New eBook: Conversational AI: The Key To Four-Star Service, Five-Star Reviews, And Bigger Profits in Travel

Written by Can Ozdoruk  on   Sep 19, 2019

Customers won’t wait, even 10 seconds, for a response. AI helps travel companies provide a better service that consumers expect today.

Modern consumers are less patient. They want personalized information with little effort. And, they are less loyal today than they have ever been.

Customer service is now tied directly with a company’s bottom line – it’s a fundamental driver of where people spend their money and is how companies differentiate in an industry which used to be ruled by loyalty programs. To meet consumer’s quick-rising demands, travel and hospitality companies need to adopt conversational AI to automatically resolve customer issues.

The Rise of AI in Travel 

Conversational AI agents are rapidly becoming table stakes for any travel brand that hopes to keep up with customer expectations. Based on a recent SITA survey, 68% of airlines planned to implement some version of AI as part of customer service interactions. The reasons for this trend are obvious.

  • Travel and hospitality brands can save billions of dollars in customer service costs by diverting easy to answer queries to a travel chatbot or other AI systems.
  • These brands can leverage AI as part of the customer support process to actually boost revenues and drive profits.
  • T&H brands can reduce agent churn by reducing their workload and allowing agents to perform more fulfilling work that involves emotional connections and higher-order thinking and problem-solving.
  • Customer service channels are now omnichannel and must operate 24/7. This increases the overall demand for customer service and mandates more scalable solutions.

Why Conversational AI Matters So Much Right Now

There is a combination of factors that have elevated the importance of Conversational AI to travel and hospitality companies, including:

  • Text is the most popular communications channel: Great customer experience must meet customers in the mediums where they are most comfortable. Text-based communications have exploded to dominate all communication.
  • Immediate responses, 24/7, are expected by consumers: 90% of customers consider an immediate response to be important for customer service queries. This is simply not possible with human agents and in an industry like travel and hospitality, where major service outages due to weather or other problems, are common.
  • Conversational AI is directly connected to the Internet and the world of apps: Conversational AI systems can pull in data from a variety of sources, empowering better personalization and localization. This, in turn, drives loyalty and revenue growth.

Use Cases For Conversational AI in Travel

There are two key levels of use cases for Conversational AI in travel. First, AI can take over repetitive tasks previously handled by agents. AI will also orchestrate wider and more complex interactions over time.

  • Taking Over Core Repetitive Tasks: In travel and hospitality, there is a high volume of repeatable queries. AI can respond to these in seconds. Examples include checking on flight status and arrival, changing flights, and making a purchase of a hotel room.
  • AI Performs More Complex and Proactive Tasks: As Conversational AI grows more knowledgeable, it’s able to conduct more complex tasks. AI will have the ability to become proactive and potentially revenue-enhancing. Virtual agents will also work with live agents in the background to augment their work.

Conversational AI represents a tectonic shift in how customers interact with brands, providing massive opportunities for travel and hospitality to reestablish their customer experience leadership. If you want to learn more about AI in travel, download our detailed analysis here.

How Media and Entertainment Companies can Boost CSAT, Increase Retention and Drive Revenue with AI

Written by Can Ozdoruk  on   Sep 17, 2019

Every aspect of the media and entertainment (M&E) industry has been impacted by technology, and AI customer service is the next frontier.

Today’s customers expect convenient, personal support on their terms – whenever and wherever they need it. As the relationship between traditional M&E companies and consumers becomes more direct, and emerging companies race to maintain subscribers and grow their base, AI can be leveraged to provide consumers with immediate resolutions to their issues across an increasing number of channels.

To meet scale high-quality, convenient and immediate support, media companies need to adopt AI into the workforce. 

Customer service trends in media and entertainment 

Perhaps more than any other industry, media and entertainment companies understand the impact of the digital age on consumer behavior. We no longer all tune in Monday night at 8pm to watch a show. We’re now binging on-the-go, on multiple screens. Networks today are racing to offer over-the-top (OTT) offerings like Disney’s highly anticipated streaming channel, Disney+.

