GFQR 1026 Lecture 3: Big Data Analytics in Business
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Questions and Answers

What type of analytics focuses on analyzing historical performance?

  • Descriptive Analytics (correct)
  • Prescriptive Analytics
  • Analytical Processing
  • Predictive Analytics

Which question is typically answered by Predictive Analytics?

  • What should be done?
  • What is the average weekly sales amount?
  • What could happen? (correct)
  • What has happened?

What is a primary function of Descriptive Analytics?

  • Analyzing customer sentiments
  • Making recommendations based on data trends
  • Mining historical data for insights (correct)
  • Estimating future business trends

What type of analytics would be best suited for forecasting smartphone sales for next year?

<p>Predictive Analytics (B)</p> Signup and view all the answers

Which analytics type utilizes techniques like data aggregation?

<p>Descriptive Analytics (D)</p> Signup and view all the answers

Which statement best describes Prescriptive Analytics?

<p>It provides insights to inform future actions. (C)</p> Signup and view all the answers

What is a core purpose of Predictive Analytics?

<p>Estimating the likelihood of future outcomes (A)</p> Signup and view all the answers

Which analytics are considered the most traditional in the field?

<p>Descriptive Analytics (C)</p> Signup and view all the answers

What is the primary input for a recommendation system?

<p>Feedback of likes and dislikes (D)</p> Signup and view all the answers

Which of the following is an example of content-based filtering?

<p>Recommending movies to someone who enjoys romantic comedies (A)</p> Signup and view all the answers

What distinguishes collaborative filtering from content-based filtering?

<p>Collaborative filtering operates on similarity measures, while content-based filtering relies on item attributes. (A)</p> Signup and view all the answers

Which of the following statement about collaborative filtering is true?

<p>It can use the same algorithm for various types of products like music and books. (B)</p> Signup and view all the answers

What type of recommendations does the 'Customers who bought this item also bought' section from Amazon.com illustrate?

<p>Collaborative filtering (B)</p> Signup and view all the answers

What does United Airlines use their 'collect, detect, act' protocol for?

<p>To analyze customer profiles and generate tailored offers. (B)</p> Signup and view all the answers

Which attribute is NOT typically considered in content-based filtering for movies?

<p>Box office revenue (B)</p> Signup and view all the answers

In user-based collaborative filtering, what is primarily utilized to make recommendations?

<p>Similarity measures between users (D)</p> Signup and view all the answers

What feature does British Airways employ to enhance customer experience?

<p>The 'Know Me' feature for personalized results. (C)</p> Signup and view all the answers

Why is content-based filtering considered domain-specific?

<p>It cannot apply the same algorithm to different types of items. (B)</p> Signup and view all the answers

How does EasyJet use artificial intelligence in their operations?

<p>To determine seat pricing based on demand. (C)</p> Signup and view all the answers

What partnership has Southwest Airlines pursued to improve safety?

<p>Teaming up with NASA. (B)</p> Signup and view all the answers

What type of data does EasyJet analyze to inform future decision-making?

<p>Historical demand patterns. (B)</p> Signup and view all the answers

What measurable impact did the 'collect, detect, act' initiative have on United Airlines?

<p>Led to a 15% increase in year-to-year revenue. (D)</p> Signup and view all the answers

What kind of offers does British Airways generate through data analysis?

<p>Personalized and targeted offers. (C)</p> Signup and view all the answers

What significant data-driven outcome is associated with Southwest Airlines' collaboration with NASA?

<p>Flagging anomalies to prevent accidents. (D)</p> Signup and view all the answers

What is the primary basis for user-based collaborative filtering?

<p>The similarities between users. (A)</p> Signup and view all the answers

Which movie can be recommended to User1 based on the provided ratings?

<p>Movie2 (A)</p> Signup and view all the answers

In item-based collaborative filtering, what is the goal when calculating a user's preference for an item they have not rated?

<p>To compute a weighted average based on similar items. (A)</p> Signup and view all the answers

Which characteristic is shared between users in user-based collaborative filtering?

<p>They have similar rating patterns. (B)</p> Signup and view all the answers

What does the P value represent in item-based collaborative filtering?

