Recruitment Day 2014, Copenhagen

1. 1
Predictive Analytics for HR and Recruitment
Aki Kakko
Co-founder, Head of Product
2nd of September, 2014 Copenhagen

2. Introduction 2
Aki Kakko
Serial Entrepreneur
Co-Founder, Head of Product, Joberate
• 2010 a recruitment agency that was used as a platform to explore
scalable business opportunities within the recruitment industry
• 2011 spin-off of the social job advertisement service that is now
operating as an independent company under Candarine
(www.candarine.com) brand
• 2014 spin-off of Joberate (www.joberate.com) - Predictive Analytics
for HR and Recruitment
• Partner of a globally operating HR event company GlobalHRU
(www.globalhru) & HRTechTank (www.hrtechtank.com)

3. Two quick words about our company 3

4. Two quick words about our company 4

5. A secular shift has occurred, data is now everywhere
Companies need to track external people data, in addition to their HRMS data
Attract people to follow you
Start following interesting people
attract talent to take interest
take interest in talent
Age of corporate dominance Age of knowledge workers
5

6. Big Data has become a disruptor for HR
A constantly evolving data stream that is “external” to current HRMS, holds tremendous potential
I think     
I know
(current state) (future state)
Investment Flow
6

7. So, we must start with understanding Big Data?
• Not looking for a needle in a
haystack (that’s easy…can
you spot it?)
- Looking for a unique piece
of hay in hundreds of
millions of haystacks
• Differs from tradition data in
three main ways (four V’s)
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8. 8
Source: IBM

9. 9
Source: IBM

10. 10
Source: IBM

11. 11
Source: IBM

12. Predictive Analytics increase value of HR services 12
Predictive Analytics
• Predictive models (i.e. credit score, life events)
• Probability of events and/or their timing
Data Analysis
• Statistical analysis, and relational models
• Understanding cause and effect
Dynamic Reporting
• Aggregate view of data sources
• Benchmarking or validation
(Traditional) Reporting
• Measure results
• Efficiency, compliance
• What can
happen?
• What is
happening now?
• Why did it
happen?
• What happened?
Extracting value from Big Data

13. Non-HR example of a Predictive Analytic 13

14. Q&A
14
Example business problems
predictive analytics
can help with…

15. HR related:
• Likelihood that someone will be a successful employee?
- Prediction of high performers for our organization / team
- Forecasting how competences we have meets the future needs
• Understanding people’s job seeking behaviors so that you can intervene and retain
potential leavers
- Ideal time to promote someone?
• Health and stress level of our people, trends and forecasts
• What could be good team combination?
• What drives innovations in the company?
• What motivates people?
- Rewards perspective?
15

16. Recruitment related:
• What is the ideal time to contact someone with a job offer?
• What are the best sources of candidates for specific roles?
• Automating matching of jobs with relevant CV profiles
• Developing an ideal job description that will generate interest
• How and where do we get more engaged with potential candidates?
• Who is attracted to us compared to the competitors?
• Likely length of employment?
• How to attract for diversity?
• How do I identify team players?
16

17. Individual level:
• How can I be more successful, motivated, happy, healthy?
- What success means for me?
• How do I best “trick” the system?
• How do I collaborate better?
• What competences are needed in the future and I should develop?
17

18. Q&A
18
Opportunities are only limited
by our imagination…

19. The Predictive Analytics lifecycle 19
Complements of the SAS Institute
Source: SAS Institute

20. How predictive analytics works
• Aggregate, input,
scrape, import, or
track information
sources
Information (could
be Big Data)
Machine learning
• Makes decisions
based on previously
validated outcomes
• Learn new outcomes
that will be used in
future decision making
• Feed/output data to
visualization or
rendering software
• Archive decision
results for future query
Display predictive
analytics
20

21. Overall technology hierarchy 21
Client HRIS or recruitment systems
Client’s User Interface variations
API and Web Services
Joberate Data validation services machine learning predictive analytics engine
(further explained on next slide)

22. Data validation services simplified 22
Data validation services

23. Some practical examples
Analyze any number of variables to understand employee job seeking trends
Analyze trends in specific groups
Trends view instantly shows how actively your
employees are looking for work, over a period of
time from three months to five years.
Quickly and intuitively identify cyclicality or
seasonality to job seeking behaviors, and
correlate data to other company initiatives.
23

24. Some practical examples
Support Workforce Planning by analyzing attrition and retention rates based on job seeking behavior
Monitor workforce development plan
The inclusion of analytics into a workforce
planning initiative are essential to mapping the
most accurate current workforce profile of any
organization.
24

25. Q&A
25
Business case examples

26. • The average cost of replacing an employee is 29%-46% of salary
• At a wage of 30k€ per annum, cost to replace is 9-12k€
• Average attrition of 8% across 3,000 employees equals 240 leavers
- Cost to replace 240 leavers x 10k€ is 2.4m€
- Cost of predictive analytics software per annum 30-80k€
26
Reduce voluntary attrition

27. 27
Reduce recruiting costs
• Most of (outbound) recruiters/researchers time is spent
talking with candidates who are not ready to make a move
• Calculate avoided time (or people) x cost = savings

28. Q&A
28
Remember, CFO’s care about €’s
not promises 

29. 29
Thank you!
Questions, comments?

30. 30
Aki Kakko, Head of Product
[m] +44 7887 473424
[e] aki@joberate.com
[t] @akikakko