Onboarding PREDICTIVE ANALYTICS REPORT

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ANALYSIS

What You Need to Know

  • Breakouts in the Onboarding predictive analytics are Human resources, Drug test, Paid time off. Seriously consider these technologies to gain a strategic advantage.
  • The technologies who are at the peak of their interest are Dress code, Drug test, Shift work.
  • By far most employment needs are found in the Life insurance, Senior management, Retirement technologies.
  • These 3 fields have the most active practitioners who have the specific skill set or experience: Recruitment, Internship, Mentoring.
  • Minimum wage, Life insurance, Internship lead in searches for information online.
  • These three technologies are receiving the highest investments to gain clients: Life insurance, Onboarding Software, Workers’ compensation.
  • These three technologies have the most active advertisers: Workers’ compensation, Work accident, Gap year.
  • In patents, these three technologies have the most coverage Productivity, Recruitment, Restructuring.
  • The most publications are available for Youth unemployment, Human resources, Professional development.
  • Instruction and courseware availability is highest in these technologies: Job interview, Recruitment, Retirement.

The Onboarding report evaluates technologies and applications in terms of their business impact, adoption rate and maturity level to help users decide where and when to invest.

The Predictive Analytics Scores below – ordered on Forecasted Future Needs and Demand from High to Low – shows you Onboarding’s Predictive Analysis. The link takes you to a corresponding product in The Art of Service’s store to get started.

The Art of Service’s predictive model results enable businesses to discover and apply the most profitable technologies and applications, attracting the most profitable customers, and therefore helping maximize value from their investments. The Predictive Analytics algorithm evaluates and scores technologies and applications.

The platform monitors over ten thousand technologies and applications for months, looking for interest swings in a topic, concept, technology or application, not just a count of mentions. It then makes forecasts about the velocity of the interest over time, with peaks representing it breaking into the mainstream. Data sources include trend data, employment data, employee skills data, and signals like advertising spent, advertisers, search-counts, Instruction and courseware available activity, patents, and books published.

Predictive Analytics Scores:

399846 – Human resources
103111 – Drug test
097139 – Paid time off
054256 – Dress code
007834 – Shift work
006258 – Employee handbook
005742 – Maximum wage
004900 – Termination of employment
004407 – Sick leave
002712 – Overtime rate
002168 – Minimum wage
001967 – Life insurance
001653 – Company culture
001513 – Work ethic
001487 – Working time
001330 – Job fraud
001320 – Job performance
001289 – Retirement
001112 – Employment contract
001051 – Senior management
000845 – Parental leave
000816 – Job interview
000797 – Living wage
000593 – Curriculum vitae
000592 – Mentorship
000569 – Professional certification
000523 – ADP SmartCompliance
000513 – Independent contractor
000487 – Full employment
000443 – Professional development
000406 – Tradesman
000382 – Leave of absence
000369 – Salary cap
000315 – Workplace harassment
000300 – Productivity
000281 – Reflective practice
000261 – Job fair
000227 – Notice period
000208 – Recruitment
000147 – Organizational performance
000140 – Professional school
000139 – Organizational communication
000137 – Organizational culture
000133 – Skilled worker
000131 – Employee benefit
000129 – Mentoring
000113 – Organizational conflict
000104 – Workers’ compensation
000100 – Onboarding Software
000095 – Retirement age
000090 – Recommendation letter
000084 – Work accident
000084 – Sabbatical
000074 – Great Recession
000072 – Social class
000064 – Exit interview
000060 – Evernote
000053 – Underemployment
000052 – Employability
000051 – Resignation
000046 – Internship
000045 – Creative class
000044 – Letter of resignation
000043 – Suggestion box
000034 – Wunderlist
000019 – Downshifting
000015 – Supernumerary
000010 – Restructuring
000008 – Gap year
000007 – Workaholic
000006 – Schedule (workplace)
000005 – Youth unemployment
000004 – Workplace bullying
000002 – Induction programme
000002 – E-recruitment
000001 – Labour law
000000 – Types of unemployment
000000 – Slow living
000000 – Severance package
000000 – Organizational citizenship behavior
000000 – Minimum Wage Ordinance
000000 – Long service leave
000000 – Long Depression
000000 – Constructive dismissal
000000 – BookedIN
-1.78098176E+9 – Hierarchical organization


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