The travel dataset provides detailed information on various trips taken by travelers, including their destination, travel dates, duration of the trip in days, traveler demographics (name, age, gender, and nationality), as well as the type and cost of accommodation and transportation. This dataset can be used to gain insights into travel patterns, preferences, and behaviors of different types of travelers. It can also be helpful for travel-related businesses, such as travel agencies, to create tailored marketing strategies and travel packages that meet the needs and preferences of different travelers. Column details: • Trip ID: A unique identifier for each trip taken by a traveler. • Destination: The name of the city or country visited by the traveler. • Start date: The date the traveler started the trip. • End date: The date the traveler ended the trip. • Duration (days): The number of days the traveler spent on the trip. • Traveler name: The name of the traveler. • Traveler age: The age of the traveler at the time of the trip. • Traveler gender: The gender of the traveler. • Traveler nationality: The nationality of the traveler. • Accommodation type: The type of accommodation the traveler stayed in, such as hotel, hostel, or Airbnb. • Accommodation cost: The cost of the accommodation for the entire trip. • Transportation type: The mode of transportation used by the traveler, such as plane, train, or car. • Transportation cost: The cost of transportation for the entire trip.

Cost-of-Goods-Sold-Including-Depreciation-and-Amortization Time Series for Global Business Travel Group Inc. Global Business Travel Group, Inc. provides business-to-business (B2B) travel platform in the United States, the United Kingdom, and internationally. The company's platform provides a suite of technology-enabled solutions to business travelers and clients; travel content suppliers, such as airlines, hotels, ground transportation providers, and aggregators; and third-party travel agencies. It also offers consulting, meetings and events planning, and outsourced services; and manages end-to-end logistics of business travel, as well as provides a link between businesses and their employees, travel suppliers, and other industry participants. In addition, the company provides Amex GBT Egencia, a digital travel platform; Amex GBT Neo1, an online spend management platform to manage business expenses, including travel; Amex GBT Neo, a customizable global travel platform; Amex GBT Select, a flexible solution to give insights and control across their travel spend; and Amex GBT Ovation, a touch travel solution and personalized corporate travel servicing platform. Global Business Travel Group, Inc. was founded in 2014 and is based in New York, New York.

Return-On-Equity Time Series for Global Business Travel Group Inc. Global Business Travel Group, Inc. provides business-to-business (B2B) travel platform in the United States, the United Kingdom, and internationally. The company's platform provides a suite of technology-enabled solutions to business travelers and clients; travel content suppliers, such as airlines, hotels, ground transportation providers, and aggregators; and third-party travel agencies. It also offers consulting, meetings and events planning, and outsourced services; and manages end-to-end logistics of business travel, as well as provides a link between businesses and their employees, travel suppliers, and other industry participants. In addition, the company provides Amex GBT Egencia, a digital travel platform; Amex GBT Neo1, an online spend management platform to manage business expenses, including travel; Amex GBT Neo, a customizable global travel platform; Amex GBT Select, a flexible solution to give insights and control across their travel spend; and Amex GBT Ovation, a touch travel solution and personalized corporate travel servicing platform. Global Business Travel Group, Inc. was founded in 2014 and is based in New York, New York.

Envestnet®| Yodlee®'s Bank Statement Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Analysis of ‘TourPackagePrediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sanamps/tourpackageprediction on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

You are a Data Scientist for a tourism company named "Lets Travel". The Policy Maker of the company wants to enable and establish a viable business model to expand the customer base. A viable business model is a central concept that helps you to understand the existing ways of doing the business and how to change the ways for the benefit of the tourism sector. One of the ways to expand the customer base is to introduce a new offering of packages. Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages. However, the marketing cost was quite high because customers were contacted at random without looking at the available information. The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being. However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient. You as a Data Scientist at "Visit with us" travel company have to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.

Objective

To predict which customer is more likely to purchase the newly introduced travel package

About the Data

Customer details: 1. CustomerID: Unique customer ID 2. ProdTaken: Whether the customer has purchased a package or not (0: No, 1: Yes) 3. Age: Age of customer 4. TypeofContact: How customer was contacted (Company Invited or Self Inquiry) 5. CityTier: City tier depends on the development of a city, population, facilities, and living standards. The categories are ordered i.e. Tier 1 > Tier 2 > Tier 3 6. Occupation: Occupation of customer 7. Gender: Gender of customer 8. NumberOfPersonVisiting: Total number of persons planning to take the trip with the customer 9. PreferredPropertyStar: Preferred hotel property rating by customer 10. MaritalStatus: Marital status of customer 11. NumberOfTrips: Average number of trips in a year by customer 12. Passport: The customer has a passport or not (0: No, 1: Yes) 13. OwnCar: Whether the customers own a car or not (0: No, 1: Yes) 14. NumberOfChildrenVisiting: Total number of children with age less than 5 planning to take the trip with the customer 15. Designation: Designation of the customer in the current organization 16. MonthlyIncome: Gross monthly income of the customer

Customer interaction data: 1. PitchSatisfactionScore: Sales pitch satisfaction score 2. ProductPitched: Product pitched by the salesperson 3. NumberOfFollowups: Total number of follow-ups has been done by the salesperson after the sales pitch 4. DurationOfPitch: Duration of the pitch by a salesperson to the customer

--- Original source retains full ownership of the source dataset ---

Context

You are a Data Scientist for a tourism company named "Lets Travel". The Policy Maker of the company wants to enable and establish a viable business model to expand the customer base. A viable business model is a central concept that helps you to understand the existing ways of doing the business and how to change the ways for the benefit of the tourism sector. One of the ways to expand the customer base is to introduce a new offering of packages. Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages. However, the marketing cost was quite high because customers were contacted at random without looking at the available information. The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being. However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient. You as a Data Scientist at "Visit with us" travel company have to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.

