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ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
ADMA Digital Certificate: Analyse to optimise
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ADMA Digital Certificate: Analyse to optimise

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  • Customer Behavior Isn't LinearIf analysis has taught us in the online marketing, where a 10 percent visit-to-purchase conversion rate is still considered extraordinary, it's that customers don't behave in a linear fashion. Customers' goals don't always align with our direct online revenue goals. Customers change their minds. They get distracted. They lose interest. They save carts, abandon carts, add items to carts, remove items from carts, and sometimes all the above -- and in no particular order. Sometimes they navigate for products, sometimes they search for products. Sometimes they do both in the same visit. So long as customers are people, customer behavior will be dynamic and at times irrational, random, and unexplainable.So why are we trying to fit the dynamic nature of online customer behavior into a linear model? I've heard this question discussed recently in online retailing circles. It will gain momentum as a better model for analyzing customer behavior for e-commerce organizations. http://www.clickz.com/showPage.html?page=3596566
  • Transcript

    • 1. [ Analyse to optimise ] Campaign tracking and analytics digital certificate guest lecture
    • 2. [ Company history ]  Datalicious was founded in late 2007  Strong Omniture web analytics history  1 of 4 Omniture Service Partners globally  Now 360 data agency with specialist team  Combination of analysts and developers  Making data accessible and actionable  Evangelizing smart data driven marketing  Driving industry best practice (ADMA) October 2010 © Datalicious Pty Ltd 2
    • 3. [ Smart data driven marketing ] October 2010 © Datalicious Pty Ltd 3 Media Attribution Optimise channel mix Testing Improve usability $$$ Targeting Increase relevance
    • 4. [ Clients across all industries ] October 2010 © Datalicious Pty Ltd 4
    • 5. Awareness Interest Desire Action Satisfaction [ AIDA and AIDAS formulas ] October 2010 © Datalicious Pty Ltd 5 Social media New media Old media
    • 6. Reach (Awareness) Engagement (Interest & Desire) Conversion (Action) +Buzz (Satisfaction) [ Simplified AIDAS funnel ] October 2010 © Datalicious Pty Ltd 6
    • 7. People Reached People Engaged People Converted People Delighted [ Marketing is about people ] October 2010 © Datalicious Pty Ltd 7 40% 10% 1% Quantitative and qualitative research data Website, call center and retail data Social media data Media and search data Social media
    • 8. [ Google data in Australia ] October 2010 © Datalicious Pty Ltd 8 Source: http://www.hitwise.com/au/datacentre
    • 9. [ Search and brand strength ] October 2010 © Datalicious Pty Ltd 9
    • 10. [ Search and the product lifecycle ] October 2010 © Datalicious Pty Ltd 10 Nokia N-Series Apple iPhone
    • 11. [ Search and media planning ] October 2010 © Datalicious Pty Ltd 11
    • 12. [ Search driving offline creative ] October 2010 © Datalicious Pty Ltd 12
    • 13. [ Facebook insights ] October 2010 © Datalicious Pty Ltd 13 Using Facebook Like buttons is a free and powerful way to gain additional insights into consumer preferences and enabling social sharing of content as well as possibly influence organic search rankings in the near future.
    • 14. [ Conversion funnel 1.0 ] October 2010 Conversion funnel Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping information, order confirmation, etc Conversion event Campaign responses © Datalicious Pty Ltd 14
    • 15. [ Conversion funnel 2.0 ] October 2010 Campaign responses (inbound spokes) Offline campaigns, banner ads, email marketing, referrals, organic search, paid search, internal promotions, etc Landing page(hub) Success events (outbound spokes) Bounce rate, add to cart, cart checkout, confirmed order, call back request, registration, product comparison, product review, forward to friend, etc © Datalicious Pty Ltd 15
    • 16. [ Additional success metrics ] October 2010 © Datalicious Pty Ltd 16 Click Through Add To Cart Click Through Page Bounce Click Through $ Click Through Call back request Store Search ? $ $ $ Cart Checkout Page Views ? Product Views
    • 17. How many survey responses do you need if you have 10,000 customers? How many email opens do you need to test 2 subject lines if your subscriber base is 50,000? How many orders do you need to test 6 banner executions if you serve 1,000,000 banners Google “nss sample size calculator”
