Optimizing Your Media Plan for the

Bought/Owned/Earned Marketing Landscape

Rolf Olsen, VP, Director of Marketing Analytics

Tuesday, June 25, 2013

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WELCOME TO PART 3!

2 weeks, 5 webcasts, improved marketing effectiveness

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SERIES SCHEDULE 1. Transformational Marketing Mix Optimization Using a Virtual Marketplace

Date: Tuesday, June 18 Time: 1 pm EDT Presenter: Jeffrey Maloy, SVP and CMO

2. Using a Virtual Marketplace to Evaluate Your Marketing Strategy Date: Wednesday, June 19 Time: 1 PM EDT Presenter: Eric Paquette, Senior Vice President

3. Optimizing Your Media Plan for the Bought-Owned-Earned Marketing Landscape Date: Tuesday, June 25 Time: 1 pm EDT Presenter: Rolf Olsen, Vice President, Director, Marketing Analytics

4. Leveraging Marketing Investments with Marketing Mix Modeling Date: Wednesday, June 26 Time: 1 pm EDT Presenter: Irina Pessin, Managing Partner, Data2Decisions US

5. Marketing Analytics: 5 Things Every CMO Should Know Date: Thursday, June 27 Time: 1 pm EDT Presenter: Peter Krieg, President and CEO

MY BACKGROUND

Leading our new Marketing Analytics practice for Copernicus

– 8+ year (and counting) tenure with Aegis Media

– Worked with clients across all major industry verticals

– Prior to transferring from the UK, I lead and developed the Social Analytics practice for Aegis in the UK

My focus – Helping our clients navigate the

increasing complex media landscape, creating analytical solutions, which help our clients maximize the impact of their marketing campaigns

ROLF OLSEN, VP, DIRECTOR OF MARKETING ANALYTICS

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THE STARTING POINT

THE INDUSTRY HAS FRAGMENTED

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THE NEED FOR INNOVATION IN MARKETING ANALYTICS

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B/O/E = ANALYTICAL COMPLEXITY

STARTING FROM A DIFFERENT PLACE

RETHINK THE OBJECTIVE

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ACCESSING THE LANDSCAPE

Agent Based Models (ABM)

VECMs / VARs (Non-Linear

Econometrics)

Bayesian Probability

Machine learning algorithms

Monte Carlo simulations

Neural Nets

COMPUTATIONAL MODEL TECHNIQUES

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COMPUTATIONAL MODELING

CREATING THE RIGHT APPLICATION

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DEVELOPING THE RIGHT FOCUS

BOE

• Evaluate all Bought, Owned & Earned channels with the same currency

Game Theory

• How to gain market share from the competition

Segments

• Evaluate the contribution from segments and define the most impactful targeting strategy

Messaging

• Identify most efficient product and attribute message strategy

Simulation Engines

• Test the unknown, validate plans and assumptions

• Maximize media performance

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ADDRESSING THE KEY CHALLENGES

ADDRESSING THE BIG MARKETING CHALLENGE

EFFECTIVE B/O/E MARKETING PLANNING

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TAKING THE EXTRA STEP

ITS NOT ABOUT STATIC OUTPUTS

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CASE STUDY

BRINGING THE APPROACH TO LIFE

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ANALYSIS OBJECTIVES

Contribution from advertising across B/O/E for product portfolio

Account for synergistic role of media across B/O/E touch points (i.e.: How does TV influence Display performance?)

How can we best optimize within digital to deliver incremental sales lift?

Quantify the potential impact of performance optimizations

TACTICAL PAID OPTIMIZATIONS

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1. B/O/E NETWORK ANALYSIS

UNCOVERING CHANNEL SYNERGIES

Non-Linear econometric models, utilizing VECMs or VARs create a structural shift from traditional econometrics by using multiple dependants vs. just one.

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ADDRESSING THE ROLE OF OWNED & EARNED

ACCESS THE BEST DESTINATION

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UNCOVERING THE DRIVERS OF EARNED

FUEL ECOSYSTEM PLANNING

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2. FLIGHTING ANALYSIS

OPTIMIZE THE FLIGHTING AND SYNERGY

CHALLENGE

•Should we leverage Continuous vs. Pulsing f lighting strategies?

•How do we best maximize the ef fectiveness of Display and TV?

