You Can Plan Media for Word of Mouth

You Can Plan Media for Word of Mouth Brad Fay, COO, Keller Fay Group Graeme Hutton, SVP, Universal McCann September 2009 © 2009 Keller Fay Group LLC Not to be quoted or distributed without written permission

Who We Are

The first research-based marketing consultancy focused on word of mouth

Metrics: measuring brands’ word of mouth and competitive context

Best practices: understanding the dynamics of word of mouth

Implications: analyzing strategic impact

TalkTrack®: award winning study of word of mouth and its impact

The Keller Fay Group 2007 ARF INNOVATION GRAND AWARD WINNER

Select Clients

UM

UM is a global media communications agency that represents the world’s leading marketers and strategic thinkers including Coca-Cola, ExxonMobil, Johnson & Johnson, MasterCard, Microsoft, Sony, Bacardi, L’Oreal, Dyson and BMW

Part of the Interpublic Group of Companies (IPG), UM has roughly 169 offices in 130 countries and more than 3,600 employees with headquarters in New York

UM provides a full spectrum of media services including media and communications planning, digital strategy consultation, analytics and economic modeling and research and consumer insight. The company’s mission is to deliver Curious Minds for Surprising Results

UM Research Finds Personal Recommendation Typically Outperforms Classic Ad Channels Source: UM Aggregated Channel Data from U.S. Consumer Fusion-Insight proprietary research, 2007-2009

We Look to Other Consumers to Help Form Our Opinions

After Personal Emails, Search and Brand Websites are the Most Common Resources People Use to Research Products

“ Thinking about the process of looking for opinions on a product, brand or service online. On average how often do you do the following when looking for information on products, brands and services?” U.S. Average. Source: UM Digital Wave Series 2008

TalkTrack ® : A Proven Approach to Measuring WOM

Keller Fay’s TalkTrack® is a syndicated research study of consumer word of mouth about products, services, and brands

All forms of WOM

Offline; online

All points of view

What “talkers” are saying; what “listeners” hear and then do

All people

Nationally representative sample of consumers, 13-69

All dimensions

Quantity, quality, and drivers of WOM (including advertising)

All brands across categories

Study was launched in the US in June 2006, and is ongoing

36,000 online interviews per year with weekly quotas;

350,000 brand conversations per year

What We’ve Learned

Most WOM happens off line

90% is face to face or over the phone

Keller Fay is only research firm that tracks off line WOM

WOM is remarkably impactful

50% of brand conversations lead to purchase intent

Most powerful “touch point” according to Zenith

There’s a lot of WOM

About 3.5 billion daily WOM impressions among US consumers 13 to 69 years of age

A key goal for brands should be to capture a larger share than competition

Word of mouth does not supplant advertising

Advertising is a key driver of WOM

But advertising and marketing need to change in a WOM era

Independent Evidence

Premise: “It is possible to quantify the way in which word-of-mouth often complements and extends the effects of advertising.”

Findings : “Using a customer lifetime value modeling approach, we show how the lifetime value of a customer acquired through advertising can increase several-fold when the effect of a customer’s WOM is included in the calculation”.

Conclusions : … “customer interactions through word-of-mouth (WOM) can have a major impact on consumer response to a product and the accompanying advertising…. advertising researchers should also make WOM analysis and its economic implications an important research issue”.

“ Quantifying the Ripple: Word-of-Mouth and Advertising Effectiveness” Hogan, Lemon & Labai, Journal of Advertising Research . Sept 2004 Page

Keller Fay Findings in Latest Journal of Advertising Research

Empirical Generalizations: How Advertising Works

About 20% of word of mouth is stimulated by advertising

About 700 million ad-influenced word of mouth impressions for brands each day in America

Opinion leaders play a 3x bigger role in ad-influenced word of mouth than the average consumer

The effectiveness of word of mouth is substantially increased when stimulated, encouraged, and/or supported by advertising

