Data-Driven Marketing

Taylor Schreiner Director, Global Marketing Solutions Insights,

LinkedIn

Audiences are not Targets

Seniority

Function

Company

Occupation

Industry

GroupFederation

Group

Membership

Title

CompanySize

Of Course, Targeting Tells You a Lot

5

Connections

Geo

But There is More to What People Do

6

Use Desktop

Use Mobile

Read InMail

Visit a Company

pages

See a Status

Updates

Group activities

And Mindset is Key

71 – The Mindset Divide research study, TNS, September 2012

Spend Time

Personal Networks

Info on friends

Info on personal interests

Entertainment updates

Invest Time

Professional Networks

Career content

Updates on brands

Connect with colleagues

1

2

3

26% higher than personal

Top 3 types of content expected 1

Goals are not Metrics

Examples of Metrics

� Brand Surveys

� Online Conversions

� Increased Online Conversation

Goals vs. Metrics

Examples of Goals

� Create Awareness

� Drive Sales

� Turn buyers into advocates

Number of studies correlating clicks and brand metrics

Conversations are not Automated

Conversational Expectations among Affluents

Accumulating Wealth

� Value

RELEVANT CONTENT

� Expect banks and credit

cards to have a social

presence

Soon to Retire

� Value

TIMELY UPDATES

� Expect brokerages to

have a social presence

Retired

� Value

CUSTOMER SERVICE

Cogent Research, U.S. March 2013

Base: Mass Affluent social media users

Case Example

Advertising is Listening

Listen to What People Say When

16

Spiking Trend

Long-TermTrending Topic

Popula

rity

Time Period

Catching trending topics

Defining content strategy

Example of Trends on LinkedIn

17

22-Apr 29-Apr 6-May 13-May 20-May

Big Data

Tumblr

US Daily Share Volume of “Big Data” and “Tumblr” Related Articles*

* Topics extracted from analyzing article text. Dips in “Big Data” due to weekend drop in activity

Same Target, Different Conversations

18

* LinkedIn internal data for August 2013. Topics extracted from analyzing article text using NLP Latent-Dirichlet classification.

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

100%

APAC article topic occurrence vs. EMEA in August

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