Critiques and use of technology

During the D&AD project, we had weekly stand up critiques of our work to assist us along the way. In some cases, these standups were really what bought ideas together after discussing them with our classmates, or challenge network.

Having access to people who understand design when working as a freelancer is not always possible. Yes, it’s possible to get some feedback from clients and from friends or family but not all feedback is good feedback (even if it’s good feedback).

With the BBC Sounds project, there were a lot of points where good critique helped me to make some choices about the direction I decided to take. I was used to working on briefs with very little details from the client, often making it work as I went along and showing them changes to see what they thought.

Starting from the beginning, the first critique we looked in detail at the brief trying to narrow down into two words what our chosen brief was trying to achieve. The BBC Sounds brief had one simple message; Listening for Everyone.

Moving into research, we looked further into the client and their competitors as well as target audience and trends. In a previous blog, I spoke in more detail about the BBC and Podcasts but to summarise the BBC has a large user base (over 300M) and podcast listening is increasing each year especially in the young adult age groups.

 

When thinking about design, I would usually jump straight into research. This project was no different however the scope of the research was different. I had to do a lot more research in a shorter time frame than I was used to. Having a weekly critique felt like I had no time to stop and think over things, and this pressure to bring something new to the table each week pushed me further.

The biggest idea I took away from all the critiques combined was the inclusion of voice and machine learning when meeting with Janja one to one. This idea developed further into my final idea. After the meeting I went away and investigated various ways in which voice works, and how Netflix / Spotify can recommend based on tastes and preferences of each user. One of the biggest challenges was to come up with a way to differentiate, and to improve the way searches work to provide relevant and interesting content to a user.

Much of the technology which the BBC is searching for through it’s brief is already in the market and available, either through open source software or implemented into services already, in this case the only thing left to do is to take advantage of the technologies and implement them in a way which benefits the user.  As an example, voice-based searching functions have been part of the “Sky Q” TV packages for a while, and voice assistance such as Amazon Alexa and Google Home are able to search based on what a user says, although sometimes specific phrases must be used.

One of the key aspects used by Spotify and Netflix are metric data for taste profiling. The basic idea is that if you listen to an artist whose genre is ‘hip-hop’ you will be more likely to listen to other artists also under the label of ‘hip-hop’. However, where there is a small disconnect is that this is a very broad label, and not all artists are the same. Personally, I like listening to storytelling rap, and there are artists who specialise in this, but if I listen to the occasional pop-rap hit, then my playlists are automatically filled with these as they are shown preference.

This is something that could differentiate the BBC Sounds app when it comes to listening to radio content, the use of more strict filtering to make sure there’s similarity in playlists or the ability to control this to allow the user to decide how broad into a genre they want to go.

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