randstad’s journey in tech and touch

To edit date & footer title choose View>Header/Footer Change fixed date to presentation date Change footer field to title of the presentation Click on apply to all for changes on all current and new slides.

[email protected]

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


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


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


embracing digital innovations agile integrator of technologies supporting data-driven services under Randstad DNA, with a place for human interface

5


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


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


leadership commitment

8


data lake

9


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


killer cases

11


how it works: can we predict demand? and can we help our salesforce to find it more easily?

12


sales navigator: product concept easy to use application | market insights | prediction model for targetting

13


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


15

search

results

including

rating


touch supported by tech

16


[email protected]

http://savagemarketingtech.com