E-Metrics and E-Business Analytics

Bamshad MobasherDePaul University

Bamshad MobasherDePaul University

Web Usage Mining & E-Business Analytics

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i The primary goal of e-business analytics is to understand and be able to predict the behavior of online customers

i Examples of questions we want to answer using the data4 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 site4 Data mining – finding deeper patterns in the data and building models

Web Usage Mining & E-Business Analytics

4 Session Analysis4 Static Aggregation and Statistics4 OLAP4 Data Mining

Different Levels of AnalysisDifferent Levels of Analysis

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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 behaviors4 Trace specific problems with the site

i Drawbacks:4 LOTS of data4 Difficult to generalize

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Static Aggregation (Reports)i Most common form of analysis (e.g., Google Analytics,

WebTrends, etc.)i Data aggregated by predetermined units such as days or

sessionsi 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.

Page Number of Average View Count View Sessions per Session

Home Page 50,000 1.5Catalog Ordering 500 1.1Shopping Cart 9000 2.3

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Static Aggregation (Reports)

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i Typical tools:4 Google Analytics4 Urchin4 WebTrends

Online Analytical Processing (OLAP)i Allows changes to aggregation level for multiple dimensionsi Generally associated with a Data Warehousei Advantages & Drawbacks

4 Very flexible4 Requires significantly more resources than static reporting.

Page Number of Average View Count View Sessions per Session

Kid's Stuff Products 2,000 5.9

Page Number of Average View Count View Sessions per Session

Kid's Stuff Products Electronics Educational 63 2.3 Radio-Controlled 93 2.5

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Data Mining: Going deeper

Sequence mining

Sequence mining

Markov chains

Markov chains

Association rules

Association rules

ClusteringClustering

Session ClusteringSession

Clustering

ClassificationClassification

Prediction of next eventPrediction of next event

Discovery of associated events or application objects

Discovery of associated events or application objects

Discovery of visitor groups with common properties and

interests

Discovery of visitor groups with common properties and

interests

Discovery of visitor groups with common behaviour

Discovery of visitor groups with common behaviour

Characterization of visitors with respect to a set of predefined

classes

Characterization of visitors with respect to a set of predefined

classes

Anomaly/attack detectionAnomaly/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 constraintsh 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 products4 Discovery of associated products

h Association rules, Sequence Mining

4 Placement of associated products on the same page4 Determining which items or product to feature on specific pages

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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 groups4 Identification of Customer Groups or Segments

h Clustering, Classification

4 Discovery of associated products of the same/different price categoriesh Association rules, Sequence Mining

4 Formulation of recommendations to the end-userh Suggestions on associated productsh Suggestions based on the preferences of similar users

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E-Metricsi Collection of aggregate statistics and metrics necessary to

4 Understand visitor/customer behavior4 Understand how visitors are using the site4 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.).

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Number of customers

Visits resulting in purchase

Average order value

Number of registered users

Origin of visitors

Customer service response time

Purchases over the last six months

Number of repeat visitors

Revenue for repeat visitors

Origin of repeat visitors

New and repeat conversion rates

Customers in a loyalty program

100%

95%

91%

88%

86%

79%

79%

74%

63%

63%

60%

47%

E-Metrics Commonly Used by Industry

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

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• 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

Basic Site Metrics

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Key Measures Needed to Compute Aggregate Site E-Metrics

Measure Measure Definition

How many users? (audience reach)

Unique users IP+User-agentCookie and/orRegistration

How often? (frequency and recency metrics)

Visit (user session) A series of one or more page impressions served to one user (gap of 30minutes=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

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More on Basic Site Metricsi Stickiness

4 measures site effectiveness in retaining visitors within a specified time period4 related to duration and frequency of visit

where

This simplifies to:

Stickiness = Frequency x Duration x Total Site ReachStickiness = Frequency x Duration x Total Site Reach

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

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

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

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More on Basic Site Metricsi Slipperiness

4 inverse of stickiness4 used for portions of the site in which it low stickiness in desired (e.g., customer

service or online support)

i Focus4 measures visit behavior within specific sections of the site

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

Either quick satisfaction orperhaps disinterest in this section.Further investigation required.

