By: Jean-Paul Isson, Global Vice President BI & Predictive Analytics, Monster Worldwide
Today’s global explosion of digital information, big data and predictive analytics is not only affecting the way organizations and businesses manage their customers and consumers, it is changing the way they look, find and retain top talent. In fact, the research firm, Gartner, predicts data will grow by 800 percent within the next five years — and 80 percent of that data will be unstructured (emails, social media posts, feed videos and resumes).
There is no doubt that big data represents a challenge and an opportunity for recruitment teams and staffing firms, and talent analytics is becoming a key component to creating a successful talent management process.
At Monster, we see the exciting opportunity to mine big data and use it with many of our forward-looking firms. We are looking forward to working with our customers to effectively leverage the power of predictive recruitment analytics solutions and big data intelligence technology to optimize their talent pipeline and resource investments.
What Predictive Analytics Means to the Staffing Industry
Traditionally, predictive analytics has helped companies to address the basic business questions of “who, when and why.” However, when applied to the staffing industry, predictive analytics helps to anticipate and optimize:
Talent acquisition: helps to identify who is the top talent? When should they be contacted? Why is this requisition/ job opportunity attractive to this top talent?
Talent pipeline planning: Predictive analytics can optimize a talent pipeline by leveraging macroeconomic and talent data to ascertain key factors that can lead to better resource allocation. For instance, identifying the best locations to invest in recruitment campaigns for certain skills.
Job-response optimization: During the recruitment process, predictive analytics helps organizations optimize their job-postings response. Data analysis can provide companies with custom recommendations and tailored best practices to help firms achieve better responses to their jobs postings based upon factors such as duration, location, occupation, and industry.
Customer acquisition: A staffing firm’s talent database is their proprietary competitive advantage and sales tool. Therefore with the power of predictive analytics to harness a staffing firm’s big data and provide valuable insight into the talent on hand, a firm is empowered to drive future sales conversations directly aligned to the talent they have.
What Big Data Intelligence Means to the Staffing Industry
To process, manage, and optimize the exponential growth of resumes and other talent data coming from multiple sources, Staffing firms have to leverage big data intelligence technology to fully understand and maximize their recruitment metrics. The benefits of performing this type of deep-dive analysis include:
Better awareness of cost-per-placement: This can improve recruiter productivity by leveraging the technology horsepower.
Analysis of the quality of the candidate: This can help recruiters to efficiently find a broader range of candidates they would not find using traditional search methods.
Improve the time-to-fill, as well as the fill ratio: This can reduce search time, provide accurate candidate ranking that leads to matching the right talent to the right job offering.
Predictive recruitment analytics and big data intelligence tools are changing the way organizations view, analyze and harness their talent data. Leveraged efficiently, predictive analytics allows staffing teams to create economic value from their talent data, helping them become more competitive and, ultimately, more successful.
Jean-Paul Isson recently co-authored an Amazon.com bestseller book in 2012 (Win with Advanced Business Analytics: Creating Business Value from your Data, Wiley, 2012.) He is an internationally recognized speaker and an expert in advanced business analytics. Mr. Isson is Global Vice President of Business Intelligence and Predictive Analytics at Monster Worldwide, Inc. where he has built his global business intelligence team from the ground up and successfully conceived and implemented global customer scoring/segmentation, predictive modeling, and Web mining applications building across North America, Europe, and Asia-Pacific.