1. Dr. Ehud Reiter, Computing Science, University of Aberdeen1 E-Commerce – customer focus l Attracting and keeping customers »Issue: security, trust l Legal issues l Personalisation l Adverts

2. Dr. Ehud Reiter, Computing Science, University of Aberdeen2 Customers are different! l Consumer types »Individual consumers »Organizational buyers l Goal of shopping »Pragmatic: buy something useful, cheaply »Hedonic: have fun l Personality »Impulsive buyers—purchase quickly »Patient buyers—make some comparisons first »Analytical buyers—do substantial research before buying

3. Dr. Ehud Reiter, Computing Science, University of Aberdeen3 Consumer Behaviour Prentice Hall, 2002

4. Dr. Ehud Reiter, Computing Science, University of Aberdeen4 Consumer Satisfaction Prentice Hall, 2002

5. Dr. Ehud Reiter, Computing Science, University of Aberdeen5 Security/Trust l Security/trust »Will the company actually deliver the correct product in reasonable shape, in a reasonable time, at correct price »Will the customer pay up (is the credit card stolen, will it be repudiated) l Technical aspects l Human aspects: Focus here on trust

6. Dr. Ehud Reiter, Computing Science, University of Aberdeen6 Trust in physical shops l Experience: shoppers trust shops they’ve used before l Appearance: shoppers trust store that look reputable l Complaints: easy to complain, shop can’t hide l Transactions are simple

7. Dr. Ehud Reiter, Computing Science, University of Aberdeen7 On-line trust l What makes you trust an e-commerce shop?

8. Dr. Ehud Reiter, Computing Science, University of Aberdeen8 On-line Trust l Experience: I trust Amazon because I’ve used them before »Reputation: because my friends use them »Very important with e-shops l Appearance: do I trust Amazon because they have a nice website??? »Less important than with physical shops »Marketing helps

9. Dr. Ehud Reiter, Computing Science, University of Aberdeen9 On-line trust l Complaints: Harder to complain since don’t know where shop is l Transactions are complex because of delivery »Where many e-shops mess up l Third-party: do I trust Amazon more if another web site says good things about Amazon (???)

10. Dr. Ehud Reiter, Computing Science, University of Aberdeen10 Does Amazon Trust Me? l Amazon trusts me because »Experience: I’ve always paid Amazon before »Reputation: I’ve used other companies and always paid up »Marketing: Amazon threatens nasty things to customers who don’t pay up

11. Dr. Ehud Reiter, Computing Science, University of Aberdeen11 Trust l We know how trust is established in physical shops. l We are developing mechanisms for establishing trust in e-shops »Partially technology, but psychology and sociology probably matter more »Lack of trust mechanisms is barrier to new e-shops

12. Dr. Ehud Reiter, Computing Science, University of Aberdeen12 Legal Issues: Tax l In USA, one driving force behind early e-store success was less tax »Because of a tax loophole, sales tax (VAT) was not charged on e-commerce sales l Automatically gave price advantage to e-commerce sites!

13. Dr. Ehud Reiter, Computing Science, University of Aberdeen13 Legal Issues: Intl E-Commerce l In theory, e-commerce means sites can sell globally l In practice, difficult because of different tax rules, regulations, customs, etc »More common to set up subsidiaries in different countries, as Amazon has done l Lack of global legal/regulatory framework hinders ecommerce

14. Dr. Ehud Reiter, Computing Science, University of Aberdeen14 Personalisation l E-Commerce sites can treat customers differently »Offer recommendations, special deals »Personalise web site »Adjust prices l In theory, “personalised shop” one of the great benefits of e-commerce

15. One-to-One Marketing Build a long term association Meeting customers cognitive needs  Customer may have novice, intermediate or expert skill E-loyalty—customer’s loyalty to an e-tailer  costs Amazon $15 to acquire a new customer  costs Amazon $2 to $4 to keep an existing customer Trust in EC  Deterrence-based —threat of punishment  Knowledge-based —reputation  Identification-based —empathy and common values  Referrals – Viral Marketing Personalisation…

