Customer centricity in airline industry


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In airline environments, information is collected for archival and historical purposes from a wide variety of sources.This creates a repository of data that is, perhaps, the most valuable asset for an airline. However, the challenges of managing and extracting information to aid an airline is increasingly difficult to resolve due to customer centricity. Siloed databases / data marts and the information they contain are typically bound to the software subsystem within an enterprise’s application, computing platform, or IT environment. To complicate matters, as businesses implement new, dissimilar technologies, the problems of inconsistency increase. The result is isolation.

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Customer centricity in airline industry

  1. 1. WHITE PAPERCustomer centricity inairline industry.
  2. 2. Summary In airline environments, information is collected for archival and historical purposes from a wide variety of sources. This creates a repository of data that is, perhaps, the most valuable asset for an airline. However, the challenges of managing and extracting information to aid an airline is increasingly difficult to resolve due to customer centricity. Siloed databases / data marts and the information they contain are typically bound to the software subsystem within an enterprise’s application, computing platform, or IT environment. To complicate matters, as businesses implement new, dissimilar technologies, the problems of inconsistency increase. The result is isolation. Making it difficult to find, deliver, assemble, or use information sources for knowledge that supports business needs across the enterprise. The purpose of this paper is to highlight the need for a real-time data warehouse coupled with an analytics engine within the airline’s IT environment. This mission critical system would ease the pressing needs of customer centricity and e-business.ContentsIntroduction 03Current state 03Airlines industry IT trends 05Real time business intelligence and analytics 05High level architecture 05Pre-packaged DWBI framework 07Conclusion 07White paper 02
  3. 3. Introduction do, they are piecemeal extracts after the fact.About ten years ago, Continental Airlines was faltering Ideally, data generated by all operational enterpriseand ranked among the lowest of the major US airlines. systems and partners across an enterprise should beThe problems the airline faced were not unique. But, siloed, automatically archived and indexed. Regardless of theunstructured data and incomplete information made application or platform that created the data. Airlinesdetermining the root cause analysis a difficult task. should also be capable of searching the entire corporate database to retrieve the relevant data. Airlines need theContinental Airlines needed a more comprehensive right information at the right time, with the right degreeview of the business in order to reduce operating expenses, of accuracy.increase commercial insights and improve time-to-market.Here, too, legacy systems were hampering the growth and For the airlines at this new frontier of innovation,profitability story. competition and productivity, it’s about analyzing masses of unstructured or semi-structured data. This, until recently,In the past decade, there has been a substantial was considered too difficult, too time consuming and / orinvestment by airlines to mothball their legacy systems too expensive. But, as airlines get closer to their customers,and move toward a modernization strategy. Some airlines they gain insight from looking at interaction patternsrealized substantial benefits by making investments in throughout the customer journey.real-time data warehousing and analytical solutions.Notably, Continental Airlines produced an ROI of over Typical attributes of airline data can be identified by four1,000% with $500 million in cost savings and revenue main attributes:generation over a six year period. 1. Volume – Airline data is massive. Typical tier 1 carriers and Global Distribution Systems (GDS) have PassengerThe challenges for implementing a real-time data Name Record (PNR) data measured in terabytes.warehouse are not alien within an airline environment. 2. Velocity – Airline data is real-time and arrives quickly,Real-time flight data feeds from flight operating systems making timely decisions difficult.and either flat-file or XML data feeds from the CRS are two 3. Variety – Ad-hoc systems traditionally have differentsolid examples of velocity and variety-related challenges structures and shapes. Data analysis on such data isthat real-time Data Warehouse and Business Intelligence difficult because of rigid schemas.(DWBI) systems have to surpass. 