Purchase Funnel Analysis - PowerPoint by xdn20552


More Info
									E-Commerce Data Analysis
     and E-Metrics

          Bamshad Mobasher
    School of CTI, DePaul University

 From before
    Review of personalization based on usage profiles
    Integration of content and usage for personalization

 E-Commerce Data Analysis
    E-Commerce Data
    Integrating E-Commerce, Usage, and Content Data
    E-Metrics

               E-Commerce Events
 Associated with a single user during a visit to a Web site
 Either product oriented or visit oriented
 Not necessarily a one-to-one correspondence with user actions
 Used to track and analyze conversion of browsers to buyers

 Product-Oriented Events
    Impression
    View
    Click-through
    Shopping Cart Change
    Buy
    Bid

            Product-Oriented Events
 Product View
    Occurs every time a product is displayed on a page view
    Typical Types: Image, Link, Text
 Product Click-through
    Occurs every time a user “clicks” on a product to get more information
      Category click-through
      Product detail or extra detail (e.g. large image) click-through
      Advertisement click-through
 Shopping Cart Changes
    Shopping Cart Add or Remove
    Shopping Cart Change - quantity or other feature (e.g. size) is changed
 Product Buy or Bid
    Separate buy event occurs for each product in the shopping cart
    Auction sites can track bid events in addition to the product purchases

       E-Commerce vs. Usage Data
 E-commerce data is product oriented while Usage data is
  page view oriented
 Usage events (page views) are well defined and have
  consistent meaning across all Web sites
 E-commerce events are often only applicable to specific
  domains, and the definition of certain events can vary from
  site to site
 Major difficulty for Usage events is getting accurate
  preprocessed data
 Major difficulty for E-commerce events is defining and
  implementing the events for a site

                  Basic Framework for E-Commerce
   Content                 Data Analysis

   Content           Data Cleaning /   Integrated          Usage
   Analysis          Sessionization    Sessionized        Analysis
   Module               Module            Data

                                       E-Commerce     Tools
                         Data           Data Mart
Web/Application       Integration
 Server Logs            Module                                        OLAP

                                                     Data Cube

  Site Map
                    products           Data Mining        Pattern
                                         Engine           Analysis

  Components of E-Commerce Data
       Analysis Framework
 Content Analysis Module
    extract linkage and semantic information from pages
    potentially used to construct the site map and site dictionary
    analysis of dynamic pages includes (partial) generation of pages based on
     templates, specified parameters, and/or databases (may be done in real time, if
     available as an extension of Web/Application servers)

 Site Map / Site Dictionary
    site map is used primarily in data preparation (e.g., required for pageview
     identification and path completion); it may be constructed through content
     analysis and/or analysis of usage data (e.g., from referrer information)
    site dictionary provides a mapping between pageview identifiers / URLs and
     content/structural information on pages; it is used primarily for “content
     labeling” both in sessionized usage data as well as integrated e-commerce data

    Components of E-Commerce Data
         Analysis Framework
 Data Integration Module
    used to integrate sessionized usage data, e-commerce data (from application
     servers), and product/user data from databases
    user data may include user profiles, demographic information, and individual
     purchase activity
    e-commerce data includes various product-oriented events, including shopping cart
     changes, purchase information, impressions, click-throughs, and other basic metrics
    primarily used for data transformation and loading mechanism for the Data Mart

 E-Commerce Data mart
    this is a multi-dimensional database integrating data from a variety of sources, and
     at different levels of aggregation
    can provide pre-computed e-metrics along multiple dimensions
    is used as the primary data source in OLAP analysis, as well as in data selection for
     a variety of data mining tasks (performed by the data mining engine

        Levels of Aggregation in Web
              Usage Analytics

user                           Loyal
             Business        Customers
              Value                        Data


                           Visits        Usage Data

                                         Raw Usage
                            Hits           Data

                        Amount of Data
How E-Business Analytics Are Used

                Modify        Change           Change           Change           Evaluate
             Site Design       Ads            Promotion       Product Mix      Partnership
                                               Strategy                         Strategies

              Source: “E-Metrics Business Metrics For The New Economy,” NetGenesis, 2000.

The Goal of E-Business Analytics

                E-Customer Life Time Value Optimization Process

       E-Customer Life Cycle
 Describes the milestones at which we:          Loyalty
    target new visitors
    acquire new visitors
    convert them into registered/paying users
    keep them as customers
    create loyalty

The Customer Life Cycle Funnel
             Untargeted Promotions                  Good Targeting
              Attract Wrong People              Ineffective Persuasion

                Good Persuasion                   Good Persuasion
                Poor Conversion                   Good Conversion

 Source: “E-Metrics Business Metrics For The New Economy,” NetGenesis, 2000.

