e-commerce data analysis and e-metrics

1. E-Commerce Data Analysisand E-Metrics

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3. 3 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

4. 4 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

5. 5 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

6. Basic Framework for E-Commerce Data Analysis

7. 7 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

8. 8 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

9. 9 Levels of Aggregation in Web Usage Analytics

10. 10 How E-Business Analytics Are Used

11. 11 The Goal of E-Business Analytics

12. 12 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

13. 13 The Customer Life Cycle Funnel

14. 14 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

15. 15 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

16. 16 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.)

17. 17 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

18. 18 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:

19. 19 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

20. 20 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.