A lot of our buying decisions are influenced by people we know- friends/ family/ acquaintances. Inspired by a personal experience, I wanted to understand the role that recommendations from known people play in making a purchase decision.
As part of my MS capstone project, I conducted ethnographic research to understand this in case of high-involvement products i.e. products which require extensive research and are expensive, like camera, car, smartphone, vacations. Worked upon the conceptual designs to assist e-comm users make better buying decisions.
Some data insights/mental models
Persona Development:
Interviewees are mapped to behavioral variables and then patterns identified.
Persona: An example
A snapshot of Insights and opportunities
Some ideas to
•Integrate friend recommendations into e-comm sites like Flipkart
•Translate technical features into lay terms to help a non-tech savvy user
•Translate technical features into lay terms to help a non-tech savvy user
Like ‘Featured books’ section, there can be a ‘View Recommended’ section where a user can view recommended products from friends, experts, or from other customers.
Sorting can be based on user preferences like a mobile which fits jeans pocket.
Technical features can be translated into lay terms to help a non-tech savvy user, who doesn't know Micro from Mini USB, or Normal from Wide selfie.
