From MIT SMR Custom Studio
Like established companies in many industries, incumbent players in the staffing and recruitment sector are encountering a competitive landscape transformed by platform businesses.
New platforms that have sprung up to connect companies with workers include online freelance marketplaces such as Fiverr, TaskRabbit, and Wonolo. While Facebook and Google are seeking a cut of recruitment advertising revenue, Microsoft-owned LinkedIn is challenging staffing firms by offering job listings and recruiter services fueled by well-maintained data. With its emphasis on professional networking, LinkedIn gives users motivation to maintain current information about their credentials, providing a rich view of where they fit into the economy and the jobs they’re qualified for.
To develop their capabilities in a platform economy, traditional staffing enterprises need to make better use of their own valuable data assets. Based on what they know and capture about both their customers’ workforce needs and job candidates’ qualifications, what new revenue streams can they create? For example, they might use in-depth knowledge of an employer’s resource needs to create road maps for workforce skills development that will generate value for that organization. When training and education providers participate in the ecosystem, staffing companies would generate revenue via recommendations that are implemented.
Using data effectively is key to efficiently matching supply and demand, the core of any platform strategy. With more and higher quality data, a company does a better job of facilitating that match. However, many traditional enterprises are not leveraging data from across the whole business, and their analytics capabilities are designed to optimize current, not future, business models.