1. Computing Science, University of Aberdeen 1 E-Commerce – customer focus l Transactions, money, trust l Attracting and keeping customers »Key issue: trust, security l Legal issues l Personalization l Adverts

2. Computing Science, University of Aberdeen 2 Transactions … in the beginning l Barter – exchange one good for another l Strictly a two way thing. l Exchange happens simultaneously (mostly). l Little, or no, trust needed

3. Computing Science, University of Aberdeen 3 Transactions – commodity money - Exchange standard items with known (supposedly intrinsic) value. - These standard items are more liquid, easier to exhange, move faster. - Can store for later use. - e.g. Weights of metal, peppercorns, sheepskin, pigs, cattle - Limited trust in retained intrinsic value

4. Computing Science, University of Aberdeen 4 Transactions – representative money Token money - Early on, might be linked to a commodity - probably not now - `just a way of keeping score’ - Even more liquid, easier to store, exchange - Need to trust that money will keep being tradeable in future, and of not much less value

5. Computing Science, University of Aberdeen 5 Transactions – money Fiat money – money that gets value, because the government and the law says it has (and because we believe it). Paper money – only used widely in Europe for last 200-ish years, initial trust problems, bank runs. http://en.wikipedia.org/wiki/John_Law_(economist)

6. Computing Science, University of Aberdeen 6 Money – modern l Cash l Money held in physical capital l Money in bank l Money invested in bonds, equities l Credit l Credit and debit cards l Electronic transfers of money

7. Computing Science, University of Aberdeen 7 Money – modern l Electronic transfer of money central to e-commerce. l Ability to accept credit (or debit) cards central to success at scale of B2C and C2C. l Standards, e.g. https://www.pcisecuritystandards.org/

8. Computing Science, University of Aberdeen 8 Transactions l Evolution of money and social mechanisms has enabled us to develop two-part transactions: money and goods (or services) exchanged at different times. l Also credit l Rely on »interpersonal trust, »social reputation, »law.

9. Computing Science, University of Aberdeen 9 Money: trust, reliability, security l Need to be sure that it will continue being acceptable l Need to be sure that it won’t lose too much value l Need to trust that our electronically stored `score’ is kept safely by bank. l Need to be sure that electronic transfers out (and in) work properly and are secure. [Banks and systems connecting them] l Need to be sure that credit and debit system work correctly and are secure. l Need to trust that traders will deliver upon payment. l Traders need to be sure that they will be paid if they deliver.

10. Computing Science, University of Aberdeen 10 PayPal l http://www.paypal.com http://www.paypal.com l Users set up an account, linked to a bank account or credit card. l Enables small businesses and consumers to accept credit card payments via paypal. »A lot less overhead than accepting and processing credit card details directly. »Reduction in overheads enables many more participants, more trade. l Various competitors available, but economic network effects in play (natural monopolies and oligopolies emerge) l In the early days, eBay tried to set-up own alternative, but users insisted on Paypal. »eBay gave up and bought Paypal instead.

11. Computing Science, University of Aberdeen 11 Internet Escrow l Escrow: money held by a third-party on behalf of transacting parties (roughy). l Used where transacting parties have limited trust in each other l Internet escrow: »Transaction between buyer and seller »Buyer places money in control of trusted, independent third party »If both verify delivery had taken place and is complete, then money is released »If not, then some dispute resolution process kicks in. l E.g. http://www.escrow.comhttp://www.escrow.com

12. Computing Science, University of Aberdeen 12 BitCoin l Emerged in last couple of years. l Open source, peer-to-peer network to track and verify transactions. l Cut-out middlemen (financial institutions) in electronic transactions using clever cryptograpic prototcols. l http://www.bitcoin.org/bitcoin.pdf http://www.bitcoin.org/bitcoin.pdf l Teething problems »No fiat from any government (relies on designer/community?) »If protocols breached, value could disappear »Value of currency is not yet sticky (no irrational, but helpful, faith in it) »Economic problems related to design (limited monetary expansion) http://krugman.blogs.nytimes.com/2011/09/07/golden-cyberfetters/