4. Value – Airline data on its own is low value until it is rolled up and analyzed. At this point data becomesFor many airlines, their current infrastructure is legacy information which, in turn, becomes knowledge for thein nature and moving to a new generation of data broader market.integration and business intelligence is an added levelof complexity. Scandinavian Airlines had to move from Storing and analyzing data is key for airlines to addressa legacy mainframe and DB2 infrastructure in order to customer satisfaction and achieve additional revenue toimplement a 4th generation data integration and business support ongoing operations. Not doing so leaves the airlineintelligence application. enterprise struggling for insightful information that can transform customer experience. For example, an airline withEven though there is tremendous complexity around the separate data marts for ticketing and flown PNR / DCS datalarge volume of data airlines produce, real-time DWBI can analyse the separate data marts and provide answers toimplementation is the need of the hour. With real-time questions pertaining to that function. However, combiningDWBI systems, historical reporting is enabled over a longer the data marts enables new cross-functional insights thatperiod of time, with an increased level of granularity. cannot be achieved with the siloed approach. By combining ticketing and flown PNR / DCS data with inventory and flight schedules, a wealth of information becomes available,Current state enabling fact-based, value-added decisions.Traditional systems that are deployed for airlines aroundthe world are siloed and very ad-hoc in nature. Rarely dothese systems share data in an economical manner. If theyWhite paper 03
  4. 4. Some key areas where real-time analytics play a vital role Passenger businesswithin airlines are: ƒƒ Missed connections ƒƒ Lost baggageManagement of the seat factor ƒƒ VIP customersƒƒ Overbooking capacity to ensure maximum seat factors ƒƒ Up sales during the booking process with minimal off-loads and downgrades ƒƒ Overhead bin space optimizationManagement of the revenue mix Flight operationsƒƒ Cabin mix via market segmentation ƒƒ Aircraft re-routingƒƒ Seat access and group acceptance ƒƒ Gate management ƒƒ Baggage managementLoyalty management ƒƒ Parts managementƒƒ Identifying the different clusters of customers ƒƒ Forecastingƒƒ Targeting clusters with appropriate messaging, ƒƒ Flight catering management promotions and placement to ensure repeat revenue and ƒƒ Ground handling services reduced churn ƒƒ Manpower planning ƒƒ Optimized staffing levelsCargo managementƒƒ Cargo capacity / demand planning Othersƒƒ Cargo rate optimization ƒƒ Sales areaƒƒ Cargo load optimization ƒƒ Managing traffic flows (O&D)ƒƒ Pallet management ƒƒ Brand buzz analysis Pricing optimization ƒƒ Sentiment analysisKey areas where real-time analytics play a vital role within airlines. Profitability analytics Loyalty points analysis Promotion effectiveness Forecast analytics Behaviour analysis Competitive analysis Fraud analytics Compliments analysis Channel analysis Customer value Complaints analysis Sales analysis Pattern analysis Media spend Lead conversion Customer profile analysis SLA analysis Booking analysis Check-in analysis Lapse analysisWhite paper 04
  5. 5. Airlines industry IT trends the data warehouse, ready for business users to view via theHaving touched upon the impact and the absolute need business intelligence platform. This is not useful for airlinesfor a world class Real-Time Data Warehouse Business that need real-time intelligence and analytical capabilities.Intelligence (RTDWBI) and analytics solution for the airline Real-time capability is the missing piece of the puzzle thatindustry, it is critical to understand key IT trends in this allows airlines to react to real-time events, such as helpingindustry that make the initiative even more important: ground operations support potentially delayed passengersƒƒ 56% of airlines will make R&D investments in passenger due to incoming flights. Furthermore, real-time capabilities services via social media provide alerts and triggers to measure tactical, operationalƒƒ 52% of airline companies will consider business and strategic key performance indicators to analysts. intelligence as their major IT investment program in the next three years A real-time data warehouse enables the warehouse toƒƒ 57% of airlines believe social media provides the extract, transform and load data almost immediately after greatest value as a marketing channel the transactional system gets updated. Thereby enablingƒƒ 78% of airlines personalize service offers to their business users to get the latest view of their enterprise data passengers though direct channels in an integrated, single version at any point in time. Latency in a real-time data warehouse is in sub-seconds asClearly, the focus of airlines beyond 2015 is to ensure opposed to incremental daily or intra-day loads. However, ifmaximization of sales through websites and smart phones. data needs to be loaded continuously in real-time, there areService personalization, as well as efficient operation, a few design challenges to be overcome:depends on the availability of data for analysis. Hence, the ƒƒ No room for system downtime and, therefore,sharing of data between stakeholders becomes essential. disaster recovery is a hurdleIn recognition of this dependency, around half of the ƒƒ If the data changes faster than the query response,airlines already share or are planning to share data with erroneous responses could be displayedthird parties. (refresh anomaly) ƒƒ Data sync is a problem because loading data to