ThinkWhy, a Dallas-based SaaS organization focused on creating a new generation of AI-driven labor market solutions, has introduced a new innovation to their popular LaborIQ platform that provides talent acquisition professionals a powerful tool to attract and retain employees. The industry-first solution delivers instant, market-driven annual salary and variable compensation answers for over 20,000 jobs, giving users the ability to uniquely customize a compensation package for each job title, adding variable compensation details, and allowing the user to create job profiles with skills and responsibilities reflective of their needs.
Every employer has unique job titles, skill requirements and compensation offerings, including bonuses and varying levels of pay incentives for their total compensation packages. And whether attracting senior executives, in-demand technology roles, sales personnel or hourly staff, the LaborIQ Total Compensation feature creates customized flexibility for each employer’s talent strategies. The platform provides rapid market-driven answers and gives HR and talent acquisition professionals the advantage in the competition for recruiting.
Beyond Salary – Total Compensation Data Represents an Unmet Need
Previously, to create compensation packages, an employer had to sort through multiple sources or rely on old methods of crowd-sourced wage data. With shifting demands in working-age populations and changes in net migration across the U.S., those spotty methods of data collection will prove insufficient in today’s competitive market. Gallup found that turnover could cost the average business with 100 employees from $600,000 to $2.5 million per year. With key roles in management, technology, and healthcare with unemployment at 2.8% or less, businesses must take action to ensure their compensation is competitive as demand for labor surges.
“One of the most common struggles talent acquisition professionals have during the offer stage is helping candidates compare and understand total compensation. These are rarely ‘apples to apples’ comparisons,” said Jim D’Amico, global talent acquisition leader at Fortune 400 chemical and manufacturing company, Celanese. “This new tool allows us to quickly and easily prepare an easy-to-read chart to provide to candidates. This is a game changer!”
With LaborIQ Total Compensation, organizations gain the ability to efficiently select specific compensation features unique to their needs, skill and education requirements, industry, company size and all U.S. metros. Users receive full calculated values with market-driven salary demands for over 20,000 job titles.
“We forecast and report on the U.S. labor market, and with over 70 million hires projected for this year, you can imagine how few of these roles are the same, each tailored to the demands of what employers need,” said Claudine Zachara, President and Co-founder at ThinkWhy. “We interviewed recruiters, HR talent acquisition professionals, and employers from all over the country to develop a better approach to talent intelligence. The mission is to connect people to opportunities, faster. This is design-thinking at work to solve unmet recruiting and talent management needs.”
Reports show that every city and industry in the U.S. is recovering at different rates, and some occupations are seeing significant escalations in wage demands. Now hiring professionals have rapid answers to compensation demands for every city, industry and role in the U.S. Users save time on creating job descriptions with alignment across the company for the specific skill requirements each role needs and accessing the right total compensation answers to attract qualified talent.
“As a growing technology company, we had our own compensation packages to consider in order to win talent,” said David Kramer, Chief Technology Officer for ThinkWhy. “We developed a proprietary, intelligent machine learning algorithm that synthesizes our research and validation methods for millions of data points in every metro in the U.S. There is simply no source more accurate than LaborIQ.”