Data Diligence LiveRamp with Nielsen Catalina Solutions

WHAT’S DATA GOT TO DO WITH IT?

ADAM PAULISICK SVP, Marke+ng & Strategy Nielsen Catalina Solu=ons

@NCSolu+ons @Paulisick

Copyright © 2015 Nielsen Catalina Solu+ons • Confiden+al & Proprietary 2 2 2 #LRroadshow

A Moment Of Silence For Our Friend, The Mobile Cookie.

#LRroadshow #LRroadshow


We Are Entering The Post-‐Cookie World

86% of mobile ,me is spent in apps, NOT the browser

Share of media time spent per day by US adults (%)

People are shi:ing from desktop to mobile

14% BROWSER 86% APPS

13.4

19.2

23.3 20.7 19.2 18.0

Mobile Desktop/laptop

Source: Flurry Analy+cs, ComScore, NetMarketShare Source: eMarketer

Social Messaging 9.5% (other)

2012 2013 2014

Gaming 32%

Facebook 17%

TwiZer 1.5%

Entertainment 4% (other)

U+li+es 8% Produc+vity 4%

News 3% Others 3%

Google Browsers 5%

Others 2%

Apple Safari 7%

YouTube 4%


What signals from a mobile device or users behavior can you use to build a media plan?


Awareness Transac+on Intent Likeability


Awareness Transac+on

Intent Likeability


Awareness


Likeability


Intent


Transac+on


Aligning Media Goals to Sales Goals

x

Reach Light Loyal Brand -‐Buyers Heavy Category-‐Buyers

Avoid Non-‐Brand Buyers Non-‐Category Buyers

Reach Medium Loyal Brand-‐Buyers Heavy Category Buyers

Avoid

Heavy Loyal Brand-‐Buyers Non-‐Category Buyers

ACTIVATE Lapsed Buyers ___

CONQUEST The Compe++on ___

Reach Heavy Loyal Brand-Buyers Heavy Category-Buyers

Avoid

Non-Loyal Non-Category Buyers

DEFEND Loyal Buyers ___


Just Put Some Data On It

(#LRroadshow)


Does the data reflect actual buyers, or are

they merely look-‐alikes?

01


Where does the data come from (and since when)?

02


How do you know your data is representa=ve

(smart) and not just big?

03


How oeen are your sources updated?

04


What control do you have of your sources?

05


Is your data related to the individual or the

household?

06


What level of granularity can you get to?

07


How much history do you have for the data (either household or individual)?

08


Where can the data be ac=vated, and how?

09


What's the typical response or reac=on

to the data?

10


Key Takeaways

§  Not all datasets are created equal. Know what ques+ons to ask

before inves+ng in enterprise level data sources.

§  Don't use datasets from companies that won't share their sources

or collec+on methods.

§  Big data can be misleading if it isn't smart data (tuned by smaller,

balanced datasets).

§  Historical collec+on, granularity of the data and how representa+ve

a dataset is affects both effec+veness and cost, not just cost.

§  #LRroadshow

Thank You!

ADAM PAULISICK SVP, Marke+ng & Strategy Nielsen Catalina Solu=ons

@NCSolu+ons @Paulisick

#LRroadshow

Documents

Data Diligence LiveRamp with Nielsen Catalina Solutions