- The document analyzes cancellation data from IndoCabs to understand the causes of trip cancellations.
- Key findings include point-to-point trips having a 10% cancellation rate, twice as high as other types, and mobile/online bookings having a 13% cancellation rate.
- Cancellations are most common in the 5-8pm timeframe and for bookings made less than 12 hours in advance.
The document analyzes cancellations at IndoCabs, a cab service company in India. The author processed a dataset of 1,956 bookings from IndoCabs to remove duplicates and errors. Key findings include: the average trip duration was 4.47 hours, the average booking window was 2.08 days, and 8.79% of bookings were cancelled. Point-to-point travel had the highest cancellation rate. Mobile bookings saw a higher cancellation rate than online bookings. Most cancellations occurred with booking windows of 1 day or less. The author recommends IndoCabs offer discounts for earlier bookings and revamp their mobile booking and phone services to reduce cancellations.
Data science project aimed at predicting hotel booking cancellations from the moment of booking. Data represents booking information form two hotels in Portugal. Results suggest lead time, market segment and average daily rate as some of the important predictors.
This document provides 20 practice questions for the CISA 100 exam. Each question includes the question prompt, possible multiple choice answers, and an explanation of the correct answer. The questions cover topics like appropriate auditor responses, reasons for controls, risk types, audit techniques, purposes of compliance tests, IS audit stages, audit charters, reporting audit results, developing risk-based audit programs, substantive versus compliance tests, segregation of duties, strategic planning, and more. The document is intended to help candidates study for the CISA exam by testing their knowledge on these important information systems auditing topics.
The document discusses strategies for telecommunications companies to improve their revenue assurance capabilities in the digital and converged space. It recommends conducting a rapid maturity assessment to evaluate the current state of a company's revenue assurance function and benchmark it against industry leaders. This will provide clarity on objectives and a roadmap for strengthening capabilities. Key areas discussed for revenue assurance include analytics-driven approaches, product margin assurance, migration assurance, customer churn assurance, control frameworks, and fraud analytics. Conducting a rapid maturity assessment is presented as the first step to understanding where a company stands and developing a robust revenue assurance strategy.
This document summarizes a project to predict cab booking cancellations for a company using data mining techniques. Visualization of the data showed bookings had the highest success rates with greater differences between booking and travel dates. Classification algorithms like random forest, Ada boost and neural networks were applied to the data and evaluated on validation data. The models showed the highest cancellation rates occurred on Sundays, Thursdays and Fridays. Developing an accurate cancellation prediction model would help the company optimize operations.
The document summarizes a report on the car leasing market in India. It provides key findings from surveys of 29 car leasing companies and 100 corporate clients. The size of the Indian car leasing market is estimated at $2.5 billion but only 10% is organized. While growth is predicted at 17% annually, weaknesses like an unorganized used car market and varying taxes across states pose challenges. Maintenance and repair are key factors for clients. Availability of qualified drivers is also a major issue according to respondents.
A Revenue Reconciliation and Settlement System that was built to provide an automated and systemic approach to recognizing revenues across the various revenue streams of the organization.
Technology Edge in Algo Trading: Traditional Vs Automated Trading System Arch...QuantInsti
The document discusses the evolution of trading system architectures from traditional to automated. Traditionally, trading systems consisted of components to access market data, store historical data, analyze data, input trades, and route orders. With automation and high-frequency trading, latency had to be reduced to milliseconds requiring event processing and order generation to move to servers. Architectures now feature complex event processing engines, risk management checks, standardized FIX protocols, data normalization, order routing, extensive data storage, and replay capabilities for backtesting. Overall the document outlines how trading systems have been optimized for speed and automation to facilitate high-frequency algorithmic trading.
The document analyzes cancellations at IndoCabs, a cab service company in India. The author processed a dataset of 1,956 bookings from IndoCabs to remove duplicates and errors. Key findings include: the average trip duration was 4.47 hours, the average booking window was 2.08 days, and 8.79% of bookings were cancelled. Point-to-point travel had the highest cancellation rate. Mobile bookings saw a higher cancellation rate than online bookings. Most cancellations occurred with booking windows of 1 day or less. The author recommends IndoCabs offer discounts for earlier bookings and revamp their mobile booking and phone services to reduce cancellations.
Data science project aimed at predicting hotel booking cancellations from the moment of booking. Data represents booking information form two hotels in Portugal. Results suggest lead time, market segment and average daily rate as some of the important predictors.
This document provides 20 practice questions for the CISA 100 exam. Each question includes the question prompt, possible multiple choice answers, and an explanation of the correct answer. The questions cover topics like appropriate auditor responses, reasons for controls, risk types, audit techniques, purposes of compliance tests, IS audit stages, audit charters, reporting audit results, developing risk-based audit programs, substantive versus compliance tests, segregation of duties, strategic planning, and more. The document is intended to help candidates study for the CISA exam by testing their knowledge on these important information systems auditing topics.
The document discusses strategies for telecommunications companies to improve their revenue assurance capabilities in the digital and converged space. It recommends conducting a rapid maturity assessment to evaluate the current state of a company's revenue assurance function and benchmark it against industry leaders. This will provide clarity on objectives and a roadmap for strengthening capabilities. Key areas discussed for revenue assurance include analytics-driven approaches, product margin assurance, migration assurance, customer churn assurance, control frameworks, and fraud analytics. Conducting a rapid maturity assessment is presented as the first step to understanding where a company stands and developing a robust revenue assurance strategy.
