In this project, I redesigned a website for rook records, an online record store for "music heads".
My main focus is to provide music lovers with automatic recommendations, but also to allow them to feel the personal connection between the seller and the buyer.
Music lover and records digger spending a lot of time by searching of new records and to find music they never heard before.
By redisigning the website and by creating a reccomendations funnel we want to help the user to find in easy way records they want to purchase, and help them to discover new music. The process comes to simulate the way collectors buy records in a record store and emphasizes the things that are important to them that we discovered in the study
In order to understand the strength and weaknesses of the competitors, I conducted a small competitor analysis. It helps me to understand which features are more relevant and what should I include on the website. Furthermore, it gave me some ideas about how to characterize the site and differentiate it from the competition. Due to my desire to learn about how the current version of Rook Records works and how it can be improved, I considered this version of the website as a competitor
After analyzing competitors and creating a provisional persona, I conducted a user interview.
Participant: I decided to conduct it with four interviews with different typecasting and shopping behavior.
Goal: To understand what is important to users when they buy records and how they behave in a record store
I asked the following questions:
Although the cover is important, the music takes precedence
Even though some interviewees mentioned they like to pick records they don't know by their covers. The preview song helped them to decide which album to purchase. At least 30 seconds from the song will help you make a decision.
Users want to know about the music they buy
Purchasing an album can be motivated by the information on it. It is important to give fun facts about the musician and the record, collaborations, producer, and more
Decisions are made by opinion leaders
Quote from a review by a radio host, opinion leader, or DJ can encourage people to purchase the album
Saving items for later is useful in record stores (Really helps, Not because it's standard on eCommerce sites)
Interviewees said they liked to save items and decided later what to buy. The item they did not buy due to budget constraints, they will purchase next time
Similar to physical stores, the styles and genres specialized are important
A store must have character, be specialized in a particular genre, and provide a pleasant shopping experience for its customers.
According to interviewees, the first impression they get of a record store influences their choices of what to look for.
Buyers enjoy communicating with sellers
The inability to communicate with a real person and get recommendations from a seller can hurt the buying experience. It is important to create an accurate system of recommendations that will enable music lovers to discover new music
To observe how records collectors behave when buying records, I spent two hours in two different record stores. In this observation, my main goal was to see what the process was from digging in the records to listening to them. How many records do they listen to before they purchase? How many times do they dig before they find something they want to buy.
This task flow shows how a user comes to the site and then goes through the recommendations funnel to get recommendations
After conducting the interviews I focused on one of the interviewees and built the user flow with a number of different scenarios. My goal was to put myself in the interviewer's shoes and see what would be going through his mind when he was making a purchase
The interview had the objective of understanding how users purchase records online or in-store and what factors influence their decision. Their answers help me to create the following funnel at the end of which users can receive recommendations for new music.
Test objectives:
Test goals:
With all the data and analyzing the results from the previous step, I built an affinity map that helped me understand the user's behavior and be ready to move on to iteration.
Based on all the conclusions from the previous step and the usability test, I made a few changes