You Can Plan Media for Word of Mouth

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    ARF 2009 re:think Convention + Expo 09/24/09 15:45 D2_KIF_Keller_Bulgrin_v04.ppt

    ARF 2009 re:think Convention + Expo 09/24/09 15:45 D2_KIF_Keller_Bulgrin_v04.ppt

    ARF 2009 re:think Convention + Expo 09/24/09 15:45 D2_KIF_Keller_Bulgrin_v04.ppt

    ARF 2009 re:think Convention + Expo 09/24/09 15:45 D2_KIF_Keller_Bulgrin_v04.ppt

    ARF 2009 re:think Convention + Expo 09/24/09 15:45 D2_KIF_Keller_Bulgrin_v04.ppt

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    You Can Plan Media for Word of Mouth - Presentation Transcript

    1. 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
    2. 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
    3. Select Clients
    4. 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
    5. 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
    6. 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
    7. 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
    8. 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
    9. 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
    10. 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
    11. 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%
    12. 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
    13. Advertising and WOM: The Tales of Two Advertisers
    14. 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
    15. 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%
    16. 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
    17. Advertiser A: The Power of Print Media in Driving WOM
    18. Step 1: Advertiser A WOM and Total AdSpend
      • No Obvious Immediate Relationship
      Source: Keller Fay, TNS Fit (r) = 50% WOM AdSpend
    19. 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
    20. 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%
    21. Step 3: WOM Regression Model Total AdSpend/WOM Fit (r) = 94% r 2 = 89%; Adjusted r 2 = 82%
    22. 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
    23. 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.
    24. Advertiser B: The Power of TV & “Non-Media” in Driving WOM
    25. 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%
    26. 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.
    27. 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
    28. 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
    29. 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
    30. Another Approach to Measuring Advertising Impact on WOM: ESPN Case Study Source : 2009 ARF Presentation by Ed Keller & Artie Bulgrin of ESPN
    31. 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
    32. 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
    33. 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
    34. 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
    35. 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.
    36. 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.
    37. Conclusions WOM Lift From Advertising Can be Quantified
      • Advertising on ESPN football generates substantial lift in word of mouth conversation about advertised brands
        • The spike in conversation can be linked clearly and directly to the media plan
      • Ad messaging does not end with the exposure
        • Engagement and share of mind continue
        • WOM proves cumulative effect of integrated campaign
      • Certain executions can drive WOM and response
        • What are the consistent elements
        • Focus on key events
      • A WOM strategy can set the tone for conversation
    38. Implications and Questions Raised
      • Think about WOM as a key outcome of advertising and marketing
        • Extends effect of advertising
        • Signals “brand engagement” generated by marketing
        • Usefully, it is an outcome that applies to all categories
      • More studies will be necessary to address other questions
        • What roles do category, channel, and creative execution play in driving WOM?
        • Is effect different for new versus established brands/products?
        • Can WOM impact be enhanced by more deliberate effort to generate WOM with advertising/marketing?
    39. Questions?
    40. Appendix: Keller Fay Methodology
    41. Keller Fay Methodology
      • Diary-assisted reporting of a day’s conversations
        • Respondents first recruited to take notes on conversations in 15 categories over 24 hours
        • Brand/company names collected on open-ended basis
        • Covers all forms of WOM: Face to face, phone, online
      • Online survey among consumers 13 to 69
        • Participants re-contacted a day later to answer questions about brands talked about during past 24 hours
        • Sample drawn from largest online consumer panels
        • 700 surveys weekly
      • Size of database
        • 36,000 interviews annually
        • About 350,000 brand mentions per year
        • Dating back to June 2006
    42. Thank You! Brad Fay (bfay@kellerfay.com) Graeme Hutton (graeme.hutton@umww.com)

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