Data warehousing and customer loyalty at Continental Airlines

¶ … Warehousing book

For an airlines company, how can strategic information increase the number of frequent flyers? Discuss giving specific details.

The insights gained from data warehousing can deliver useful intelligence for redesigning marketing, selling, customer service and customer loyalty programs (Birkner, 2008). In addition, data warehousing is actively being used to understand the difference between customers' expectations and experiences, including which factors and touch points in service delivery contributed most and least to customer satisfaction. Services businesses are finding that the ability to mine their massive amounts of customer data and architect entirely new strategies as a result can lead to significant cost reductions and increased profits as well (Johnson, 2004).

Airlines are continually fighting a paradox of having to reduce their costs while also increasing customer loyalty. Continental Airlines was able to transform itself by using data warehousing and the business intelligence gained through the use of analytics to completely redesign its customer loyalty program and hold into customers who had been leaving in droves due to extremely low levels of satisfaction with the air carrier (Britt, 2007). Continental learned that the greater the level of systems integration across the many diverse, disparate systems in the company, many of which had never been used as part of a broader enterprise strategy in the past, the greater the Return on Investments (ROI) of their data warehousing and business intelligence systems (Dev, Buschman, Bowen, 2010).

All of these factors contributed to Continental Airlines being able to capture more customers by continually making enhancements to their Continental Airlines OnePass Frequent Flyer Program. Based on the insights gained from data warehousing strategies and the use of predictive analytics, Continental was also to determine what factors most contributed to customer churn as well (Qian, Jiang, Tsul, 2006). Knowing why customers were leaving gave Continental exceptional insight that competitors lacked and misunderstood to be pricing-related dissatisfaction (Britt, 2007). With the U.S. airline industry in pricing free-fall during this time period, Continental was generating insights and intelligence into the unmet needs of airline passengers, getting a firm grasp of what was causing churn, while the rest of the industry was reading the churn as price sensitivity (Ku, Fan, 2009). Continental drastically re-vamped their OnePass Program, creating marketing programs to show how they were working hard to get the roadblocks and impediments out of the way of travelers. The results were impressive, with customer satisfaction rankings of Continental going from among the worst in the airline industry to among the best (Britt, 2007). In the airlines industry, Continental is considered one of the most skilled at using data warehousing to increase customer loyalty while reducing churn and increasing profitability (Qian, Jiang, Tsul, 2006).

Exercise #4 -- pg. 44 -- Chapter Two Data Warehousing book

For an airlines company, identify three operational applications that would feed into the data warehouse. What would be the data load and refresh cycles for each?

The data warehouse of an airlines company is comprised of a myriad of accounting, finance, pricing, transaction, and services databases and systems. Studies indicate that the greater the number of integration points and the relative speed with which they share information, the greater the level of decision latency and accuracy in an organization's decisions (Watson, 2005). For an airline, the three operational applications that need to be integrated into a data warehouse to ensure the profitable operation of the business include its Customer Relationship Management (CRM), pricing management and optimization, and its operations systems that include on-time measures of performance and cost-per-seat analytics. These three systems are critical because for any airline, the costs of operation dictate what levels of service can be delivered at what price. The pricing system must be integrated into the data warehouse as this provides insights into the elasticity of demand in the markets the airline serves (Payton, Zahay, 2005). All three of these systems are also critically important for creating an analysis to guide fuel hedging forecasts and strategies (Birkner, 2008). Fuel hedging is the strategy Southwest uses to save up to 80% of their fuel costs over time, which leads the industry on this practice. Using customer history by flight, cost per seat, frequency of flight data and a forecast of fuel costs, the Southwest senior management teams devise strategies to buy fuel contracts using a strategy called hedging (Birkner, 2008). It is a strategy largely believed to be responsible for Southwest being the only airline in the U.S. To have never declared bankruptcy. Taking fuel hedging as an example, it can be seen that pricing and operations performance must be integrated and supported with batch updates on a daily basis to ensure the best possible decisions are made. The pricing systems of airlines have become increasingly sophisticated, using algorithms to define constraint-based pricing strategies that take into account elasticity of demand by flight (Wong, Chung, 2008). Pricing and fuel cost estimates must be real-time as they are updated with flight-by-flight analysis and also defined by the speculative price analysis, two critical real-time data needs within an airline's business model (Watson, Wixom, Hoffer, Anderson-Lehman, Reynolds, 2009). Data load and refresh rates for CRM systems can be less frequently done, yet need to be completed at a minimum on a weekly basis to ensure that the latest customer data is available for planning and implementing strategies. CRM systems need to have pricing data and customer satisfaction data from flights also integrated to ensure that the airline can index which flights are generating the highest profit per customer segment as well.