<p>The estimated preference for an unrated item. (D)</p> Signup and view all the answers

What is a common method to calculate the similarity between items in item-based collaborative filtering?

<p>Counting the number of shared ratings by users. (D)</p> Signup and view all the answers

What can be inferred if User1 and User3 have rated Movie1 and Movie4 similarly?

<p>User1 and User3 are likely to rate other movies similarly. (A)</p> Signup and view all the answers

What might happen if a user's calculated preference for an item exceeds a certain threshold in item-based collaborative filtering?

<p>The item can be recommended to the user. (D)</p> Signup and view all the answers

What is the primary purpose of cookies when a user browses a website?

<p>To remember user inputs and browsing activity (B)</p> Signup and view all the answers

Which of the following best describes how cookies function?

<p>They are small data files that persist across multiple sessions. (D)</p> Signup and view all the answers

In the case of Chow Tai Fook Jewelry, what was the purpose of collecting massive amounts of data?

<p>To identify customer behaviors and preferences (A)</p> Signup and view all the answers

What feature distinguishes Explorium, the experimental shopping mall in Shanghai?

<p>It integrates cutting-edge technologies to understand shopper preferences. (A)</p> Signup and view all the answers

What type of data do cookies primarily store?

<p>Information entered into forms and browsing history (A)</p> Signup and view all the answers

Which aspect of big data analysis is highlighted in the case of Li & Fung in China?

<p>Improving inventory turnover by understanding buying habits (C)</p> Signup and view all the answers

What can cookies help a website to do when a user returns?

<p>Show previous orders and last pages visited (D)</p> Signup and view all the answers

How do cookies enhance user experience on websites?

<p>By remembering user preferences and entered information (D)</p> Signup and view all the answers

What is a primary factor WeLend uses to determine creditworthiness?

<p>Proprietary big data technology (A)</p> Signup and view all the answers

How quickly can WeLend approve a loan application?

<p>In 1.7 seconds (D)</p> Signup and view all the answers

Which company has implemented big data analytics for customer retention?

<p>Citibank (B)</p> Signup and view all the answers

What information does WeLend collect to assess user profiles?

<p>Type of phone and installed apps (C)</p> Signup and view all the answers

How does Citibank utilize big data analytics?

<p>To scan for fraudulent charges (A)</p> Signup and view all the answers

What unique aspect does WeLend consider in addition to credit history?

<p>Social networks and engagement time (B)</p> Signup and view all the answers

What is a benefit of big data for banks like Hang Seng Bank?

<p>Better analysis of customer behavior (C)</p> Signup and view all the answers

What is the loan delinquency rate reported by WeLend in Hong Kong?

<p>1% (C)</p> Signup and view all the answers

Flashcards

Recommendation Systems

Systems that suggest items (e.g., movies, music) based on user feedback.

Content-based Filtering

Recommends items similar to what a user has liked based on item attributes (e.g., genre).

Collaborative Filtering

Recommends items based on preferences of similar users.

Item attributes

Characteristics of an item used for recommendations (e.g., genre, actor).

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User's taste

User's preferences or liking regarding an item's attributes.

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Similarity measures

Methods used to determine how similar items or users are.

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User-based CF

Collaborative Filtering that recommends items based on preferences of similar users.

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Item-based CF

Collaborative Filtering that recommends items based on similarity between items.

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Descriptive Analytics

Examines past performance by analyzing historical data to understand success or failure.

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Predictive Analytics

Uses statistical models and forecasts to predict future outcomes.

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Prescriptive Analytics

Suggests actions based on predictions to optimize future outcomes.

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Analytics

The process of extracting insights and knowledge from data.

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Past Performance

Information from prior periods that can be analyzed to understand the past.

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Future Outcomes

Potential future events or results, such as anticipated market trends.

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Data Mining

The process of discovering patterns and insights from large datasets.

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Actionable Insights

Information that can guide decision-making and lead to a desired outcome.

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User-Based Collaborative Filtering

Recommends items based on the preferences of users similar to the target user. It identifies users with similar tastes and recommends items that those similar users liked.

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Item-Based Collaborative Filtering

Recommends items based on the similarity between items. Similar items are often preferred by the same users.

