Computing Science, University of Aberdeen 1
E-Commerce – customer focus
Attracting and keeping customers» Key issue: trust, security
Legal issues Personalization Adverts
Computing Science, University of Aberdeen 2
Customers are not all the same!
Consumer types» Individual consumers» Organizational buyers
Goal of shopping» Pragmatic: buy something useful, cheaply» Hedonistic: have fun
Personality» Impulsive buyers — purchase quickly» Patient buyers — make some comparisons first» Analytical buyers — do substantial research before buying
Computing Science, University of Aberdeen 3
Consumer Behaviour
Prentice Hall, 2002
Computing Science, University of Aberdeen 4
Consumer Satisfaction
Prentice Hall, 2002
Computing Science, University of Aberdeen 5
Trust/Security 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)
Technical aspects Human aspects: Focus here on trust and, to some
extent, policies
Computing Science, University of Aberdeen 6
Trust in physical shops Experience: shoppers trust shops
they’ve used before Appearance: shoppers trust store that
look reputable Complaints: easy to complain, shop
can’t hide Transactions are simple
Computing Science, University of Aberdeen 7
On-line trust What makes you trust an e-commerce
shop?
Computing Science, University of Aberdeen 8
On-line Trust 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? Appearance: Do I trust Amazon because they have a
nice website?» Less important than with physical shops» Marketing helps
Computing Science, University of Aberdeen 9
On-line trust Complaints: Harder to complain since
don’t know where shop is Transactions are complex because of
delivery» Where many e-shops mess up
Third-party: do I trust Amazon more if another web site says good things about Amazon?
Computing Science, University of Aberdeen 10
Does Amazon Trust Me? 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
Computing Science, University of Aberdeen 11
Trust We know how trust is established in
physical shops. 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
Computing Science, University of Aberdeen 12
Legal Issues: Tax 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 Automatically gave price advantage to
e-commerce sites!
Computing Science, University of Aberdeen 13
Legal Issues: Intl E-Commerce
In theory, e-commerce means sites can sell globally
In practice, difficult because of different tax rules, regulations, customs, etc» More common to set up subsidiaries in
different countries, as Amazon has done Lack of global legal/regulatory
framework hinders ecommerce
Computing Science, University of Aberdeen 14
Personalization E-Commerce sites can treat customers
differently» Offer recommendations, special deals» Personalise web site» Adjust prices
In theory, “personalised shop” one of the great benefits of e-commerce
One-to-One MarketingBuild a long term associationMeeting customers cognitive needs
Customer may have novice, intermediate or expert skillE-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…
Personalisation - Marketing Model“Treat different customers differently”
Prentice Hall, 2002
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
Computing Science, University of Aberdeen 18
Recommendation Build profiles
» What has X bought?» What has X looked at?» Demographics: age, gender, etc
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
Data Mining
Automated prediction of trends and behaviors Example: from data on past promotional mailings, find out
targets most likely to respond in futureAutomated 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
Computing Science, University of Aberdeen 20
Recommendations If done well, perceived very positively
» Real benefit, not just marketing spam» Credit-card companies have done this well
– Have the most purchasing data? Data privacy issues
» Can Visa sell data about you to Amazon?» Spyware to track all of your web browsing?
Computing Science, University of Aberdeen 21
Personalise Web Sites Let customers create their own “shop
front” focusing on their interest Adjust appearance (eg, for visually
disabled, or strict Muslims) Doable, not huge success
Computing Science, University of Aberdeen 22
Personalised Pricing 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
Computing Science, University of Aberdeen 23
Personalised Pricing Amazon, etc have tried this, but
customers hated it. So has gone “underground” for now. Technology permits this, but society’s
expectations does not allow it
Computing Science, University of Aberdeen 24
Advertising E-Shops (and other sites) can make
money via advertising» Google makes billions from its “sponsored
links”» Amazon has adverts as well
Computing Science, University of Aberdeen 25
Web Advertising Conventional advertising focuses on
visual appeal Less successful on web
» Flashy animated banner adverts are a nuisance and distraction
Computing Science, University of Aberdeen 26
Targeted adverts 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 Very effective if done well
» So Web sites can charge more for targeted adverts
Computing Science, University of Aberdeen 27
Web adverts Initially treated like TV adverts, put huge
effort into flashy multimedia banner ads Now focusing on simple targeted
adverts instead Advertising models cannot be blindly
moved from TV to web» need new models!
Computing Science, University of Aberdeen 28
E-Commerce Summary 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
Computing Science, University of Aberdeen 29
E-Commerce Summary Sometimes technology really helps
» Recommender systems, targeted adverts Sometimes technology works but
society doesn’t like it» Differential pricing