E-Metrics and E-Business Analytics

Bamshad MobasherSchool of Computing, DePaul University

Bamshad MobasherSchool of Computing, DePaul University

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Analyzing e-Customer Behavior In general, analyzing purchase behavior for online purchases is

similar to analyzing any purchase behavior, but we can do more on the Web

First it is possible and desirable to tie each purchase to an identified customer Can be done through Site registration information, shipping address, cookies,

credit card numbers

Some characteristics important for analyzing online purchases Frequency of purchases Average size of market basket Total number of different items purchased Total number of different item categories purchased Day of week and time of day Response to recommendations and online specials Comparison of online purchases to offline purchases

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What We Want to Know Are we attracting new people to our site? Is our site ‘sticky’? Which regions in it are not? What is the health of our lead qualification process? How adept is our conversion of browsers to buyers? What behavior indicates purchase propensity? What site navigation do we wish to encourage? How can profiling help use cross-sell and up-sell? How do customer segments differ? What attributes describe our best customers? Can we target other prospects like them? What makes customers loyal? How do we measure loyalty?

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Using Analytics for E-Business Management

Navigation Calibration Calculating Content Conversion Quotient Interaction Computation Customer Service Assessment Customer Experience Evaluation Branding

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Analyzing e-Customer Behavior

Single Visit Behavior - what happens during a particular session or visit to the site:

Did the customer make a purchase? What pages did a customer visit prior to making a purchase? How many different products did a customer consider? How many different products did the customer purchase? How many different product categories did the customer visit? How many different product categories did the customer purchase? What ratio of the customer session was spent at pages containing products

that the customer purchased during this session? Is the shipping address the same as the billing address? If not, did the

customer request gift‑wrapping?

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Analyzing e-Customer Behavior Multiple Visit Behavior - The ability to tie together customer behavior over

time is one of the key new capabilities enabled in the online world

Do customers first come to the site to browse and only then make purchases? This might suggest a segment of customers who compare prices before making a purchase.

Do customers who make repeated purchases broaden or narrow their purchase patterns? This might give insight into customer loyalty.

Do customers visit the site at relatively predictable intervals? This might give information about the time to next visit, so we can know when we need to start worrying because a particular customer has not been around for a while.

Over time, are repeat purchasers turning into more visitors, or are visitors turning more into repeat purchasers?

Are customers interested in the same categories every time they return to the site? This might suggest natural interest segments among customers.

Are there particular patterns among customers who have not returned in a long time? Were these customers one‑time purchasers? Did they purchase particular products? And so on.

Does responding to a special offer encourage customers to return?

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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

Metrics That Sites Track and Analyze at Least Once a MonthSource: Jupiter Communications, 2000

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The Goal of E-Business Analytics

E-Customer Life Time Value Optimization ProcessE-Customer Life Time Value Optimization Process

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Reach

Acquisition

Conversion

Retention

Loyalty

Abandonm

ent

Attrition

Churn

Reactivatio

n

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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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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Untargeted PromotionsAttract Wrong People

Good TargetingIneffective Persuasion

Good PersuasionPoor Conversion

Good PersuasionGood Conversion

The Customer Life Cycle Funnel

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

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Basic E-Customer Life cycle Metrics

W (Target Market)

S (Suspects / 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 NC

NM2 NC

NM3 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 NC

NM1 NC

NM2 NC

NM3 NC

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Basic E-Customer Metrics - RFM 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

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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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 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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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)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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Using E-Metrics - Case of LandsEnd.com

Goals: Keep entire interactive team apprised of key metrics so that they make decisions and execute initiatives in concert and in real-time

Metrics tracked daily by LandsEnd.com Sales revenues Number of orders Average order values Total visits Revenues per visit Conversion rate Total page views Visits by source (e.g., entering URL directly, bookmark, e-mail,

referring site) Revenues by source (as above) Conversion rate by source (as above)

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Using E-Metrics - Case of LandsEnd.com Not Enough

needed to cut each metric by new visitors and returning visitors, as well as new customers and returning customers

This led to the following additional metrics tracked daily: Percentage of traffic and page views from new vs. repeat visitors Average order from new vs. repeat customers Conversion rate for first-time visitors and customers Conversion rate for repeat visitors and customers Page views for new vs. repeat customers and visitors How much portals and affiliates are aiding in customer acquisition, and in

retention

The bottom line tracking the highest-level key metrics (traffic, revenues, average order)

day-to-day is standard operating procedure for commerce businesses distinguishing between behaviors of the first-time and repeat customers

allows the company to determine what constitutes the “trial” phase of the customer relationship, and how to move customers toward loyalty. Lands’ End does not consider somebody a “customer” until that person makes a second purchase

