Fallon Brainfood: Fall0nylitics 2.1


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"Fall0nylitics 2.1: 17 Things You Probably Don’t Know About Online Advertising (And 1 Thing You Probably Do)"

Did you know that 80% of clicks are done by 20% of people—and they’re probably not your target? That the biggest impact of online display ads could be on search? The explosion of data available to digital marketers often creates as much confusion as opportunity. This presentation aims to dispel the confusion so you can focus on seizing the opportunity.

Join Marty Kihn, Director of Strategic Analysis at Fallon, (http://www.fallon.com/blog/news/fallon-adds-depth-to-digital/) as he breaks down the most important things for marketers and creatives—and not just digital creatives—to know about online advertising.

Using case studies and the results of recent findings in the field, Marty lays out the key counter-intuitive insights we should know to make smarter decisions online. Learn who’s clicking, why landing pages matter, and how much of a difference the creative really makes.

In addition, Marty introduces Fallon's own developing campaign optimization and indexing tool. Look for an announcement of this innovative open source solution in the coming months.

Whether you are in charge of making digital investment decisions or are involved in optimizing specific campaigns, you'll find information you can use in this presentation.

In addition, Marty presents his data sources and is available to talk about additional details and implication of the proprietary case studies.

Brainfood is a series of presentations developed by thought leaders at Fallon that started several years ago. Brainfood's wide-ranging topics explore trends, innovations, business issues, and opportunities for marketers and brands. Moreover, Brainfood offers a chance to come together and engage in a stimulating discussion on a variety of interesting topics that affect our business.

Check out previous Fallon Brainfood presentations at http://www.slideshare.net/group/we-are-fallon.

