5 tips for implementing conversational experiences in travel

Current events have revealed business continuity shortcomings across every single industry, especially in travel. Call centers weren’t prepared with readily available, scalable technology, and didn’t have the resources to properly withstand the increase in customer touchpoints.

And what was left? A swarm of confused travelers needing to cancel or reschedule flights, hotels, and cruises. Met with long wait times and unorganized responses, customers felt frustrated and unimportant.

As customers rebook their vacations, they expect an enthusiastic “Welcome back!” type of customer experience making flight and hotel reservations a breeze. But, as travel companies try ramping up activity, they’re still contending with the business continuity and communication plans that left them empty-handed before. Here’s what needs to change.

Power conversational experiences with AI and automation.

Customer sentiment toward the traditional communication channels like phone or email has shifted, and now we’ve entered the “post-app” era. In a post-app era, fewer customers use the apps companies built to solve their problems and instead desire an exclusively personalized experience. It seems daunting at first, but companies can accomplish it by using their extensive datasets to “conversationalize” their brand for a far more customizable, DTC-friendly experience.

Enter conversational AI, which automates communication and creates personalized experiences at scale between humans and machines. It’s more than chat: chatbots are confined to just sending messages while companies with conversational AI can offer more immersive brand experiences. For example, conversational experiences can deflect high volumes of calls and emails and provide a direct path for customers to sort through problems, address concerns and accomplish their own goals.

Natural language understanding enables conversational AI to interact with customers as intelligent assistants, trained to provide concise or in-depth content. Without conversational AI, these interactions would instead be a series of rules-based, canned questions and answers that exhaust customers.

This new customer experience (CX) advancement can handle multi-layered customer challenges and provide features like videos, carousels, and survey buttons for an intuitive experience. It brings a variety of useful functions to CX including:

• Transactional functionality — conversational AI can manage customers’ credit card details, handle payments, and transfer funds through a messaging carrier — all with military-grade security and PCI compliance.
• Rich, dynamic UX that makes things effortless — all of a customer’s business can be conducted in one platform, updated in real-time using data and insights powered by conversational AI.
• User context for hyperpersonalization — customer information can be integrated seamlessly using conversational AI so customers get personalized information and details delivered within the messaging experience.

When connected to a scalable computing service like AWS, conversational AI can handle high traffic volumes and deliver trustworthy, memorable CX. Customers can update flights, change hotel reservations, pay in advance and more, all via messaging and without the need for human intervention.

5 ways travel brands can implement conversational experiences

Conversational experiences help the travel industry prepare to handle the forthcoming influx of customers and the issues they need help resolving. But, how can travel brands use this technology to redevelop their CX initiatives and emerge more resilient from this pandemic?

1. Use automation and conversational AI to create always-on CX so customers can instantly access reliable, consistent, and accurate information.

In our digital-first society, the world never shuts down, and neither should a brand’s CX. Strict adherence to the 9 to 5 customer service schedule is outdated; consumers used to consider it inconvenient, now they consider it unacceptable. And they don’t want to call a call center. In fact, 60% of consumers say their go-to channel for simple customer support inquiries is a digital self-service tool.

Because conversational AI is highly scalable, available 24/7 and can instantly address issues customers experience without input from representatives or managers, it modernizes a brand’s CX orientation. Customers can also access information securely and quickly whenever they want. With conversational experiences, customers walk away with a much more positive brand impression, while increased automation better deploys your CX team’s resources organization-wide. You enhance productivity and workflow and, ultimately, increase profits.

Royal Caribbean shared how the company uses Pypestream to provide instant support and improve service. “Pypestream has turned us into an always-on brand. No matter the contact volume or the time of day, we’re available to provide service via messaging,” said Juan Silva, Associate Vice President, Product Digital Sales, at Royal Caribbean. “Pypestream’s AI and automation has also unlocked new insights and allowed us to continuously optimize the design and quality of our engagements with the flexibility to deploy new use cases at rapid speed. This transformative approach has changed the game for our business and travel partners.”

2. Connect backend systems to more self-service channels.

When customer service agents answer calls to handle customer inquiries, it costs an average of $6-15 per call. We know that customers already want self-service experiences like messaging. And now it can decrease CX costs for brands, too. It is, simply put, a win-win scenario.

To create a meaningful conversational experience, conversational AI needs access to customer information stored within backend systems. Then, it can automatically surface personalized information about each customer. Through sentiment, tone, and even emoji analysis, AI adapts to the customer — not the other way around.

For example, a customer wants to rebook a flight purchased before the pandemic. Without backend system integration, the customer must call the center and communicate back and forth with an agent to handle rebooking. And a chatbot, confined to text-based capabilities, confuses the customer with a sluggish, clunky process. Using conversational AI, powered with backend integrations, the customer can enter a messaging experience, access all their pertinent information, and make changes and pay within the interface. Whether the customer responds to prompts with text, a click on a certain visual or an emoji, the system will still select the best response to produce a successful outcome.

3. Leverage conversational analytics to understand customer intents.

When customers communicate with your brand, they have specific intentions behind their messages. Conversational AI can parse that intent by analyzing the many different ways consumers might pose the same question. AI then generates useful, helpful insights to assist your team and customers.

This puts your CX function on a proactive stance. For too long, analytics have depended upon reactive approaches like measuring website clicks or call volume by topic. These don’t illustrate the root causes of recurring issues and don’t help your team solve the real pain points. Conversational analytics powered by AI uses data to accelerate product and service feedback loops. It uses customers’ own words to show what they want so brands can adapt proactively and create the experiences consumers desire.

4. Select marketing and CX partners who appreciate the importance of UX design.

The goal of digitization efforts should be to advance specific business objectives. And vendors and partners must share an understanding of an irreplaceable UX design. While the functional components of conversational AI matter, customers want a seamless and pleasant experience.

As you consider how conversational AI could support your CX efforts, research the partners tasked to build your system. Third-party providers should bring specific knowledge to the table, such as design thinking, user experience expertise, and content creation capabilities. Otherwise, the project’s focus might shift toward technical issues, losing sight of the bigger, broader picture you’re creating: excellent customer experience.

5. Empower CX, rather than IT teams, when starting new transformations.

Your CX leadership should drive a CX transformation. When implementation fails, it’s often because IT teams were mandated to lead the project, leading to distractions like the “build versus buy” debate.

It doesn’t mean other teams like IT are intentionally preventing successful business outcomes. Teams simply have their own goals, and mashing them together increases confusion and muddies priorities. Keeping teams targeted on customer satisfaction, revenue generation and reduced costs can align efforts toward the best outcomes. In the end, teams with an eye for user experience and customer engagement should lead experience design.

While the pandemic has fundamentally altered the travel industry, brands have an opportunity to reinvent themselves. Customers will be eager to create new memories, and equally as eager to skip the traditional customer service hassles associated with travel. By fostering conversational experiences, travel brands will build loyalty and showcase a new degree of resilience.

A conversational platform built on AWS.

At Pypestream, we use Infrastructure-as-as-Service (IaaS) on AWS as underlying hardware for our conversational AI platform. This consists of two virtual private clouds: one for the conversational AI application, which customers interact with every second of the day, and the other for an administration cluster to run jobs including orchestration, monitoring, and backup. Clones of both exist for disaster recovery (DR) purposes.

Pypestream also uses AWS Key Management System (KMS) for encryption. Each tenant has its own keys stored in KMS, and Pypestream calls this system every time data is written to its database. Moreover, Pypestream uses Amazon Spot Instances for efficiency and scalability for dynamic adaptation to usage requirements.

This article was originally published by AWS.