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The CRM ‘Virtuous Circle’

Purchase response

Customer knowledge

Buying decision/proces

s

E-CRM – The case of Amazon.com

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The continuing relationship …Amazon.com “Loyalty” model

Need CreationNeed Creation

Information search Information search

Evaluate alternatives Evaluate alternatives

Purchase transaction Purchase transaction

Post purchase experiencePost purchase experience

provide /assist

anticipate/stimulate

assist / negate

optimise /reward

add value

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Need Creation (attract to website)

Need CreationNeed Creation anticipate/stimulate

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Further Need Creation(upon reaching website)

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provide /assistInformation searchInformation search

Information Search

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Evaluation of Alternatives

assist / negateEvaluate alternativesEvaluate alternatives

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Purchase Optimisation/Reward

optimise /rewardPurchase transaction Purchase transaction

•1-click purchase1-click purchase•‘‘slippery check out counter’ vs. ‘sticky aisles’slippery check out counter’ vs. ‘sticky aisles’

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Post-purchase experience

add value Post purchase experiencePost purchase experience

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Account Management

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Is “loyalty” a relevant concept?

Amazon’s ‘customer lifetime value’ model (for book buyers) Average $50 for first time purchase Average $40 per visit thereafter Average of one visit per 2 months Assume customer will be active for 10 years

“4 buys and you are hooked” empirical law

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Shopping Pipeline Analysis

Shopping pipeline modeled as state transition diagram Sensitivity analysis of state transition probabilities Promotion opportunities identified 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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Additional Case Studies(Blue Martini Software)

MEC (Mountain Equipment Co-op) Canadian company selling sport and mountain

climbing gear leading supplier of quality outdoor gear and

clothing Consumer cooperative that sells to members only

DEBENHAMS Department store chain in UK 102 stores across the UK and Republic of Ireland

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Bot Detection Significant traffic may be generated by bots Can you guess what percentage of sessions are generated

by bots?

23% at MEC (outdoor gear)

40% at Debenhams

Without bot removal, your metrics willbe inaccurate

More than 150 different bot families on most sites.

Very challenging problem!

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Example: Web Traffic

Weekends

Sept-11 Note significant drop in human traffic, not bot

traffic

Registration at Search Engine sites

Internal Perfor-

mance bot

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Search Effectiveness at MEC Customers that search are worth two times as much as

customers that do not search. Failed searches hurt sales

Visit

Search(64% successful)

No Search

Last Search SucceededLast Search Failed

10%90%

Avg sale per visit: 2.2X

Avg sale per visit: $X

Avg sale per visit: 2.8XAvg sale per visit: 0.9X

70% 30%

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Referrers at Debenhams

Top Referrers

MSN (including search and shopping) Average purchase per visit = X

Google Average purchase per visit = 1.8X

AOL search Average purchase per visit = 4.8X

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Page Effectiveness Percentage of visits clicking on different links

14% 13% 9% Top Menu 6%8%

Any product link 7%18% of visits exit at the welcome page

3%

3% 2% 2%

0.3% 2%2%2%

0.6%

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Top Links followed from the Welcome Page:Revenue per session associated with visits

10.2X

1.4X 4.2X 1.4X Top Menu 0.2X 2.3X

Product Links 2.1X

10X

2.3X X 1.3X

5X

3.3X 1.7X 1.2X

Note how effective physical catalog item #s are

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Product Affinities at MEC

Minimum support for the associations is 80 customers Confidence: 37% of people who purchased Orbit Sleeping Pad also purchased Orbit Stuff Sack Lift: People who purchased Orbit Sleeping Pad were 222 times more likely to purchase the Orbit Stuff

Sack compared to the general population

Product Association Lift Confidence

Orbit Sleeping Pad Cygnet

Sleeping Bag Aladdin 2Backpack

Primus Stove

OrbitStuff Sack

WebsiteRecommended Products

222 37%

Bambini Tights Children’s

Bambini CrewneckSweater Children’s

195 52%

Yeti Crew NeckPullover Children’s

Beneficial T’sOrganic LongSleeve T-Shirt Kids’

