eCommerce Technology 20-751 Lecture 10: Mass Personalization

1. eCommerce Technology20-751Lecture 10:Mass Personalization

2. Personalization • “Real world”: humans conduct the customer relationship • Web: Too many users, too few humans, too little time • Answer: mediate the relationship by machine • How to get the necessary information? How to use it? • What is the underlying technology? • Benefit to vendor • Higher sales per visit • More return visits • Benefit to customer • Tailored, efficient web experience • Saving in time and convenience

3. Bricks v. Clicks DATA-POOR BRICKS CLICKS DATA-RICH SOURCE: NUMA-Q (IBM)

4. “Real World” v. Web Personalization • In the real-world • Too expensive (requires humans) • Too intrusive (customers don’t like being watched) • Too slow (capture-analyze-decide cycle inefficient) • Data not centralized • On the Web • Cheap (performed by computer) • Non-intrusive (user is not aware of monitoring) • Fast (real-time, while user is visiting) • Data captured permanently and centrally

5. Web Personalization • Creates “relationship” • Human tendency to show loyalty to the familiar (McKinsey) • Stickiness • Tendency of customers to return • Tendency to stay longer (“shelf space in the brain”) • Competitive necessity • Effective use of customers’ time • Increased “cleverness” + convenience

6. Empowering the User • Users have tremendous leverage on the Web • Leave your site freely • Compare prices cheaply • Exchange information quickly • Gómez.com, epinions.com • How can business recover the advantage? • Personalize! If everyone is treated differently, power cannot be concentrated

7. What is Personalization? • Addressing customers by name and remembering their preferences • Empowering the customer. Examples: Land’s End, llbean • Showing customers specific content based on who they are and their past behavior • Product tailoring. Example: dell.com • Connecting to a human being when necessary • Allowing visitors to customize a site for their specific purposes • Users are 20%-25% more likely to return to a site that they tailored (Jupiter Communications, Inc.)

8. The Secret: Know the User • IP address, e.g. 205.228.12.204 Look it up. • Anonymous, but I might know your employer • Domain name, e.g. www.mckinsey.com • I probably know your employer • Name, address, phone no. • A good start • Social security number • I know everything

9. Know Your Customer • Insider trades (search PNC) • Inmate release (search Jones with photos) • Tax records • Marriage records (look up Snelling in Berks Co.) • Property assessments (look up 101 Main) • Home sale prices (search zip 10471, $1.5-$3 million, year 2000) • Phone number by address (look up 5026 Arlington Bronx) • Census data (look up 5026 Arlington 10463) • Altavista (search “jonathan bram”, “susan bram”) • Nationwide searches • Death index

10. Prime Personalization Candidates Companies with: • Many products/services • Complex products/services • Many customers • Competitive environment Industries: • Newspapers/Magazines/Research • Catalogs/Retail • High Tech • Financial Services

11. Cookies • Scratchpad memory for the web (typically 4KB) • Small files maintained on user’s hard disk, readable only by the site that created them (up to 20 per site) • Internet Explorer keeps them in \windows\Cookies • Netscape keeps them in a file cookies.txt in the Netscape directory • Used for • website tracking, online ordering, targeted adverts • Can be disabled • Visit Cookie Central • We have no privacy left anyway. See Anonymizer

12. DoubleClick’s Cookie on my laptop! idd75ae834doubleclick.net/014689387523158341357021488029320845*

13. How DoubleClick Works Merchant Cookie Client 1. Client requests a page Merchant Server e.g. Altavista DoubleClick Cookie 2. Server sends a page with a DoubleClick URL 3. Text is displayed 4. Client requests the DoubleClick page Web Page 5. DoubleClick reads its cookie DoubleClick Server If you choose to give u personal information via the Internet that we or our business partners may need -- to correspond with you, process an order or provide you with a subscription, for example -- it is our intent to let you know how we will use such information. If you tell us that you do not wish to have this information used as a basis for further contact with you, we will respect your wishes. We do keep track of the domains from which people visit us. We analyze this data for trends and statistics, and then we discard it. 6. DoubleClick decides which ads to send