Published in: Business

Fallon Brainfood: Fall0nylitics 2.1

  1. 1. Fallon Worldwide “Brainfood” POV“17 Things You May Not Know About OnlineAdvertising (And 1 Thing You Probably Do)”January 25, 2011
  2. 2. I’m here today to talk about analytics in the contextof creative digital advertising•  Creative agencies (like mine) are founded on the concept of “creative leverage”—that big ideas can work harder than big budgets.•  Analytics as a discipline has been driven by the explosion of data available online.•  To succeed in digital, good ideas are not good enough – they need to be supported, informed and changed by numbers.Today I’m going to present 17 surprising facts about online advertising culled from recent studies and client work – starting with one thing you probably know . . .
  3. 3. Online advertising (OLA) (n.) – “a thing that seemsto be getting less effective and more expensive”US Online $25B 0.50% Avg. OLAAd Spend CTR (%) ($) $20 0.40 $15 0.30 $10 0.20 $5 0.10 0 0 2000 2002 2004 2006 2008 2010 Source: Fallon analysis; Google Benchmarks; Forrester
  4. 4. #1. Digital spend is still too low.•  This is the first of the “17 Things You May Not Know About Online Advertising” . . .
  5. 5. Internet media budgets do not reflect howconsumers actually spend their time.
  6. 6. Display ads really do impact brand metrics. Brand Impact of Online Display (Control v. Exposed)Source: Dynamic Logic MarketNorms, 2008, Fixed frequency level of 1. Campaigns using online display advertising of any format.
  7. 7. #2. 80% of clicks are done by 20% of people. CLICKERS
  8. 8. Online clickers follow the 80/20 (Pareto) rule.“Despite only accounting for 6% of the total Internet population,heavy clickers accounted for 50% of clicks in the month.” ComScoreSource: comScore Inc., custom analysis, total US online population, XPC Persons Panel, July 2007 data periodNote: “Heavy” clickers defined as 4+ clicks on online advertising per month; “Moderate” = 2-3 clicks; “Light” = <1 click per month
  9. 9. #3. Clickers are probably NOT your best customers.
  10. 10. “Natural Born Clickers” •  Age: 25-44 •  Lower income: $20-40K •  2x more spend online •  Heavy Internet use:   5X higher time on site   8X more pages per visitor vs. online pop •  Non-clickers visit: portals, search, news, finance •  Heavy clickers visit: gambling, job Source: comScore Inc., custom analysis, total US online searching, games … and porn* population, XPC Persons Panel, July 2007 data period Note: “Heavy” clickers defined as 4+ clicks on online advertising per month; “Moderate” = 2-3 clicks; “Light” = <1 click per month * Just a guess
  11. 11. Clickers Are Generally Less Credit Worthy. Approval Rates•  Financial services client Financial Services Client View-Through Study case study•  Users who were exposed but did NOT click were 179% approved 180% more often Lift than users who clicked•  “Casino” mentality•  More leisure time = More time to click Control Exposed Clicked Source: Publicis financial services client case study (2007-2008)
  12. 12. #4. Your best customers may be invisible.
  13. 13. The Cookie Deletion Dilemma:Overstated Reach but Understated Frequency.Based on a comScore study of Yahoo and DoubleClick cookies:•  30% of Internet users delete their cookies in a month•  These deleters do so an average of 4x per month•  True for 1st party (e.g., NYT sign-in) and 3rd party (e.g., ad server) cookies   Up to 2.5x overstatement of “unique visitors”   Understatement of campaign ROI   Worse for higher-end and technology brands
  14. 14. #5. 80% of OLA’s effectivenesshas nothing to do with the CLICK.
  15. 15. It’s been conclusively shown across many industries that banners impact people who don’t click—like TV. Client Example: Latency Window Client Example: VT* Incrementality Conversions Acquired 0.14% Click-Based 0.0200% VT Incremental 0.12% 15.7% Lift* 0.0180% Days 0 -5: 341% lift over baseline 0.0160% 0.10% Net Yield by Unique UserConversion Probability (Hazard) 0.0140% 0.0120% 0.08% 0.0100% VT Days 5 -10: 124% lift over baseline 0.06% Non-Incremental 0.0080% Days 10 -15: 94% lift over baseline 0.0060% 0.04% 0.0040% 0.0020% Red Baseline = conversion probability for days 15 -60 0.02% 0.0000% 5 10 15 20 25 30 35 40 45 50 55 60 Length in Days of Advertising Effect 0.00% Control Ad Exposed * VT = View-through, defined as post-impression activity observed among users who were exposed to a display ad but did not click
  16. 16. #6. Most of a banner’s effect is probably on search.
  17. 17. Effect of Display Ads on Search by Industry. Observed Lift for Seachers Exposed to Banner Ads vs. Control +206% +144% +125% +69%Source: comScore, “How Online Advertising Works: Whither the Click?” Jan. 2009, presentation for ARF [meta-study of 200]; Specific Media study of 12 months.* Publicis client case study (2008-09)
  18. 18. #7. Same with TV.
  19. 19. Search Reacts to Television Advertising. TV GRPs vs. Search Clicks for National BrandSource: Omniture Inc (The Omniture Summit 2009, p8); case study client name suppressed