This document summarizes a project to predict cab booking cancellations for a company using data mining techniques. Visualization of the data showed bookings had the highest success rates with greater differences between booking and travel dates. Classification algorithms like random forest, Ada boost and neural networks were applied to the data and evaluated on validation data. The models showed the highest cancellation rates occurred on Sundays, Thursdays and Fridays. Developing an accurate cancellation prediction model would help the company optimize operations.
The document summarizes a report on the car leasing market in India. It provides key findings from surveys of 29 car leasing companies and 100 corporate clients. The size of the Indian car leasing market is estimated at $2.5 billion but only 10% is organized. While growth is predicted at 17% annually, weaknesses like an unorganized used car market and varying taxes across states pose challenges. Maintenance and repair are key factors for clients. Availability of qualified drivers is also a major issue according to respondents.
A Revenue Reconciliation and Settlement System that was built to provide an automated and systemic approach to recognizing revenues across the various revenue streams of the organization.
Technology Edge in Algo Trading: Traditional Vs Automated Trading System Arch...QuantInsti
The document discusses the evolution of trading system architectures from traditional to automated. Traditionally, trading systems consisted of components to access market data, store historical data, analyze data, input trades, and route orders. With automation and high-frequency trading, latency had to be reduced to milliseconds requiring event processing and order generation to move to servers. Architectures now feature complex event processing engines, risk management checks, standardized FIX protocols, data normalization, order routing, extensive data storage, and replay capabilities for backtesting. Overall the document outlines how trading systems have been optimized for speed and automation to facilitate high-frequency algorithmic trading.
This survey summarizes customer satisfaction with the European airline ES-JET. Most customers took 3 round trips in the past year, primarily for leisure. Older customers tended to book through travel agents while younger customers booked directly. Some procedures like ticket counters caused dissatisfaction due to long queues. Overall satisfaction with ES-JET was good, but over half of customers were dissatisfied with travel agent services. Internet retail sales are growing faster than other retail sales and show a high positive correlation, though many other factors also influence sales.
Sophisticated online marketers are realizing that email, mobile and desktop applications all belong in their messaging ecosystem, and they must work and be measured together to achieve optimal results. Case study examples from the Royal Caribbean VIP Cruise Pass, the Tahiti Live and Vail Resorts SnowMate will demonstrate how these brands are using Web 3.0 tactics to deliver the right message, at the right time in the right channel.
Providing quality for travel solution version1Sujith C Saji
1. Testing quality is essential for travel sites due to the complexity of the travel industry and dependencies between components like hotels, flights, and travel agencies. Issues during booking can negatively impact customers' experiences.
2. The Global Distribution System (GDS) is critical as it holds real-time availability and processes bookings, cancellations, and changes. Thorough testing is needed to ensure accurate passenger name records and itineraries.
3. Setting up an end-to-end test workflow connecting to different GDSs and supplier systems is important to simulate the full customer booking process and identify any issues.
This document discusses how local searches on the internet often fail to return the desired results because they are typically filtered by distance rather than travel time. While distance is the most common local filter offered, searching by travel time would be more useful to local searchers as it accounts for real-world factors like traffic that impact how long it takes to travel somewhere. The author proposes that search platforms that allow filtering by travel time could significantly improve local search success rates and better satisfy the needs of online visitors seeking local businesses and services.
The document discusses the hidden costs of business travel programs, including productivity costs and intangible costs. It provides examples of how online booking tools can help reduce productivity costs by saving travel managers and travelers time spent on booking and approvals. Poorly designed online tools can increase productivity costs if they are difficult to use or do not provide the necessary information. Intangible costs include impacts on employee morale from lack of good travel support tools. Providing personalized, easy-to-use online booking tools can increase traveler satisfaction and compliance with company policies. Overall, the hidden costs of business travel programs should be considered in addition to direct travel costs.
This document summarizes a capstone project analyzing hotel booking data from 2015 to 2017. The team imported necessary libraries and cleaned the data by replacing null values. Their analysis found that most bookings were for city hotels by transient customers online. August had the most bookings but cancellations varied by year. Portugal, Germany, and France had the most bookings while Portugal, Germany, and Spain saw the most cancellations. Average daily rates increased over time with the highest in 2017. The team drew several conclusions and recommendations for hotels based on their findings.
This document provides an overview of an online hotel management system project. It includes an introduction to online hotel management and the benefits it provides. The document then outlines the various sections that will be included in the project such as requirements, objectives, analysis, design, and implementation. It discusses the key modules that will be developed including booking management, payment processing, and reporting. The goals of the project are to create a web-based system that allows customers to book hotels online and for hotels to manage reservations and payments electronically.
The meeting discussed transitioning from paper forms to an electronic Personnel Action Request (ePAR) system. Currently there are many paper forms used for personnel actions across institutions. The ePAR system will streamline processes by allowing electronic signatures, tracking of workflow, and reducing data entry errors. A pilot program is underway involving several departments. The goals are to improve efficiencies and reduce costs through the consolidated electronic system.
