2 minute read
wallet research I
Market Survey
I conducted a survey into the current usage of wallets. By using an anonymous survey, I would be looking to find out what a typical user looks for in a wallet. The survey consisted of the following nine questions: • What is your approximate age?
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This multiple choice question broke age down into categories in order to get an insight into the changing of wallet use with the user’s age. • What features do you value most in a wallet? This tick-box question allowed users to check multiple things that they found important in wallet design, as well as add their own opinions in the form of an other box • How happy are you with your current wallet - This linear scale question was designed to show me the potential market for my product, as if people are already happy with their current
wallet, there’s no space for a new one on the market. • What, if anything would improve your current wallet? -This short answer question encourages people to air their gripes with their current wallet, showing me potential pitfalls in current wallet design. • How many cards/notes or tickets/coins do you usually carry in your wallet? - This set of three short answer questions would help me look at the size and features that are desirable in a wallet. • Is there anything else standout that you carry in your wallet? -
This short answer question was designed to catch any items that I didn’t originally think people would carry with them.
Initial Analysis
I got a good reception to my survey, with 17 responses over 2 days. This produced some very useful data that I can use to influence my design decisions as well as outline a second, amended PDS. From my initial question, I found that a majority of my survey respondents were under 21. This isn’t ideal as my target market for this project is completely different. However, as can be seen from [Venkatasamy, Nithyaprakash. (2015)] this is the age bracket that has the most impact on the fashion choices of the rest of the population, so I have chosen not to disclude them from the results, and treat the data as one class.
