E-Metrics and E-Business Analytics

1. E-Metrics and E-Business Analytics Bamshad Mobasher School of Computing, DePaul University

2. 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

3. 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?

4. Using Analytics for E-Business Management • Navigation Calibration • Calculating Content • Conversion Quotient • Interaction Computation • Customer Service Assessment • Customer Experience Evaluation • Branding

5. 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?

6. 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?

7. 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 60% Customers in a loyalty program 47% Metrics That Sites Track and Analyze at Least Once a Month Source: Jupiter Communications, 2000 E-Metrics Commonly Used by Industry

8. The Goal of E-Business Analytics E-Customer Life Time Value Optimization Process

9. Loyalty Retention Conversion Acquisition Attrition Churn Reactivation Reach Abandonment 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

10. 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

11. 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

12. 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.)

13. Good Targeting Ineffective Persuasion Untargeted Promotions Attract Wrong People Good Persuasion Good Conversion Good Persuasion Poor Conversion The Customer Life Cycle Funnel Source: “E-Metrics Business Metrics For The New Economy,” NetGenesis, 2000.

14. W(Target Market) NS S(Suspects / Site Visitors) P(Prospects / Active Investigators) NP NC C(Customers) CB(Abandon Cart) CR (Repeat Customers) C1 (one-time Customers) CA (Attrited Customers) 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.

15. 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

16. 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)

17. Monetary Value 5 4 3 2 1 5 4 3 2 1 Recency 1 2 3 4 5 Frequency 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

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: 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)

19. High Stickiness 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. Narrow Focus Attempting to locate the correct information. Enjoyable browsing indicates a site ”magnet area”. Wide Focus 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)

20. 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)

21. 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

22. E-CRM – The case of Amazon.com The CRM ‘Virtuous Circle’ Buying decision/process Purchase response Customer knowledge

23. 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

24. Need Creation(attract to website) anticipate/stimulate Need Creation

25. Further Need Creation (upon reaching website)

26. provide /assist Information search Information Search

27. assist / negate Evaluate alternatives Evaluation of Alternatives

28. optimise /reward Purchase transaction Purchase Optimisation/Reward • 1-click purchase • ‘slippery check out counter’ vs. ‘sticky aisles’

29. add value Post purchase experience Post-purchase experience

30. Account Management

31. 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

32. Browse catalog Complete purchase Select items Enter store Shopping Pipeline Analysis ‘sticky’ states • 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 ‘slippery’ state, i.e. 1-click buy cross-sell promotions up-sell promotions

33. 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

34. 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!

35. Example: Web Traffic Sept-11 Note significant drop in human traffic, not bot traffic Weekends Internal Perfor-mance bot Registration at Search Engine sites

36. Visit 90% 10% No Search Search(64% successful) Avg sale per visit: $X Avg sale per visit: 2.2X 30% 70% Last Search Failed Last Search Succeeded Avg sale per visit: 0.9X Avg sale per visit: 2.8X Search Effectiveness at MEC • Customers that search are worth two times as much as customers that do not search. Failed searches hurt sales

37. 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

38. 14% 3% 2% 9% 0.6% 8% 2% 13% Top Menu 6% 3% 2% 2% 0.3% 2% 18% of visits exit at the welcome page Any product link 7% Page Effectiveness Percentage of visits clicking on different links

39. 5X X 2.3X 1.3X 4.2X 1.4X 2.3X 1.4X Top Menu 0.2X 10X 3.3X 10.2X 1.2X 1.7X Note how effective physical catalog item #s are Product Links 2.1X Top Links followed from the Welcome Page:Revenue per session associated with visits

40. Website Recommended Products Product Association Lift Confidence Orbit Sleeping Pad Orbit Stuff Sack 222 37% Cygnet Sleeping Bag Primus Stove Aladdin 2 Backpack Bambini Crewneck Sweater Children’s Bambini Tights Children’s 195 52% Yeti Crew Neck Pullover Children’s Beneficial T’s Organic Long Sleeve T-Shirt Kids’ Silk Long Johns Women’s Silk Crew Women’s 304 73% Micro Check Vee Sweater Volant Pants Composite Jacket Cascade Entrant Overmitts Polartec 300 Double Mitts 51 48% Windstopper Alpine Hat Volant Pants Tremblant 575 Vest Women’s 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

41. Website Recommended Products Product Association Lift Confidence Fully Reversible Mats Egyptian Cotton Towels J Jasper Towels 456 41% Confidence 1.4% Confidence 1% White Cotton T-Shirt Bra Plunge T-Shirt Bra Black embroidered underwired bra 246 25% 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

42. 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

43. 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%)

44. 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.

45. Black dots show store locations. 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.

46. Distance From Nearest Store (MEC) • People farther away from retail stores • spend more on average • Account for most of the revenues

47. 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

48. Debenhams card ownersLarge group (> 1000)High average order amountPurchased once (F = 5)Not purchased recently (R=5) RFM for Debenhams Card Owners Recommendation Send targeted email campaign since these are Debenham’s customers. Try to “awaken” them! Low Medium High Low Medium High

49. 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

50. 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