Customer-Centric Sales

Maximizing Sales Through Better Approaches To Product Distribution

Forward-looking distribution methods are associated with greater personalization characteristics, resulting in improved opportunities for an airline to offer the right product at the right price to the right customer at the right time.

To get closer to their customers, travel suppliers including airlines, hotels, cruise lines and rental-car companies need to understand customer preferences and purchase behaviors to better serve customers across all channels of distribution. Demanding customers who have high expectations for personalized services are a reality in today’s extremely competitive airline marketplace.

Successful customer experiences are fundamental drivers for airline revenue growth, and those experiences represent heavy influences on product investments from suppliers, intermediaries and software vendors to help ensure customer loyalty.

Travel suppliers are also investing in advanced analytics to understand customer traits, behaviors and preferences to support customer acquisition, maximize revenue-generation potential from the customer base and retain the most profitable customers.

To enable customers with targeted options, next-generation applications collect information from customers in real-time. Analytics have advanced from descriptive analytics to predictive analytics (in other words, what will happen) to prescriptive analytics (recommending options).

Targeted Content

A common theme across the travel value chain is to display targeted content to a given customer based on implicit or explicit information that is known about the customer. This type of deep understanding can be derived from alternate data sources such as transactional booking and ticketing data, demographic data, social media, survey data and other sources to segment and target customers with relevant offers.

Segmentation of customers based on purchase-behavior patterns is never perfect and should be refined on a periodic basis as there are new data sources and as customer behaviors change over time.

In the competitive marketplace, travel solutions should focus on products that resonate with customers to improve conversion rates and maximize revenues for suppliers with every interaction by offering the ideal product or bundle to a customer at the right price when he or she is ready to make a purchase based on customer-behavior data.

Attribute-Based Shopping

Attribute-based shopping, pricing and booking now characterize air travel. When attributes specific to a trip are indicated by a user, the search space should be refined to return content with the specified attributes.

For example, airline customers like to select itineraries based on seat availability, looking at premium seats (with more leg room), aisle seats, window seats, exit-row seats, seats together for a family, and other important characteristics such as free bags, loyalty tier, pre-reserved seats, and schedule and fare preferences.

In this new paradigm, the attributes specified by a user come first, before itineraries (the available flights themselves) are returned for air shopping. However, attribute-based shopping is only the first step in a longer journey to target customers with precise offers, and thereby generate incremental revenues.

Trip Segmentation And Personalization

From the advent of airline reservations systems and global distribution systems in the 1960s, airlines have applied the reservations-booking designator (RBD), commonly referred to as a booking-class code, as a surrogate for customer segmentation that is used in fare management, seat availability and inventory control, shopping, booking, pricing and ticketing processes. Multiple fare-basis codes, each with its associated rules map, are assigned to a reservations-booking designator.

While RBDs serve a purpose in displaying seat availability, which is subsequently used in air shopping, booking and itinerary pricing, airlines today are looking beyond traditional RBDs for greater flexibility in customer segmentation, influenced by purchase-behavior patterns that cannot be encapsulated based on information available in a reservations-booking designator.

Revealed Preferences

An approach that is more closely aligned with revenue management is trip-purpose segmentation based on revealed preferences. Trip segmentation is the first step toward the process of “offer creation,” and it works even when the customer’s identity is not known.

The value of trip segmentation is rooted in the fact that a typical customer has multiple profiles based on the context for travel: business, leisure getaway, family vacation or whatever other context in which the traveler is planning the trip.


Advanced Statistical Techniques

Generating targeted offers to customers during shopping and booking requires advanced statistical techniques for trip-purpose segmentation.

To generate relevant offers when the customer has not been identified, trip segmentation is required. When the customer has been identified, one-to-one personalization of offers is feasible and can be influenced by prior history.

Determining the trip purpose (such as a short business trip, a long business, a leisure vacation for a single traveler, a family vacation with children) has a strong influence on customer preferences and price sensitivity.

Trip-purpose segmentation is the first practical step toward grouping customers with similar purchase-behavior characteristics. However, not all customers are registered users, and they can be anonymous when they book with a travel agent or book on a website without cookies enabled. Additionally, the typical traveler has multiple profiles, depending on the purpose of the trip.

Trip segmentation can be augmented with customer-specific data that are resident in a customer profile, including name, credit card, frequent-flyer status, past trips and other factors, allowing fine-tuning of offer recommendations.

Typical parameter ranges that can be considered in combination to identify trip segments include:

  • Advance-purchase period,
  • Length of stay,
  • Saturday night stay,
  • Number in party,
  • Distance traveled,
  • Temperature differentiation between the origin and destination,
  • Channel.

As the first level of customer centricity, this phase personalizes the shopping experience to provide the most relevant information during the sales process.

Hierarchical-clustering-based and CART-based (classification and regression trees) techniques can be used for trip segmentation. Subsequently, when the customer has been identified, recommendations can be refined by augmenting trip-segmentation recommendations with past behaviors and stated preferences.

Customer Segmentation

Trip segmentation is vastly different from customer segmentation, which is only applicable when the customer’s identity is known. Common customer-segmentation techniques used in customer-relationship-management applications encompass RFMTV (recency, frequency, monetary, tenure and value), or tiers based on the total or remaining customer lifetime value. Customer segmentation can work with trip segmentation to fine-tune the offer based on the customer’s prior travel history.

More modern techniques rely on natural language processing of user-review data for sentiment analysis of airline brands to recommend products that closely match the stated preferences on a customer’s profile. When a customer is “declared,” past travel history also plays a role in determining the components of the offer as to products and price.

A/B Testing

To evaluate alternate segmentation strategies or determine the validity of displays generated for an alternate customer segment, A/B testing (also sometimes referred to as “split” or “bucket” testing) has a significant validation role.

