big data: to smart data

1 © GfK 2014 Big Data to Smart Data

BIG DATA:

IT MAY BE BIG BUT IS IT SMART? Turning Big Data into winning strategies

A GfK

Point-of-view


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Variety (data in many forms)

Data in different formats,

versions, from different

sources, with different

dimensionality and structure

Velocity (data in motion)

Welcome to the fire hose.

The real-time data flow never

stops. Today's data will be

history tomorrow

Volume (data in huge quantity)

Data in massive quantity.

"Collect first, think later".

Big Data requires sophisticated

infrastructures

Veracity (data in doubt)

Volume doesn't automatically

increase precision. Managing

accuracy and reliability requires

significant analytical expertise

Big Data is complex…

Typical ‘Big Data’ characteristics


…and can be misleading

So what is required? Thorough analytics expertise and a reference frame

Biasedness Big Data is often highly selective and

unsystematic

Analytic limitations Standard approaches to data processing,

modeling and analytics not feasible

Incompleteness Data streams are "happening", not designed for

information gathering


Elements of our Big Data Experience @ GfK

Established Pilot projects, R&D

Mobile Insights

GfK proprietary Big Data Algorithms

Social Media Intelligence (SMI)

Location Insights

Digital Behavioural Tracking

Cookie tracking

Analytic Integration of SMI & Survey Data

Datafication of Games

Social Network Analysis

Integration of (Digital)

Behavioural Tracking and SMI

Analytic Integration of SMI & Sales Data


Smart Data in the real world Use cases


Use Case #1: Purchase Journey


Use case 1: Purchase journeys

Therefore, marketers have new information needs….

Understand how

digital & offline

channels interact,

what message is

best, at each

step in the buying

process

Context

Consumers are increasingly

using technology along

purchase journey and

sharing experience in

real-time

Traditional

research can

provide some

insight – but not

the complete

picture

Technology allows

us to get more data on the path

to purchase

Business question: How do I best allocate on/offline marcoms spend to maximize

sales?


Use case 1: Purchase Journeys Example: Magnitude of digital behavioral tracking data in the travel industry

• 10,000 Users in the panel

• 2,100,000 Page Impressions at major

search engine

• 30,000,000 Navigation events overall

• 1,140,000,000 Server requests

• 1,500,000,000 MB of textual content

• Categorize 1,322 Websites as

Retailers, Aggregators,

Accommodation, Airlines,

Destinations, etc..

• Categorize 16,011 Search Keywords as

Travel Organization, Accommodation,

Generic, etc..

• Identify relevant data on which we can now complete analysis

• 59 Offline Bookers, 148 online Bookers, 20 Bookers @ specific portal

• 33,570 relevant Navigation Events (0,1%)

One month of data in our GfK

Media Efficiency Panel

E.g. create a taxonomy for

the travel industry

Analyze purchase journeys for a

selected client

1 2 3 The haystack Organize the haystack Find the needle


Source: Smartphone Purchase Journey, Russia

The purchase journey can be really complex, but still there are

certain patterns…

Finish

Click & mortar

Start

Forums /

blogs / review

sites

Brand sites

Social

networks

Pure online

retailers

Aggregators

2 Search 1

3


Click & mortar

Search engines

Pure players

Aggregators

Manufacturers

Telecom operators

Social networks

Forums / blogs

Which touchpoints have a higher impact on brand purchase?


Typical purchase pathway – Consumer Segment X

Visits in

online:

25

Offline

contacts:

3

Trigger:

keep up

with trends

Purchased

brand:

Samsung

purchased

ONLINE

Ag

gre

-

gato

rs

Sea

rch

eng

ine

Cli

ck &

mort

ar

Pu

re

on

lin

e

Foru

ms

&

blo

gs

Man

u-

fact

ure

rs


Use Case #2: Integrating Social Media Intelligence

into Decision Making


Use case 2: Social media Business question: how is social media affecting product sales?

Therefore, organizations are no longer in control

of their own brand

Understanding what is being said

about you (and competitors) in earned media,

how sentiment is trending, what is

influencing trends

Traditional research still

provides valuable insight – but not

the complete picture.

Technology allows us to take the pulse of the digital world – in near real-time.

