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.
Enterprises must prioritize the development of a consistent data model that goes across the entire organization; they must be able to capture, codify, and access all customer interactions and all supplier interactions. That will allow them to compete much more effectively than if they maintain the fragmented systems that often result from growth through acquisitions or growth via market-focused divisions. Operating in silos may have been efficient enough at the time, but now, it’s a real liability — especially when competing with digital-native firms that have committed to a common data model from the start.
Staffing firms might also consider how platforms can enhance trust by collecting and providing more information to participants. Job platform Wonolo, for example, offers a robust recommendation and rating system that can generate value for parties on both sides of the transaction: Employers can rate an individual based on whether they showed up on time and worked all the hours contracted, for example, while workers can rate employers based on factors like timely payment of wages or safe working conditions. This ability to offer bidirectional ratings, much like an Uber or an Airbnb, can generate significant value.
At the same time, companies developing platform strategies must invest in governance to monitor network effects, creating positive ones and screening out bad actors. Failure to do so could mean losing the trust and confidence that keeps people on the platform.
AI and algorithms are important tools for screening, but enterprises in the staffing business should proceed with extra care when applying them to the task of evaluating candidates. There are real concerns about the risk of denying opportunities algorithmically. They should guard against algorithmic biases, even unintended, that may effectively deny opportunities to people based on characteristics such as gender, race, or geographical origin. Nonetheless, companies must invest in and can offer value by capturing data in a reputation system that allows people to get better job and employment offers and developing recommendation systems to identify training opportunities that can help in career development.
While incumbents in staffing are confronting new competitors that have vast amounts of data at their disposal, companies in the sector do have domain expertise at their advantage. When they apply data, they should be able to do it more effectively than a technology company.
This article is excerpted from Executive Scholar Exchange “Transforming to Compete With Data: Strategies to Win in the New Staffing Industry Landscape.”
Geoffrey Parker is a visiting scholar and fellow at the MIT Sloan Initiative on the Digital Economy and a professor of engineering at Dartmouth College. He co-chairs the annual MIT Platform Strategy Summit.