13. Computing Science, University of Aberdeen 13 Customer Focus

14. Computing Science, University of Aberdeen 14 Customers are not all the same! l Consumer types »Individual consumers »Organizational buyers

15. Computing Science, University of Aberdeen 15 Customers are not all the same! l Consumer types »Individual consumers »Organizational buyers l Goal of shopping »Pragmatic: buy something useful, cheaply »Hedonistic: have fun

16. Computing Science, University of Aberdeen 16 Customers are not all the same! l Consumer types »Individual consumers »Organizational buyers l Goal of shopping »Pragmatic: buy something useful, cheaply »Hedonistic: have fun l Personality »Impulsive buyers — purchase quickly »Patient buyers — make some comparisons first »Analytical buyers — do substantial research before buying

17. Computing Science, University of Aberdeen 17 Consumer Behaviour Prentice Hall, 2002

18. Computing Science, University of Aberdeen 18 Consumer Satisfaction Prentice Hall, 2002

19. Computing Science, University of Aberdeen 19 Trust/Security l Trust/Security »Will the company actually deliver the correct product/service 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 and, to some extent, policies

20. Computing Science, University of Aberdeen 20 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

21. Computing Science, University of Aberdeen 21 On-line trust l What makes you trust an e-commerce shop?

22. Computing Science, University of Aberdeen 22 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 »Specific technicalities; for example, accounts/cards compromised or not? l Appearance: Do I trust Amazon because they have a nice website? »Less important than with physical shops »Marketing helps

23. Computing Science, University of Aberdeen 23 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?

24. Computing Science, University of Aberdeen 24 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: vendors generally signal that nasty things happen to customers who don’t pay up –credit record affected –legal consequences

25. Computing Science, University of Aberdeen 25 Trust l We know quite a bit about how trust is established in physical shops. l We are developing mechanisms for establishing trust in e-shops »Partially technology, but human factors (psychology, sociology, economics, law) probably matter as much »Lack of trust mechanisms is barrier to new e- shops

26. Computing Science, University of Aberdeen 26 Legal Issues: Tax l In USA, one driving force behind early e-store success was lower tax »Because of a tax loophole, sales tax (VAT) was not charged on e-commerce sales »Automatically gave price advantage to e- commerce sites!

27. Computing Science, University of Aberdeen 27 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

28. Computing Science, University of Aberdeen 28 Personalization 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 l Can also take advantage of more of long tail »Don’t need to keep stock in same way as traditional shop »Can do things like Print On Demand

29. 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…

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

31. 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

32. Computing Science, University of Aberdeen 32 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

33. 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

34. Computing Science, University of Aberdeen 34 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?

35. Computing Science, University of Aberdeen 35 Personalise Web Sites l Let customers create their own “shop front” focusing on their interest l Adjust appearance (eg, for visually disabled, or strict, religious consumers) l Do-able, not huge success

36. Computing Science, University of Aberdeen 36 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

37. Computing Science, University of Aberdeen 37 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

38. Computing Science, University of Aberdeen 38 Advertising l E-Shops (and other sites) can make money via advertising »Google makes billions from its “sponsored links” »Amazon has adverts as well

39. Computing Science, University of Aberdeen 39 Web Advertising l Conventional advertising focuses on visual appeal l Less successful on web »Flashy animated banner adverts are a nuisance and distraction

40. Computing Science, University of Aberdeen 40 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

41. Computing Science, University of Aberdeen 41 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!

42. Computing Science, University of Aberdeen 42 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

43. Computing Science, University of Aberdeen 43 Customer Focus Summary l Sometimes technology really helps »Recommender systems, targeted adverts l Sometimes technology works, but society doesn’t like it »Differential pricing l Trust – sine qua non

44. Computing Science, University of Aberdeen 44 Assessment 1 l Essay due 18 th November. l Without delay, go to http://www.abdn.ac.uk/~csc245/teachin g/CS5038/assessment/ http://www.abdn.ac.uk/~csc245/teachin g/CS5038/assessment/ for more detailed instructions. l Please read the instructions very carefully – and follow them!