Context

Fast adoption and

widespread use of social

media = everything more

spontaneous, immediate,

and dynamic


• 3,162,185 pieces of content found

• 11,579 single domains crawled

• 44,942 API requests

• After cleaning 1,786,353 pieces

of content for further analysis we…

• Categorized content into 8 channels:

Blogs, Forums, Video, Web, SocialNets,

MicroBlogs, News and eCommerce

• Separated user and non-user content

• Clustered content into pre-defined topics

• Identified the best days for launch

announcement and first client feedback

• Identified top 15 domains for ongoing

monitoring

• Identified top 5 key issues that are

evaluated negatively by customers

• Identified impact of social media

communication on retail sales

One month of data for an Social Media Intelligence Monitoring in six countries for a product launch

Create a basis for analysis

Put the social media results into context for the client and connect it with other data sources e.g. retail sales data

1 2 3 The haystack Organize the haystack Find the needle

Use case 2: Social media Example: Magnitude of digital information available to monitor a product

launch in the tech industry


Integrating Social Media Analysis into Brand Trackers

Case study: Sam Adams vs. Budweiser

2012 – 2013 Trend | Action Signals

Budweiser Sam Adams

Buy

Use

No Action

Not Use

Drop

0%10%20%30%40%50%60%70% 0% 10% 20% 30% 40% 50% 60% 70%

Positive: 37% 30%

Negative: 4% 2%

Net: +33% +28%

Positive: 61% 66%

Negative: 3% 1%

Net: +39% +65%

Wave 1

Wave 2

Use (Have, use or consume a product)

Buy (Wanting, buying or planning on buying a product, shop for, search for, looking for a product)

Not Use (Not have, not use or not consume a product)

Drop (Switching, getting rid of,

dropping rejecting a product, stop using)


Integrating Social Media and real observed purchase behavior* to

reliably calculate Social ROI

Unique User (in Tsd.) selected URLs

*GfK Media Efficiency Panel, Germany

94

291

299

86

121

506

230

475

482

+ 19.6% € 14.13 € 11.85 + 7.7% € 12.72 € 11.85 + 9.1% € 12.89 € 11.85

Change in % Customer Value SoM Customer Value Buyers Total

Facebook-Sites YouTube-Channels Blogs & Boards


Integrating Social Media Listening and GfK Retail Sales Data (example of GfK dashboards)


Use Case #3: Mobile Insights


Mobile Audience Measurement

Define mobile internet audience by demographics, device usage and behavior.

• What is the mobile web behavior of a certain target audience?

• How and where are brand’s targets spending mobile web time?

• How does mobile relate to their total media usage?

• How is the competition doing compared to a specific brand in reaching its own targets?


Household income • Ikea.com’s penetration is similar

across the income brackets, but peaks

in the low end (under £20k).

Ikea.com vs. Johnlewis.com – Oct ‘13 John Lewis saw over 50k more mobile site visitors than IKEA

50k 100k 150k 200k 250k

Device

Location Demographics

Johnlewis.com is stronger amongst slightly

older females (25-54 years old), who

represent a potentially more affluent sector.

3.9% Ikea.com penetration

amongst females.

• In contrast, Johnlewis.com peaks

within the highest income bracket

(over £50k).

• Therefore, this represent a key target

area for IKEA.

Ikea.com penetration

amongst under 25 year

olds.

3.7%

Ikea.com sees its highest level of

penetration in London (4.5%).

However, opportunities remain in Northern

Ireland & Yorkshire, where penetration is

only 2%.

Both ikea.com &

johnlewis.com see a

higher level of

penetration on iOS

devices. Therefore,

the opportunity

remains for IKEA to

cement themselves

on Android.

% = penetration of retail sites.

Mobile websites

£20k

3.5%

Source: GfK Mobile Insights, UK (based on mobile operator data analysis)


Fashion retail mobile websites 2.1 million unique users in November 2013

Unique users Fashion retail

penetration

Sessions per

user

Duration per

user (mins)

155k 7.4% 1.5 6.3

Unique users Fashion retail

penetration

Sessions per

user

Duration per

user (mins)

132k 6.3% 1.9 14.0

18% 82% 13% 87%

36% users 18-24 44% users 18-24

27% social class C1 31% social class C1

Brand X Brand Y

0%

5%

10%

15%

20%

00-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 08-09 09-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24

When do users visit the sites? brand-y.combrand-x.com

Source: GfK Mobile Insights, UK (based on mobile operator data analysis)


Key take-aways

Don’t necessarily rely on ‘Big Data’ on its own – it might not tell you the

whole story

Without understanding the consumer context, the value of Big Data for

marketers is limited

Combine consumer data with ‘reference’ data for better insights


Date post:	17-Jul-2015
Category:	Data & Analytics
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big data: to smart data

Data & Analytics