Key CRO Metrics to Analyze for Successful Landing PagesHanapin Marketing
The webinar discusses key CRO metrics to analyze for successful landing pages, including bounce rate, exit rate, average time on page, pages per session, session duration, and conversion rate. It covers analyzing metrics by demographics, new vs. returning users, device type, page speed, and specific landing pages. The goal is to understand audience behavior and determine where to focus optimizations by finding problems in the data and testing hypotheses.
In order to have a successful CRO program, you need to focus in on the data behind your landing pages. However, you might find yourself logging into Google Analytics with your heart rate at 250 because you know there are a million metrics you could be analyzing. Which ones do you need to spend your time on?
In this new live webinar, Hanapin Marketing’s CRO Manager Samantha Kerr will help you narrow down your focus. She’ll walk you through the key metrics she analyzes when creating landing page optimizations.
You’ll learn tips like:
Why bounce rate is important to analyze and what it means for your site
Why it’s important to look at the engagement metrics for specific users
On which areas of your site you should focus your optimizations
BEST PRACTICES IN TRAVEL WEBSITE
TESTING AND OPTIMIZATION 2 - Search
Download at: http://www.travelweekly.com/uploadedFiles/PDFs/021213BestPractices-Search.pdf
This document discusses travel time reliability and how it is measured. It defines travel time reliability as the consistency or dependability of travel times from day to day. It describes several ways to measure reliability, including the 90th/95th percentile travel times, buffer index, and planning time index. It provides examples of agencies like FHWA, MnDOT and WSDOT that are using reliability measures to monitor traffic conditions and performance.
Most riders travel between 2-4pm and 11pm, with those aged 36-45 having the highest average ride durations. Offering discounts to casual riders aged 18-35 (male) and 18-35 and 36-45 (female) during peak times could increase loyalty. The highest proportion of riders are aged 0-17, suggesting an experience-focused demographic. Changing regional sales managers most impacts customer attrition, so reducing such changes is recommended alongside offering discounts to price-sensitive customer segments.
1Executive Summary IntroductionPriceline.com is anAnastaciaShadelb
1
Executive Summary
Introduction
Priceline.com is an agency that aims to make traveling easier by providing online travel-related services such as finding flights, hotel stays, and car bookings. They act as an intermediary between customers and providers (Etzioni et al., 2003). Priceline.com started its roots in the business industry of online travel companies in 1997; they are a sub-part of Booking Holdings. Their main purpose in joining this industry was to attract the two unsaturated markets and come up with a way of mixing market penetration with market development by using the demand and power of the Internet. Commonly, airlines work on a regular basis, and individuals always travel. Priceline.com took this perfect opportunity by giving the hustled clients an easier way to travel with a stress-free process of online booking options. This site also provides suitable hotels in the neighborhood of the client’s desired destination.
It is an online travel company with a total of 40% share in the global travel and tourism market, according to the report of Statista (2021). According to the financial statements and account handling of yahoo finance, Priceline.com is one of the third largest public travel companies by market share.
The department that works for the informational security of Priceline.com is very reliable and secure. They make sure that their technical, administrative, and physical safeguards and databases are manufactured to block unauthorized access and maintain zero percent data error with increased efficiency (Huang et al., 2014). They also do not lead any personal information of their clients and make sure that clients' personal data is not used for any other purpose, such as digital marketing. All of this is done by collaborating with multiple departments but mainly by MIS experts and the Cyber security department. These all are comprised of one structure known as the privacy department.
The overall organization of Priceline has a vertical organizational structure where the decisions are made by the top management and descended to employees through their hierarchy. Similarly, their privacy department has a vertical organizational structure too (Huang et al., 2014).
Priceline is able to operate with only 12,700 employees worldwide. It has a high revenue margin, including a gross margin of 88.90%, an operating margin of 36.00%, and a profit margin of 27.40%, allowing Priceline to produce higher income from its sales. According to this data, each departmental unit's budget is reasonably high as they operate in an international market. The privacy department is owned by the Chief Technology Officer (CTO). CTO is a higher authority that under-looks every technological matter where technological units, systems, and management evolve and continuously change (Privacy & Cookies Policy, 2021). According to the 2021 Annual Report, the information technology budget was around $412 million, which also accounts for 3.8% of the reven ...
Mobile Commerce – today’s e-commerce. How successfully to take advantage of consumer behavior? How to measure the value of mobile communications? These and other m-commerce-related questions will be answered during this session.
This presentation used in electronic commerce conference "E-komercija '16" by Rytis Meškauskas from company pigu.lt.
Online Hotel & Ticket Booking Sites in Indonesia 2014
Omnibus Popular Brand Index 2014
A. Detail findings
1.Popular Brand Index
2.Brand awareness
3.Expansive
4.Frequent User
5.Future Intention
6.Switching
7.General Information
WHITEPAPER - Fine Tune Your Hotel Forecast with Big DataDuetto
The document discusses how hotels can improve demand forecasting through the use of big data. It outlines seven key types of data that can be analyzed: 1) historical booking data, 2) competitor pricing, 3) events and macroeconomic factors, 4) air traffic data, 5) social reviews and ratings, 6) weather data, and 7) web shopping behavior data. When these various data sources are combined and analyzed, it allows hotels to generate highly accurate forecasts that can optimize pricing and drive greater profits.
http://thebestcompanys.com/hotel-booking/company/expedia-com/
Expedia offers hotel booking services to customers all around the world. They are a big competitor to other major brands in the hotel booking industry like Kayak.com, Orbitz and HotelBooking.com.
