E-commerce and Data Mining Flashcards

Flashcards covering key concepts in e-commerce, digital markets, and data mining techniques.

What are 'Digital Goods'?

Products that can be delivered over a digital network; characteristics include nearly zero marginal cost and easy duplication.

What is 'Information Asymmetry'?

A situation where one party in a transaction has more or better information than the other.

Explain 'Disintermediation'.

The removal of intermediaries in a supply chain, allowing producers to sell directly to consumers.

What are the two main types of Recommendation Systems?

Collaborative filtering and content-based filtering.

Define 'Filter Bubble' and name one pro and one con.

A situation where algorithms limit exposure to diverse content. Pro: personalized experience. Con: reduced exposure to different perspectives.

List three key features of digital markets.

Information asymmetry, network effects, and dynamic pricing.

Differentiate IT and IS.

IT refers to hardware and software; IS includes people, processes, and technology to achieve business objectives.

What is the 'Longtail Theory'?

Selling a large number of niche products can collectively match or exceed hits in traditional markets.

Name four types of e-commerce.

B2B, B2C, C2B, C2C.

Match business model to example: Market Creator, Content Provider, E-tailer, Transaction Broker.

Market Creator: eBay; Content Provider: Netflix; E-tailer: Nike online; Transaction Broker: Expedia.

List three revenue models and give an example for each.

Subscription (Netflix), Advertising (Facebook), Transaction Fee (eBay commission).

What defines a 'Platform' in online platforms?

A digital service facilitating interactions between two or more distinct user groups.

Explain 'Same-Side' vs 'Cross-Side' network effects.

Same-side: more users on one side affect those on the same side. Cross-side: users on one side affect the other side.

What is the difference between supervised and unsupervised data mining?

Supervised uses labeled data; unsupervised finds patterns without labels.

Describe 'Clustering' in data mining.

An unsupervised technique grouping similar data points based on attributes.

Define 'Support' in association rules.

The proportion of transactions containing a given itemset.

Define 'Confidence' in association rules.

The probability that a transaction containing item A also contains item B.

Define 'Lift' in association rules.

Ratio of observed support to expected support if A and B were independent.

List two strengths and two weaknesses of current AI.

Strengths: pattern finding, data analysis. Weaknesses: creative tasks, intuitive decisions.

Give an example of dynamic pricing.

Airline ticket prices changing based on demand and time to departure.