Objective

To predict which customer is more likely to purchase the newly introduced travel package

About the Data

Customer details: 1. CustomerID: Unique customer ID 2. ProdTaken: Whether the customer has purchased a package or not (0: No, 1: Yes) 3. Age: Age of customer 4. TypeofContact: How customer was contacted (Company Invited or Self Inquiry) 5. CityTier: City tier depends on the development of a city, population, facilities, and living standards. The categories are ordered i.e. Tier 1 > Tier 2 > Tier 3 6. Occupation: Occupation of customer 7. Gender: Gender of customer 8. NumberOfPersonVisiting: Total number of persons planning to take the trip with the customer 9. PreferredPropertyStar: Preferred hotel property rating by customer 10. MaritalStatus: Marital status of customer 11. NumberOfTrips: Average number of trips in a year by customer 12. Passport: The customer has a passport or not (0: No, 1: Yes) 13. OwnCar: Whether the customers own a car or not (0: No, 1: Yes) 14. NumberOfChildrenVisiting: Total number of children with age less than 5 planning to take the trip with the customer 15. Designation: Designation of the customer in the current organization 16. MonthlyIncome: Gross monthly income of the customer

Customer interaction data: 1. PitchSatisfactionScore: Sales pitch satisfaction score 2. ProductPitched: Product pitched by the salesperson 3. NumberOfFollowups: Total number of follow-ups has been done by the salesperson after the sales pitch 4. DurationOfPitch: Duration of the pitch by a salesperson to the customer

Analysis of ‘🗺️ Holiday_Package_Prediction’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/susant4learning/holiday-package-purchase-prediction on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

"Trips & Travel.Com" company wants to enable and establish a viable business model to expand the customer base. One of the ways to expand the customer base is to introduce a new offering of packages. Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages. However, the marketing cost was quite high because customers were contacted at random without looking at the available information. The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being. However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. - Most important features that have an impact on Product taken: Designation, Passport, Tier City, Martial status, occupation - Customers with Designation as Executive should be the target customers for the company .Customers who have passport and are from tier 3 city and are single or unmarried, have large business such customers have higher chances of taking new package. - Customers monthly income in range of 15000- 25000, and age range 15-30, prefer 5 star properties also have higher chances of taking new package based on EDA.

Inspiration

We need to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.

Tasks to Solve :

To predict which customer is more likely to purchase the newly introduced travel package Which variables are most significant. Which segment of customers should be targeted more.

--- Original source retains full ownership of the source dataset ---

Stock Price Time Series for Equals Group PLC. Equals Group plc, through its subsidiaries, engages in the provision of financial technology payment services in the United Kingdom. It operates through the International Payments, Solutions, Currency Cards, Banking, Travel Cash, Europe, and Central segments. The company offers Equals Money, a card payment platform that includes international payments, spend management software, multi-currency business accounts, prepaid business cards, business debit cards, corporate cards, domestic payments, and brokers platform; FairFX, a retail-focused card product that includes multi-currency cards, international payments, and travel money; CardOneMoney, which includes business current and current accounts; and Roqqet, an open banking platform. The company was formerly known as FairFX Group Plc and changed its name to Equals Group plc in June 2019. Equals Group plc was founded in 2005 and is headquartered in London, the United Kingdom. As of April 14, 2025, Equals Group plc was taken private.

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision USA includes consumer transaction data on 100M+ credit and debit cards, including 35M+ with activity in the past 12 months and 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants, 800+ parent companies, 80+ same store sales metrics, and deep demographic and geographic breakouts. Review data by ticker in our Investor Relations module. Brick & mortar and ecommerce direct-to-consumer sales are recorded on transaction date and purchase data is available for most companies as early as 6 days post-swipe.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

Private equity and venture capital firms can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights teams and retailers can gain visibility into transaction data’s potential for competitive analysis, shopper behavior, and market intelligence.

CE Vision Benefits • Discover new competitors • Compare sales, average ticket & transactions across competition • Evaluate demographic and geographic drivers of growth • Assess customer loyalty • Explore granularity by geos • Benchmark market share vs. competition • Analyze business performance with advanced cross-cut queries

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Use Case: Private Equity, Growth and Venture

Problem A $35B Private Equity company focused on growth & venture, credit, and public equity investing in later-stage companies was looking for a data solution to enable them to source and vet the health of potential investments vs. their peers and their industry. With limited visibility, they were seeking a data solution that would seamlessly and easily provide concrete data and analytics for their assessments.

Solution The firm leveraged Consumer Edge's Vision Pro platform and alternative dataset to monitor and report weekly on: • Sourcing: With the support of Consumer Edge’s Insight team, the firm set up dashboard views to find and track the struggling firms that are open to capital needs. • Diligence: The firm vetted the health of a potential investment target vs. their peers and their industry by monitoring key metrics such as YoY growth, spend amount % growth, transactions, and of transactions % growth. Impact The diligence team was able to: • Identify three target acquisition companies based on historic performance • Set benchmarks vs. competition and monitor growth trends • Develop growth plans for post-acquisition strategy