Travel industry publications are awash in stories about new technologies incorporating machine learning and artificial intelligence for purposes like improving equipment operations and maintenance efficiencies, and even making corporate travel booking less of a headache. Yet, while machine learning and AI platforms can help travel industry execs gain insights and improve decision-making, many of these systems fall short.
Unlike most systems that take a purely technological approach to decision-making, the Decision Cloud incorporates human intuition into the decision support process. Rather than making enterprise decisions for the user, the Decision Intelligence-based Decision Cloud platform complements the human approach to decisioning. The platform was built to help enterprises clarify and balance the factors affecting even the most complex decisions. The platform’s process illuminates the tradeoffs consumers make when formulating a decision, providing insight into the natural intuition and cognitive aspects of human decision making.
Here are a few of the myriad ways in which travel industry executives can use Decision Cloud:
Improving B-to-B Airline Reservation Booking
Airlines, cruise lines, train and ferry companies have call center data they could tap for valuable insights about their customers and their buying preferences. An airline, for instance, wants to make smarter decisions about how best to respond to business clients in order to improve sales ROI. Call center data provides clues and Decision Cloud can uncover them.
In an effort to learn more about how individual business customers prefer to purchase tickets, the Decision Cloud platform ingests the airline’s b-to-b call center information. The deep learning technology determines which specific factors affected how customer travel decisions were made, not only providing more information about individual preferences, but rare insight into the buyer’s emotions, psychographic attributes and other factors that led to those decisions – “The Why” behind the purchases.
The platform’s process illuminates the tradeoffs enterprise customers make when formulating a decision, providing insight into the natural intuition and cognitive aspects of human decision making. The airline gets insight into why some customers prefer to purchase via an agent while others prefer to purchase directly from the airline, how customers rank decision tradeoffs and which emotions drive customer decisions.
Decision Cloud attributes a confidence score to each caller which predicts the likelihood an individual will make a purchase, and in which channel – online or via phone.
Hotel and Resort Marketing ROI
In order to grow its business and book reservations for three new destination resorts, a lodging brand aims to identify new customers and increase conversions:
Identify New Customers: To help the resort brand make more effective use of its marketing dollars, Decision Cloud analyzes its client data and publicly-available information to identify signals indicating when consumers are ready to buy.
These signals inform customized marketing messages for improved click-through and purchase rates by matching offers to each client’s individual interests, hobbies or even preferences for destination-area cuisine. For instance, a prospect planning a thirtieth wedding anniversary trip might be interested in a quiet romantic destination, while an extroverted wine enthusiast might appreciate offers featuring winery tour group side trips.
Boost Conversions: To increase conversions, Decision Cloud delivers a set of prospects which the resort brand can use to target offers through social media, email and direct mail. Using in-market testing, the brand can determine the quality of the prospects delivered via Decision Cloud compared to existing methodologies.
Smarter Hotel Procurement
A hotel chain procurement manager evaluates refrigerator models in order to make the smartest decision about which new refrigerators to purchase for the chain’s luxury suites.
The standard AI or business intelligence system would require the procurement manager to set narrow parameters that cannot be weighted or prioritized, forcing her to set a price range or choose only from specific brands or vendors. The user most likely would be required to define a maximum price on each refrigerator unit, for instance. But by doing so, it would eliminate what might have been the best option: refrigerators from an unchosen brand with an extended warranty and a sleek modern design for $150 above the defined price threshold.
The typical human decision-making process would deem that tradeoff worthwhile considering the longer life of the product — not to mention the Instagram-effect generated by the cool fridge design. While most rules-based AI systems would simply rule out that option, Decision Cloud would allow the procurement manager to prioritize a wide array of criteria on a sliding scale, resulting in a nuanced approach to uncovering the best decision.
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