By Naveen Kapoor and Love Ojha.
The travel and hospitality industry is a rapidly growing Customer-Centric Business. For Airlines, Hotels, Cruise lines and TMC’s, the need to know and uniquely identify their valued customers is a business imperative which helps them optimize their operations and increase the impact of their marketing strategies, ultimately keeping them ahead of the competition.
However, in this dynamic environment, many organizations struggle to formulate a strategy optimal to their business model which can make the most of their existing customer data.
It usually costs ten times more to acquire a customer than to retain one, and a hundred times more to win back a customer who is already disillusioned with the company. The key is to finding out which customers are valuable, and how many of them are dissatisfied. Organizations have a hard time maintaining an up-to-date and integrated knowledge base essential to understand customers. It might be due to the organization’s inability to maintain a complete and consistent customer profile (which records all customer activities) since there are multiple disparate systems maintaining disintegrated local copies of the same customer. Even when a repository is present, organizations face challenges due to an incomplete understanding on how to best utilize such a repository
It is difficult to identify the true value of a customer primarily because there are multiple dynamic factors contributing to this value. To add to the complexity, factors valid today might not stay relevant tomorrow.
Therefore, it becomes imperative for organizations to formulate a Customer Value Implementation strategy and have a robust model which can not only gauge the net worth of a customer based on factors that are present today, but is also flexible enough to adapt to changing market scenarios tomorrow.
Typical Data Challenges for an Organization
For an organization attempting to extract the most from their customer data, problems can be identified at each step. Take an example of a marketing campaign scenario, starting from preliminary information retrieval to final campaign creation.
Localized Disintegrated Customer Knowledge: Big Organizations have multiple channels of interactions with the customer, resulting in multiple entries being maintained across various systems. In a typical Airline operation, data about the same customer can be found in the GDS, Reservation System, IBE, Ground Operations (Check-in, Boarding) etc. These functional silos work independently, with little or no interaction between them.
Unknown Customer Value: In cases where functional silos have been integrated and a comprehensive customer knowledge base exists, organizations are often found wanting on a customer ranking system front.
Unfocused Campaigns: Without the knowledge of the customer value, organizations are unable to segregate customers and treat them equally, requiring higher investments, and a lack of visible differentiation for highly valuable customers
Rigid Static Calculation Models: Organizations which do have the ability to arrive at a “Customer Value score” after significant investment tend to opt for static calculation models which are designed in a way that renders them inflexible to change. Any changes to the way business functions leads to changes in business parameters for the calculation of the customer value and should necessarily trigger changes in the calculation model.
The need of the hour: A Comprehensive Customer Value Framework
A comprehensive “Customer Value Framework” is required to enable the airline, hotel, cruise line or a TMC to come up with a solution to the problem of customer value determination. The framework should ideally consist of three major processes:
Implementation of the Unified Customer Database: In order to make sense of customer data, an integrated repository needs to be in place. A unified Customer Data Model which can be used to keep all the customer details would be a single source of truth about the customer data. This information needs to be propagated to all the customer touch points. Any new customer entries captured in these systems need to be replicated and maintained in a consistent manner in the unified Customer Database.
Identification of Major Contributing Factors: What are the major parameters contributing to the customer value for a customer centric business? For a travel industry organization, the major factors affecting customer value include Travel Information, Passenger Information, Ancillary Service Spend, Social Influence Score, Financial Information, Frequent Flyer Information and Service Quality offered to the customer. This list may shrink or increase depending on the Organization’s choice on how it wants to evaluate its customers.
Customer Value Calculation Model: A generic calculation methodology, which also considers non loyal customers, should be adopted to cater to the need of any customer centric travel organization. The calculation method should have a flexible approach which allows changes in the weight and choice of parameters and the way they may affect or contribute to the customer value.
Identifying the true Customer Value will help organizations unlock valuable insights, which when combined with analytics and creativity can achieve amazing results. Customer Value helps in providing increased customer retention, better ROIs with targeted promotions and more focused customer service (in line with the customer’s profitability to the business). It also creates a consensus across the business units – a common base for decision making on investments.
About the Authors
Naveen Kapoor is the CoE Practice Head for IGT Data Warehouse and Business Intelligence Practice. Naveen holds a Masters in Business Administration degree from University of Dallas, Texas and has 18+ years of IT experience. He has been involved in many “end to end” enterprise level DW/BI projects and worked with many companies like Sabre, EDS, HP and American Airlines in Travel domain. He can be reached at email@example.com.
Love Ojha is a Lead BI Architect with IGT Data Warehouse and Business Intelligence Practice. Love holds a Masters in Computer Application degree from MIS University, Udaipur (Rajasthan) and has 8+ years of experience in the field of DW/BI. He has worked in the entire life cycle of the DW/BI, including the phases of requirements gathering, ETL Architecture Design, BI layer Design and implementation. He can be reached at firstname.lastname@example.org