Exercise #4 -- pg. 138 -- Chapter Six Data Warehousing book

You are the manager for the data warehouse project at a retail chain with stores all across the country and users in every store. How will you ensure that all the details necessary to decide on the DBMS are gathered during the requirements phase? Write a memo to the Senior Analyst directly responsible to coordinate the requirements definition phase.

As the greatest challenge of implement a new system is overcoming resistance to change through the use of change management strategies, the most critical focus needs to be on capturing the unmet needs of users in every store quickly, thoroughly and with a high degree of transparency. The results of this effort needs to be shared electronically online to ensure everyone is kept informed about its progress.

Memo:

With the data warehouse project entering the requirements definition phase, it's important to start setting the foundation for users across the network of retail stores to begin having ownership of the new system. Getting them interested in the new data warehouse, and to take ownership of it will require getting their inputs early on during the requirements definition phase.

I'd like to suggest the following steps be taken to ensure their needs are captured as part of the requirements definition phase:

1. Create an online, protected portal and have a series of questions and applications on it to give each store user a chance to contribute to not only the content of the new data warehouse, but its usability as well. Use this data as a means to create bi-weekly conference calls to discuss the status of the data-warehousing project.

2. Enroll each store in Skype and get budget to make sure they can all participate on video conferences with regard to the user interface design and contents of the data warehouse. This will be critical for ensuring each person has an opportunity to contribute to the process as well.

3. Post the matrix of all user needs and show the date and revision fo the data warehouse when their needs were included in the design. Also be sure to recognize with praise and recognition those users in each retail store who work hard to provide feedback.

4. Give the stores an opportunity to work with early beta versions of the data warehouse software and the interfaces used for querying and using it. This will also provide a greater level of ownership as well.

5. Sponsor a contest to see which user in which store can create the best tutorial demo. Whoever wins gets to go around to all the stores and host training, at the company's expense and also gets a weekend in any city of their choice also on the company. This will drive up ownership of the data warehouse immediately and will also lead to competition to learn as much as possible.

Remember the challenge is to get the users in each store to change their behavior and support the new data warehouse initiative. Taking these steps will lead to much greater levels of ownership in the project.

Business Intelligence Text -- Cases.

Question 4-page 27 - What benefits are being derived from this initiative?

Vodaphone's many benefits of creating an enterprise data warehouse (EDW) include gaining greater accuracy and precision in the marketing, selling and customer churn reduction strategies, in addition to increasing profit margins over time. The company had also been facing a stagnating 56% market share, yet with the insights gained from the EDW and the business intelligence applications it supports, Vodaphone was able to determine the best possible message to deliver to the right customers at the right time. The formation of the customer knowledge and analysis department also served as a catalyst for more effective customer offer optimization, campaign effectiveness analysis, customer insights and the development of trigger-based marketing campaigns as well.

Question 5-page 78 - What strategic advantage can Continental derive from the real-time system as opposed to a traditional information system?

Continental was able to transform their entire business model based on the real-time systems they evolved their EDW into from their traditional information systems. The creation of the Go Forward plan and a data warehousing award presented in 2004 (Johnson, 2004) solidified the company's position as a leader in the area of BI and analytics for retaining and growing airline customers. The strategic advantage of real-time systems was most valuable in analyzing what customers' needs were, integrating real-time data from pricing, customer satisfaction and data, and analyzing operations data to ensure customer satisfaction. The benefits of the real time system soon became significant with fraud detection saving the company $7M and greater accuracy in demand forecasting and tracking also serving as a catalyst of accelerated growth. In total, the real-time systems became an essential catalyst in the turn-around the Continental and saved them from having to file for bankruptcy a third time. It was the catalyst of the business model also focusing first on customers, trimming costs to serve them better instead of being only concerned with trimming expenses alone.

Question 6-page 127 - What can businesses learn from a government initiative like the CPR?

There are many lessons learned about how best to integrate analytics and BI into the daily workflows of businesses, in addition to normalizing and aggregating massive amounts of data so it can be effectively used. The CPR initiative provides insights into how metro-wide EDW and data marts can be created and maintained to fulfill the needs of a diverse user base. Included are performance management applications, analytics and dashboards configurable for specific reporting requirements, and data definition analysis of key variables and attributes as well. An excellent lesson learned is the metrics and key performance indicators (KPIs) only matter when they are in the context of promoting collaboration and knowledge transfer. As the case alludes to, when this happens the entire organization benefits and decision outcomes improve due to data accuracy. The need for creating data warehouses as a catalyst of collaboration and rewarding shared task ownership and accomplishment through the use of the data is also evident. The case shows how a government agency can cut through bureaucracy by being goal-centered in their approach to managing data.