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How does one of the world's most highly-trafficked websites, takes a best-of-breed approach to data analytics to fully understand and engage online participants. AOL utilizes clickstream data to …

How does one of the world's most highly-trafficked websites, takes a best-of-breed approach to data analytics to fully understand and engage online participants. AOL utilizes clickstream data to create person-centric views of individual visitors and leverages behavioral targeting and predictive analysis techniques to optimize every message across every web interaction for individual visitors, increasing customer loyalty. Senthil describes how AOL links web data to actionable systems (email marketing, site personalization, etc.) to maximize the value of website optimization initiatives and how they mine social network behaviors and interactions to customize offers for increased conversion rates.

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  • How does one of the world's most highly-trafficked websites, takes a best-of-breed approach to data analytics to fully understand and engage online participants. AOL utilizes clickstream data to create person-centric views of individual visitors and leverages behavioral targeting and predictive analysis techniques to optimize every message across every web interaction for individual visitors, increasing customer loyalty. Senthil Mohan describes how AOL links web data to actionable systems (email marketing, site personalization, etc.) to maximize the value of website optimization initiatives and how they mine social network behaviors and interactions to customize offers for increased conversion rates.
  • What is Projection? Projection is a voyage that collects data over time to predict and target behavioral patterns by combining web analytics data with back-office systems to achieve actionable solutions.  Here, I want to emphasize the word back-office, which will play a critical role in web analytics paradigm. Here is a quick formulae to derive projections, we will come back to this equation later in the presentation.
  • This diagram depicts the eco-system of all the different touch points where Web Analytics should interface with, starting with Finance, CRM and ERP systems.
  • Here is a simple example:50 customers performing 10 web actions across 2 geoswithin two time periods for 12 months computes to 24,000 combinations Now, let’s look at a real-world scenario for a high traffic website: For 30 Million customers, with the same parameters, we are dealing with over 14 Billion combinations. So, let’s image if we have to map the demographic data, like age by income by gender; product by income by age; This is called Data Explosion.
  • Traditional web analytics are good in delivering static and drill down reports on a limited data set. There are several short falls of the current web analytics tools such as near real-time forecasting and predictive modeling on big data.Optimization : What is the nest that can happenPredictive : What will happen next ?Forecasting : What if these trends continue ?Ad Hoc : How Many and How often ?Drill Down : Where exactly the problem ?Reports : What happened ?What we can Optimize in this one ? Improve in this model. ..Able to process High Volume of DataNear Real-Time ForecastingCreatingPredictive ModelingDefine and drive new metrics on the fly to meeting business demands.
  • Traditional web analytics are good in delivering static and drill down reports on a limited data set. There are several short falls of the current web analytics tools such as near real-time forecasting and predictive modeling on big data.Optimization : What is the nest that can happenPredictive : What will happen next ?Forecasting : What if these trends continue ?Ad Hoc : How Many and How often ?Drill Down : Where exactly the problem ?Reports : What happened ?Able to process High Volume of DataNear Real-Time ForecastingCreatingPredictive ModelingDefine and drive new metrics on the fly to meeting business demands.
  • Data sets are raw data.Data Movement multiple data-store.
  • Data Movement to centralized data-store.
  • Sampling fails on the following scenarios: Change in Business Logic Multi Domain Integration Segment Analysis Behavioral Targeting Forecasting Predictive Analysis The reason that solving these problems is challenging is because web analytics data volumes are very high, and they involve high cardinality – sampling would reduce the accuracy and in most cases for qualitative data, would return incorrect results.
  • So what is a Netezza data warehouse appliance?Netezza has taken the concept of a relational database and instantiated in hardware – taking what historically has been 3 independent technology silos – database technology, server technology, and storage technology – and re-integrating them into an appliance architecture, which opened up opportunities to innovate across that entire technology stack and deliver 100x the performance, at less than half the cost of traditional data warehouse solutions, with unmatched simplicity of deployment and operation
  • Purpose built, Analytics AppliancePerformance10-100x faster than traditional systemsFrom Terabytes to Petabytes of online capacitySimplicityAppliance simplifies installation, configuration, & upgradesInnovative architecture alleviates indexing, tuning, etc.Save time, improve productivity, release projects fasterValueTime to Value – install and running applications in 2-3 daysLowest TCO – low price, cost effective configurationsGreen Solution - 75% data center resource savings
  • Segmentation is the single most important capability underlying customer marketing and customer communications, and yet the type of segmentation that Omniture provides is really nothing more than primitive filtering. It is not real segmentation. Real segmentation requires a visitor-centric view of web visitors.And importantly, while there are strong benefits to all of these different types of segmentation strategies, behavioral segmentation provides the most powerful indication of audience intent, and because a Netezza data warehouse appliance enables you to store and query your granular audience data down to a segment of one, this capability is often the most significant value driver we see in our customers’ deployments.
  • How do you implement it?  Watching the clickstream, seeing that the user has clicked on these links, and changing the website behavior based on their prior clickstream pattern.   The story here is around “passive personalization of web pages”.  Creating a user cache to do personalization for the user
  • We started at the theory of projection and optimization of web analytics, then we looked at managing data explosion. Also, we talked about the different targeting models. Now, let’s correlate the theory with the actual practice. The sweet-spot of targeting is going to help us achieve the break-even point. Once we have derived the break-even point, it’s time for optimization, which is an iterative continuous process, which can be achieved through research, development and industry influence. Now, let’s see how this equation fits into our projection. Here we are starting with Optimization, which is the driving factor that is used to align the traditional web analytics metrics.  Then we continue to collect all predictive analysis factors till we reach our basic hypothesis, in our equation it’s called Forecasting, it’s also our control.  Forecasting is based on facts, from which we derive highly probable outcome or actions. Combining these models, we can achieve actual projection by factoring the entire data
  • Transcript

