This study is designed to examine how freelancers are looking for their next job, unmet needs, proof of concept, distress of job seekers, what features are important to users, attitudes towards the product, target audience identification, the correctness of use, concept differentiation, application name and publishing platform.
For this research I use quantitative statistical method. The population is freelancers seeking jobs, aged 21+, professionals with technical skills. Size of sample: 60 peoples, 30 women and 30 men. 41 people have an academic education, 4 people have a high school education, 6 people have a tertiary education, and 9 people have a profession education.
The important features are sending CVs, receiving information about the reason(s) for failure, having a conversation with colleagues and checking the status of the application. There is a gap between the ideal search and the actual one. On the one hand, the respondents claim that an ideal job search is through friends and the Internet. On the other hand, a large part of it is done by employment agencies.
According to the breakdown by education, the most salient problems relate to the difficulty of emphasizing oneself over other candidates and the difficulty in talking with colleagues in the professional sector. The app’s attributes increase the likelihood of acceptance for candidates by knowing the reason(s) for refusal and exposure to relevant professional content.
Most respondents are positive about the app concept and therefore are willing to use it. Exposure to educational content and possibility of uploading self-video are features that vital for the application. Also, the best places to advertise the app are technology content sites, Facebook groups and YouTube.
In order to map correctly all user steps in his app journey, I started with initial flow for the unemployment user. Then, I was need to feel the app more tangible so I’ve moved to wireframes.
When a person sends a CV, in fact he provides us with all his profile. From their reading, we can deduce many things, for example, what qualities are strong for him, does he aspire to excellence? Does he have the ability to self-study? Today most of the CVs are sent in Word and PDF format, but we had limited resources, and we could not decipher the information inside the files to infer about the professional profile and submit appropriate proposals
Our solution was to build the professional profile in stages. To understand who the candidate and his abilities are from two kinds of information: Declared Information – This information is provided to us by the user at the time of registration. Aggregate information – This is information that is extracted from the user’s activity in the app.
At this point we have solved the problem of resources, but now we have faced a new problem. Mobile users do not like to type, it is an action that requires great effort, so they will not rush to give us the information declared. Considering that a professional profile contains lots of information and requires typing a lot of text, the problem becomes even more difficult.
So how we can start building a professional profile that does not require a lot of typing? The solution we thought about was to divide the registry into three short tasks, this will reduce cognetive effort. In each task we ask for the most necessary information to help us display matches.
A quick prototype showing declared information in three steps with minimum typing.