The presence of advertising in word of mouth conversation increases the probability by ~ 20% that a consumer will make a strong recommendation to buy or try a product

Page

Keller Fay Finds About One-Fifth of WOM Directly Stimulated by Ads

Chart reflects percentage of conversations about brands where participants say somebody directly referred to advertising as a source of brand information in the conversations

Not included are conversations influenced by advertising that was unmentioned by any conversation participant

Base: Brand Conversations Influenced by Advertising, n=32,496 Source: TalkTrack ® , October 2007 through September 2008 Industry % of WOM Influenced by Advertising Entertainment/Movies 27.2% Technology 25.6% Personal Care/Beauty 25.0% Automotive 24.4% Telecommunications 24.4% Shopping/Retail 22.6% Household Products 22.0% All Category Average 21.6% The Home 21.5% Travel Services 21.4% Food/Dining 20.0% Children 18.8% Beverages 17.7% Sports/Recreation 16.6% Financial 16.2% Health/Healthcare 14.8%

New Media Planning Tool: 100 Media Outlets Included in TalkTrack®

CABLE CHANNELS

A&E Television Network

ABC Family

AMC

BET

Bravo

Cartoon Network

CNN

Comedy Central

Discovery

ESPN

ESPN 2

Food Network

FOX News

FX

Hallmark

History

Home & Garden Television (HGTV)

Lifetime

MSNBC

MTV

Nick at Night

Oxygen

Sci Fi

Spike TV

TBS

The Learning Channel (TLC)

TNT

Tru TV

TV Land

USA

VH1

MAGAZINES

AARP The Magazine

Better Homes & Gardens

Car and Driver

Cooking Light

Cosmopolitan

Country Living

Ebony

Entertainment Weekly

ESPN The Magazine

Family Circle

Glamour

Good Housekeeping

Ladies' Home Journal

Martha Stewart Living

Maxim

Men's Health

National Geographic

Newsweek

O, The Oprah Magazine

Parenting

Parents

People

Prevention

Reader's Digest

Rolling Stone

Southern Living

Sports Illustrated

Star

Time

TV Guide

Us Weekly

Vogue

Weight Watchers

Woman's Day

WEBSITES

About.com

Amazon.com

AOL.com

Ask.com

CNET.com

CNN.com

Craigslist.org

Disney.com

Ebay.com

ESPN Sportszone (ESPN.go.com)

Facebook.com

Foxnews.com

Foxsports.com

Go.com

Google.com

Hulu.com

Live.com

MBL.com

MSN.com

MSNBC.com

MySpace.com

NFL.com

NYTimes.com

People.com

Target.com

USAToday.com

Walmart.com

Washingtonpost.com

Weather.com

WSJ.com

Yahoo.com

YouTube.com

NEWSPAPERS

Chicago Tribune

Los Angeles Times

New York Times

USA Today

Wall Street Journal

Washington Post

Advertising and WOM: The Tales of Two Advertisers

Our Two Advertisers

Advertiser A

A “top 50” WOM brand in terms of WOM “quantity”

Has outstanding WOM “quality”

Very positive, little negative

High recommending

High credibility, pass-along, and purchase intent related to WOM conversations

Advertiser B

A “top 10” WOM brand with high WOM “quantity”

Lost of WOM, both online and offline

WOM “quality” a bit above average

Using WOM as an Effectiveness Metric

Aim

To determine the relationship between Advertiser media weight and Word of Mouth (WOM)

To assess if this metric can be embraced:

Predictively, to assess likely forward WOM based on media spend levels

Historically, to evaluate actual campaign consumer effects as an interim action standard between tracking recall and sales

Result

Advertising media have a measurable effect on WOM

Using Keller Fay TalkTrack®, we have been able to explain the relationship between media weight and Advertiser A at 89% and Advertiser B at 80%

Approach in This Review

Step 1

Source Keller Fay TalkTrack®, a weekly tracker of online and face to face WOM, to determine Advertisers’ WOM levels and shifts

