EMetrics and EBusiness Analytics Bamshad Mobasher De Paul

E-Metrics and E-Business Analytics Bamshad Mobasher De. Paul University

Web Usage Mining & E-Business Analytics i The primary goal of e-business analytics is to understand be able to predict the behavior of online customers i Examples of questions we want to answer using the data 4 Where did visitors come from? 4 What do they do when they get to the site? 4 How happy are the visitors/customers? 4 What are the outcomes: conversions, repeat visits, loyalty? 4 What types of content attracts which types of customers? 4 Which customers are profitable? 4 How profitable are different products or product categories? i Where do data-driven answers to these question come from? 4 E-metrics – metrics/statistics that tell us something about online behavior of the user on the site 4 Data mining – finding deeper patterns in the data and building models 2

Web Usage Mining & E-Business Analytics Different Levels of Analysis 4 Session Analysis 4 Static Aggregation and Statistics 4 OLAP 4 Data Mining 3

Session Analysis i Simplest form of analysis: examine individual or groups of user sessions and/or e-commerce transactions i Advantages: 4 Gain insight into typical customer behaviors 4 Trace specific problems with the site i Drawbacks: 4 LOTS of data 4 Difficult to generalize 4

Static Aggregation (Reports) i Most common form of analysis (e. g. , Google Analytics, Web. Trends, etc. ) i Data aggregated by predetermined units such as days or sessions i Generally gives most “bang for the buck. ” i Advantages: 4 Gives quick overview of how a site is being used. 4 Minimal disk space or processing power required. i Drawbacks: 4 No ability to “dig deeper” into the data. 5

Static Aggregation (Reports) i Typical tools: 4 Google Analytics 4 Urchin 4 Web. Trends 6

Online Analytical Processing (OLAP) i Allows changes to aggregation level for multiple dimensions i Generally associated with a Data Warehouse i Advantages & Drawbacks 4 Very flexible 4 Requires significantly more resources than static reporting. 7

Data Mining: Going deeper Prediction of next event Discovery of associated events or application objects Sequence mining Markov chains Association rules Discovery of visitor groups with common properties and interests Clustering Discovery of visitor groups with common behaviour Session Clustering Characterization of visitors with respect to a set of predefined classes Classification Anomaly/attack detection

How Data Mining is Used - Examples i Calibration of a Web server: 4 Prediction of the next page invocation over a group of concurrent Web users under certain constraints h. Sequence mining, Markov chains 4 Prefetching resources that are likely to be accessed next i Cross-selling of products: 4 Mapping of Web pages/objects to products 4 Discovery of associated products h. Association rules, Sequence Mining 4 Placement of associated products on the same page 4 Determining which items or product to feature on specific pages 9

How Data Mining is Used - Examples i Sophisticated cross-selling and up-selling of products: 4 Mapping of pages/objects to products of different price groups 4 Identification of Customer Groups or Segments h. Clustering, Classification 4 Discovery of associated products of the same/different price categories h. Association rules, Sequence Mining 4 Formulation of recommendations to the end-user h. Suggestions on associated products h. Suggestions based on the preferences of similar users 10

E-Metrics i Collection of aggregate statistics and metrics necessary to 4 Understand visitor/customer behavior 4 Understand how visitors are using the site 4 Measure e-business outcomes such as conversion, loyalty, etc. 4 Monitor factors that prevent successful outcomes i Basic Types of E-Metrics (not necessarily mutually exclusive) 4 Site e-metrics – metrics that tell us something about how the site as a whole or specific components (pages, categories, tools, functions) are being used and how to improve the site or its content 4 Customer e-metrics – metrics that characterize the behavior of visitor or visitor segments and measure the propensity of visitors convert 4 Basic business metrics – general metrics to measure how successfully overall business objectives are being met (revenue, profitability, etc. ). 11

E-Metrics Commonly Used by Industry Number of customers 100% 95% Visits resulting in purchase Average order value 91% Number of registered users 88% Origin of visitors 86% Customer service response time 79% Purchases over the last six months 79% Number of repeat visitors 74% Revenue for repeat visitors 63% Origin of repeat visitors 63% New and repeat conversion rates Customers in a loyalty program 60% 47% 12

Basic Site Metrics • Which site “referred” them – – – Search engine Affiliate site Partner Advertisement Contribution to sales or other desired outcome • Measures - allows the evaluation of the referrer – What percentage of all referrals came from this source? – Calculation of the cost of acquisition of each visitor 13

Basic Site Metrics • We can monitor – Which content is accessed by users – When they visit – How long they stay – Whether interaction with content leads to sales or other desired outcome • Measures – eg. – Bounce rate: proportion of visitors to a page who leave immediately – Stickiness: how long a visitor stays on the site, and how many repeat visits they make – Conversion rate: % of visitors who perform a desired action 14

Key Measures Needed to Compute Aggregate Site E-Metrics Measure Definition How many users? (audience Unique users reach) IP+User-agent Cookie and/or Registration How often? (frequency and recency metrics) Visit (user session) A series of one or more page impressions served to one user (gap of 30 minutes=end of visit) How many views? (volume metric) Page impression File (or files) sent to a user as a result of a server request by that user How many Ad views? Ad impressions A file (or files) sent to a user as an individual ad as a result of a server request by that user What do they do? Ad clicks? An ad impression clicked on by a valid user 15