If you're looking for the best hotel booking services then we recommend you visit our Top Ranked and Recommended Companies by visiting TheBestCompanys.com
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART
This survey summarizes customer satisfaction with the European airline ES-JET. Most customers took 3 round trips in the past year, primarily for leisure. Older customers tended to book through travel agents while younger customers booked directly. Some procedures like ticket counters caused dissatisfaction due to long queues. Overall satisfaction with ES-JET was good, but over half of customers were dissatisfied with travel agent services. Internet retail sales are growing faster than other retail sales and show a high positive correlation, though many other factors also influence sales.
Sophisticated online marketers are realizing that email, mobile and desktop applications all belong in their messaging ecosystem, and they must work and be measured together to achieve optimal results. Case study examples from the Royal Caribbean VIP Cruise Pass, the Tahiti Live and Vail Resorts SnowMate will demonstrate how these brands are using Web 3.0 tactics to deliver the right message, at the right time in the right channel.
Providing quality for travel solution version1Sujith C Saji
1. Testing quality is essential for travel sites due to the complexity of the travel industry and dependencies between components like hotels, flights, and travel agencies. Issues during booking can negatively impact customers' experiences.
2. The Global Distribution System (GDS) is critical as it holds real-time availability and processes bookings, cancellations, and changes. Thorough testing is needed to ensure accurate passenger name records and itineraries.
3. Setting up an end-to-end test workflow connecting to different GDSs and supplier systems is important to simulate the full customer booking process and identify any issues.
This document discusses how local searches on the internet often fail to return the desired results because they are typically filtered by distance rather than travel time. While distance is the most common local filter offered, searching by travel time would be more useful to local searchers as it accounts for real-world factors like traffic that impact how long it takes to travel somewhere. The author proposes that search platforms that allow filtering by travel time could significantly improve local search success rates and better satisfy the needs of online visitors seeking local businesses and services.
The document discusses the hidden costs of business travel programs, including productivity costs and intangible costs. It provides examples of how online booking tools can help reduce productivity costs by saving travel managers and travelers time spent on booking and approvals. Poorly designed online tools can increase productivity costs if they are difficult to use or do not provide the necessary information. Intangible costs include impacts on employee morale from lack of good travel support tools. Providing personalized, easy-to-use online booking tools can increase traveler satisfaction and compliance with company policies. Overall, the hidden costs of business travel programs should be considered in addition to direct travel costs.
This document summarizes a capstone project analyzing hotel booking data from 2015 to 2017. The team imported necessary libraries and cleaned the data by replacing null values. Their analysis found that most bookings were for city hotels by transient customers online. August had the most bookings but cancellations varied by year. Portugal, Germany, and France had the most bookings while Portugal, Germany, and Spain saw the most cancellations. Average daily rates increased over time with the highest in 2017. The team drew several conclusions and recommendations for hotels based on their findings.
This document provides an overview of an online hotel management system project. It includes an introduction to online hotel management and the benefits it provides. The document then outlines the various sections that will be included in the project such as requirements, objectives, analysis, design, and implementation. It discusses the key modules that will be developed including booking management, payment processing, and reporting. The goals of the project are to create a web-based system that allows customers to book hotels online and for hotels to manage reservations and payments electronically.
The meeting discussed transitioning from paper forms to an electronic Personnel Action Request (ePAR) system. Currently there are many paper forms used for personnel actions across institutions. The ePAR system will streamline processes by allowing electronic signatures, tracking of workflow, and reducing data entry errors. A pilot program is underway involving several departments. The goals are to improve efficiencies and reduce costs through the consolidated electronic system.
Key CRO Metrics to Analyze for Successful Landing PagesHanapin Marketing
The webinar discusses key CRO metrics to analyze for successful landing pages, including bounce rate, exit rate, average time on page, pages per session, session duration, and conversion rate. It covers analyzing metrics by demographics, new vs. returning users, device type, page speed, and specific landing pages. The goal is to understand audience behavior and determine where to focus optimizations by finding problems in the data and testing hypotheses.
In order to have a successful CRO program, you need to focus in on the data behind your landing pages. However, you might find yourself logging into Google Analytics with your heart rate at 250 because you know there are a million metrics you could be analyzing. Which ones do you need to spend your time on?
In this new live webinar, Hanapin Marketing’s CRO Manager Samantha Kerr will help you narrow down your focus. She’ll walk you through the key metrics she analyzes when creating landing page optimizations.
You’ll learn tips like:
Why bounce rate is important to analyze and what it means for your site
Why it’s important to look at the engagement metrics for specific users
On which areas of your site you should focus your optimizations
BEST PRACTICES IN TRAVEL WEBSITE
TESTING AND OPTIMIZATION 2 - Search
Download at: http://www.travelweekly.com/uploadedFiles/PDFs/021213BestPractices-Search.pdf
This document discusses travel time reliability and how it is measured. It defines travel time reliability as the consistency or dependability of travel times from day to day. It describes several ways to measure reliability, including the 90th/95th percentile travel times, buffer index, and planning time index. It provides examples of agencies like FHWA, MnDOT and WSDOT that are using reliability measures to monitor traffic conditions and performance.