Digital has disrupted every facet of the content consumption experience. Rather than downloading songs to iTunes, we’re barking orders at Alexa to play our favorite songs. Instead of picking up the paper at the end of our driveway, we get real-time breaking news alerts on our smart watches. Traditional publishers now even have dedicated teams to disseminate stories on Snapchat and other digital-first channels 1.

This digital era has also drastically changed the customers’ expectations for support when they encounter a problem or have a question. Customer service increasingly dictates where people spend money, maintain subscriptions and foster brand loyalty. The expectations for customer service are higher than ever before:

  • 59% of consumers have higher expectations for customer service than they did just one year ago 2 
  • Customers expect to receive service through any channel and on any device: 59% of people having used multiple channels to get questions answered 2
  • 66% of US online adults say that valuing their time is the most important thing a company can do to provide them with good online customer experience 3

AI in Entertainment: Use Cases 

In M&E, everything is on-demand. Your audience now expects support, anytime and anywhere.

AI can help reduce costs by deflecting support tickets for simple queries. This enables human customer service agents to focus on high-touch, complex needs. An AI-powered virtual agent can resolve a range of issues in less than one second, delighting customers with the personal and immediate support they crave, driving loyalty and reducing churn. Here are a few examples of the types of customer service needs that can be delegated to AI:

  • Free Trials: Free trials are a great way to lure new customers, especially for streaming companies like Hulu, Netflix and Spotify. With nearly 1/3 of free trial subscribers converting to paying customers 4, they are critical for growing an audience. An AI Agent can reassure someone wondering things like “I thought this was a free trial. Why do I have to add a card?” at the exact moment they can be persuaded to move forward. 
  • Signing up: As soon as a person is interested in a service, companies can’t afford to frustrate them when questions or issues arise during the on-boarding process. Help a viewer understand the differences between various plans and options. Or how your service compares to a competitive service. An AI Agent can reply immediately when the brand is top-of-mind and has the person’s undivided attention.
  • Canceling or suspending a subscription: When a customer has requested to cancel a subscription, an AI Agent can pull in compelling offers from back-end systems to tempt her to stick around. This can be highly targeted based on customer traits (i.e. how long a person has been a customer). This will ensure high-value customers are provided with the most compelling offers. If a person wants to suspend service due to extended travel, AI can be the first line of defense. AI can gather key information like the pause and restart date before elevating to a human agent. A virtual agent can even be given the authority to update the system autonomously on the back-end.
  • Content availability: If your content systems have an open API, virtual agents can look up in real-time the status and availability of specific titles (i.e. is “A Star is Born” available yet?) or by genre, artist / actor and other parameters (i.e. When is the new Taylor Swift song going to be here?). AI Agents can also proactively reach out on a customer’s preferred channel with content recommendations, highly targeted to their tastes, to drive engagement.
  • Troubleshooting and Technical Help: When a customer experiences a technical issue, they are more often than not needing help immediately: Whether it’s resetting a Roku, using a VR headset, resetting a password or signing in, fixing your customer’s technical issues in their moment of need is essential to consumer satisfaction (CSAT). An AI Agent can walk a person through the steps to fix their problems without any delay.
  • Account Management: A virtual agent can help readers adjust their news alert preferences or change their settings in a music streaming service. Resolving issues like “Please only send alerts once a day” or “How can I add an artist to my favorites” can be facilitated without ever getting in a human agent’s queue.
  • Billing information, clarifications and payments: By integrating into billing and accounting systems, an AI Agent can resolve issues related to payment information (Can I update my credit card?) or billing questions (Can I see my last bill please?), and even facilitate monthly payments.

As M&E companies continue to innovate in content delivery, advertising solutions and hardware like VR headsets, customer service cannot be forgotten. Customers expect more from companies than ever before. We don’t want to wait a week before watching the next episode. In the same vein, we people want on-demand, convenient and cross-channel support. Bringing AI into the workforce enables M&E companies to meet these quick-rising demands, increasing customer satisfaction and scaling high-quality customer service across customer support email, messaging, chat and voice. 