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Item similarity

How alike or similar items are based on user preferences. (e.g., similar ratings by a large number of users)

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User Similarity

How alike users are based on item ratings (e.g., rating the same items similarly).

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Weighted Average

A calculation that estimates the preference for an item based on the similarity of that item to items the user likes.

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Preference Threshold

A minimum value for preference score that must be exceeded for a recommendation to be made.

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Recommendation

Suggestions for items a user might like, based on their preferences and similarities to others.

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Collaborative Filtering

A recommendation method that leverages the preferences of other users to suggest new items. (User-based and Item-based are two ways to do this)

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United Airlines Big Data

Analyzing 150+ customer data points (purchases, preferences) to create personalized offers, increasing revenue by 15% annually.

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British Airways 'Know Me'

Personalized flight search results based on customer past flight patterns.

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EasyJet's AI Seat Pricing

Artificial intelligence automatically sets seat prices based on demand, predicting demand up to a year in advance.

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Southwest & NASA

Collaboration to improve safety by creating a system generating vast amounts of data to identify & prevent accidents.

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Big Data Aviation

Leveraging immense data sets from customer journeys to airlines like United, British Airways, EasyJet, Southwest for tailored services and process improvement.

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Collect, Detect, Act Protocol

United Airlines' data analysis methodology, including collecting customer data, detecting trends, and acting on insights for personalized offers.

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Personalized Search Results

Providing search results specifically tailored to a user's previous search history or preferences.

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Airline Safety System

A system developed by Southwest and NASA to analyze data for potential hazards and accidents in flight.

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Big Data in Banking

Using large datasets to analyze customer behavior, creditworthiness, and fraud in banking.

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Online Loan Application

Applying for loans without in-person meetings using online platforms.

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Customer Creditworthiness

Assessing a customer's ability to repay a loan based on data analysis.

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Fraud Detection

Using data analysis to identify unusual or incorrect transactions.

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Loan Approval Speed

Time taken to approve a loan application.

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Big Data Analysis in WeLend

Using big data to assess the creditworthiness of customers in Hong Kong's online lender.

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Data Sources in WeLend

WeLend uses user's credit history, phone data, apps, and social network activities for loan approvals.

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Loan Delinquency Rate

Percentage of loans that are not being repaid on time.

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Cookies (web)

Small data files stored on a user's computer when browsing a website, recording browsing activity and remembering user-entered information.

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Browsing Activity

Actions a user takes while exploring a website, like visiting pages and filling forms.

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Big Data in Retail

Using massive amounts of data from various sources (stores, online, memberships) to understand customer behavior for better business performance.

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Customer Preferences

Individual customer's likes and dislikes related to products or services.

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Inventory Turnover

How quickly a business sells its products.

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Chow Tai Fook (Case Study)

A case example of a jewelry company that uses data to understand customer behavior and predict business performance.

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Explorium (Case Study)

A retail shopping mall in Shanghai using technology like big data analysis to improve customer experience and understand buying patterns.

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Li & Fung (Case Study)

A company that utilized the Explorium, a shopping mall, to enhance its knowledge of customer behavior and preferences through data analysis.

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Study Notes

Course Information

  • Course title: GFQR 1026: Big Data in “X”
  • Lecture 3: Big Data Analytics in Business (1)
  • Course offered by: Department of Computer Science, Hong Kong Baptist University

Lecture 3: Outline

  • Business Intelligence
  • Types of Big Data Analytics used in Business
  • Examples of Big Data Analytics in Business
  • Technologies used in Business: Recommendation Systems & Cookies

Business Intelligence (BI)

  • Businesses have used business intelligence (BI) tools for decades
  • BI covers many applications and technologies to gather, store, analyze, and provide data access
  • BI provides historical, current, and predictive views of business operations
  • Combine BI with big data

Revenue from Big Data and Business Analytics

  • Worldwide revenue from big data and business analytics increased from 2015 to 2022
  • In 2018, the revenue was expected to be 166 billion U.S. dollars

Big Data Analytics

  • Big data analytics is used in many fields: healthcare, telecom, insurance, government, finance, automobile, education, and retail
  • This lecture focuses on big data in business

