As tech redefines businesses’ workforce strategies, it is also redefining the business of providing workforce services. The following perspectives from industry and academia suggest how staffing companies can innovate to thrive in the face of new competition.
An executive perspective by Nagaraj Ijari, VP and Global Head, HiTech & Professional Services, TCS
Businesses’ workforce strategies are undergoing a seismic shift in response to the rise in automation, the increasing ease of hiring “just-in-time” short-term workers, and the scarcity of the right talent. That shift has created both opportunities and challenges for companies in the staffing and recruitment sector. To meet the evolving needs of their customers, they must now deliver more customized services, and do so more quickly and efficiently than ever before.
New Business Models, New Competitors Disrupt the Staffing Industry
Temporary staffing, especially for industrial and office workers, has become commoditized, limiting the pricing power of even large, established players in the industry. The traditional, recruiter-centric business model carries not only a high cost of operations but is also less relevant, as online social networks displace recruiters’ personal connections as a source of candidates, particularly when hiring for highly skilled professional roles. Corporate customers may value the ability to fill jobs quickly with quality candidates more than they prize a long-term relationship with a staffing company.
At the same time that their customers’ needs are changing, staffing companies face new competitors that include AI-powered startups, gig-economy platforms, and tech giants such as Google, Microsoft, and Facebook.
Startups in the staffing and recruitment industry are using social media, mobile technology, and advanced, AI-based analytics to better serve the needs of candidates and customers, while keeping operations lean and highly automated. Many succeed at winning market share from established competitors by focusing on particular regions or industries.
Meanwhile, the gig-economy platform companies threaten to cut staffing services out of the equation by enabling a direct connection between job providers and job seekers, especially in the freelance and short-term labor marketplace. And the big tech firms are looming over the staffing market: Microsoft’s 2016 acquisition of professional network site LinkedIn has given it a strong presence, while Google offers job posting and search with Google for Jobs and its applicant tracking system Google for Hire. Facebook provides the ability to post and apply for jobs directly via its social media platform. All of these developments have an impact on the business of traditional staffing companies.
Playing to Their Strengths — Starting With Data
Rising to the challenge of industry disruption will require traditional staffing companies to recognize and make the most of their competitive differentiators. The most important ones are, first, their proprietary candidate databases and, second, long-term relationships with customers that rely on their contingent staffing platforms. Despite the changes in customer needs, relationships will remain an asset and an opportunity if staffing companies make good use of the knowledge they have amassed over the course of the relationship.
Fully tapping the power of candidate data assets will require technology that can speed the process of searching, matching, acquiring, and nurturing talent — finding the right candidate for a particular job, gaining their interest, and building a relationship. And staffing companies must do all that more quickly than a competitor. Some may bring in enabling technology by acquiring or partnering with a startup, while others build their own platforms and solutions.
Regardless of how they choose to equip themselves with technology for the journey, staffing companies should have a road map to data-driven recruitment. The advantages to be gained are numerous, including:
1. Improve time to fill and the fill ratio. Data analytics can help reduce the time it takes to search for candidates by assigning an accurate ranking to candidates and helping to match the right candidate to the right job offer. This will help improve the critical metrics of time to fill (the period between a job posting and an accepted offer) and the fill ratio (the ratio of job orders received to job orders filled).
2. Drive recruiter productivity. Because recruiters drive the staffing process, it’s critical to have data-based insights into their performance. Building a culture of continuous improvement requires tracking productivity metrics such as new candidates brought in, placements made, revenues earned, and margins achieved.
3. Identify and analyze talent sources. Data analytics can help staffing companies identify and reach a broader range of candidates, including those who are hidden from traditional sources. It can also identify the most promising talent, indicate when those individuals should be contacted, and highlight why a given requisition or job opportunity may be attractive to a particular individual.
4. Maximize job posting responses. Predictive analytics can help organizations improve the response to their job postings during the recruitment process. Data analytics can be used to provide custom recommendations and identify best practices to maximize job posting responses based on factors such as industry, place, time, and occupation.
5. Attract and retain customers. The internal candidate database is a staffing firm’s proprietary competitive advantage and an important sales asset. A company can drive sales discussions by showcasing how it uses the power of predictive analytics to harness big data and gain valuable insight into its talent pool. Recruitment firms can also use external sources of data to proactively identify client requirements and reach out to prospects. For example, using relevant information collected from online sources, they can identify business events and map them to potential staffing opportunities.
6. Improve insight into costs and return on investment. Data analytics can help reduce cost per placement. It can also help identify which candidates and types of assignments lead to the greatest return on investment.
If staffing companies can build on their brand value, leverage the assets they have nurtured over time, and adopt new business models enabled through technology, they can effectively defend and grow their market share.
Staffing Firms Must Use Data Assets to Compete with New Platforms
A scholar perspective by Geoffrey Parker, Professor, Dartmouth; Fellow, MIT Initiative on the Digital Economy
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.