If you’re a company in M&E and are not using AI in customer support, you’re at risk of losing loyalty and revenue. Don’t fall too far behind. Let’s chat.

References: 

  • What’s New in Publishing: Reinventing the newspaper for the digital age, March 13th, 2019
  • Microsoft: Now available: the 2018 State of Global Customer Service Report, August 30, 2018
  • Forrester: 2018 Customer Service Trends: How Operations Become Faster, Cheaper — And Yet, More Human, January 24, 2018
  • CNET: On a Netflix free trial? A third of you will likely pay up, April 24, 2017

Returns are the new black: How retailers can manage the influx of returns

Written by Can Ozdoruk  on   Sep 12, 2019

Finding the best RMA software is only the beginning as the customer experience takes center stage

If you’re selling online, you’re dealing with returns. Companies have widely adopted Return Merchandise Authorization (RMA) software to manage this burdensome process, but AI is emerging as a powerful counterpart to increase customer satisfaction during the often tedious returns process. When used during the pre-sale process, AI can answer critical information related to sizing and care, identify potential returns based on items in a person’s cart to preemptively intervene, and, if a return is initiated, a virtual agent can keep a customer informed throughout the process, which increases the likelihood of future business. 

Returns are part of the business, leading to widespread RMA software adoption 

Returns are a key component of the online shopping experience. As eCommerce sales continue to skyrocket, topping $500 billion in 20181, so do returns (It’s estimated that over 30% of all online purchases are returned2. In fact, if you want to make money online, you have to have an effortless and often generous return policy as people base their decisions increasingly on how easy they can return something. Sixty-seven percent of online shoppers check the company’s return policy before shopping2

RMA solutions have become incredibly advanced. Companies like Return Magic, AfterShip Returns and other RMA software providers have eased the burden on operational teams to execute returns more easily and alleviate the pain points that marred the consumer experience for so long. However, as the sheer volume of returns is increasing at such an incredible rate, retailers are starting to look more holistically at the returns process to try and mitigate returns and ensure the customer experience is best-in-class throughout the process. As we look into 2020, AI-powered virtual agents will be a key technology in the returns management process.  

Optimize the Customer Experience Before Checkout

With estimates of return deliveries reaching $550 billion by 20203, trying to minimize returns will be a primary focus for retailers of all sizes moving forward. Improving customer service has become a lot more metric-driven over the last two decades. The latest customer service stats point to one key initiative: growing customer happiness predictably and at scale.

One way organizations are tackling this challenge is with artificial intelligence.

Companies are launching conversational AI Agents – you might know them as retail chatbots – to help customers throughout the online shopping experience to answer questions a person might have. With an easily accessible shopping assistant, consumers can feel empowered in their decisions and be less likely to have surprises when they receive an item.

AI Agents can take cues from real-time behavior to be able to anticipate when a person is not 100% sure of a decision. For instance, if a person has clicked on the size guide a few times, the company would presume that she is curious about which size she should get.  A virtual agent could preemptively reach out offering advice (This dress is small and will shrink a little in the wash. I’d recommend ordering up a size!). Research has found that sizing issues are a domineering reason that people return items, with 30% of items returned as they were too small and 22% returned for being too big4. If a virtual agent is deployed to help in these situations alone, the impact on the returns volume would be immense. 

Identify and court likely returners 

Forty-one percent of people buy variations of a product with the intention of returning items4. During the checkout process, a virtual agent could identify like products (i.e. the same product in various sizes or colors) and proactively send the customer a message to see if it could help to find the customer the right size (and also determine that the intention is not to gift a like item, but find the right one for themselves). Many companies might find that offering a 10% discount in this critical moment if the customer purchases only one size far outweighs logistical and inventory losses of managing a potential return.

