Mass Personalization

1. Mass Personalization

2. Outline • What is personalization? • Personalization is based on data • Acquiring data about people • From people themselves • From their clickstream • From outside data sources • Using the data in the relationship (CRM) • Improve the customer’s experience • Help the company • Data mining

3. Need For Personalization • In the real-world • Customer relationship is mediated by people • Personalization is critical: PEOPLE are PEOPLE • On the Web • Too many customers; too few employees • Orders are entered by machine; follow-up is by machine • Customer relationship is mediated by machines • Personalization is critical • Uniqueness (everyone is different) • Efficiency (everyone has limited time)

4. Store Visitors in the Real World • Casual store visitor: • no intention of buying • Prospecting store visitor: • wants to buy, maybe not here • Add, marketing target: • in store because of ad or promotion • Customer: • buys something • pays cash • uses a credit card • uses a store charge card DATA COLLECTED ONLY IF VISITOR BUYS SOMETHING IDENTITY UNKNOWN PRODUCT/TIME KNOWN IDENTITY KNOWN IDENTITY, JOB, INCOME KNOWN

5. Store Visitors in Cyberspace • Casual site visitor: • no intention of buying • Prospecting site visitor: • wants to buy, maybe not here • Add, marketing target: • in store because of ad or promotion • Customer: • buys something • pays cash • uses a credit card • uses a store charge card CAN EASILY DETECT THE DIFFERENCE WE KNOW HOW HE GOT HERE AND WHAT HE WANTS TO BUY WE HAVE HIS WHOLE FILE WE KNOW WHAT OTHER PEOPLE LIKE HIM ARE BUYING

6. Click Behavior CASUAL VISITOR STORE HOME PAGE OFFICEPRODUCTS SPORTING GOODS HOUSEWARES PRESENTATION ITEMS KITCHEN HUNTING GOLF LASER POINTERS TOASTERS RIFLES CLUBS LASER 1 LASER 2 LASER 3 CALLAWAY

7. Click Behavior PROSPECTING VISITOR STORE HOME PAGE OFFICEPRODUCTS SPORTING GOODS HOUSEWARES PRESENTATION ITEMS KITCHEN HUNTING GOLF LASER POINTERS TOASTERS RIFLES CLUBS LASER 1 LASER 2 LASER 3 CALLAWAY

8. What is Personalization? • Addressing customers by name and remembering their preferences • Showing customers specific content based on who they are and their past behavior • Empowering the customer. Examples: Land’s End, llbean • Product tailoring. Example: dell.com • Connecting to a human being when necessary. We Call You, Adeptra • 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.)

9. Adeptra Response Solutions SOURCE: ADEPTRA

10. The Secret: Know the User • IP address, e.g. 192.151.11.40. Look it up. • Anonymous, but I might know your employer • Domain name, e.g. hp.com • I probably know your employer • Name, address, phone no. • A good start • Social security number • I know everything

11. Know Your Customer • Insider trades (search AMZN) • Inmate release (search Jones with photos) • Marriage records (look up Snelling in Berks Co.) • Land records (look up “shamos”) • Home sale prices (search zip 10471, $2.2-$5 million, 1997-2001) • Name by address (look up 5026 Arlington Bronx) • Phone number by name (Bram, Jonathan, Bronx, NY) • Census data (look up 5026 Arlington 10463) • Altavista (search “jonathan bram”, “susan bram”) • Death index • Index of over 16,500 public databases

12. Customer Profiling Geographic (How are customers distributed?) Cultural and Ethnic (What languages do customers prefer? Does ethnicity affect their tastes or buying behavior?) Economic conditions, income and/or purchasing power (What is the purchasing power of your customer? Power (What is title and the decision-making power of the customer?) Size of company (How big is the customer?) Age (How old is the customer? Family? Children?) SOURCE: K. GARVIE BROWN

13. Customer Profiling Values, attitudes, beliefs (Predominant values your customers have in common; their attitude toward your kind of product Knowledge and awareness (How much do customers know about your product or service, about your industry?) Lifestyle (How many lifestyle characteristics can you name about your purchasers? UpMyStreet) Buying patterns (How consumers of different ages and demographic groups shop on the Web.) Media Used (How do your targeted customers learn? What do they read? What magazines do they subscribe to? What are their favorite websites ...?) SOURCE: K. GARVIE BROWN

14. Cookies • Post-it notes 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) • Used for • website tracking, online ordering, targeted adverts • Can be disabled • To learn about cookies, see Cookie Central • Internet Explorer keeps cookies in \windows\Cookies • Netscape keeps them in cookies.txt in the Netscape directory

15. 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

16. Filtering Techniques • Rule-based filtering • Ask user questions to elicit preferences, adaptive sequencing • Phone Wizard (uses Active Product Spex from ActiveDecisions) • Credit card finder • Learning agents (nonintrusive personalization) • implicit profiling • webgroove.com • Collaborative filtering • base decisions on preferences of like-minded users • movielens • amazon.com

17. Active Decisions 7 SOURCE: ACTIVE DECISIONS

18. Web Servers Navigational Data Recommend Request Recommend™ Front-end Server Recommend™ Back-end Server Real Time Recorder Matching Agent Recorder Predictor Real Time Predictor Analyzer Synchronization Personalization Database Operational Database Cache Database Real-Time CRM SOURCE: PIONSOFT

19. 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

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

21. Portals • Universal entry points for corporate information • Employees • Customers • Potential employees • Press • Investors • Must allow some personalization • Too much information • CMU portal:

22. Infomaster Guides Access Discussion Database Community Groupware Apps Fubar Corp. New products Re: Fubar Corp New products Not a big deal in my client base Seeing interest out west. Help! Help from engineering Thanks. How about … Try the attached slides Marketing will prepare a paper Customer Satisfaction survey Looking for more responses Real-Time Info. Feed Memos People Project X External Plans Status You have a meeting in ... Real-Time Chat & Net Meetings Search In: Search For: All Sources Bixbie Intl. Who’s Online? Matt Cain David Cearley Mike Gotta Steve Kleynhans Dale Kutnick Options Search Search & Retrieval Buddy List Document Sharing BI Report Viewer Knowledge Mgmt. Related Links (Sites & Apps) Interest Group Sites (Internet, Extranet, Intranet) Enterprise Portals - “Context is King” • Characteristics • Focused membership targeting projects, teams, and “communities” • Hub for interactions (both structured & unstructured) • Includes unique & “guided” content & content/app linking and/or integration • Used to capture & access knowledge • Rich BCM services behind the portal with varying degrees of integration SOURCE: META GROUP

23. Anonymizers • Server that “launders” IP addresses to allow anonymous browsing • List of Web anonymizers • The Cloak • JAP • Issues • Blocking by administrators • Subpoenas • Anonymous email • Escrow agents • anonymous purchases and payments

24. 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 DATE REFERRING QUERY REQUESTING IP ADDRESS REQUESTING DOMAIN

25. Analysis SOURCE: WEBTRENDS CORP.

26. 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

27. Key Takeaways • People want to be treated as individuals • There’s nothing wrong with entertaining the user • Everyone has a frustration limit • We can learn who a user is and what he wants to buy • Use data to alter the web experience in real-time • Users have high privacy sensitivity

28. Q A &