AI-Driven Recommendation Engine Boosts Engagement for Fuzu

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Fuzu, a Helsinki-based company, provides job opportunities, career advice, and new skills to young East African professionals. The company faced a challenge in streamlining its user onboarding experience and boosting engagement through personalized recommendations. The onboarding process was cumbersome due to the need to obtain considerable amounts of information from new users. Additionally, Fuzu wanted to leverage the data from pre-written resumes, cover letters, and diplomas available in the form of text documents to facilitate the onboarding process. The company also aimed to keep users engaged with personalized offers and recommendations. However, the question was how to leverage the data and system to provide additional benefits to the customers.

Fuzu is a Helsinki-based company that launched a comprehensive career platform for the East African job market in 2015. The company aims to provide ambitious young East African professionals with job opportunities, career advice, and new skills. Fuzu serves a wide range of job seekers, but a typical user is a young university graduate in a major city, such as Nairobi or Mombasa, who may or may not already have some work experience. Fuzu is often a launch pad for people to land their first job. The company currently serves more than one million users in Kenya, Uganda, and Nigeria.

Fuzu partnered with MindTitan to create machine learning models that would provide users with a streamlined, hassle-free onboarding experience and relevant job recommendations. MindTitan used the datasets from Fuzu to build and train machine learning models. Free text extracted from the resumes and cover letters uploaded by users was classified into skills, seniority, industry, and education level among others. This not only facilitated the onboarding process but also enabled the platform to provide job recommendations that fit the users' needs. Fuzu also started sending out regular emails with personalized suggestions for jobs that candidates may like to apply for. The platform made recommendations based on users’ previous interactions with the platform, including past job applications. Fuzu also used categorization algorithms to identify and post content from partner platforms that is of interest to their users.

• The implementation of machine learning models and the collaboration with MindTitan enabled Fuzu to streamline its onboarding process and provide personalized job recommendations. This led to an increase in user engagement and satisfaction. The platform was able to provide companies and headhunters with potential employee profiles with the skill sets they’re looking for. The use of categorization algorithms allowed Fuzu to spend less time on identifying and posting content from partner platforms that is of interest to their users. The company was able to recognize specific industries with a high level of accuracy and pre-fill lots of fields, like educational level and job category, thanks to these predictive models. The collaboration with MindTitan also laid the groundwork for Fuzu to explore new areas where they can use these technologies.

• Click-through rate for job applications increased by 30%

• Classifies education level with 90% accuracy

• Enabled Fuzu to become increasingly data-driven