Data Driven Digital Marketing

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Digital Marketing Webinar Data Driven Digital Marketing Apr 2, 2014 | Manish Vij (Founder, SVUG) www.digitalvidya.com

Transcript of Data Driven Digital Marketing

Digital Marketing WebinarData Driven Digital Marketing

Apr 2, 2014 | Manish Vij (Founder, SVUG)

www.digitalvidya.com

Data Driven Digital MarketingApril 4, 2014

AGENDA

1. What is data driven digital marketing?

2. How can an organization use 1st/3rd party data to fine-

tune their digital marketing campaigns?

3. How can data driven digital campaigns impact an

organization’s revenue goals?

4. What are the latest trends in data-driven marketing-

navigating the complex world of Big Data?

5. What are the best practices for targeting and retargeting

ads and fine-tuning segmentation strategies?

6. How to be cognizant of the latest privacy regulations?

Data Targeted Advertising

ONLINE AUDIENCE

DataAggregation

Data Point 1

Data Point 2

Data Point 3

Data Point 4

Data Management

Platform (DMP)

Data Analysis

Trait 1

Trait 2

Trait 3

Trait 4

cookies classified

into

Segments

classified as

Intent

Interest

Descriptors

availablevia

connected to

Multiple browsing behavior data points captured from various sources

Data collected and analyzed to allow effective classification of audience cookies

Cookies assigned to various usable segments, e.g. New readers, Auto Interest/Intent etc.

RTB inventory used to run campaigns against

relevant audience segments available via

DSPs

Look Alike Modeling

People who click on an Ad

PrecisionMatch 3rd

Party Data Set

Converting Audiences

People who look like

Converting Audiences

CASE STUDY – LOOK-ALIKE MODELLINGAUDIENCE DATA EXTENSION – POLITICAL PARTY

Data Seen on Platform

• Overall trend of the Party’s website users as seen on DMP from 14-Jan onwards

Data Seen on Platform

• Multiple attributes (e.g. PageType) captured for analysis

Sample Audience Profile• Based on analysis of the sample group, following are their

interests/profile –– Microblogging users (e.g. Twitter)– Social Media users and followers– Financial Enthusiasts– Brand followers on blogging sites– Technology savvy (smartphones, tablets etc.)– Value seekers (e.g. Couponing Website visitors)– News Shows Enthusiasts– Travel Intenders– Online Shoppers– …

• A number of these profiles would be receptive to the Party’s core messages of growth and anti-corruption

Power of Look Alike Modeling

• The visitors that come to the Party’s website are a

relatively small %age of the overall internet user

population

• While retargeting of these users can continuously

engage them, look alike modeling can find other similar

users at scale that have not visited the Party’s websites

• Targeting these users should increase effectiveness of

the Party’s campaigns since these users have similar

issues and profiles as the Party’s site’s current visitors

Look Alike Model

13.6M new

potential

recipients at

82%

Accuracy!

*Based on sample size

of just about 77k from

the Party’s website

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