Here at Kustomer, we believe artificial intelligence plays an essential role in helping companies scale customer service and efficiently deliver exceptional results. And we aren’t alone. Gartner predicts that 72% of customer interactions will involve technology such as machine learning and chatbots by 2022. That’s why we are excited to share that Kustomer is acquiring Reply.ai to deliver even deeper intelligent self-service and agent assistance to our customers.
As a long-time partner of Kustomer, Reply is able to seamlessly integrate their tools into the Kustomer platform and help brands efficiently scale without compromising quality of service. The partnership furthers our commitment to integrating Kustomer IQ, our artificial intelligence engine, throughout the customer journey, while providing some powerful benefits to businesses and customers.
Reply is ranked as one of Forrester’s Top 10 AI Providers for Customer Service Automation, leveraging sophisticated machine learning models to power incredibly accurate self-service chatbots and deflection tools. With an astounding 40% average deflection rate, Reply, now a part of Kustomer IQ, can successfully resolve nearly half of all initial customer communications without the need for live interaction with a service agent. And with 67% of customers preferring self-service over talking to a company representative, deflection tools are not only a win for businesses, but also for customers.
With Reply now a part of Kustomer IQ, our customers will save thousands of hours spent answering simple questions, so they can focus on the most important cases that have a much larger impact on business and loyalty. At a time when customer service teams are being asked to do more with less, our suite of AI tools can tackle your growing queue of inquiries around the clock, while drastically minimizing costs.
We are so excited to welcome co-founders Omar and Pablo Pera, and the entire Reply team of world class data scientists and engineers to the Kustomer Krew. With Reply’s engineering offices in Madrid, this acquisition will also expand our presence in Europe and accelerate our growth in the region.
It goes without saying that we’ve always been committed to revolutionizing customer service. Today’s acquisition of Reply marks one more step in that journey.
What Customers Can Now Expect From Kustomer IQ
Built within Reply’s natural language processing engine, these best-in-class chatbots feature visual flow builders and templates for easy one-time creation and deployment across multiple channels and languages, providing effortless experiences by connecting customers to the right information.
Knowledge Base Deflection
An enhanced deflection widget can be embedded in forms, email, and chat, and features a powerful information retrieval system in which a semantic search engine and answer extractor not only provide relevant articles and content, but the exact answer to a question.
The platform can plug into third-party APIs to leverage a company’s most critical customer data points when deflecting. For example: a chatbot can answer the question “where is my order” or “when does my policy expire”.
Relevant answers and subsequent actions can be suggested to agents based on historical behavior, user text and conversation context. Actions include routing or auto-tagging conversations, as well as responding with relevant templated content.
Deflection success rates are measured and chatbot behavior can be evaluated, with functionality to support custom events, set conversion goals and segment audiences.
Reply is the first acquisition for Kustomer, reinforcing Kustomer’s commitment to AI and machine learning capabilities throughout the customer journey. With Reply, Kustomer will now offer enhanced chatbot and deflection capabilities through its customer service platform.
New York, NY – May 14, 2020 — Kustomer, the omnichannel SaaS platform reimagining enterprise customer service to deliver standout experiences, announced today it has signed an agreement to acquire Reply.ai, a customer service automation company founded in 2016 that helps companies scale intelligent customer service without compromising experience. Reply leverages artificial intelligence and machine learning models to improve agent efficiency through self-service chatbot and deflection capabilities. This announcement comes on the heels of the expanded roll-out of Kustomer IQ, the artificial intelligence engine embedded across Kustomer’s CRM platform. With Reply, Kustomer can provide even deeper intelligent self-service and assistance via Natural Language Processing (NLP) based chatbots, enhanced omnichannel customer deflection and machine learning based response suggestions. Madrid based Reply will also accelerate Kustomer’s European growth by significantly increasing its presence in the region.
“We believe artificial intelligence is essential to helping today’s enterprises scale customer service and efficiently deliver exceptional results. We recently rolled out Kustomer IQ to meet the growing need for companies to have access to the power of AI, and with today’s acquisition, we continue our investment in bringing self-service tools and intelligence capabilities to our clients,” said Brad Birnbaum, CEO and Co-Founder of Kustomer. “Reply has built deflection and self-service chatbots that help companies effectively deflect initial customer communications at an astounding rate of 40 percent. This means that almost half of all initial customer communications can be successfully resolved without requiring live interaction with a service agent, bringing greater efficiency to the entire customer service function. We are excited to welcome co-founders Omar and Pablo Pera and the entire Reply team of world class data scientists and engineers to the Kustomer Krew.”
