LinkedIn and Monster Look to Gain an Edge from Innovative Recruiting Tools
Competition between LinkedIn (NYSE:LNKD) and Monster (NYSE:MWW) has sparked innovation in the recruitment solutions business. LinkedIn has introduced quite a few new products to help companies and recruiters find talented candidates via LinkedIn’s network of professional profiles. These tools include LinkedIn Recruiter, LinkedIn Referral Engine and LinkedIn Recruitment Media to name a few.
On the other hand, Monster is ramping up its efforts to improve resume search by incorporating its intelligent search technology (Semantic 6Sense) into the next generation of products for both employers and job seekers. These two companies also compete with sites like CollegeRecruiter.com, Naukri.com and efinancialcareers.com.
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We currently maintain a $30 price estimate for LinkedIn stock, well below the +$100 market price that shares hit within the first few hours of trading on Thursday.
New Products Bolster Recruitment Service Revenues
LinkedIn generates revenues from client companies that purchase advanced recruitment solutions from LinkedIn. The average revenue per client company has declined from around $26,000 in 2008 to around $22,000 in 2009 and 2010 as the mix of smaller customers has grown. These customers typically spend a bit less than larger companies.
However, we believe that continued innovation will benefit LinkedIn by maintaining pricing levels over the long term. For example, LinkedIn introduced LinkedIn Recruiter, which is the company’s flagship hiring solutions product that enables enterprises to find, contact and hire highly qualified candidates. The service allows recruiters to search and view every profile on LinkedIn.
The company also introduced LinkedIn Referral Engine, a tool that enables enterprises and professional organizations to leverage the networks of their employees to find qualified candidates for open positions. Similarly, Monster’s Semantic 6Sense is a search technology that is designed to be smarter than traditional keyword search by incorporating more weighting factors for searches.