Proactive Communication at every step of the journey 

Even with a powerful virtual agent running defense during the shopping and checkout experience, returns are inevitable. As your RMA solution takes care of the logistics, use a virtual agent to ensure the customer is always happy. During the returns process, happiness breeds from communication: 56% of people say they want notifications about the status of their refund and 50% want to know the status of their return package5

A virtual agent can keep a customer updated along various parts of the process through deep integrations with the RMA platform. It can automatically reach out with key information, while also enabling a customer to ask any follow-up questions they might have.  

While some RMA solutions might have the ability to send an email, they lack the ability to respond to any forthcoming questions without routing to traditional customer service channels. If a person comes back with “What happens now?” or “When will I see my money” to a note that their package was received, a virtual agent could immediately respond whereas if the customer was routed to an agent, there would likely be a significant delay in a reply and the CX would suffer.

Hyper-personalized Recommendations to Recover Lost Revenue 

In a recent study, 57% of people exchanged or replaced the last item they returned 5, but not necessarily with the same retailer. 

A virtual agent can provide hyper-personalized recommendations to spur replacement purchases by following up at the optimal time during the returns process (for some companies this is after the refund has shown up in an account, while others this might be when the package is on the way back to the distribution center). Machine learning technology can identify patterns based on consumer profile attributes and cross-analyzing with items returned in order to hyper-target recommendations. Customers would enjoy their own virtual assistant that curates individualized recommendations.  

Returns are not going anywhere

Returns are a necessary part of doing business online today. Accepting returns and offering a generous returns policy, is the way to make money. In fact, 96% of shoppers will shop with a retailer again if the returns process was easy 5.  For retailers, optimizing the returns process goes well beyond finding the best RMA software to manage the back-end. It takes a holistic approach to the entire customer experience. Bringing AI customer service into the customer experience pre-checkout and proactively keeping the customer informed throughout the process can elevate the customer experience and keep customers happy and coming back.

Can we discuss how we can marry our AI with your best-in-class RMA software to keep customers satisfied during the returns process? Let’s chat. 

To get more of our insights on retail customer service before you contact us to request a demo.

References 

  1. Internet Retailer: US ecommerce sales grow 15.0% in 2018, February 28, 2019 
  2. Invesp: E-commerce Product Return Rate – Statistics and Trends [Infographic]
  3. Stastisa: Costs of return deliveries in the United States from 2016 to 2020 (in billion U.S. dollars),  Jun 18, 2018
  4. Return Magic: The State Of Online Returns In 2018 
  5. Narvar: The State of Returns 2018: What Today’s Shoppers Expect

AI in Real Estate: Leverage Machine Learning to Provide an Effortless, Instantaneous Experience for Buyers, Sellers and Agents

Written by Emily Cummins  on   Sep 3, 2019

Technology has changed every aspect of the real estate experience and AI is the next frontier. 

The digital disruption machine learning in real estate has caused is undeniable: home buyers and sellers are more empowered than ever to own more of the process. Whether or not they realize it, homebuyers are now using AI in the real estate process from the start.

Nowadays, home buyers are the ones telling their agents the homes they want to see from online searches. Over 89% of people now search online for homes on sites like Zillow, Trulia, Redfin and Realtor.com (1). This is a far cry from a person slowly driving through neighborhoods looking for yard signs.

Sellers, too, are leveraging intuitive online platforms and opting to sell their own homes: a recent study found that 25% of people who sold a home did so without the help of a full-service agent (2).

With this influx of technology, the day-to-day for agents has also changed completely. They juggle between various software platforms to create contracts for e-Signing, schedule drone shots and text 24/7 with their clients. Agents also are dealing with a more informed client, who often looks at sites like Zillow as the bearer of truth for listing availability, home value, and other pertinent information. More and more, agents are also relying on these platforms as a critical tool. Agents can help their clients find and sell homes, as well as market themselves to generate new leads. 

As Zillow and others continue to have greater influence in the real estate process, these platforms have to cater to both the public and agents. When an issue or question arises, these companies must provide an immediate resolution across many digital channels. 