Most riders travel between 2-4pm and 11pm, with those aged 36-45 having the highest average ride durations. Offering discounts to casual riders aged 18-35 (male) and 18-35 and 36-45 (female) during peak times could increase loyalty. The highest proportion of riders are aged 0-17, suggesting an experience-focused demographic. Changing regional sales managers most impacts customer attrition, so reducing such changes is recommended alongside offering discounts to price-sensitive customer segments.
1Executive Summary IntroductionPriceline.com is anAnastaciaShadelb
1
Executive Summary
Introduction
Priceline.com is an agency that aims to make traveling easier by providing online travel-related services such as finding flights, hotel stays, and car bookings. They act as an intermediary between customers and providers (Etzioni et al., 2003). Priceline.com started its roots in the business industry of online travel companies in 1997; they are a sub-part of Booking Holdings. Their main purpose in joining this industry was to attract the two unsaturated markets and come up with a way of mixing market penetration with market development by using the demand and power of the Internet. Commonly, airlines work on a regular basis, and individuals always travel. Priceline.com took this perfect opportunity by giving the hustled clients an easier way to travel with a stress-free process of online booking options. This site also provides suitable hotels in the neighborhood of the client’s desired destination.
It is an online travel company with a total of 40% share in the global travel and tourism market, according to the report of Statista (2021). According to the financial statements and account handling of yahoo finance, Priceline.com is one of the third largest public travel companies by market share.
The department that works for the informational security of Priceline.com is very reliable and secure. They make sure that their technical, administrative, and physical safeguards and databases are manufactured to block unauthorized access and maintain zero percent data error with increased efficiency (Huang et al., 2014). They also do not lead any personal information of their clients and make sure that clients' personal data is not used for any other purpose, such as digital marketing. All of this is done by collaborating with multiple departments but mainly by MIS experts and the Cyber security department. These all are comprised of one structure known as the privacy department.
The overall organization of Priceline has a vertical organizational structure where the decisions are made by the top management and descended to employees through their hierarchy. Similarly, their privacy department has a vertical organizational structure too (Huang et al., 2014).
Priceline is able to operate with only 12,700 employees worldwide. It has a high revenue margin, including a gross margin of 88.90%, an operating margin of 36.00%, and a profit margin of 27.40%, allowing Priceline to produce higher income from its sales. According to this data, each departmental unit's budget is reasonably high as they operate in an international market. The privacy department is owned by the Chief Technology Officer (CTO). CTO is a higher authority that under-looks every technological matter where technological units, systems, and management evolve and continuously change (Privacy & Cookies Policy, 2021). According to the 2021 Annual Report, the information technology budget was around $412 million, which also accounts for 3.8% of the reven ...
Mobile Commerce – today’s e-commerce. How successfully to take advantage of consumer behavior? How to measure the value of mobile communications? These and other m-commerce-related questions will be answered during this session.
This presentation used in electronic commerce conference "E-komercija '16" by Rytis Meškauskas from company pigu.lt.
Online Hotel & Ticket Booking Sites in Indonesia 2014
Omnibus Popular Brand Index 2014
A. Detail findings
1.Popular Brand Index
2.Brand awareness
3.Expansive
4.Frequent User
5.Future Intention
6.Switching
7.General Information
WHITEPAPER - Fine Tune Your Hotel Forecast with Big DataDuetto
The document discusses how hotels can improve demand forecasting through the use of big data. It outlines seven key types of data that can be analyzed: 1) historical booking data, 2) competitor pricing, 3) events and macroeconomic factors, 4) air traffic data, 5) social reviews and ratings, 6) weather data, and 7) web shopping behavior data. When these various data sources are combined and analyzed, it allows hotels to generate highly accurate forecasts that can optimize pricing and drive greater profits.
http://thebestcompanys.com/hotel-booking/company/expedia-com/
Expedia offers hotel booking services to customers all around the world. They are a big competitor to other major brands in the hotel booking industry like Kayak.com, Orbitz and HotelBooking.com.
If you're looking for the best hotel booking services then we recommend you visit our Top Ranked and Recommended Companies by visiting TheBestCompanys.com
SATTA MATKA DPBOSS KALYAN MATKA RESULTS KALYAN CHART KALYAN MATKA MATKA RESULT KALYAN MATKA TIPS SATTA MATKA MATKA COM MATKA PANA JODI TODAY BATTA SATKA MATKA PATTI JODI NUMBER MATKA RESULTS MATKA CHART MATKA JODI SATTA COM INDIA SATTA MATKA MATKA TIPS MATKA WAPKA ALL MATKA RESULT LIVE ONLINE MATKA RESULT KALYAN MATKA RESULT DPBOSS MATKA 143 MAIN MATKA KALYAN MATKA RESULTS KALYAN CHART
𝐔𝐧𝐯𝐞𝐢𝐥 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐄𝐧𝐞𝐫𝐠𝐲 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡 𝐍𝐄𝐖𝐍𝐓𝐈𝐃𝐄’𝐬 𝐋𝐚𝐭𝐞𝐬𝐭 𝐎𝐟𝐟𝐞𝐫𝐢𝐧𝐠𝐬
Explore the details in our newly released product manual, which showcases NEWNTIDE's advanced heat pump technologies. Delve into our energy-efficient and eco-friendly solutions tailored for diverse global markets.
