E-Metrics%20and%20E-Business%20Analytics

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E-Metrics and E-Business Analytics
Bamshad Mobasher School 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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E-Metrics Commonly Used by Industry
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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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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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The Customer Life Cycle Funnel
Source E-Metrics Business Metrics For The New
Economy, NetGenesis, 2000.
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Basic E-Customer Life cycle Metrics
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)
NM1 Í NC
M2 (performed product click through)
NM2 Í NC
M3 (placed product in shopping cart)
NM3 Í NC
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Micro-Conversion Rates
P
NP Í NC
M1 (saw product impression)
NM1 Í NC
M2 (performed product click through)
NM2 Í NC
M3 (placed product in shopping cart)
NM3 Í NC
M4 C (made purchase)
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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

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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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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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E-CRM The case of Amazon.com
The CRM Virtuous Circle
Buying decision/process
Purchase response
Customer knowledge
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The continuing relationship Amazon.com
Loyalty model
anticipate/stimulate
Need Creation
provide /assist
Information search
assist / negate
Evaluate alternatives
optimise /reward
Purchase transaction
add value
Post purchase experience
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Need Creation (attract to website)
anticipate/stimulate
Need Creation
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Further Need Creation (upon reaching website)
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Information Search
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Evaluation of Alternatives
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Purchase Optimisation/Reward

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

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Post-purchase experience
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Account Management
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Is loyalty a relevant concept?

• Amazons 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
sticky states

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

slippery state, i.e. 1-click buy
cross-sell promotions
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

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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
Sept-11 Note significant drop in human traffic,
not bot traffic
Weekends
Internal Perfor-mance bot
Registration at Search Engine sites
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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

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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
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Top Links followed from the Welcome PageRevenue
per session associated with visits
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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

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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

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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 Customers Postal Code

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Migration Study - MEC

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

Apr 2002 Sep 2002
Oct 2001 Mar 2002
Spent over 200
Spent over 200
Migrators
(5.5)
Spent under 200
Spent 1 to 200
(94.5)
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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
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.
Map of Canada with store locations.
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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)

• 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

Majority of customers have purchased once
Low
Medium
High
Low
Medium
High
More frequent customers have higher average order
amount
Recommendation Targeted marketing campaigns to
convert people to repeat purchasers, if they did
not opt-out of e-mails
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RFM for Debenhams Card Owners
Recommendation Send targeted email campaign since
these are Debenhams 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, Drivers 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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Demographics - Income

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

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Demographics Credit Cards

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