Exploration: AI Shopping Experience
An AI Shopping experience exploration.
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A take on how can AI and machine learning help in making the shopping experience easier and reliable.
Targeted pain points
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1. Shopping for multiple items can sometimes become so much messed up and clumsy, and sometimes the user might even miss a few things that they intend to buy or might need the help of with the items to complete what they are trying to achieve.
Ex: Furnishing a room, Buying a good pair of pants and a matching shirt which might go well with a specific set of jacket or shoes or a tie.
2. While searching for multiple items sometimes a user needs to make sure that the things they purchase go well with each other.
Ex: A matching pair of pants for the shirt a user put in a cart, A storage box for the cabinet they just chose to buy.
3. A user sometimes has an idea of what to buy but has no idea of where to start and is looking for some inspiration or ideas to get what he wants.
4. The search results consist of many unnecessary items that are not specifically useful for the user and his search.
Ex: A user might have a smaller storage cabinet in his list and is looking for a storage box that goes with it and fits in the cabinet. Showing some random large size boxes in the search results on the top doesn't help the user for what he is searching.
5. Sometimes user knows what exactly they need to buy and what they are looking for. But the filters that are present have so many options and makes it inefficient and makes the user annoyed as the user has to search all the way through the options to find what he is looking for.
Ex: A user is looking for a pair of pants from Calvin Klein and Hugo Boss. The user has to go to the filter and search Calvin Klein and Hugo Boss among hundreds of brands the filter provides. This method even though it works makes it inefficient.
Implementation
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1. An updated search where a user can add a sub-item to further filter out the results
2. Proper suggestions and sections to provide all the necessary options to the user.
3. Pushing the cart to the top to show what items a user has in his cart and keep track of the items and suggest new items that go well with them.
4. Providing a complete set of options for the user to complete any type of set that the user is trying to complete.
Ex: Furnishing a Living room. We provide the user with all the appropriate items that go in the living room and keeping track of the users' activity and providing necessary conditions along his way.
5. Keeping a track of the activity also helps us in providing appropriate suggestions of what goes well with what.
Ex: When a user selects a small room, we can know that all the items he would expect are the ones that fit well in a smaller room. So, showing a big 5-seat sectional sofa would be inappropriate.