How Predictive Analytics Elevate Airlines' Customer Centricity,
Driving Competitive Advantage
Contributed by Shannon Warner, Samrat Sen
The unbundling of rate structures and ancillary services, combined with escalating demands of the “always connected” consumer, have made the similarities between airlines and retail more significant than ever.
Retail and airline customers are influenced by many of the same factors. The two are increasingly comparing and contrasting products and researching their options before making purchases. Both are looking for inspiration to buy through many channels, such as online and physical world promotions and coupons, social media and input from friends.
The payoff for delivering on those high customer expectations can be great. Loyal customers are truly the backbone of any successful business, given the high percentage of revenue derived from them. It costs significantly more to acquire a new customer than to retain an existing one.
For this reason, many retailers have shifted to a customercentric business model, focusing on individual customer needs and buying patterns. They have discovered that understanding customer behavior is critical to building and maintaining true customer loyalty. That understanding begins with data analysis. Once data is captured and analyzed, the retailer develops a strategy to focus on its most profitable customers.
Most airlines have strong loyalty programs that bind their best customers and keep them returning to the same carrier. These loyalty programs are so powerful that they have become the sole retention strategy for many airlines. The “stickiness” of these programs has allowed airlines to focus largely on revenue optimization and airline operations, and less on customer satisfaction.
However, with the changing dynamics of the airline industry, competing airline loyalty programs (such as credit card programs that offer travel benefits with any carrier) and the change in customer behavior (including the use of technology to research and engage their social networks), it is time for airlines to begin differentiating their customer acquisition and retention strategies by becoming more customercentric. They need to learn from the retail handbook.
Predictive Analytics is the Key to CustomerCentricity
How is it that so many retailers are able to turn data about the buying habits and patterns of their customers into actionable insights? For years, they have been using data analysis techniques like data mining, as well as applying predictive analytics, to better understand the varying needs of their customers. Predictive analytics is a form of statistical analysis that is used in predicting behavioral patterns to shape business decisions, forecast trends and ultimately improve performance. Instead of looking backward to analyze “what happened,” predictive analytics help answer the question, “what's next?” and “what should we do about it?”
Predictive analytics is applied in many ways to help businesses make wellinformed decisions at a micro level. The core difference from one mode of application to another is what's being predicted. Whether predicting customer response, traffic patterns or defection, each requires different models and delivery of business value in different ways.
Today, predictive analytics is primarily used by companies with a strong consumer focus, such as retail, financial, communication and marketing organizations. It enables these companies to determine relationships among internal factors such as price, product positioning or staff skills, as well as the external factors of economic indicators, competition and customer demographics. It also allows them to determine the impact on sales, customer satisfaction and corporate profits.
Not only does being customercentric enable companies to gain market share by being more relevant to their customers, but it can also help them to reduce promotional expense by delivering the right messages and offers to customers at the right time.
Unfortunately, customercentric business models have been slow to catch on in the airline industry. When looking for new streams of revenue, many airlines have resorted to increased fees vs. introducing new valueadded products or services. And, with the exception of special treatment for some passengers with a high loyalty program status, airlines generally do not differentiate the way they treat individual customers.
Meet Today's Escalating Customer Expectations
As the choice of travel options grows, and customers are empowered with more knowledge, they have higher expectations. To keep up with these demands, airlines must find new ways to enhance their products, prices, promotions, operations and customer service. Predictive analytics can help determine how an airline should move forward by systematically learning from organizational experience. Improvements delivered through analytical quality control, reliability modeling, streamlined services and expedited application processing can all help to identify customer needs and wants, thereby enabling carriers to respond more quickly to meet escalating customer requirements.
For example, predictive modeling can assist in moving from mass marketing to more personalized, targeted campaigns and offers. It can also provide insights into where airlines are or are not meeting traveler expectations. Rather than relying on intuition or limited data (such as revenue forecasts or focus groups) for pricing products, managing inventory or staffing, airlines can use predictive analytics against customer and operational data to improve efficiency, reduce risk and increase profits.
There is little doubt about the escalating significance of predictive analytics in the airline industry. Whether it is used to predict customer behavior, set promotion strategy, optimize ad spending or manage risk, predictive analytics is moving to the top of the management agenda.
Read the full white paper, How Predictive Analytics Elevate Airlines' Customer Centricity and Competitive Advantage (PDF) or learn more about Cognizant's Travel & Hospitality Consulting Practice.