Silk CrewWomen’s

SilkLong JohnsWomen’s

304 73%

Micro Check Vee Sweater

VolantPants

Composite Jacket

CascadeEntrant Overmitts

Polartec300 DoubleMitts

51 48%

VolantPants

WindstopperAlpine Hat

Tremblant 575Vest Women’s

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Product Affinities at Debenhams

Minimum support: 50 customers Confidence: 41% of people who purchased Fully

Reversible Mats also purchased Egyptian Cotton Towels Lift: People who purchased Fully Reversible Mats were 456 times more likely

to purchase the Egyptian Cotton Towels compared to the general population

Product Association Lift Confidence

WebsiteRecommended Products

J Jasper Towels

FullyReversibleMats

456 41%Egyptian CottonTowels

White CottonT-Shirt Bra

PlungeT-Shirt Bra 246 25%

Black embroidered underwired bra

Confidence 1.4%

Confidence 1%

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Building The Customer Signature

Building a customer signature is a significant effort, but well worth the effort

A signature summarizes customer or visitor behavior across hundreds of attributes, many which are specific to the site

Once a signature is built, it can be used to answer many questions The mining algorithms will pick the most important attributes for

each question Example attributes computed:

Total Visits and Sales Revenue by Product Family Revenue by Month Customer State and Country Recency, Frequency, Monetary Latitude/Longitude from the Customer’s Postal Code

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Migration Study - MEC

Oct 2001 – Mar 2002 Apr 2002 – Sep 2002

Migrators

Spent $1 to $200

Spent over $200

Spent over $200

Spent under $200

(5.5%)

(94.5%)

Customers who migrated from low spenders in one 6 month period to high spenders in the following 6 month period

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Key Characteristics of Migrators at MEC

During October 2001 – March 2002 (Initial 6 months) Purchased at least $70 of merchandise Purchased at least twice Largest single order was at least $40 Used free shipping, not express shipping Live over 60 aerial kilometers from an MEC retail store Bought from these product families, such as socks, t-shirts, and accessories Customers who purchased shoulder bags and child carriers were LESS

LIKELY to migrate

Recommendation: Score light spending customers based on their likelihood of migrating and market to high scorers.

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Customer Locations Relative to Retail Stores

Map of Canada with store locations.

Black dots show store locations.

Heavy purchasing areas away from retail stores can suggest new retail store locations No stores in several hot areas:

MEC is building a store in Montreal right now.

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Distance From Nearest Store (MEC)

People farther away from retail stores

spend more on average

Account for most of the revenues

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RFM Analysis (Debenhams)

Recommendation: Targeted marketing campaigns to convert people to repeat purchasers, if they did not opt-out of e-mails

Recommendation: Targeted marketing campaigns to convert people to repeat purchasers, if they did not opt-out of e-mails

Majority of customers have purchased once

More frequent customers have higher average order amount

Low Medium High Low Medium High

Anonymous purchasers have lower average order amount Customers who have opted out [e-mail] tend to have higher average order amount People in the age range 30-40 and 40-50 spend more on average

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RFM for Debenhams Card Owners

Debenhams card ownersLarge group (> 1000)High average order amountPurchased once (F = 5)Not purchased recently (R=5)

Recommendation

Send targeted email campaign since these are Debenham’s customers. Try to “awaken” them!

Low Medium High Low Medium High

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Consumer Demographics - Acxiom ADN – Acxiom Data Network Comprehensive collection of US consumer and telephone data

available via the internet Multi-sourced database Demographic, socioeconomic, and lifestyle information. Information on most U.S. households Contributors’ files refreshed a minimum of 3-12 times per year. Data sources include: County Real Estate Property Records, U.S. Telephone

Directories, Public Information, Motor Vehicle Registrations, Census Directories, Credit Grantors, Public Records and Consumer Data, Driver’s Licenses, Voter Registrations, Product Registration Questionnaires, Catalogers, Magazines, Specialty Retailers, Packaged Goods Manufacturers, Accounts Receivable Files, Warranty Cards

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Consumer Demographics Using Acxiom, we can compare online shoppers to a

sample of the population People who have a Travel and Entertainment credit card are

48% more likely to be online shoppers (27% for people with premium credit card)

People whose home was built after 1990 are 45% more likely to be online shoppers

Households with income over $100K are 31% more likely to be online shoppers

People under the age of 45 are 17% morelikely to be online shoppers

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A higher household income means you are more likely to be an online shopper

Demographics - Income

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Demographics – Credit Cards

The more credit cards, the more likely you are to be an online shopper