Either consuming interest on thepart of users, or users are stuck.Further investigation required.

Narrow Focus

Wide Focus

High Stickiness Low Stickiness

Attempting to locate the correctinformation.

Enjoyable browsing indicates asite ”magnet area”.

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Shopping Pipeline Analysis

i Shopping pipeline modeled as state transition diagrami Sensitivity analysis of state transition probabilitiesi Promotion opportunities identifiedi E-metrics and ROI used to measure effectiveness

Overall goal:• Maximize probability of reaching final state• Maximize expected sales from each visit

Enterstore

Browsecatalog

Selectitems

Completepurchase

cross-sellpromotions

up-sellpromotions

‘sticky’states

‘slippery’state, i.e.1-click buy

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Reach

Acquisition

Conversion

Retention

Loyalty

Abandonm

ent

Attrition

Churn

Reactivatio

n

Metrics for E-Customer Life Cyclei Describe the milestones at which we:

4 target new visitors4 acquire new visitors4 convert them into registered/paying users4 keep them as customers4 create loyalty

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Elements of E-Customer Life Cycle

i Reach4 targeting new potential visitors4 can be measured as a percentage of the total market or based on other measures

of new unique users visiting the site

i Acquisition4 transformation of targeting to active interaction with the site4 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 Conversion4 a conversion rate is the ratio of “completers” to total “starters” for any

predetermined activity that is more than one logical step in length4 examples: percentage of site visitors who perform a particular action such as

registering for a newsletter, subscribing to an RSS feed, or making a purchase4 We can get more fine-grained measures: micro-conversion rates

h look-to-click rate; click-to-basket rate; basket-to-buy rate

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Elements of E-Customer Life Cyclei Retention

4 difficult to measure and metrics may need to be time/domain dependent4 usually measured in terms of visit/purchase frequency within a given time

period and in a given product/content category4 time-based thresholds may need to be used to distinguish between retained

users and deactivated-reactivated users

i Loyalty4 loyalty is indicated by more than purchase/visit frequency; it also indicates

loyalty to the site or company as a whole4 special referral or “bonus” campaigns may be used to determine loyal

customers who refer products or the site to others4 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

i Abandonment4 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 process4 micro-conversion ratios are useful in measuring this type of event

i Attrition4 applies to users/customers that have already been converted4 usually measures the % of converted users who have ceased/reduced their

activity within the site in a given period of time

i Churn4 is measured based on attrition rates within a given time period (ratio of

attritions to total number of customers4 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 Life cycle Metrics

W (Target Market)

S (Site Visitors)

P (Prospects / Active Investigators)

C (Customers)

CR (Repeat Customers)

NS

NP

CB (Abandon

Cart)

NC

CA(Attrited Customers)

C1(one-time Customers)

Note: Each of W, S, P, C and CR must be defined based on site characteristics and business objectives.

Note: Each of W, S, P, C and CR must be defined based on site characteristics and business objectives.

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Micro-Conversion Rates

M1 (saw product impression)

M2 (performed product click through)

M3 (placed product in shopping cart)

NM1 Í NC

NM2 Í NC

NM3 Í NC

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Micro-Conversion RatesP

M1 (saw product impression)

M2 (performed product click through)

M3 (placed product in shopping cart)

M4 = C (made purchase)

NP Í NC

NM1 Í NC

NM2 Í NC

NM3 Í NC

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Basic E-Customer Metrics - RFMi 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

5 4 3 2 1

Recency 1 2 3 4 5Frequency

Mo

ne

tary

Va

lue

5 4

3 2

1

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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 questioni Example attributes computed:

4 Total Visits and Sales4 Revenue by Product Family4 Revenue by Month4 Customer State and Country4 Recency, Frequency, Monetary (RFM)4 Latitude/Longitude from the Customer’s Postal Code

E-Metrics and E-Business Analytics

Bamshad MobasherDePaul University

Bamshad MobasherDePaul University