big data and predictive analytics sept 2014 helsinki #agilehr

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Predictive Analytics for HR and Recruitment

Aki KakkoCo-founder, Head of Product

3rd of September, 2014 Helsinki

Introduction 2

Aki KakkoSerial EntrepreneurCo-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)

Two quick words about our company 3

Two quick words about our company 4

A secular shift has occurred, data is now everywhere

Age of corporate dominance

Age of knowledge workers

Att

ract

peop

le t

o f

ollow

you S

tart fo

llow

ing

inte

restin

g

peop

le

Companies need to track external people data, in addition to their HRMS data

attract talent to take interest

take interest in talent

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I think

Big Data has become a disruptor for HR

I know

Investment Flow

A constantly evolving data stream that is “external” to current HRMS, holds tremendous potential

(current state) (future state)

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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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Source: IBM

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Source: IBM

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Source: IBM

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Source: IBM

Predictive Analytics increase value of HR services

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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

Ente

rpri

se V

alu

e

• What is happening now?

• Why did it happen?

• What happened?

• What can happen?

Extracting value from Big Data

Non-HR example of a Predictive Analytic 13

Q&A

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Example business problems predictive

analytics can help with…

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?

• What else?

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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?

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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?

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Q&A

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Opportunities are only limited by our imagination…

The Predictive Analytics lifecycle 19

Complements of the SAS Institute

Source: SAS Institute

How predictive analytics works

• Aggregate, input, scrape, import, or track information sources

Information (could be Big

Data)

• Makes decisions based on previously validated outcomes

• Learn new outcomes that will be used in future decision making

Machine learning • Feed/output data to

visualization or rendering software

• Archive decision results for future query

Display predictive analytics

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Overall technology hierarchy 21

Client HRIS or recruitment systems

Client’s User Interface variations

API and Web Services

Joberate machine learning predictive analytics engineData validation services(further explained on next

slide)

Data validation services simplified 22

Data validation services

Some practical examplesAnalyze any number of variables to understand employee job seeking trends

Analyze trends in specific groupsTrends 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.

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Some practical examplesSupport Workforce Planning by analyzing attrition and retention rates based on job seeking behavior

Monitor workforce development planThe inclusion of analytics into a workforce planning initiative are essential to mapping the most accurate current workforce profile of any organization.

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Q&A

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Business case examples

• 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€

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Reduce voluntary attrition

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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

Q&A

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Remember, CFO’s care about €’s not promises

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Thank you!

Questions, comments?

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