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The Genesis of BriansClub.cm Famous Dark WEb PlatformSabaaSudozai
BriansClub.cm, a famous platform on the dark web, has become one of the most infamous carding marketplaces, specializing in the sale of stolen credit card data.
Zodiac Signs and Food Preferences_ What Your Sign Says About Your Tastemy Pandit
Know what your zodiac sign says about your taste in food! Explore how the 12 zodiac signs influence your culinary preferences with insights from MyPandit. Dive into astrology and flavors!
Discover the Beauty and Functionality of The Expert Remodeling Serviceobriengroupinc04
Unlock your kitchen's true potential with expert remodeling services from O'Brien Group Inc. Transform your space into a functional, modern, and luxurious haven with their experienced professionals. From layout reconfiguration to high-end upgrades, they deliver stunning results tailored to your style and needs. Visit obriengroupinc.com to elevate your kitchen's beauty and functionality today.
Starting a business is like embarking on an unpredictable adventure. It’s a journey filled with highs and lows, victories and defeats. But what if I told you that those setbacks and failures could be the very stepping stones that lead you to fortune? Let’s explore how resilience, adaptability, and strategic thinking can transform adversity into opportunity.
The Steadfast and Reliable Bull: Taurus Zodiac Signmy Pandit
Explore the steadfast and reliable nature of the Taurus Zodiac Sign. Discover the personality traits, key dates, and horoscope insights that define the determined and practical Taurus, and learn how their grounded nature makes them the anchor of the zodiac.
How are Lilac French Bulldogs Beauty Charming the World and Capturing Hearts....Lacey Max
“After being the most listed dog breed in the United States for 31
years in a row, the Labrador Retriever has dropped to second place
in the American Kennel Club's annual survey of the country's most
popular canines. The French Bulldog is the new top dog in the
United States as of 2022. The stylish puppy has ascended the
rankings in rapid time despite having health concerns and limited
color choices.”
Part 2 Deep Dive: Navigating the 2024 Slowdownjeffkluth1
Introduction
The global retail industry has weathered numerous storms, with the financial crisis of 2008 serving as a poignant reminder of the sector's resilience and adaptability. However, as we navigate the complex landscape of 2024, retailers face a unique set of challenges that demand innovative strategies and a fundamental shift in mindset. This white paper contrasts the impact of the 2008 recession on the retail sector with the current headwinds retailers are grappling with, while offering a comprehensive roadmap for success in this new paradigm.
Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
The APCO Geopolitical Radar - Q3 2024 The Global Operating Environment for Bu...APCO
The Radar reflects input from APCO’s teams located around the world. It distils a host of interconnected events and trends into insights to inform operational and strategic decisions. Issues covered in this edition include:
Unlocking WhatsApp Marketing with HubSpot: Integrating Messaging into Your Ma...Niswey
50 million companies worldwide leverage WhatsApp as a key marketing channel. You may have considered adding it to your marketing mix, or probably already driving impressive conversions with WhatsApp.
But wait. What happens when you fully integrate your WhatsApp campaigns with HubSpot?
That's exactly what we explored in this session.
We take a look at everything that you need to know in order to deploy effective WhatsApp marketing strategies, and integrate it with your buyer journey in HubSpot. From technical requirements to innovative campaign strategies, to advanced campaign reporting - we discuss all that and more, to leverage WhatsApp for maximum impact. Check out more details about the event here https://events.hubspot.com/events/details/hubspot-new-delhi-presents-unlocking-whatsapp-marketing-with-hubspot-integrating-messaging-into-your-marketing-strategy/
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1. Michael Mitroff IndoCabs Report.pdf
Analysis of Cancellations at a Cab
Portal Company
MICHAEL MITROFF
WISCONSIN SCHOOL OF BUSINESS
OCTOBER 2021
2. Executive Summary
When first looking at the data given to us, we realized that the data needed to be processed and cleaned up
before it was analyzed. To do this we deleted data that contained errors and data that had duplicates. The
erroneous data contained dates that were before 2013, many of these trips contained dates in the 1970s. We
then created more variables to get a better understanding of the data we were looking at. These variables
included "fromdate", "Dayoftheweek", "Duration(hours)" "Duration(days)", and “BookingWindow". In
total the data provided showed 2972 trips. To better understand these trips, we looked at information on
trip duration and booking window, including variables on the mean, standard deviation, and range. We
used the interpretation of this data to then look at the problem of cancellations at IndoCabs. The total
cancellations at IndoCabs are 263 which is 9% of all the bookings created. After analyzing our data on
travel type, we realized that point to point travel has the highest cancellation rate of 10% more than double,
long distance or hourly rental travel. This led us to then look at the cancellation by type of booking. Here
we discovered that mobile and online booking have a 13% cancellation rate, over 4% of the base
cancellation rate. We decided to analyze the proportion of bookings cancelled by day of the week. Here we
noticed some days have higher cancellation rates, but these were random and not correlated in any manner.
Finally, we analyzed the relationship between booking windows, cancellation and trip timing. We found
noticeable increases in cancellation rates from 5 to 8pm. Also discovering that most bookings have a
booking window and are cancelled in under 12 hours.