    • 1.
    • 2. AOL and Netezza-Powered Web Analytics
      Transforming Our Business with
      a Best-of-Breed Approach
      Senthil Kumar Mohan
    • 3. Projection
      Projection is a voyage that collects data over time to predict and target behavioral patterns by combining web analytics data with back-office systems to achieve actionable solutions.
      Forecasting

      f
      ( )
      Projection =
      Predictive Modeling
      Optimization
    • 4. Web Analytics?
      “Web Analytics is the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage. “
      Advertising
      Page View
      Visitor
    • 5. New view of Web Analytics
      “ Web Analytics is an instrument for measuring both Quantitative and Qualitative metrics across the organization. ”
      Sales
      Channels
      Customers
      CRM
      Advertising
      Audit & Control
      FINANCE
      Web
      Analytics
      OPERATIONS
      Accounting
      HRM
      Budgeting
      Procurement
      ERP / SCM
      Logistics
      Partners
      Suppliers
      Distribution
    • 6. Metrics
      Sales
      Channels
      Customers
      CRM
      Advertising
      Audit & Control
      FINANCE
      Web
      Analytics
      OPERATIONS
      Accounting
      HRM
      Budgeting
      Procurement
      ERP / SCM
      Logistics
      Partners
      Suppliers
      Distribution
    • 7. Data Explosion
      50 customers x 10 web actions x 2 geo x 2 time period
      x 12 months = 24,000 combinations
      30M customers x 10 web actions x 2 geo x 2 time period
      x 12 months = 14,400,000,000 combinations
    • 8. Traditional Web Analytics
      Knowledge
      Optimization
      Lost Value
      Predictive Modeling
      Forecasting
      Ad Hoc Reports
      Value Delivered By Traditional Web Analytics Tools
      Drill Down
      Reports
      Data
    • 9. Alternate View
      Revenue
      Knowledge
      Cost
      Profit
      Predictive Modeling
      Lost Value
      Optimization
      Forecasting
      Loss
      Ad Hoc Reports
      Fixed Cost
      Value Delivered By Traditional Web Analytics Tools
      Drill Down
      Reports
      Data
    • 10. Industry Approach
      BI Analytical / Reporting Tools
      Data Marts
      Data Stores
      Data Transformation
      Data Sets
      Data Sets
      Data Sets
      Data Sets
    • 11. Optimized Approach
      BI Analytical / Reporting Tools
      Large Massively Parallel Processing RDBMS
      Data Transformation
      Data Sets
      Data Sets
      Data Sets
      Data Sets
    • 12. Sampling Vs Full Dataset
      Sampling fails on the following scenarios:
      • Change in Business Logic
      • 13. Multi Domain Integration
      • 14. Segment Analysis
      • 15. Behavioral Targeting
      • 16. Forecasting
      • 17. Predictive Analysis
    • Database – Server – Storage: Consolidated
      100x THE PERFORMANCE
      ½ THE COST
      UNMATCHED
      SIMPLICITY
    • 18. The NetezzaTwinFin™ Appliance
      Slice of User Data
      Swap and Mirror partitions
      High speed data streaming
      Disk Enclosures
      SQL Compiler
      Query Plan
      Optimize
      Admin
      Hosts
      Processor &
      streaming DB logic
      Snippet Blades™ (S-Blades™)
      High-performance database
      engine streaming joins,
      aggregations, sorts, etc.
    • 19. Case Study : Netezza Appliance
    • 20. Key benefits of a Netezza-powered approach to web analytics
      100x THE
      PERFORMANCE
      ½ THE
      COST
      UNMATCHED
      SIMPLICITY
    • 21. Best-of-breed analytics tools deliver competitive advantage
    • 22. Targeting disparate data silos
      Customer
      Research
      Orders/Leads
      Satisfaction
      A/B Testing
      Revenue: How/Why
      Conversion Rates
      Income Group
      Demographic
      User Data
      Age Group
      Problem Resolution
      Gender
      Textual Analysis
      Segmentation
      Logistics
      Actionable Insights & Metrics
      Look-alike Modeling
    • 23. Technology
      AOL has developed the gold standard in Behavioral Targeting
      Scale
      AOL Media sites and advertising network reach 9 out of 10 consumers online