•Can we provide insight on ef fectiveness decay?

INSIGHT

•Contribution f rom Display impressions indicate poor sales contribution for both Brand X & Y

•Network analysis highlights the two-way impact of TV & Display on channel performance

•As seen with in this category, creative/media wear out occurs within three months

ACTION

•Adjusting the display f lighting strategy to a staggered 1-2-3 burst (high to low), with a one week break, will maximize the response curve function for display

•Flighting strategy will also help to maximize the performance of a creative within the three month cycle

•Optimize display f lighting strategy to maximize synergistic relationship between TV and Display

IMPACT

•The estimate impact f rom the display pulsing strategy is $4,816,675

•Direct contribution f rom Display Impressions increases f rom 0.3 to 0.8% for both Brand X & Y

•Maximizing this Display f lighting strategy with TV has the potential to deliver incremental sales of $17,103 for Brand X & $146,928 for Brand Y, by maximizing the impact for Display during TV f lighting

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Response Curves

Observed Response Predicted Response

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5,000

10,000

15,000

20,000

25,000

30,000

35,000

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Current Flighting

Current Flighting Observed Response

0.83%

Contribution

from Display

Impressions

Zero-Sum

Difference 0.3% & 0.2%

Contribution

from Display

Impressions

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5,000

10,000

15,000

20,000

25,000

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Impression Flighting

Current Flighting Proposed Flighting

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10,000

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25,000

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35,000

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Proposed Flighting

Proposed Flighting Predicted Response

Brand X – 55,509 Incremental Units at a value of $2,357,562

Brand Y – 46,089 Incremental Units at a value of $2,459,113

FLIGHTING OPTIMIZATION IMPACT

“ZERO SUM BUDGET CONSTRAINTS”

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3. SCENARIO PLANNER

• Having completed the Network analysis, we now want to leverage the model outputs within the scenario planner for testing possible optimization scenarios

Overview

• Test the impact of inter-channel optimizations

• i.e. within digital as in this case

• Quantify the performance impact of optimizations with incremental sales

Analysis Objective

OPTIMIZING DISPLAY PERFORMANCE

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SCENARIO PLANNER IN ACTION

OPTIMIZE WITHIN DISPLAY

CHALLENGE

• How do we best optimize within digital to deliver incremental volume or ROI?

INSIGHT

• The scenario planner facilitates the ability to optimize Display performance, using the modeled Display cuts

• This allows us to highlight good and poorly performing site buckets

• USH cuts indicate strong performance

ACTION

• Using the site bucket contribution grid as a starting point, we created a number of scenario optimizations, while not moving more than 10% within buckets

IMPACT

• The estimate impact from the proposed optimizations is $2,224,805

• 70% Brand X

• 30% Brand Y

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“ZERO SUM” PERFORMANCE OPTIMIZATION F

LIG

HT

ING

AN

ALY

SIS

• Performance Summary

• Brand X • 55,509 Inc Units

• $2,357,562 Sales cont.

• Brand Y • 46,089 Inc Units

• $2,459,113 Sales cont.

DIG

ITA

L O

PT

IMIZ

AT

ION

• Performance Summary

• Total impact • $2,224,805

• Brand X

• $1,557,363 Sales cont.

• Brand Y • $667,441 Sales

cont. TV

/DIS

PLA

Y S

YN

ER

GY

• Performance Summary

• Total impact • $164,031

• Brand X

• $17,103 Sales cont.

• Brand Y • $146,928 Sales

cont.

OPPORTUNITY SUMMARY

TOTAL POTENTIAL OPTIMIZATION IMPACT = $7,205,511

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THANK YOU FOR YOUR TIME TODAY!

QUESTIONS?

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For a PDF of this presentation and our advertorial on using big data for marketing planning.

Email ami.bowen@copernicusmarketing.com

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Leveraging Marketing Investments With

Marketing Mix Modeling

Irina Pessin, Data2Decisions US

June 26, 2013

Data2Decisions

Irina Pessin

Managing Partner, Data2Decisions US

+1 347 406 0247

Irina.Pessin@d2dlimited.com

(917) 326-7451

rolf.olsen@copernicusmarketing.com

goo.gl/eydHg

Rolf Olsen

Vice President, Director, Marketing Analytics