Michael Cao | Mood-Filters
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Mood-Filters

What if mood/occasion was a filter for online retail or hospitality apps? Like the case of Spotify, where pre-set playlists are generated by mood, online retail can leverage this for optimal product placement. Imagine being directed a curated collection per occasions such as: first dates, interviews, etc.

What will this look like? Imagine being able to sort the collection of your favourite clothing brand by your mood or logging onto an experience website such as Airbnb and filtering by occasion? What about just being able type in “dinner party” on a box-store website and have all the essential items for one appear. For some, this might simplify the search process and save both valuable time and “decision making” brain cells. For retailers, this might be an opportunity to bring high probability conversion items directly in front of your valuable “decision making” eyeballs.

A simple reframe into being able to search and filter products by mood and/or occasion, so that the perfect product(s) just appear

As this is a curation-heavy endeavour, this “what-if” is already beginning to emerge in many industries. For Spotify, it’s as easy as grouping songs based on EQ and audio profiles using AI and human crowd-sourcing; for box stores, it’s just a matter of tagging the products under various categories. An opportunity for further research to elevate this type of sorting is a qualitative one on how people describe their moods. On average, people can only name around 9 moods off the top of their heads, and so there is some interesting white space to explore into how people define these 9 moods.

As retails moves into more and more consumer-centered design, there will undoubtedly be a shift in sentiment towards creating personalized or personalized-apparent content. This filter based on very human emotions will just be the start.

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