutual notes of “catchy beat”.
Ken has a song that’s stuck in his head that he can’t put a name to. He knows how the beat goes and it reminds him of Uptown Funk, so he searches for this song. He then traverses connecting paths with songs in the same style, but with slower tempos. He goes through 5 songs before This is America by Childish Gambino appears, which is really close to what he’s thinking of. He looks at other songs by this artist and finds Redbone, which is the song stuck in his head. He adds it to his library and pop playlist.
Kate has just been through a bad breakup and she wants music that relates to her situation and mood. She looks through her library and starts listening to Bad Blood by Taylor Swift, however the other songs in this album and in her pop playlist are not breakup songs and do not convey the same tone. So Kate traverses the system for songs with lyrics about heartbreak and gets numerous suggestions. She filters them based on their mood, depending on if the lyrics are more angry or sad, listens to previews of ones that interest her, and adds the ones she likes to a “Breakup” playlist.
Harry has an extremely large and diverse music, with self-made playlists that are poorly organized and frustrating to browse. He erases these playlists and visualizes his library on the platform without any self-imposed organizational categories. He then tells the system to organize his music by genre, language, and decade. He adds songs from each generated cluster into a different playlist with the filters specified in the titles. He deletes songs he no longer likes to declutter his collection as he moves from cluster to cluster creating fresh, updated playlists.
Sean is a wedding planner and wants to create the perfect playlist for his clients’ reception dance party. He wants upbeat, popular music guests can sing along to so he searches for songs that are considered “music to play at a wedding reception”, “fun”, and “pop”. He adds songs he recognizes and likes from the generated cluster to his playlist. Sean also knows that the bride and groom love The Beatles, so he filters the cluster for songs that are by this artist and adds them to the mix. Lastly, the wedding is taking place on a beach in Hawaii, so he adds “beachy” and “beach wedding” to the original search terms and picks out some songs that will tie in with the theme and location of the wedding while still matching the mood of the other music in the playlist.