Calculate Advertisers’ share of WOM (all mentions) to eliminate seasonality

Compare direct monthly Advertiser share of WOM to TNS AdSpend data to assess possible relationship

Step 2

Roll up data to smooth out noise and improve sample size

Reassess correlation between media and total AdSpend

Step 3

Build multiple regression model to explain all major media weight variables

Advertiser A: The Power of Print Media in Driving WOM

Step 1: Advertiser A WOM and Total AdSpend

No Obvious Immediate Relationship

Source: Keller Fay, TNS Fit (r) = 50% WOM AdSpend

Similar to Advertising Awareness Effects…

At an aggregate level, advertising’s relationship with WOM is unlikely to be on a direct month to month basis

Data should be rolled and lagged to take account of delayed ad effects in the mid-term, reduce sampling noise, etc., to determine underlying relationships in the data

Interrogating various iterations of this data indicated one of the more powerful roll ups was three-months rolling data, where we additionally lagged magazines by a month

Step 2: Advertiser A’s Lagged Data

July 2007 – March 2009

Total AdSpend Share of WOM Source: Keller Fay, TNS (Data rolled up 3 months) Total AdSpend/WOM Fit (r) = 85%

Step 3: WOM Regression Model Total AdSpend/WOM Fit (r) = 94% r 2 = 89%; Adjusted r 2 = 82%

Advertiser A’s Primary Coefficients Reveal Higher WOM ROI for Print Media Media Coefficient Index per $100,000 TNS Spend TV 4 Magazines 100 Newspapers 64

Evidence of WOM Effects for Advertiser A at “Micro-Planning” Level Fit (r) = 78% KF WOM index is based on the proportion of magazine readers talking about Advertiser A. UM Media in Mind Index is based on the proportion of Advertiser A customers who read the magazine vs. total population.

Advertiser B: The Power of TV & “Non-Media” in Driving WOM

Step 1: Advertiser B WOM and Monthly AdSpend

No Immediately Apparent Strong Relationship

Total TNS Monthly AdSpend Share of WOM Source: Keller Fay, TNS Data shown here is the raw monthly AdSpend and share of WOM related to one another but the relationship is only modest at best Fit (r) = 49%

Step 2: Advertiser B Lagged Data

December 2007 – December 2008

Total TNS AdSpend Total AdSpend/WOM Fit (r) = 72% Share of WOM Note the increase in the correlation to 72% Source: Keller Fay, TNS (Data rolled up 3 months, magazines lagged by one month) This chart takes data seen earlier and rolls up data for three months and lags magazines by one month so that its delayed effect can be more precisely accounted for.

Step 3: Multiple Regression Model for Advertiser B Predicted WOM Actual WOM Total AdSpend/WOM Fit (r) = 89% r 2 = 80%; Adjusted r 2 = 60% Share of WOM This model is a standard multiple regression model which weights the effect of each medium by its calculated effectiveness contribution, or coefficient, driving WOM. Degree of explanation is (r2) 80%. Note: non-advertising baseline is estimated to account for 77% of all brand WOM mentions

Advertiser B’s Primary Coefficients Reveal Higher WOM ROI for TV The coefficient index indicates the relative power of the channel to affect WOM The model on the previous slide indicates that TV followed by magazines clearly have a major relationship to Advertiser B’s short-term WOM 1. Coefficient Index: the relative contribution of each channel to boosting WOM per $100K of TNS reported AdSpend 2. p-value: the probability the index does NOT effect WOM, i.e. there is 96% probability TV does influence WOM, 90% magazines Coefficient Index per $100,000 TNS AdSpend 1 p -value 2 Magazines 13 0.095 TV 100 0.044

Conclusions WOM Can Be a Broad Effectiveness Metric

There is a clear relationship between Ad Spend and WOM

Can be estimated statistically over a rolling three months – with magazines lagged by a further month

