Personalisation, behavioral targeting and online mkt optimisation


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On personalisation and behavioural targeting

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Personalisation, behavioral targeting and online mkt optimisation

  2. 2. AGENDAOn the subject of Behavioural Targeting,Personalisation and Online MarketingOptimisation.Headline topics for the content are1. Explain each concept2. Highlight some goodbad examples3. Describe how the Coremetrics applications can be used to help our clients in this area.
  4. 4. Behavioural TargetingMaking the Online Experience RelevantBehavioural advertising enables advertisers to reach valuable customers no matterwhere they are surfing on the web. By targeting consumers behaviourally, advertiserscan engage people when they are most receptive to their message delivering thesewith frequency and at scale. Rob Blake, head of agency sales, AOL AdvertisingWe can attract prospects with customized campaigns according to their interests,engage site visitors with dynamic content in response to their conduct and desires, andput the right message in front of the right person at the right time. We can create amore pleasant and more individual buying experience. We can quickly identify theoffers that will more likely convert those prospects to buyers. Jim SterneGOALS: IMPROVING EFFECTIVENESS OF YOU MARKETING STRATEGY IMPROVING EFFICIENCY RE-TARGETING ADVERTS Sources: IAB UK, AOL Advertising, eConsultancy, Wikipedia,
  5. 5. Behavioural TargetingMaking the Online Experience RelevantIQOLA Case Study The Idea: Iqola was an invented energy drink – “the first drink to improve your IQ” – to build awareness for a new brand amongst a niche audience of senior media executives purely via targeted display advertising. The Campaign: Spread over a three week period, the iqola campaign re-targeted everyone who registered online to attend a Microsoft Advertising organised conference on behavioural advertising in late September 2009. Impressions were delivered across AOL, Facebook, MSN, Specific Media Network and Yahoo! The Result: 50,000 impressions served.216 unique users. 67% of those polled recognised iqola as a brand they had seen online.Sources: IAB UK
  6. 6. Behavioural TargetingMaking the Online Experience RelevantYAHOO PORTAL Yahoo has a 2-petabyte, specially built data warehouse, which it uses to analyze the behavior of its half-billion Web visitors per month, processing 24 billion events a day. Yahoo! retains search requests for a period of 13 months. In response to European Regulators Yahoo scrambles the last eight digits of a users IP address after three months, rendering them partially anonymous.
  7. 7. PersonalisationMaking the Online Experience RelevantWeb personalisation is about delivering targeted content and adaptive webexperiences based on what you know about each visitor. eConsultancyDelivering customised content for the individual through web pages, e-mail or pushtechnology. Dave ChaffeyPersonalisation involves using technology to accommodate the differences betweenindividuals. WikipediaLeveraging site visitor and customer profile data with marketing technology to createrelevant and target conversations between customers and merchandises. Coremetrics GOAL: IMPROVING EFFECTIVENESS OF YOU MARKETING STRATEGYSources: eConsultancy, Wikipedia, Web Analytics Demystifies, Dave Chaffey
  8. 8. Behavioural Targeting : Personalisation
  9. 9. PersonalisationMaking the Online Experience Relevant 1. Knowing your customer Buying Decision Making Process a. New vs. Returning b. Historical buying behavior models c. Immediate behavior d. Sources e. The Context f. The dynamic environment 2. Proactively anticipating their needs (Predictive Modeling) 3. Create “Real Time” Dynamic individual profiles. 4. Automatically served them the right product at the right time using the right channel using the right media format Context – Relevant – Engaging – Automatic – Real Time – Enhancing Users Experience
  10. 10. PersonalisationMaking the Online Experience RelevantIMPLICIT PERSONALISATION EXAMPLE 1. Early adopter of personalisation technology to recommend products 2. Tracked/monitored user behaviour as they navigate the website (Registered user). 3. Use a rules-based filtering model, based on "if this, then that" rules processing, couple with a collaborative filtering approach, which serves relevant material to customers by combining their own personal preferences with the preferences of like- minded others. 4. Email Re-Targeting model
  11. 11. PersonalisationMaking the Online Experience RelevantIMPLICIT PERSONALISATION – SEARCH EXAMPLE Google uses factor weighting model which includes as variables factors as user history, bookmarks, community behaviour, site CRT and stickiness. Need to be logged in Google Account Bing personalises search results for all users based on an individuals previous searches.
  12. 12. PersonalisationMaking the Online Experience RelevantEXPLICIT PERSONALISATION EXAMPLE When a visitor elects a profile that determines they will see content in a manner that it is specific to their requirements. Also known as Customisation This is something like recommendation button shown along with the each search result, by pressing this button it will be shown in search results to your friends(your friends, chat and contacts in Google) that you have “+1’d this” (that you like or recommend this) if they have search for the same term.
  13. 13. PersonalisationMaking the Online Experience RelevantIMPLICIT PERSONALISATION EXAMPLE Personalised Subject Line: “I coulda been a contender”). Strong, Simple Image: Timberland = boots – sponsoring the Sundance Film Festival in Park City = Snow Simple Yet Provocative Headline Content Navigation, Pre-header, and Mobile are all
  14. 14. ISSUES WITH PERSONALISATION & BTWhy some people are simply not interested1. Anonymity preferred. Cookies deletion, log out.2. Lack of relevance. People do not want a relationship with companies that have no real relevance to them.3. Lack of credibility. Relevant vs. Intrusion.4. Lack of security. What about my personal details?5. Technological barriers. Latest Mobile phone apps.6. Infrequent contact.7. Misunderstood terminology.
  15. 15. PersonalisationWhen things go wrongWRONG PERSONAS Build around Company’s needs, not customers
  16. 16. PersonalisationWhen things go wrongAN EMAIL EXAMPLE – WRONG TARGETED SEGMENT A “targeted” email campaign, Dedicate a Flower for Mothers Day, supporting the initiative with "send to a friend" viral options and a link to the Interflora Site for information and purchase. Mums received an email from Interfloras epartner Communicator, which included a link to the page where her message is featured, and a confirmation email is sent to the poster. Badly targeted: It was sent to everyone in their mailing list
  17. 17. PersonalisationWhen things go wrongAN EMAIL EXAMPLE – WRONG TECHNOLOGY SO Big Brother
  18. 18. Behavioural targetingWhen things go wrongINTRUSIVE AND OUT OF CONTEXTADS I made the mistake of clicking an ad re those websites once, more than 2 months ago. They are still appearing in disparate and not contextual sites.
  19. 19. ONLINE MARKETING OPTIMISATIONTo capture and maintain your most valuable segment Inaccurate Attribution Online Marketing Model (last session or last click 100% attribution) 1. Technological Barriers (first vs. last touch strategy), vendors limitations 2. Historical data not fully available or too difficult conciliating diverse data sources 3. Segmentation based on inaccurate Personas 4. High Risk in developing model fitting the past but predicting poorly the future 5. Lack of Resources/Budget
  20. 20. ONLINE MARKETING OPTIMISATIONTo capture and maintain your most valuable segment In Order to Boost Avg Order Value, Increase Conversion, strength loyalty and retention we need to unlock the Jewel Box 1. Identify an universal key point, e.g. Order ID – depending on business objectives. 2. Conduct Path Analysis, Historical Customer Purchasing Cycle, e.g. last 3-6 months days data (proper segmentation) 3. Click sequence analysis before purchasing – timing depends on product/industry. Use at least the last known 4 touch points 4. Build a MKT attribution model 5. Predicting Model and automatic services 6. Continually test your results Econometrics↔ Multivariate Regression↔ (Math) Optimisation Model ↔ ?
  21. 21. Q&A?