Tom Kelly 2014 UGA MMR Research Summit Preso
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Tom Kelly 2014 UGA MMR Research Summit Preso
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Tom Kelly 2014 UGA MMR Research Summit Preso
- 3. WRITE MEDIA PLANS REFINE AND SCALE AD EFFECTIVENESS PRODUCTS COLLECT AND STITCH DIFFUSE DATA SETS BUILD AD EFFECTIVENESS PRODUCT MAKE OUR COLLEAGUES SMARTER KANTAR
- 5. 700 | 5,000 | $1.8B STAKEHOLDERS GLOBAL ANNUAL
- 6. WHO ISAOL– “commercial” TOP 2 IN VIDEO AD SERVING $1,800,000,000 AD REVENUE TOP 2 IN AD TECH & PROGRAMMATIC ADS
- 8. “prove it . . . .”
- 10. DATASOURCES- COMPLEX Purchase Intent Ad Awareness Brand Favorability Message Association Brand Awareness Interaction Rate Interaction Time Completion Rate Click Through Rate Conversion Rate QSR Tech Entertainment Finance CPG Travel Autos Retail Govt / Edu Pharma / Health
- 15. MAKEYOURSELF SMARTER 4) YOU DON’T NEED A MILLION TO LOOK LIKE A MILLION 1) CASE STUDIES ARE USEFUL BUT NORMATIVE DATA IS BELIEVABLE 2) GIVE ME A DASHBOARD IS TO GIVE ME DEATH 3) META-TAG ME
- 17. SAMPLE SIZE = 1
- 20. Industry Norm – 4% Interaction Rate Hunger Games Case Study – 26% Interaction Rate AOL Norm – 13% Interaction Rate
- 21. GIVE MEADASHBOARD ISTO GIVE ME DEATH
- 23. OUTPUT – CLIENT READY
- 24. META-TAG ME
- 25. Purchase Intent Ad Awareness Brand Favorability Message Association Brand Awareness Interaction Rate Interaction Time Completion Rate Click Through Rate Conversion Rate QSR Tech Entertainment Finance CPG Travel Autos Retail Govt / Edu Pharma / Health SCIENCEOF ORGANIZATION
- 26. KPI Brand Awareness Ad Awareness Message Association Interaction Rate Interaction Time Video Completion Rate Conversion Rate Click Through Rate Category Automobile CPG Education / Government Entertainment Finance Pharma / Health QSR Retail Travel Property AOL Jobs AOL.com AOL On Daily Finance Engadget Homesessive Huffington Post HuffPost Celebrity HuffPost Sports Mapquest Moviefone Mandatory Kitchen Daily TechCrunch TUAW Stylelist Cambio Autoblog Product Premium Formats Display Video Mobile Sponsorships Roadblocks Devil Network Branded Entertainment Live Advertising Native Campaign Ratings Advertiser Bank of America Audi American Express Zillow Miller Coors L’oreal LendingTree Toyota Vanguard Home Depot Target J Crew Visa University of Georgia Major League Baseball Staples T Mobile General Mills Unilever Brand Favorability Purchase Intent Tech AOL Travel Joystiq Devil Targeting HP
- 27. KPI Brand Awareness Ad Awareness Brand Favorability Message Association Purchase Intent Interaction Rate Interaction Time Video Completion Rate Conversion Rate Click Through Rate Category Automobile CPG Education / Government Entertainment Finance Pharma / Health QSR Retail Tech Travel Property AOL Jobs AOL Travel AOL.com AOL On Daily Finance Engadget Homesessive Huffington Post HuffPost Celebrity HuffPost Sports Joystiq Mapquest Moviefone Mandatory Kitchen Daily TechCrunch TUAW Stylelist Cambio Autoblog Product Premium Formats Devil Display Video Mobile Sponsorships Roadblocks Targeting Devil Network Branded Entertainment Live Advertising Native Advertiser Bank of America Audi American Express Zillow Miller Coors L’oreal LendingTree HP Toyota Vanguard Home Depot Target J Crew Visa University of Georgia Major League Baseball Staples T Mobile General Mills Unilever AdEffectiveness
- 28. KPI Brand Awareness Ad Awareness Brand Favorability Message Association Purchase Intent Interaction Rate Interaction Time Video Completion Rate Conversion Rate Click Through Rate Category Automobile CPG Education / Government Entertainment Finance Pharma / Health QSR Retail Tech Travel Property AOL Jobs AOL Travel AOL.com AOL On Daily Finance Engadget Homesessive Huffington Post HuffPost Celebrity HuffPost Sports Joystiq Mapquest Moviefone Mandatory Kitchen Daily TechCrunch TUAW Stylelist Cambio Autoblog Product Premium Formats Devil Display Video Mobile Sponsorships Roadblocks Targeting Devil Network Branded Entertainment Live Advertising Native Advertiser Bank of America Audi American Express Zillow Miller Coors L’oreal LendingTree HP Toyota Vanguard Home Depot Target J Crew Visa University of Georgia Major League Baseball Nissan T Mobile General Mills Unilever PostCampaign Analysis