In an A/B testing framework, alternate versions defined as “current” (control) versus “proposed” (variation) split the incoming traffic and can be compared against each other with statistical analysis to determine if there is a positive, negative or neutral impact on a metric such as conversion rates.

Successful customer experiences are fundamental drivers for airline revenue growth, and those experiences represent heavy influences on product investments from suppliers, intermediaries and software vendors to help ensure customer loyalty.

Power Shopping

A typical shopping response generates itineraries that are based on the parameters of the shopping request. Shopping responses are not optimized to return itineraries by trip segment. For example, for identical shopping requests, business travelers may prefer a short connection, while a family with children may want longer connect times.

The definition of trip segments is a first step toward creation of rules or shopping parameters to narrow the search and return the most relevant set of options. For corporate travel, corporate compliance also serves as a filter by augmenting the trip-purpose segment shopping parameters.

Establishing rules by trip segment requires validation with A/B testing to determine the best set of shopping parameters to be deployed to narrow the search and determine specific trip-segment schedules to help maximize conversion rates. Trip segmentation is a learning process that must be fine-tuned and updated over time as market conditions and customer behaviors change.

Preference-Driven Air Shopping

Today, a typical shopping response generates 10 to 1,000-plus itineraries, but does not reflect attributes provided by a user. From such a large set of itineraries, it is difficult to review and make a selection.

Online travel agencies employ filters to prune the set of itineraries returned. However, doing so automatically introduces limitations since the filters are hard constraints.

Sabre has developed several prototypes for preference-driven air shopping, which allows an individual user, with a desktop, tablet or mobile phone, to effortlessly get an ideal itinerary based on travel-preference tradeoffs, which represent a radical departure from filters.

Typical preferences that can be provided by a user include: schedule preferences (such as departure time, return-arrival time, connecting airport, connecting time and carriers); fare (refundable or non-refundable); seats; and ancillary products.

The preferences should be stored in the customer profile to ensure the information is accessible and can be used, if needed, for every customer interaction. The preference-driven air shopping display algorithm provides a way for customers to state their preferences based on what is important to them. It also enables an airline to quickly hone in on specific flights that meet customer needs.


Offer Management

Offer management is the modern term used to describe an expansion of traditional revenue management to include ancillary products that are bundled with the base fare to create a “bundled offer.”

Surveys have shown that travelers would pay for extra perks, such as more frequent-flyer miles, pre-reserved seats and the extra legroom. In fact, ancillary revenues and new product offerings in the form of airline branded fares have grown rapidly during the past decade, and new decision-support tools are emerging in this area.

The offer-management process begins with trip segmentation. Trip-segmentation data can be analyzed against historical booking and ticketed data to determine the dominant and less-significant ancillaries that resonate with each trip segment. This is called “market-basket analysis” in traditional brick-and-mortar and online retailing. The information can then be used to determine the composition of the most appropriate bundles for each trip segment.

Determining the price for a bundle requires estimates of a customer’s willingness to pay, which should be calibrated for each trip segment. The customer’s willingness to pay is an estimate that can be derived from A/B testing or other analysis. With the estimates of price elasticity by trip segment, the discount for the bundle can be determined, and the price of the bundle can be set.

Dynamic Availability And Pricing

For the base fare, there are two methods for market-adaptive pricing, involving modifying the inventory controls or pricing an itinerary based on prevailing competitive market conditions. It all requires monitoring of competitive selling fares from shopping responses to determine the optimal inventory controls.

To determine the attractiveness of each itinerary in a shopping response requires a “choice” model to be calibrated from a shopping request and response data, with pertinent variables such as displacement time, elapsed time, fare and screen presence.

Dynamic pricing is closely related to dynamic availability in that both techniques leverage competitive selling fares to arrive at an inventory-control or dynamic-price recommendation.

The session-based fare optimizer already determines the optimal price for the host airline, based on the competitive set and current selling fares of competing airlines in the marketplace. Instead of converting the optimal price point to an inventory-control recommendation, the dynamic price is used to approximate the ticketed price.

Dynamic pricing addresses specific problems that traditional revenue-management methods, and even the more advanced capability of dynamic availability, do not.

First, dynamic pricing bridges the gap between seat availability and pricing by ensuring the ticketed fare is greater than the total bid price for the itinerary generated by revenue management. Second, for the first time, a supplier will be able to enforce a consistent, repeatable and identical response in the shopping, booking and ticketing processes.


When a customer’s identity is known, the offer can be personalized. Past history with the customer, in the form of transactional data and web-behavior data, can be taken into consideration to determine how the offer should be personalized, by adding components to the bundle either at cost or at no cost to the traveler, or simply modifying the price for the bundle.

Using branded fares is also an important consideration in creating the final offer. When a bundled offer is determined, it should be mapped to the attributes included in each branded-fare product to ensure the lowest-cost option is offered to the customer.

For example, it may be less expensive to offer a customer a cheaper branded-fare product and an ancillary priced separately, rather than offering the higher-valued branded-fare product with the ancillary included.

Continually Developing Options

Displaying targeted offers that truly resonate with customers requires an investment in a data infrastructure and advanced analytics to understand consumer behavior and preferences to generate incremental revenues with targeted offers and ensure repeat profitable customers.

Understanding customer preferences and generating pertinent targeted responses to customer requests during the sales-process lifecycle represents the new reality.

Generating a multitude of offers to customers who are then required to select from myriad options essentially slows down the sales process and leads to abandonment. Customer preferences should work in tandem with attribute-based shopping and pricing.

A key to customer centricity is the ability to offer the right product or bundle to the right customer at the right price at the right time, based on deduced or stated customer preferences.

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