The Reply suite of tools include deflection capabilities that look at historical and contextual data, continuously improving over time, as well as deflection widgets that can be embedded in forms and email, and features a powerful information retrieval system that extracts relevant answers to customer questions from a company knowledge base. Reply also features a platform to build chatbots that can be deployed across multiple channels and languages, and agent-assist tools that suggest relevant answers to messages and subsequent actions, such as routing or auto-tagging conversations.
“We are excited for Reply to join Kustomer and share its mission to make customer service more efficient, effective and personalized. As a long-time partner of Kustomer, we are able to seamlessly integrate our deflection and chatbots technologies into Kustomer’s platform and help brands more cost-effectively increase efficiency. We look forward to working with Brad and the entire team,” said Omar Pera, Co-Founder of Reply.
“By leveraging advanced AI capabilities and Kustomer’s robust CRM platform, combined with self-service deflection tools, Kustomer is uniquely built for the needs of today’s enterprise companies,” adds Birnbaum. “Since 2015, we have been committed to revolutionizing customer service and today’s acquisition of Reply marks one more step in our journey.”
Kustomer is the omnichannel SaaS CRM platform reimagining enterprise customer service to deliver standout experiences. Built with intelligent automation, Kustomer scales to meet the needs of any contact center and business by unifying data from multiple sources and enabling companies to deliver effortless, consistent and personalized service and support through a single timeline view. Today, Kustomer is the core platform of some of the leading customer service brands like Ring, Glovo, Glossier and Sweetgreen. Headquartered in NYC, Kustomer was founded in 2015 by serial entrepreneurs Brad Birnbaum and Jeremy Suriel, has raised over $174M in venture funding, and is backed by leading VCs including: Coatue, Tiger Global Management, Battery Ventures, Redpoint Ventures, Cisco Investments, Canaan Partners, Boldstart Ventures and Social Leverage.
Reply.ai helps brands scale customer service by providing AI-powered solutions that instantly resolve common customer questions on chat and ticketing channels. The two core products, Deflect for Ticketing and Deflect for Chat, reduce consumer frustration and deliver 24/7 personalized service, ultimately increasing self-service effectiveness and support team capacity. Reply’s customers, like The Cosmopolitan Of Las Vegas, Vail Resorts, Honeywell, Glovo and Paula’s Choice, rely on Reply for innovative and industry-focused solutions to customer service problems. Reply was founded in 2016 by former Google and CERN engineers and is headquartered in New York City and Madrid, Spain.
Life has become a series of trade-offs and workarounds in light of the pandemic. Curbside pick-ups are my new norm. My inbox is a litany of order confirmations and estimated delivery times. Last weekend, I drove to a local hardware store and found the following handwritten message on a sign at their front door: “Know what you want. Get in and get out.” At times, my interactions with people feel purely transactional.
The world has changed, and customer service is changing right along with it. Businesses are being challenged with a paradoxical conundrum: how do we retain our humanness? How do we maintain trust in a time of uncertainty? Below are three ways customer service teams must adjust in light of the global pandemic.
Empathy Is #1.
Companies who approach customer service with a deeper level of empathy are more likely to maintain loyalty and win new business. This concept is not a newfound revelation. In fact, The Empathy Business has studied the efficacy of empathy in business for years. And what have they found? Organizations that focus on the “emotional impact” they have on employees, customers, and society are valued higher and earn more than their counterparts.
In the world of COVID-19, empathy is even more desperately needed. Quarantine measures and social isolation mean a rise in loneliness and other mental health issues. Think about it this way: what if your organization delivers the only social interaction an individual will experience for a full day? Armed with that information, how should you change your customer journey?
Start small. Use your data. Study the way your customers use your tools and services. Where are they running into roadblocks? Where are you making your customers’ lives easier? Take note and adjust. Document your FAQs in an accessible location, like a Knowledge Base. Have the patience to clearly explain the nuances of your business and policies. Above all else, practice kindness in all of your communication.
“One-Size-Fits-All” Won’t Succeed.
As the pandemic spreads, we’ve seen a spike in conversations for many of our clients. And according to a recent survey by Kustomer, there has been a 17% increase in inquiries across industries. With this influx in communication, it no longer makes sense to force every customer to call the same number to contact your company. Instead, it’s time to get smart about the channels you employ to manage customer interactions, and it’s time to invest in a fully-fledged omnichannel experience.
But beware: you should avoid blindly adding new service channels without a strategy in place. Dig deep into your customer personas and understand their respective beliefs and behaviors. McKinsey notes that “not all customers are the same, and it’s how they differ in their behavior and preferences—particularly on digital—that should have an outsize influence on how service journeys are designed.” Keep this in mind, too: a small percentage of customers — classified as the “offline society” -— may suddenly be forced into adopting digital communication in light of shelter-in-place orders. Take these different customers into account when adapting your customer service strategies.