- 29. YOU DON’T NEEDAMILLION TO LOOK LIKEAMILLION
- 31. To Democratize Data Be Neither Slave Nor Master – But Liberator
Notes de l'éditeur
- Thank you very much, Niels – it’s an honor to be here at The University of Georgia to talk about How to Make Your Stakeholder Smarter I always wanted to get my education in the South And after getting accepted at a couple of my safe schools down here – I was quickly DENIED by Chapel Hill up in North Carolina At 18 this was one of the major REJECTIONS of my life – 2nd to only Debbie Bentley in 6th grade (the 1st time I expressed interest in a girl) And while 1991 Tom Kelly may not have gotten into Chapel Hill, I’m convinced 2014 Tom Kelly would. But that’s only because I’m Older . . . More experienced . . . . And I’m smarter Over the past 20 years or so I’ve become smarter professionally as well as personally . . . . .
- Until 3rd day at Aol Sr exec “how make sales team smarter” so daunting here I had become SMARTER both pers and prof – but no idea how make others smarter
- The answer to this question is NO relief off thook
- Tall order 5,000 . . . . .
- 5 yrs ago emerged WORST merger in global business history FACEBOOK GOOGLE AND $1.8B in advertising revenue – REVENUE THAT’S GETTING MUCH HARDER TO WIN – WHICH IS EXACTLY WHY I WAS BEING ASKED TO MAKE MY SALES COLLEAGUES SMARTER
- B/C the way advertising bought/sold changed YES . . . Jeans parties
- Heard the pitch, seen present, lunch was nice NOW PROVE
- & client fdbk says our PROOF needs align w/ THEIR KPIs . . . . . KPIs like To further this problem – most media seller don’t know the answers & materials don’t help
- Not easy to prove 2 ways to address & get insight into hands of lrg sales team PEOPLE and TECHNOLOGY
- Going to show TECH solution (Aol Ad Works) few mins 2 yrs ago we solved our Insight Sharing challenge with PEOPLE – this is what that looked like
- PLAY MOVIE
- AFTER VIDEO: Now FF 2 years 3,000 data projects / yr EXPANDED OVERTAXED AUTOMATION On that note let’s dive right in to our Configurable Insights tool called AOL Advertising Works – it’s simple and fast SO we’re going to show 2 run throughs
- PLAY MOVIE
- So was interesting to see People sol juxtaposed against Technology solution – BOTH WORK and BOTH WORK TOGETHER but Tech solution more elegant and scalable Along build process 4 important truths share w/ you now
- NOT showing Case Studies here – Proof Points tied to Normative Data This is 1 of most MIS-UNDERTSOOD concepts in marketing insights world
- To be sure, CS ARE useful Large format ad – DEVIL – helped Hunger Games crush opening day ticket sales 26% INT RATE BUT sample sizes of 1 OUTLIERS Here’s a few others All have in common?
- Fine print
- In last yr AOL measured 36T on OUR OWN SITES Typical, Projectable, Forward Looking
- What one have to do to arrive?
- The next natural question along the build path was not WHAT should we communicate – but HOW – and 1 answer jumped out at us - Don’t build another dashboard
- Where the output only data – with no insight or interpretation, usage low across 700 people This is b/c dashboards are to client friendly output what little yellow sticky notes are to client friendly output
- This is what typical AOL Advertising Works output is like – ready to be presented to a client – and it makes all the difference
- BUT THE REAL KEY IS HOW YOU ORGANIZE YOUR DATA AND HOW ACCURATELY YOU BUILD A TAXONOMY ACROSS ALL DATA SOURCES
- How do you go from this cacaphonous mess . . . .
- To this? Meta-tagging
- metatag process this is what makes easy to understand Proof oriented Results Combos
- It’s easy to see how simple, elegant and scalable the AOL Advertising Works platform is versus the People approach - and it didn’t take as much investment as you think
- Easy roadmap to solve for the mounting demands from clients for PROOF In the end we didn’t make our stakeholders smarter, we built a platform to make themselves smarter