There is variability in the role of advertising in driving WOM

Advertising was very important for driving WOM for Advertiser A, but other factors account for 77% of WOM for Advertiser B

Advertising-related WOM is primarily seen to be driven by TV and print, but this is currently based on TNS data

We see that TV and print can perform very differently for different advertisers and/or categories

Since advertising can be effectively modeled and predicted, WOM could be used as a broad ad effectiveness metric

Provided that other unexpected factors do not swamp the data, e.g., a product recall

Another Approach to Measuring Advertising Impact on WOM: ESPN Case Study Source : 2009 ARF Presentation by Ed Keller & Artie Bulgrin of ESPN

55% of Males Report “Regular” Exposure to One or More ESPN Platforms Base: Male Respondents n=6,216 Question: We are interested in your exposure to sports content on ESPN properties. Please indicate which of the following you regularly watch or use: [Above properties included] Source: TalkTrack ® , August – December 2008 Proportion of Males Who Report “Regularly Watching/Using” ESPN Platforms

The Approach

Track WOM levels for NFL and CFL advertiser brands during football season

Compare trend for those men in ESPN audiences versus those not in the audience during prior 7 days

Inference is that increase in WOM for audience relative to non-audience attributable to ad campaign

Opportunity to estimate the incremental WOM related to advertising on ESPN

Retailer “A”: WOM Surged Among SportsCenter and Live Game Viewers in Sept 2008, and Again in November Base: Male respondents during average 4-week period (ESPN Viewership/Use Net, n=766; ESPN Broadcast of Live Games, n=632; ESPN Broadcast of SportsCenter, n=438; Non-Viewers of ESPN Platforms, n=589) *Due to base issues, unable to show ESPN.com results for first 3 periods; 8-week rolling average shown for ESPN.com (n=493) Source: TalkTrack ® , August – December 2008 Note: 8-week rolling average shown for ESPN.com on this slide and all those that follow. (Percentage of males engaging in WOM about Retail Brand “A” on a daily basis, rolling 4-week avg.) Drop in weekly TV impressions for MNF Start of weekly presence in MNF Co-branded spots airing 10/27-11/26

Athletic Apparel Brand “B”: WOM Peaked among ESPN.com Users in November (Percentage of males engaging in WOM about Apparel Brand “B” on a daily basis, rolling 4-week avg.) Base: Male respondents during average 4-week period (ESPN Viewership/Use Net, n=766; ESPN Broadcast of Live Games, n=632; ESPN Broadcast of SportsCenter, n=438; Non-Viewers of ESPN Platforms, n=589) *Due to base issues, unable to show ESPN.com results for first 3 periods; 8-week rolling average shown for ESPN.com (n=493) Source: TalkTrack ® , August – December 2008 Sponsor of Big League Bash (arcade section ESPN.com)/ Home Run Derby-style game

Male ESPN Viewers Generated 82 Million More Conversations About Retailer “A” than Non-Viewers (Projected weekly brand conversations about Retailer “A,” among ESPN Viewers & Non-Viewers) Base: Brand mentions among male respondents during average 4-week period (ESPN Male Viewers, n=6,487; Male Non-Viewers, n=2,699) Source: TalkTrack ® , August – December 2008 From late August to late December, Male viewers of ESPN generated an additional 82 million conversations about Brand “A” than non-viewers.

Male ESPN Viewers Generated 91 Million More Conversations about Apparel Brand “B” than Non-Viewers (Projected weekly brand conversations about Apparel Brand “B,” among ESPN Viewers & Non-Viewers Base: Brand mentions among male respondents during average 4-week period (ESPN Male Viewers, n=6,487; Male Non-Viewers, n=2,699) Source: TalkTrack ® , August – December 2008 Male viewers of ESPN generated an additional 91 million more conversations about Brand “B” than non-viewers.

Conclusions WOM Lift From Advertising Can be Quantified