[email protected] http://savagemarketingtech.com
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http://savagemarketingtech.com
Randstad’s Journey in Tech and Touch
Vaishali Sachdev - Head of e-commerce Randstad Holding NV Jildert Huitema - Head Marketing & Market Intelligence Randstad Nederland
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agenda
3
about
Randstad
embracing
digital
innovations
where we are;
where we are
going
four
pre-conditions
for successful
data-driven
Randstad
killer app:
Sales
Navigator
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2001 2004 2005 2008 2009 2010 2011 2012 2014 2013 2002 2003 2006 2007
Uitzendbureau Amstelveen founded
1960
Company name changed to Randstad Holding nv
1987 1990
Listed on AEX
1998
Frits Goldschmeding, Founder, retires
Presented in 20 countries, promoted
to the AEX index
Randstad Vedior merger
Acquire SFN Group
Acquire part of USG
in EU
RIF started
2015
RiseSmart Acquisition
Founded
IPO
Founded IPO Founded Founded Founded Founded
(as Elance)
Acquire FujiStaff (JP)
Revenues €6.6 billion
3x
4
Randstad innovating agile, local, based on client needs
Revenues €19.2 billion
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embracing digital innovations agile integrator of technologies supporting data-driven services under Randstad DNA, with a place for human interface
5
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where we are; where we are going…
6
entry
transf
orm
advanced
optim
ize
complexity
busi
ness
im
pact
Monetize our Data
Data
Warehouse
Optimization
Big
Data
Exploration
Customer 360 Degree
View
Streamlined Data
Refinery
Talent 360 degree
profile
Harnessing external
Data
Intelligent matching & Ranking
Big Data Predictive Analytics
Pricing Intelligence
Randstad Current State
Descriptive & Diagnostic
Randstad Future State
Predictive & Prescriptive
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data driven program
7
labour market Predict scarcity and skills
gaining in popularity
candidates Predict the perfect and
available candidate
companies Predict demand of every single
company on any moment
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leadership commitment
8
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data lake
9
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new roles and skills
10
Business sponsor
Program manager
Business consultant
IT analyst
Data scientist
Data engineer
Business analyst
Benefit reporter
UX developer
Implementation manager
Community builder
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killer cases
11
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how it works: can we predict demand? and can we help our salesforce to find it more easily?
12
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sales navigator: product concept easy to use application | market insights | prediction model for targetting
13
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market data & prediction
14
step 1: define goal and hypotheses
step 2: use the data (variables) available and scope
step 3: built algorithm, test, refine
step 4: present algorithm through web service to front end
1. hypotheses 2. sources 3. prediction model
• company info from Chamber
of Commerce
• market information from
Bureau of Statistics
• internal data from CRM system
• e-mail data
• vacancy data
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15
search
results
including
rating
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touch supported by tech
16
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http://savagemarketingtech.com