a factThe SITA airline survey results clearly indicate that the table happens several times a day, while dimensionsvelocity and volume of data for the airline industry are only loaded nightlyis going to increase drastically in the next few years. ƒƒ Scalability could be an issue if not handled early enoughTherefore, airline enterprises need to prepare with a robust during the architecture phaseintelligence infrastructure. So they not only manage growth,but also stay ahead of competition by their ability to High level architectureabstract insights from large volumes of data. The majority of airlines intend to leverage websites, smart phones and social media as the preferred sales channel. AReal time business intelligence and analytics surge of data is therefore expected to flood transactionalOne of the simplest ways to increase intelligence in systems, in increased frequency and varied formats. Thisoperational systems is to embed simple business rules will make data feed into the upstream data warehouseand models. They could then help analyze transactional even more challenging. The triple V – Volume, Velocity anddata on the fly and provide alerts triggered by specific Variety – constraint will define the future landscape of largeevents or threshold limits. Of course, care should be taken scale DWBI applications.not to create performance bottlenecks on mission criticaloperational systems. However, this approach would not Real-time DWBI architecture needs to address extracting,match up to the analytics that could be modelled over an transforming and loading, as shown below (Fig. 02):integrated enterprise data warehouse with change captureenabled as a key differentiator.Most data warehouse designs implemented for airlines aredesigned for daily, weekly, or monthly data refresh fromunderlying source systems. So it would take an entire dayfor changes in the transactional systems to be loaded intoWhite paper 05
  6. 6. Fig. 02. Real-time DWBI architecture showing extracting, transforming and loading. Source system Booking / Standby DW reservation Report layer Distribution system (GDS) Warehouse layer Dashboards Real time Master Control Fact data Summary data storage Real time data Historical systems data feed reports Partnerships Metadata OLAP Self service BI Shared repository metadata layers Revenue / yield Batch feed Advanced analytics & alert Sales Pattern Business dashboards recognition rules Alerts based on Loyalty Real time data integration: business rules ƒƒ Parallel ETL engine derived from pattern ƒƒ MQ series queues recognition and data Flight data ƒƒ CDC (log based & others) mining algorithms ƒƒ Web service Real time data propagation: ƒƒ Unidirectional query off-loading with zero downtime migration ƒƒ Bi directional _ hot standby ƒƒ Peer to peer _ load balancing ƒƒ Broadcasting ƒƒ Messaging based data distribution _ BAM, CEP, BPMAs mentioned above, a few design challenges need to be For data loading and replication, there are tools like IBMovercome. The architecture should account for no system DataMirror, Oracle GoldenGate and MetaMatrix that supportdowntime. Because data changes faster than a query real-time data movement into a warehouse. Enterpriseresponse, users could be faced with “refresh anomaly”. Application Integration (EAI) vendors such as Tibco, MuleData sync needs to be addressed, as the loading of data into Enterprise Service Bus (ESB) and IBM WebSphere Messagefact tables occurs several times a day, whereas dimensions Broker (WMB), leverage a Service Oriented Architectureare only loaded nightly. Database backups become (SOA) and provide solutions for real-time data transport.cumbersome to the real-time nature of the warehouse. And Incremental data load design could be applied usingfinally, scalability could become an issue. Change Data Capture (CDC) techniques such as log-based and time stamps, database triggers and message queuesFortunately, solutions and design approaches currently (e.g., IBM MQ series).available can help overcome the challenges ofimplementing a real-time data warehouse.White paper 06
  7. 7. Data federation tools can provide data abstraction. ƒƒ Predefined airline dashboardsThis enables users and clients to store and retrieve data ƒƒ Integration of airline data model to standard industryin multiple non-contiguous databases with a single query, source interfaceseven if the constituent databases are heterogeneous.Enterprise Information Integration (EII) ensure that the This framework reduces implementation time by two-thirdsprovision of a federated query server can retrieve and of a full blown development effort, helping airlines realizeintegrate data from multiple data sources. Including ROI much sooner.unstructured data and message queuing software. Somekey EII products include Actuate Enterprise Reporting As with all frameworks, customizations on the data model,Platform (ERP), BEA Liquid Data, Composite Software ETL, reports and dashboards cannot be ruled out. FurtherInformation Server, IBM DB2 Information Integrator and maintenance includes regular updates to the analyticalMetaMatrix server. models based on on-going accuracy measurements.Data could be combined from multiple sources, making it Mindtree is currently moving