 Elements of E-Customer Life Cycle
 Reach
    targeting new potential visitors
    can be measured as a percentage of the total market or based on other measures
     of new unique users visiting the site
 Acquisition
    transformation of targeting to active interaction with the site
    e.g., how many new users sessions have a referrer with a banner ad?
    e.g., what percentage of targeted audience base is visiting the site?
 Conversion
    persuasion of browsers to interact more deeply with the site (registration,
     customization, purchasing, etc.)
    conversion rate usually refers to ratio of visitors to buyers
    but, we need a more fine grained measure: micro-conversion rates
         look-to-click rate
         click-to-basket rate    Also: registration & customization ratios
         basket-to-buy rate

 Elements of E-Customer Life Cycle
 Retention
    difficult to measure and metrics may need to be time/domain dependent
    usually measured in terms of visit/purchase frequency within a given time
     period and in a given product/content category
    time-based thresholds may need to be used to distinguish between retained
     users and deactivated-reactivated users
 Loyalty
    loyalty is indicated by more than purchase/visit frequency; it also indicates
     loyalty to the site or company as a whole
    special referral or “bonus” campaigns may be used to determine loyal
     customers who refer products or the site to others
    in the absence of other information, combinations of measures such as
     frequency, recency, and monetary value could be used to distinguish loyal

 Elements of E-Customer Life Cycle
                  Interruptions in the Life Cycle
 Abandonment
    measures the degree to which users may abandon partial transactions (e.g.,
     shopping cart abandonment, etc.)
    the goal is to measure the abandonment of the conversion process
    micro-conversion ratios are useful in measuring this type of event
 Attrition
    applies to users/customers that have already been converted
    usually measures the % of converted users who have ceased/reduced their
     activity within the site in a given period of time
 Churn
    is measured based on attrition rates within a given time period (ratio of
     attritions to total number of customers
    goal is to measure “roll-overs’ in the customer life cycle (e.g., percentage
     loss/gain in subscribed users in a month, etc.)

              Basic E-Customer Metrics
 RFM (Recency, Frequency, Monetary Value)
    each user/customer can be scored along 3 dimensions, each providing unique
     insights into that customers behavior
    Recency - inverse of the time duration in which the user has been inactive
    Frequency - the ratio of visit/purchase frequency to specific time duration
    Monetary Value - total $ amount of purchases (or profitability) within a given time period

                                                   Monetary Value
                                      1 2 3 4 5     5 4 3 2 1

                     Basic Site Metrics
 Stickiness
    measures site effectiveness in retaining visitors within a specified time period
    related to duration and frequency of visit

           Stickiness = Frequency x Duration x Total Site Reach

     Frequency = (Visits in time period T) / (Unique users who visited in T)

         Duration = (Total View Time) / (Unique users who visited in T)

    Total Site Reach = (Unique users who visited in T) / (Total Unique Users)

      This simplifies to:

               Stickiness = (Total View Time) / (Total Unique Users)

                     Basic Site Metrics
 Slipperiness
    inverse of stickiness
    used for portions of the site in which it low stickiness in desired (e.g., customer
     service or online support)
 Focus
    measures visit behavior within specific sections of the site

  Focus = (Avg. no. of pages visited in section S) / (Total no. of pages in S)

                         High Stickiness                       Low Stickiness

                   Either consuming interest on the     Either quick satisfaction or
 Narrow Focus      part of users, or users are stuck.   perhaps disinterest in this section.
                   Further investigation required.      Further investigation required.

                   Enjoyable browsing indicates a       Attempting to locate the correct
 Wide Focus
                   site ”magnet area”.                  information.

  E-Metrics, OALP, and Data Mining
 It is important to note that E-Metrics do not take the place of
  OLAP analysis or data mining:
    E-metrics are good for providing basic measures related to site effectiveness
     and individual visitor behavior beyond simple usage analysis.

    OLAP analysis can be used to gain an understanding of relationships at higher
     or lower levels of aggregation among or between objects (products or pages)
     and subjects (users, visitors, customers). But, it requires prior knowledge
     (hypothesis testing), and is not automated.

    Data mining can discover patterns which may be unexpected and lead to the
     discovery of deeper knowledge about subjects and objects.


To top