14. Personalization Techniques • Content targeting rules • “Show all announcements whose subject contains ‘mortgage’ to anyone whose DwellingStatus is HomeOwner • User segmentation rules (cluster the audience) • “Include anyone whose PastPurchases in the last 12 months is above $10,000 in group BigSpenders" • Behavior-based profiling rules • “When a person views Home Equity Loan Information set DwellingStatus to HomeOwner”

15. Filtering Techniques • Rule-based filtering • Ask user questions to elicit preferences, adaptive sequencing • Personalogic • Learning agents (nonintrusive personalization) • implicit profiling • webgroove.com • Collaborative filtering • base decisions on preferences of like-minded users • moviecritic.com • amazon.com

16. Clickstream Analysis • Determine distinct visitors • Determine repeat visits • Effectiveness of marketing campaigns • Path to revenue generation • Popularity of different sections of the site • Understand when and where people leave the site • ROI on marketing and advertising expenditures

17. Clickstream Analysis Examples • MatchLogic www.matchlogic.com • Andromedia www.andromedia.com • E.piphany www.epiphany.com • Broadvision www.broadvision.com • Personify www.personify.com • net.Genesis www.netgen.com • Accrue Software www.accrue.com

18. Server Log Analysis • Servers maintain logs of all resource requests remotehost name authuser [date] "request" status bytes gateway.iso.com - - [10/MAY/1999:00:10:30] "GET /class.html HTTP/1.1" 200 10000 • Referrer logs 08/02/99, 12:02:35,http://ink.yahoo.com/bin/query?p="sample+log+file"&b=21&hc=0&hs=0, 130.132.232.48, biomed.med.yale.edu • Analog (Cambridge) DATE REFERRING QUERY REQUESTING IP ADDRESS REQUESTING DOMAIN

19. Analysis SOURCE: WEBTRENDS CORP.

20. Analysis HitsNumber of Successful Hits for Entire Site184,558 Average Number of Hits Per Day15,379 Number of Hits for Home Page2,248 Page ViewsNumber of Page Views (Impressions)46,438 Average Number of Page Views Per Day 3,952 Document Views43,829 Visitor SessionsNumber of User Sessions13,564 Average Number of User Sessions Per Day1,130 Average User Session Length00:03:09 International User Sessions26.13% User Sessions of Unknown Origin31.01% User Sessions from United States42.81% VisitorsNumber of Unique Visitors11,685 Number of Visitors Who Visited Once10,720 Number of Visitors Who Visited More Than Once 959 SOURCE: WEBTRENDS CORP.

21. LikeMinds WebSell (Collaborative)

22. Dynamo (Art Technology Group)

23. Web Ad Targeting - Landscape Consumer Data Ad / Page Recommendation Ad / Page Presentment Ad Inventory - Clickstream data - Behavioral - Ad Networks aggregate advertisers - Collaborative filtering software recommends ad from user profile - Selects an ad or webpage on Ad Server. - Site data Key Points: Companies: NetGravity Accipiter Net Perceptions MatchLogic DoubleClick AdSmart AdForce Flycast NetGravity Engage Vignette Firefly Source: BancBoston Robertson Stephens

24. Broadvision SOURCE: BROADVISION

25. Personalization Roadblocks SOURCE: FORRESTER RESEARCH (12/98)

26. Personalization Pitfalls • Only ask for information you need • Never ask for information before you need it • Respect the privacy of your customers • Do not underestimate response • Be prepared for sales credit issues • Be aware of scalability issues

27. Personalization Tools • Blue Martini www.bluemartini.com • Art Technology Group www.atg.com • RightPoint www.rightpoint.com • HNC www.ehnc.com • GuestTrack www.guesttrack.com • Net Perceptions www.netperceptions.com • Manna (FrontMinds) www.mannainc.com • Engage www.engage.com

28. Q A &