AI in Real Estate: Alleviating the Pain Points in the Buyer and Seller experience 

More and more, people are taking the reins in their real estate journey. Whether buying or selling a home, the public is more empowered than ever before thanks to powerful online real estate sites. When issues arise, whether it’s updating a listing to a For Sale By Owner (FSBO) or troubleshooting a common account problem, people expect immediate resolutions.

AI in real estate can be leveraged alongside human agents to immediately resolve over 50% of customer issues. Here are some examples: 

  • Managing FSBO:  As more people opt to sell their home as a FSBO, the burden is on them to create a compelling listing to get people in the door. It’s an experience that is unbelievably stressful (take it from me, I did it earlier this year). Machine learning in real estate enables a virtual agent to reply instantaneously to questions like
    • “Why did my property get deleted?”
    • “How do I change the featured image” or
    • “ I need to change the time of my open house.” 
  • Changing Home Facts: Whether a person is currently selling a home, planning to or is happily in their nest, people want accuracy in their home facts. Machine learning in real estate can now enable a virtual agent to help address concerns like My square footage should be 2,500 – not 1,800” or My home is inaccurately listed as a pre-foreclosure.” The Zillow Zestimate, for instance, also raises a lot of concerns for home sellers as buyers increasingly use it as a reference point when making an offer. An AI Agent can be the first line of defense when questions arise like “How is this calculated?” or “My Zestimate is inaccurate. How can I update it?” 
  • Updating Preferences: Homebuyers will often change their criteria as their search matures. Say a person reaches out to customer service to modify their search parameters with questions like “I want to add 32082 to my search area” or “I actually want to see only pool homes.” An AI Agent that leverages machine learning for real estate can make the changes to the buyer’s profile on the back-end, without burdening human agents with mundane tasks like these. 
  • Account Management: An AI Agent can automatically troubleshoot common issues like resetting passwords, deferring costly customer service tickets away from human customer service agents. For example, if a seller reaches out saying they can’t see their requested changes to the online listing, the virtual agent can provide the steps to clear their cookies. 

AI in Real Estate: Streamlining Support to Empower Agents to Generate More Leads and Close More Deals

A powerful customer segment that online real estate companies can’t ignore is the agent/broker community. There’s a mutually-beneficial relationship. Agents can glean new leads and market their listings on these sites.  These platforms benefit from accurate data, advertising revenue and providing an added benefit to the public through agent finder tools. 

Online real estate platforms can leverage AI to immediately resolve issues that arise related to advertising, listings, account management and other issues. Here are a few examples: 

  • Advertising / Paid Marketing: Whether an agent is interested or is currently advertising on a platform, an AI-powered Customer Service Agent can get back to agents immediately for questions related to benefits/costs or even facilitate the start or stop of a campaign. A virtual agent can also automatically provide insights based on current campaign. Or provide recommendations on how campaigns can be improved through better targeting or adjusting other parameters. 
  • Listing Information: If a listing has any inaccuracies, the agent is going to work to get it updated as quickly as possible. If an agent runs into an issue making changes, they would look for a company to respond immediately. An AI Agent can troubleshoot issues or even make edits to a listing itself on behalf of the agent. 
  • Account Management: If an agent wants to start, pause or stop a premier / paid program, an AI Agent can be given the authority to facilitate a request. In certain situations, for instance, a high-value agent is requesting to close their account, an AI Agent can ask “Why are you looking to close your account?” before being elevated to a human agent to try and coerce to stay.

AI in Real Estate: How to modernize customer support and exceed customer expectations

Real estate can be high-pressure. If a buyer/seller or agent runs into an issue on influential real estate platforms, they expect immediate resolutions. AI can respond instantaneously to over 50% of repeatable tickets. This enables human customer service agents to focus on more complex customer needs.  

Are you an online real estate company looking to capture more market share and increase CSAT through customer-first support? We’d love to chat. 

References

  1. Investopedia: Zillow vs. Realtor vs. Redfin (June 25, 2019)
  2. Forbes: Selling Your Home ‘By Owner’: What’s Really Happening? (January 17, 2017)