  20. 20. #8. Never do display without search.
  21. 21. Paid Search + Display Works Better Than Either Channel Alone. Incremental Impact on Offline Sales per (’000) Exposed Search & Search Display Display Only OnlySource: comScore, “How Online Advertising Works: Whither the Click?” Jan. 2009, presentation for ARF
  22. 22. #9. Impact is largely determined by your industry.
  23. 23. Display Ads’ Impact on Site Visitation by Industry. Advertiser Site Reach (Weeks 1-4 After First Exposure)Source: comScore, “How Online Advertising Works: Whither the Click?” Jan. 2009, presentation for ARF
  24. 24. #10. Rich media may not work the way you think it does.
  25. 25. Rich Media can work for aided awareness, but it may be distracting/confusing the brand’s message. Rich Media with Video has the MOST …But is significantly LESS Effective Impact on Aided Brand Awareness… at Impacting Message AssociationSource: Dynamic Logic MarketNorms Study (2008)
  26. 26. #11. Banners are often lessimportant than LANDING PAGES.
  27. 27. Most people who clickdon’t make it to the landing page. Banner Click-to-Quote Start Traffic for Insurance AdvertiserDrivers of Drop-off: (Click to Quote Start Traffic) 376,426•  Cookie rejection•  Long load times•  Natural defection -70.1% (user clicks stop, back, or close window)•  Connectivity issues (111,886)•  Improper tagging (263,882) -29.7% (-99.4% of LP traffic) 658 Source: DFA reporting Jan. 2009; Fallon analysis
  28. 28. Landing Pages Matter: One of these options drove a20-25% better response rate than the others. Landing Pages Tested •  Driven to a product-specific •  Driven to the application detail page •  Driven to less detail around more card products •  Result: Performed the best •  Result: Depressed •  Result: Performed the worst •  Significantly higher responses more than 20% among all variations (double-digit) response vs. single-product page rate Source: Confidential client analysis
  29. 29. #12. Landing pages need to MIRROR your creative.
  30. 30. Landing Pages must register as “not a mistake” <1 second after loading, or the user will bounce. •  Messaging expands & adds Visual brand and infoConsistency elements •  Identical visual element is the link •  Further down the funnel Keyword: iPod •  Transactional key word •  Driven to the iPod mall ContextualConsistency •  Higher up in the funnel – less transactional Keyword: MP3 Player •  Key words are made visual
  31. 31. #13. If you want good response, customize your landing page.
  32. 32. Different Targets = Different Landing Pages. Florida drivers   Examples of Landing Pages with images and “Review Your Texas drivers Quote” – Banner for content tied to a customer quote abandoners groupSavings Message Interactive Cross-sell Tabs – Students “Early Bird (Car, Home, Boat) Special” – Seniors Discount for Military“Welcome to yournew life” – Movers Alumni “could save even more” Find an Agent OR get a quote online
  33. 33. #14. Load times trump everything.
  34. 34. Longer load times may be the single biggestdriver of online response rates Response Rates vs. Load TimesResponse 30Rate Index* 20 15 10 0 0 5 10 15 20 Total Waiting-to-Load Time (Click to Quote Start – Paid Search Only, seconds)* Total responses (#) / Total impressions from online media (paid search and display). Does not include conversion rate or quality.
  35. 35. #15. The only way to measure trueimpact is to set up a test in advance.
  36. 36. Post-View tests can be set up relatively easily through an ad server like DoubleClick. Test Group- Views Ad ClickWeb Audience Brand Banner View Responses (no click) and Conversions Control Group- View Views PSA (not Ad) (no click) - Runs on same sites/placements Use cookie-level log files (from ad server) to differentiate between conversions that would have occurred naturally, versus those influenced by the online media. Appropriate latency window and attribution percentage is decided.
  37. 37. #16. If you fail to plan campaign measurement—plan to fail.
  38. 38. Best in class digital players start their measurementprocess well before assets are built.
  39. 39. #17. Creative can be less important than other factors in digital.
  40. 40. OLA and Paid Search often function more like Direct Mail than above-the-line advertising (e.g., TV). Impact of Different Elements on Prospect Response to DM Creative Elements -  Hooks to provoke immediate response (sample, etc.) -  Bullets, lists, dashes to catch skimmers Creative Targeting & List Selection -  Headline / “Johnson Box” above 20% Carefully select and cultivate letter salutation outlining key benefit customer and prospect lists Targeting -  Clear call-to-action to drive response 40% Value of Offer 40% Value of Offer The ideal DM offer: -  Fulfills a perceived need -  Conveys strong perceived value (compared to competitors) -  Is unique -  Is practical -  Has a clear connection with brandSource: CRM Trends; confidential DM client study (2008)
  41. 41. Bonus #18. But great creative + great product always wins . . .
  42. 42. Thanks.martin.kihn@fallon.com office: 612.758.2748