ECommerce Data Analysis and EMetrics

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E-Commerce Data Analysisand E-Metrics
Bamshad Mobasher School of CTI, DePaul University
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Today

• 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

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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

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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

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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

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Basic Framework for E-Commerce Data Analysis
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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

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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

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Levels of Aggregation in Web Usage Analytics
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How E-Business Analytics Are Used
Source E-Metrics Business Metrics For The New
Economy, NetGenesis, 2000.
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The Goal of E-Business Analytics
E-Customer Life Time Value Optimization Process
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E-Customer Life Cycle

• Describes the milestones at which we
• target new visitors
• acquire new visitors
• convert them into registered/paying users
• keep them as customers
• create loyalty

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The Customer Life Cycle Funnel
Source E-Metrics Business Metrics For The New
Economy, NetGenesis, 2000.
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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
• basket-to-buy rate

Also registration customization ratios
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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
  users/customers

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Elements of E-Customer Life CycleInterruptions
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.)

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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

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Basic Site Metrics

• Stickiness
• measures site effectiveness in retaining visitors
  within a specified time period
• related to duration and frequency of visit
• where
• This simplifies to

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