    • 18. How many survey responses do you need if you have 10,000 customers? 369 for each question or 369 complete responses How many email opens do you need to test 2 subject lines if your subscriber base is 50,000? And email sends? 381 per subject line or 381 x 2 = 762 email opens How many orders do you need to test 6 banner executions if you serve 1,000,000 banners? 383 sales per banner execution or 383 x 6 = 2,298 sales Google “nss sample size calculator”
    • 19. [ De-duplication across channels ] October 2010 © Datalicious Pty Ltd 19 Banner Ads Email Blast Paid Search Organic Search $ Bid Mgmt Ad Server Email Platform Google Analytics $ $ $ Central Analytics Platform $ $ $
    • 20. [ Exercise: Duplication impact ]  Double-counting of conversions across channels can have a significant impact on key metrics, especially CPA  Example: Display ads and paid search – Total media budget of $10,000 of which 50% is spend on paid search and 50% on display ads – Total of 100 conversions across both channels with a channel overlap of 50%, i.e. both channels claim 100% of conversions based on their own reporting but once de-duplicated they each only contributed 50% of conversions – What are the initial CPA values and what is the true CPA?  Solution: $50 initial CPA and $100 true CPA – $5,000 / 100 = $50 initial CPA and $5,000 / 50 = $100 true CPA (which represents a 100% increase) October 2010 © Datalicious Pty Ltd 20
    • 21. [ Success attribution models ] Banner Ad $100 Email Blast Paid Search $100 Banner Ad $100 Affiliate Referral $100 Success $100 Success $100 Banner Ad Paid Search Organic Search $100 Success $100 Last channel gets all credit First channel gets all credit All channels get equal credit Print Ad $33 Social Media $33 Paid Search $33 Success $100 All channels get partial credit Paid Search October 2010 21© Datalicious Pty Ltd
    • 22. [ First and last click attribution ] October 2010 © Datalicious Pty Ltd 22 Chart shows percentage of channel touch points that lead to a conversion. Neither first nor last-click measurement would provide true picture Paid/Organic Search Emails/Shopping Engines
    • 23. Closer SEM Generic Banner View TV Ad [ Full path to purchase ] Influencer Influencer October 2010 23© Datalicious Pty Ltd $ Banner Click $ SEO Generic Affiliate Click $ SEO Branded Direct Visit Email Update Abandon Direct Visit Social Media SEO Branded Introducer
    • 24. [ Understanding channel overlap ] October 2010 © Datalicious Pty Ltd 24
    • 25. [ Website entry survey ] October 2010 © Datalicious Pty Ltd 25 Channel % of Conversions Straight to Site 27% SEO Branded 15% SEM Branded 9% SEO Generic 7% SEM Generic 14% Display Advertising 7% Affiliate Marketing 9% Referrals 5% Email Marketing 7% De-duped Campaign Report } Channel % of Influence Word of Mouth 32% Blogging & Social Media 24% Newspaper Advertising 9% Display Advertising 14% Email Marketing 7% Retail Promotions 14% Greatest Influencer on Branded Search / STS Conversions attributed to search terms that contain brand keywords and direct website visits are most likely not the originating channel that generated the awareness and as such conversion credits should be re-allocated.
    • 26. The study examined data from two of the UK’s busiest ecommerce websites, ASDA and William Hill. Given that more than half of all page impressions on these sites are from logged-in users, they provided a robust sample to compare IP-based and cookie-based analysis against. The results were staggering, for example an IP-based approach overestimated visitors by up to 7.6 times whilst a cookie-based approach overestimated visitors by up to 2.3 times. [ Unique visitor overestimation ] October 2010 © Datalicious Pty Ltd 26 Source: White Paper, RedEye, 2007
    • 27. [ Importance of calendar events ] October 2010 © Datalicious Pty Ltd 27 Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
    • 28. © Datalicious Pty Ltd [ Useful analytics tools ]  http://labs.google.com/sets  http://www.google.com/trends  http://www.google.com/insights/search  http://www.google.com/sktool  http://bit.ly/googlekeywordtoolexternal  http://www.google.com/webmasters  http://www.google.com/adplanner  http://www.google.com/videotargeting  http://www.keywordspy.com  http://www.compete.com October 2010 28
    • 29. © Datalicious Pty Ltd [ Useful analytics tools ]  http://bit.ly/hitwisedatacenter  http://www.socialmention.com  http://twittersentiment.appspot.com  http://bit.ly/twitterstreamgraphs  http://twitrratr.com  http://bit.ly/listoftools1  http://bit.ly/listoftools2  http://manyeyes.alphaworks.ibm.com  http://www.wordle.net  http://www.tagxedo.com October 2010 29
    • 30. ADMA short course “Analyse to optimise” In Melbourne & Sydney October/November By Datalicious
    • 31. Contact us cbartens@datalicious.com Follow us twitter.com/datalicious Learn more blog.datalicious.com

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