      Quality
      We combine best-in-class data providers with clean, well-lit inventory
      Optimization
      Results-based performanceusing the Internet’s best optimization technology
      Integrity
      AOL operates with full respect for consumer privacy; we allow users to opt-out and we do not collect any personally identifiable information
      Behavioral Targeting
      Behavioral Targeting is about reaching people, NOT pages
    • 24. Behavioral Modeling
      …delivered to“AOL Living audience”
      USERS ARE SLOTTED INTO RESPECTIVE AOL MEDIA BEHAVIORAL CATEGORIES
      SERVE RELEVANTADVERTISING
      CONSUMERS BROWSE AOL MEDIA SITES ONLINE
      AOL Technology
      User A recently visited
      AOL Living
      Moviefone
      AOL Living
      AOL Health
      parentdish
      The segment population is eligible for relevant targeting across the AOL Media sites
      Specific content consumption on AOL Media sites, and the recency and frequency of that consumption, is mined and modeled to define and create AOL Media segments. Users are slotted into their respective segment(s) based on observed visitation behaviors and these are stored in their cookie file
      Users on average spend more than 3 hours and 45 minutes longer on AOL than Yahoo! and Facebook*
      *Nielsen Online, June 2010
    • 25. Behavioral Targeting Suite
      OUR SUITE INCLUDES THE FOLLOWING SOLUTIONS
      BEHAVIORAL TARGETING
      Behavioral Targeting is an opportunity to reach users based on displayed behaviors online: content visited, search queries, ads clicked and/or viewed, actions taken on a website.
      Audience BehaviorsTarget one of 150 behavioral segments (e.g. Auto Intender, Apparel Shopper)
      Custom Audience BehaviorsTarget a custom segment of users who have displayed relevant, discrete behaviors
      Audience ExtensionTarget a specific set of AOL Media users outside of AOL Media content at a lower CPM
      Advertiser LeadBackRe-target users who visit your website
      Creative LeadBackRe-target users who have clicked or seen an ad banner
      Sponsorship LeadBackRetarget users who have been to specific sponsorship area of AOL
      Reverse LeadBackTarget anyone but your users
      SearchBackTarget users who have made a category-related search on AOL Search or AOL properties
    • 26. Demographics Targeting
      High
      Age: 18-24
      Income: High
      Recency: Low
      Age: 25-35
      Income: High
      Recency: High
      Income
      Recency of visits to YourWebSite.com
      High
      Age: 50-65
      Income: Low
      Recency: Low
      Age
      Age: 36-49
      Income: Low
      Recency: High
      High
    • 27. Geographic/Daypart Targeting
      User: Target users based on the DMA look-up, or destination query data
      Site: Target users while they consume geo-focused content
      Target users by time of day – or day of week – based on user time zones or
      Eastern Standard Time ad server configuration.
    • 28. Content targeting
      How it Works:
      Content targeting is the by-product of Behavioral and Geographic targeting. By identifying the customers behavior, location and time we can deliver the relevant content.
      1
      2
      3
      Identify the
      DMA / Geo segment.
      We deliver relevant content.
      Identify the
      Behavioral segment
    • 29. Case Study : Look-alike modeling
      Demo, Geo and Behavioral data is used to create look-alike models.
      This model is an anonymous method of identifying users and matching them with relevant content.
      For example, people from specific DMA code typically like product X and tend to drive Y.  Then whenever a visitor shares those characteristics, we can make more intelligent decisions about which content and ads to present. 
    • 30. Projection
      Revenue
      Knowledge
      Cost
      Profit
      Predictive Modeling
      Optimization
      Forecasting
      Loss
      Ad Hoc Reports
      Fixed Cost
      Drill Down
      Reports
      Data
    • 31. Thank you
      http://www.linkedin.com/in/senthilkmohan

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