Introduction
IndoCabs is currently experiencing a problem with company induced cancellations that are stemming from
an unknown source. In order the combat this issue we have been given data on cancelled and noncancelled
trips taken by IndoCabs clients. We have processed and analyzed this data to look for correlations in trip
cancellations.
Analysis
A Look at Trip Durations
To begin to understand the problems currently at IndoCabs we need to start by looking at data on trip
duration and booking window. Below I have compiled the mean, standard deviation and range of our
clients’ trip duration and booking window
3. Summary Statistics of Trip Duration and Booking Window
Trip
Duration
(Hours)
Booking
Window
(Days)
Mean 4.14 1.95
Standard
Deviation 10.84 5.01
Range 253.26 79.45
I choose these three measurements of data to show you because of how they interact with one another. The
mean is the average of the data set, the average time a trip takes for our clients is approximately 4 hours,
while the standard deviation is 68%. So, 68% of all of our clients trips are in the range of 0 to 15 hours,
while we have a total range of 253 hours of trip time. We can take this information about our measurements
and apply it to booking window data. Booking window is the time from when a client books a cab to when
their trip starts. The average booking window is approximately 2 days. While the standard deviation of
booking window is 5 days. Meaning that 68% of all of clients book their cabs from 0 to 7 days in advance,
while the range of booking windows are approximately 79 days. These condensed booking window time
and trip duration show us that while people may have really long trips or plan for them well in advance
most people are taking trips under a day’s time and plan for them only a week in advance at most.
The Magnitude of the Cancellation Problem at IndoCabs
From our data processing we find that 263 of a total of 2972 total bookings are cancelled. Meaning that 9
percent of all bookings created are cancelled before the trip starts.
An important part of our data is the type of travel the client is booking. We have three types of travel
including long distance, point to point and hourly rental travel. From our data we notice that that point to
point is the most popular type of travel, hourly rentals and long distance follow respectfully. In fact, point
to point travel make up 79% of travel. We notice that the number of cancellations is higher in point to point
travel than in the other two types, which makes sense since it is the most popular. But the proportion of
cancellations in point to point is more than twice the other two. Point to point travel experiences a 10%
cancellation rate while long distance and hourly travel both only experience 4% cancellation rate. I did
expect there to be a pattern by travel type. People who are booking long distance trips are going to be paying
more than the other travel types and are going to make sure their trips are not cancelled. Also from our data
we know that the average duration of long distance trips is on average 35 hours, compared to the
approximately 2 and 6 hours for point to point and hourly rental respectfully. This could suggest that the
shorter the trip the higher the likelihood of it being cancelled
Number of Bookings
Number of
Cancellations
Proportion
Cancelled
Long Distance 130 5 0.04
Point to Point 2344 239 0.10
Hourly Rental 498 19 0.04
The number of bookings made online versus on the mobile app are 1,207 and 136 respectfully. The number
of bookings made online is much higher than those made on the mobile app. While together these make up
1,343 of total bookings or 45% of total bookings. What is interesting about these types of bookings are that
4. their cancellation rates are both at 13%, 4% over the average cancellation rate. These two booking types
bring up big concerns for IndoCabs. While IndoCabs does offer other types of booking like text, e-mail,
phone call, or webchats, the mobile and online options will continue to grow as most people prefer to use
their phones and the internet due to the convenience it brings. Shutting down two methods of booking which
contribute up to 45% of all bookings is not a good option for IndoCabs. IndoCabs should focus on fixing
the problems that contribute to the high proportion of mobile and online bookings
This is not the pattern I was expecting to see from the percentage of bookings cancelled by weekday. I
would have expected the percentages to be much higher on weekends when people are in a higher need
for IndoCabs services. I find it most interesting that Thursday is above the average cancellation rate while
Saturday is much lower that the average.
0.00
0.02
0.04
0.06
0.08
0.10
0.12
Percentage
Day of the Week
Percentage of Bookings Cancelled on each
Weekday
5. The Relationship between Booking Windows, Cancellations, and Trip Timing
The chart above is a scatterplot of the number of hourly bookings and cancellations. The R-squared for
this data is .3622. This suggests that the data is not very well correlated. We notice this as well in the
graph as after 100 bookings the data becomes much more scattered and uncorrelated.
R² = 0.3622
0
5
10
15
20
25
30
35
40
0 50 100 150 200 250 300
Number
of
Cancellations
Number of Bookings
Scatterplot of the Number of Bookings and
Cancellations
0
50
100
150
200
250
300
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Number
of
Bookings
Hour of the Day
Number of Bookings by Hour of Trip Start
6. Of all of these charts the one that is the most informative is the final chart on the percentage of car
cancellations by hour. Specifically, the chart shows us that the highest percentage of car cancellations
occur at night between the 17th
and 20th
hour of the day or 5pm to 8pm. This finding is also supported by
the other chart which shows the number of cancellations, of which most seem to be occurring between the
17th
and 20th
hour of the day. IndoCabs should investigate what is happening during the night which
causes their system to cancel trips. By narrowing down the time of the day in which most cancellations
occur they will have an easier time discovering why cancellations are occurring.