ahead on the roadmap toaccessible with a simple service call using a common XML or enhance and host the RAPID framework as a trademarkJSON format. Systems based on the latest Java technologies industry solution for the airline industry. Starting with(Java Messaging Service) could be used to transmit each DWBI and big data analytics (a process of examiningnew data element from the source system to a lightweight large amounts and different types of data to make betterlistener application. This in turn inserts the new data into business decisions) on a cloud platform.warehouse tables. Data received over the Internet could betransmitted in XML via HTTP using the Simple Object Access ConclusionProtocol (SOAP) and then loaded into the warehouse. Analytics has been defined as, “The extensive use of data,Data caching techniques could be leveraged, e.g., page statistical and quantitative analysis, explanatory andcaching, tag caching, template caching and dynamic predictive models and fact-based management to drivechannel query caching. decisions and actions.” (Davenport and Harris, Competing on Analytics, 2007).Eventually, it boils down to two things: the right kind ofexperience and expertise-led design implementation; and Airlines have been traditional users of analytics in termsleveraging the latest technologies supporting a real-time of dynamic pricing, optimum capacity and utilization ofdata warehouse and business intelligence application. cargo allotments, to name a few. But today’s competitiveSome key features of a data warehouse appliance include: environment is filled with an influx of low cost carriers,ƒƒ Real-time query and loading an increase in cross-border players and an ever-changingƒƒ MPP architecture pool of consumer desires. This makes the understanding ofƒƒ Automatic high availability customers and the business landscape utterly importantƒƒ Aggressive data compression for survival. With the co-joined service offering of real-timeƒƒ Extensible in-database analytics framework data warehouse and analytics, airlines have another veryƒƒ In-database analytics library important tool at their disposal to bring to market enhanced customer centricity and e-business.Pre-packaged DWBI frameworkApart from product vendors, Mindtree builds solutions Apart from American Airlines, most of the other airlines inand frameworks for the airline industry. Our custom airline the US have reported profits this past quarter. The race is onindustry offering is called Real-time Airlines Predictive for airlines to win and retain each and every customer. ThisIntelligence Design (RAPID) framework. It is based on our means they not only need to cater to customer desires, theyvast experience in building DWBI and analytics solutions also need to understand how competitors are wooing theirfor leading airlines around the world. The RAPID framework customers. Stickiness with airline customers is even moreoffers Mindtree’s Intellectual Property (IP) assets for the important now. It’s an oft repeated lie, “Airline service is aairline industry and the IP stack includes: commodity to consumers”. Airlines can no longer behaveƒƒ Airline data model as a mere commodity. A huge part of stickiness dependsƒƒ Airline analytics modelsWhite paper 07
  8. 8. on customer experience at the various touch points in the stickiness. These are the moments of truth that differentiatecustomer journey. one company from another. In the airline industry where customers don’t see any difference between one airline andAirlines need to see the journey through the eyes of the another, the level of service can’t be average. Imagine thiscustomers. Countless books and research articles have – Southwest Airlines has about 500,000 moments of truth.been written about customer interaction. Every moment on the customer journey leads to customer stickiness. Anthony O. Putnam, in the book, “MarketingMany airlines have even gone as far as identifying all Your Services,” says, “Alignment comes from getting allthe touch points. But very few have implemented a unified your energy focused on one direction – in making sure thatcustomer experience. Each touch point might just as well everything you say and do is congruent and seen that waybelong to any other airline; there is no uniformity or point by your clients.”of view. This points to a very important fact – many airlinesare yet to map out the customer journey as a customer Real-time DWBI can positively impact airline operations,retention strategy. Airlines still have the opportunity to greatly increasing customer centricity and satisfaction. It’sreach out to their customers and provide them with an critical to understand that real-time data warehouse andexperience is relevant and makes a difference. analytics support the key strategies of the airline business,Each customer has a story to tell that’s good, bad, or namely supporting enterprise vision and their goals.indifferent. Chances are that good stories lead to customer About the authors: Ajay Paul GM – Alliances & Strategic Relationships Sreejit Ulatil Menon Associate Director Analytics & Information Management Practice Jayashree Iyangar Manager Analytics & Information Management Practice Jayashree_Iyangar@mindtree.comWhite paper 08