More on Basic Site Metrics i Stickiness 4 measures site effectiveness in retaining visitors within a specified time period 4 related to duration and frequency of visit Stickiness = Frequency x Duration x Total Site Reach where 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) 16

More on Basic Site Metrics i Slipperiness 4 inverse of stickiness 4 used for portions of the site in which it low stickiness in desired (e. g. , customer service or online support) i Focus 4 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 Narrow Focus Wide Focus Low Stickiness Either consuming interest on the part of users, or users are stuck. Further investigation required. Either quick satisfaction or perhaps disinterest in this section. Further investigation required. Enjoyable browsing indicates a site ”magnet area”. Attempting to locate the correct information. 17

Shopping Pipeline Analysis ‘sticky’ states Browse catalog Complete purchase Enter store Select items cross-sell promotions i i Overall goal: • Maximize probability of reaching final state • Maximize expected sales from each visit ‘slippery’ state, i. e. 1 -click buy up-sell promotions Shopping pipeline modeled as state transition diagram Sensitivity analysis of state transition probabilities Promotion opportunities identified E-metrics and ROI used to measure effectiveness 18

Metrics for E-Customer Life Cycle i Describe the milestones at which we: 4 target new visitors 4 acquire new visitors 4 convert them into registered/paying users 4 keep them as customers 4 create loyalty Loyalty n o nti te e R At tio tri i u cq an n t en nm do R Ab h n A c ea n n io t i s ur e nv o C Ch n o i rs o ati v Re ti c a 19

Elements of E-Customer Life Cycle i Reach 4 targeting new potential visitors 4 can be measured as a percentage of the total market or based on other measures of new unique users visiting the site i Acquisition 4 transformation of targeting to active interaction with the site 4 e. g. , how many new users sessions have a referrer with a banner ad? 4 e. g. , what percentage of targeted audience base is visiting the site? i Conversion 4 a conversion rate is the ratio of “completers” to total “starters” for any predetermined activity that is more than one logical step in length 4 examples: percentage of site visitors who perform a particular action such as registering for a newsletter, subscribing to an RSS feed, or making a purchase 4 We can get more fine-grained measures: micro-conversion rates h look-to-click rate; click-to-basket rate; basket-to-buy rate 20

Elements of E-Customer Life Cycle i Retention 4 difficult to measure and metrics may need to be time/domain dependent 4 usually measured in terms of visit/purchase frequency within a given time period and in a given product/content category 4 time-based thresholds may need to be used to distinguish between retained users and deactivated-reactivated users i Loyalty 4 loyalty is indicated by more than purchase/visit frequency; it also indicates loyalty to the site or company as a whole 4 special referral or “bonus” campaigns may be used to determine loyal customers who refer products or the site to others 4 in the absence of other information, combinations of measures such as frequency, recency, and monetary value could be used to distinguish loyal users/customers 21

Elements of E-Customer Life Cycle Interruptions in the Life Cycle i Abandonment 4 measures the degree to which users may abandon partial transactions (e. g. , shopping cart abandonment, etc. ) 4 the goal is to measure the abandonment of the conversion process 4 micro-conversion ratios are useful in measuring this type of event i Attrition 4 applies to users/customers that have already been converted 4 usually measures the % of converted users who have ceased/reduced their activity within the site in a given period of time i Churn 4 is measured based on attrition rates within a given time period (ratio of attritions to total number of customers 4 goal is to measure “roll-overs’ in the customer life cycle (e. g. , percentage loss/gain in subscribed users in a month, etc. ) 22

Basic E-Customer Life cycle Metrics W (Target Market) NS S (Site Visitors) Note: Each of W, S, P, C and CR must be defined based on site characteristics and business objectives. P (Prospects / Active NP Investigators) NC C (Customers) CB (Abandon Cart) C 1 CA (one-time Customers) (Attrited Customers) CR (Repeat Customers) 23

Micro-Conversion Rates M 1 (saw product impression) NM 1 Í NC M 2 (performed product click through) NM 2 Í NC M 3 (placed product in shopping cart) NM 3 Í NC 24

Micro-Conversion Rates P NP Í NC M 1 (saw product impression) NM 1 Í NC M 2 (performed product click through) NM 2 Í NC M 3 (placed product in shopping cart) NM 3 Í NC M 4 = C (made purchase) 25

Basic E-Customer Metrics - RFM i RFM (Recency, Frequency, Monetary Value) 4 each user/customer can be scored along 3 dimensions, each providing unique insights into that customers behavior 4 Recency - inverse of the time duration in which the user has been inactive 4 Frequency - the ratio of visit/purchase frequency to specific time duration 4 Monetary Value - total $ amount of purchases (or profitability) within a given time period Monetary Value 5 4 3 2 1 y nc ce Re 1 2 3 4 5 Frequency 26

Building The Customer Signature i Building a customer signature is a significant effort, but well worth the effort i A signature summarizes customer or visitor behavior across hundreds of attributes, many which are specific to the site i Once a signature is built, it can be used to answer many questions i The mining algorithms will pick the most important attributes for each question i Example attributes computed: 4 Total Visits and Sales 4 Revenue by Product Family 4 Revenue by Month 4 Customer State and Country 4 Recency, Frequency, Monetary (RFM) 4 Latitude/Longitude from the Customer’s Postal Code 27

E-Metrics and E-Business Analytics Bamshad Mobasher De. Paul University