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Number
of
Cancellations
Hour of the Day
Number of Car Cancelations by Hour of Trip Start
0
0.05
0.1
0.15
0.2
0.25
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Percentage
of
Cancellations
Hour of the Day
Percentage of Car Cancellations by Hour
7. The two histograms above show the proportion of bookings by booking window. The histogram with
booking windows of .25 day bins is a more useful chart than the one with only 1-day bins. This chart
shows us that 57% of all bookings have a booking window of a half day or less, which a 1-day bin could
not tell us. This tells us that most people are planning their trips within of half day of taking them.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1 2 3 4 5 6 7 More
Proportion
of
bookings
Booking window by 1-day
Length of Booking Windows (1-day bins)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Proportion
of
Bookings
Booking Window by .25-day
Length of Booking Windows (.25-day bins)
8. 0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1 2 3 4 5 6 7 More
Percentage
of
Cancelled
Bookings
Booking Window by 1-day Bins
Proportion of Cancelled Bookings (1-day bins)
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
1 2 3 4 5 6 7 More
Percentage
of
Bookings
Booking Window by 1-day Bins
Proportion of Non-cancelled Bookings (1-day
bins)
9. The four histograms above show the proportion of bookings that were either cancelled or not cancelled in
1 and .25-day bins. The .25-day bins give us much more information on the probability of a booking
window being cancelled. We can see that most bookings are cancelled within a half booking window.
Which tells us that the .25-day bins are much more effective in communicating that the shorter the
booking window the more likely a trip will be cancelled.
Recommendations and Conclusion
By analyzing the data provided to us we have found a lot of useful correlations that could point us in the
direction of solving the trip cancellation problem that IndoCabs is currently facing. First off, we
discovered that IndoCabs is experiencing a 9% cancellation rate. Although the cancellation rate is 9% we
discovered several correlations that have a higher cancellation rate. For instance, point to point travel type
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.25
0.5
0.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
3.25
3.5
3.75
4
4.25
4.5
4.75
5
5.25
5.5
5.75
6
6.25
6.5
6.75
7
Proportion
of
Cancelled
Bookings
Booking Window by .25-day Bins
Proportion of Cancelled Bookings (.25-day bins)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.25
0.5
0.75
1
1.25
1.5
1.75
2
2.25
2.5
2.75
3
3.25
3.5
3.75
4
4.25
4.5
4.75
5
5.25
5.5
5.75
6
6.25
6.5
6.75
7
Proportion
of
Non-cancelled
Bookings
Booking window by.25 day bins
Proportion of Non-cancelled Bookings (.25-day bins)
10. has more than double the cancellation rate compared to the other two travel types with a cancellation rate
of 10%. It is important to figure out why this is the case and how to prevent it. Those who are using point
to point travel contribute to a significant majority of the travel IndoCabs provides. So it is important for
IndoCabs to keep this majority happy to continue to remain profitable. We notice a higher cancellation
rate in the type of bookings. Where mobile and online booking both have a cancellation rate of 13%. I
recommend IndoCabs look deeper into what is causing these booking types to have such a high
cancellation rate. These booking types make up over 45% of bookings, which is contributing a significant
amount to the trip cancellation rate. Mobile and online booking will most likely rise in popularity so
fixing this problem sooner will be important to the future of the company. We found out a lot about the
timing of trip cancellations, as many cancellations occur from 5-8pm along with occurring mostly within
12 hours of creating the trip. IndoCabs should investigate what causes cancellations to occur at these
times. It could be due to the high amount of people requesting cabs, something that their system could be
poorly equipped to handle. Implementing incentives to not cancel your trip are very important. IndoCabs
could use cancellation fees to prevent people and drivers from cancelling their trip. Also, IndoCabs could
create a ranking system for their clients to prevent them from cancelling their trips very often.
Elevator Charts
Looking at all the charts we have compiled I would use the percentage of car cancellations by hour,
proportion of cancelled bookings by .25-day bins and the length of booking windows by .25-days in an
elevator pitch. Starting off I would use the length of booking window by .25-day graph. This shows us
initially that most bookings have a booking window of 12 hours or under. So, when we look for why
cancellations occur, we can refine our search to this period. This graph also goes along with the proportion
of cancelled bookings by .25-day bins. In that graph as well, we notice that most cancellations have a
booking window of 12 hours or less. This correlates itself with the previous graph and forces us to look at
the shorter range of booking windows to solve our cancellation problem. Finally, I would use the car
cancellations by hour graph. The graph has a very striking and obvious spike in the range of 5pm to 8pm.
This will catch anyone’s attention and highlights the need for IndoCabs to look at what is happening during
this time to cause a rise in car cancellations.
Notes on Data Preparation
There were many steps taken to clean and remove errors from the data provided to us. We made a new
spreadsheet with only the processed data titled “ProcessedData” to make sure we still had our original data.
In our processed data sheet, we first removed duplicate data by using the remove duplicates function in
excel. Next, we had to remove any errors in our data. By sorting our data in terms of date we discovered
that the data contained dates that were from before 2013. After doing this we created several new variables
to understand the data better. These include, "fromdate", "Dayoftheweek", "Duration(hours)"
"Duration(days)", and “BookingWindow". By creating these variables, we were better able to interpret and
analyze the data provided. In terms of data collections, steps could be taken to fix these problems. There
may be problems in the collection of data causing dates mismatched with other information making them
older than 2013. Also, there may be some bad code causing data to be duplicated. I suggest IndoCabs
investigate the cause of this bad data, by looking into their data collection software.