Types of Big Data Analytics

  • Descriptive Analytics: This is the most traditional type of business analytics which analyzes past performance using historical data to find the reasons for success or failure
    • Example: Average weekly sales amount
  • Predictive Analytics: This uses statistical models and forecasting techniques to understand future trends or potential outcomes
    • Example: Forecasting smart phone sales in the next year
  • Prescriptive Analytics: This goes beyond predicting future outcomes by suggesting actions to benefit from the predictions. It looks at "why" things will happen, proposes decision options, and considers the impact of each option.
    • Example: Google self-driving car

Big Data in Aviation Industry

  • Examples of big data in this industry: departure/arrival times of flights, social media activity, in-flight food preferences, and number of passengers
  • Usages: aircraft maintenance, inventory management, fleet management, operations management (pilots, crew), staff management
  • Benefits: smarter maintenance, improved safety, better service, reduced costs
  • Related use cases from different airlines: United Airlines, British Airways, easyJet, Southwest Airlines, and Cathay Pacific are discussed.
    • These examples demonstrate how big data helps with tailoring customer experience through personalization, efficiency, and cost reduction.

Big Data in Retail Industry

  • Case: Alibaba
    • One of Asia's top Big Data users
    • Online transactions reached US$ 84.5 billion (HK$ 659 billion) in 2021
    • Utilizes big data for personalized recommendations to cross-sell and up-sell products
  • Other use cases discuss in detail how big data supports businesses in the retail industry.
    • There are example cases to show that big data supports businesses to understand and predict customer behavior.

Big Data in Banking & Finance Industry

  • Case: Hang Seng Bank
    • Using online applications for loans without requiring income or address verification
    • Analyzing customer behavior and creditworthiness via Big Data
  • Case: Citibank
    • Tracking more than 200 million customer accounts globally
    • Identifying and managing fraudulent transactions using Big Data
  • Case: WeLend (by WeLab)
    • Hong Kong's first online lending platform founded in 2013
    • Loans to customers without direct meetings, relying on Big Data for approval and user risk assessment

Big Data in Insurance Industry

  • Case: HSBC Insurance
    • Uses big data analysis for product innovations, such as the HSBC Term Protector and HSBC Cancer Term Protector
    • Understands customer needs and requirements better with big data
  • Case: MLC Life Insurance
    • Using wearable technology to improve health activity and better provide insurance premiums
    • Focuses on product innovation, customer experience, and customer engagement with big data
  • Case: ZhongAn Online Casualty and Property Insurance
    • China's first internet-only insurer
    • Utilizes a one-stop shop for buyers and owners of cars
  • Offers discounts and better policies based on driver's behavior.

Big Data used for Hospitality Industry

  • Case: Marriott
    • Uses unstructured/semi-structured data for demand forecasting, and optimizes room prices
  • Case: Starwood Hotels and Resorts
    • Optimizing room pricing globally based on local and worldwide factors, events and weather and increase yearly revenue by 5%

Big Data Applications In Business Sectors

  • Airline sector: using sensors to improve safety
  • Retail sector: using analytics to optimize the business
  • Bank and Finance sector: using analytics to differentiate fraudulent interactions and transactions
  • Insurance sector: using analytics for risk assessment, fraud detection, marketing, customer experience.
  • Hospitality sector: using analytics for optimal pricing and personalized marketing campaigns.

Big Data Around You

  • Increasing number of connected devices per person.
  • Big data is growing rapidly
  • The technologies related to big data are in many areas.

Summary of Lecture 3

  • This lecture covers business intelligence, big data analytics types, business examples, recommendation and technologies

Cookies

  • Small data files on a user's hard drive
  • Cookies record browsing activity.
  • Cookies remember and access information for later use (e.g., previous orders, pages visited).
  • Helps personalize customer experiences from websites.

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GFQR 1026 Lecture 3: Big Data Analytics in Business (1) PDF

Description

Explore the impact of big data analytics on business intelligence in this quiz. Learn about various types of analytics, technologies, and the growing revenue in this field. Test your understanding of how businesses leverage big data for better decision making.

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