16. Personalisation - Marketing Model “Treat different customers differently” Prentice Hall, 2002

17. Personalisation “Process of matching content, services, or products to individuals’ preferences” Build profiles – N.B. Privacy Issues  Solicit information from users  Use cookies to observe online behavior  Use data or Web mining

18. Dr. Ehud Reiter, Computing Science, University of Aberdeen18 Recommendation l Build profiles »What has X bought? »What has X looked at? »Demographics: age, gender, etc l Recommendation »Rules: If X buys Harry Potter 6, recommend HP 7 »Data Mining: Other people who bought Harry Potter also bought Lord of the Rings »Collaborative: X’s overall buying profile is similar to Y, so recommend whatever Y bought

19. Data Mining Automated prediction of trends and behaviors  Example: from data on past promotional mailings, find out targets most likely to respond in future Automated discovery of previously unknown patterns  Example: find seemingly unrelated products often purchased together  Example: Find anomalous data representing data entry errors Mining tools:  Neural computing  Intelligent agents  Association analysis - statistical rules Web Mining - Mining meaningful patterns from Web resources  Web content mining – searching Web documents  Web usage mining – searching Web access logs searching for valuable information in extremely large databases

20. Dr. Ehud Reiter, Computing Science, University of Aberdeen20 Recommendations l If done well, perceived very positively »Real benefit, not just marketing spam »Credit-card companies have done this well –Have the most purchasing data? l Data privacy issues »Can Visa sell data about you to Amazon? »Spyware to track all of your web browsing?

21. Dr. Ehud Reiter, Computing Science, University of Aberdeen21 Personalise Web Sites l Let customers create their own “shop front” focusing on their interest l Adjust appearance (eg, for visually disabled, or strict Muslims) l Doable, not huge success

22. Dr. Ehud Reiter, Computing Science, University of Aberdeen22 Personalised Pricing l Companies would love to be able to charge people different amounts for the same product »Airline seats, cars, etc »Full price for people who are keen, in a rush, don’t care about money »Discount for choosy/finicky

23. Dr. Ehud Reiter, Computing Science, University of Aberdeen23 Personalised Pricing l Amazon, etc have tried this, but customers hated it. l So has gone “underground” for now. l Technology permits this, but society’s expectations does not allow it

24. Dr. Ehud Reiter, Computing Science, University of Aberdeen24 Advertising l E-Shops (and other sites) can make money via advertising »Google makes billions from its “sponsored links” »Amazon has adverts as well

25. Dr. Ehud Reiter, Computing Science, University of Aberdeen25 Web Advertising l Conventional advertising focuses on visual appeal l Less successful on web »Flashy animated banner adverts are a nuisance and distraction

26. Dr. Ehud Reiter, Computing Science, University of Aberdeen26 Targeted adverts l Web allows relevant adverts to be associated with a web page »Google sponsored links based on search »Amazon could display different adverts for sci-fi and romance novel l Very effective if done well »So Web sites can charge more for targeted adverts

27. Dr. Ehud Reiter, Computing Science, University of Aberdeen27 Web adverts l Initially treated like TV adverts, put huge effort into flashy multimedia banner ads l Now focusing on simple targeted adverts instead l Advertising models cannot be blindly moved from TV to web »need new models!

28. Dr. Ehud Reiter, Computing Science, University of Aberdeen28 E-Commerce Summary l Initially tried to make e-shops similar to high street shops. But »Need different business model »Trust issues much more important »Need appropriate legal framework

29. Dr. Ehud Reiter, Computing Science, University of Aberdeen29 E-Commerce Summary l Sometimes technology really helps »Recommender systems, targeted adverts l Sometimes technology works but society doesn’t like it »Differential pricing