Automation Is a Necessity, Not a Luxury.
As we’ve seen an increase in the number of inquiries, we’ve also seen an increase in the need for artificial intelligence and machine learning technologies. Agents can become easily overwhelmed by an onslaught of new messages. AI can automate some of the more tedious tasks that those agents might encounter, thereby freeing their time for more important work.
Consider how you can deflect commonly asked questions and save your agents valuable time. Let’s say you’re an airline in today’s world. With the rise of the pandemic, you’re being flooded with requests for information about your refund policy. Instead of directing your team to answer each inquiry individually, you could use automation to serve up pre-written articles that align with the inquiry’s keywords. Not only do you protect your team’s time, but you also deliver a better customer experience as customers receive near-instant answers to their questions. Beyond that, unsuccessful deflections can provide a treasure trove of data to guide your future content.
Those who adapt and adjust their strategies now can influence their fate in the post-pandemic world. The opportunity is there. We have to be good stewards of our time and resources to capitalize on it.
The holiday season has only just wrapped, and it was arguably the most important season for any retail customer service organization. When there are issues with holiday orders, you encounter the very real possibility of unhappy customers who are angry and embarrassed by missing or incorrect gifts. Customer expectations are at an all time high, and organization must over-deliver during their greatest times of need. Whether it’s handling simple product questions in real time, proactively alerting customers when there is an issue with their order, or rectifying any subsequent issues upon delivery, customer service during the holiday season could be the difference between a lifelong customer and one lost to the competition.
How Peak Season Customer Service Has Changed
The digital and direct-to-consumer shifts have had a huge impact on retail customer service. While customers now expect instantaneous service on a multitude of channels, they also expect personal and helpful interactions, similar to face-to-face interactions with in-store associates. In fact, according to a recent Kustomer survey, 75% of consumers aged 25-34 said they expected personalized communications from retailers. And that is 15% higher than those 65 and older, meaning personalization is becoming increasingly expected with younger generations.
2019 Holiday Season Developments
Last year there was a drastic increase in multichannel shopping (both online and in-store), and multichannel support inquiries are also on the rise. That means consumers may start an inquiry on one channel, and finish it on another. Whether the channel switch is because they are on the go, or they didn’t get a prompt response on their original channel, multichannel inquiries can cause duplicative work resulting in agent collision as well as the unfortunate need for customers to repeat information. It is important that retailers have a strong omnichannel support solution in place for any peak shopping period. A true omnichannel support solution can integrate your combination of communication channels in order to capture the free flow of conversations across channels and display the data in a single screen. This ensures seamless transitions and consistent experiences from one channel to the next.
Peak Season Challenges
The constant struggle for customer service organizations during peak shopping periods is sheer volume. With more shopping comes more support inquiries, and businesses that don’t have a scalable strategy in place, supported by the right technology, may not be able to deliver on customer expectations. Additionally, many businesses hire “seasonal employees” to help with the busy periods that they must heavily train to ensure they provide a consistent brand experience. With software that does the heavy lifting for them, providing unified customer history in a single screen and delivering standardized responses via dynamic content, the onboarding burden during an already busy time will be lessened.
Additionally, with a high volume of inquiries, customer service organizations often have trouble prioritizing the most urgent or pressing issues, and simply stick everyone in a queue, which is often unbearably long. Retailers can use AI and automation to intelligently route the most pressing issues to the most appropriate agents, or even prioritize loyal customers.
Overwhelmed customer service organizations often fall into the unfortunate habit of delivering bare minimum support in order to complete inquiries as quickly as possible. It’s important to realize that during peak shopping seasons, your customers are also stressed out, and expect retailers to deliver on their usually stellar service just as thoroughly as they would on the slowest day of the year. That means delivering real-time support, on any channel they choose, in a personalized manner.
Overcoming Peak Season Challenges
Whether it’s Cyber Monday, Valentine’s Day or Back to School season, brands should not only have a scalable strategy in place, but also technology that enables them to be more efficient and effective. AI and automation can improve the precision and speed of service by automating repetitive, manual tasks. While there is always fear of losing personalization when using AI and automation, with the right platform, businesses can actually do the opposite. For instance, if a business leverages customer data properly, AI could ask personalized questions based on an individual’s purchase or browsing history. These interventions save time for both the customer and agent, and increase the time spent on the actual issue rather than information gathering and low-level support.
To learn more about the results from the 2019 holiday shopping season, and how to properly prepare for future peak shopping periods, download the full report.
It’s undeniable that customer experience is only becoming more central to business success. But being thought of as an “icon” in the customer service space is challenging. A surefire way to succeed? Prepare for the future now. According to a CCW Digital survey of contact center professionals, the future of customer service is already within grasp. Read on to learn what pros in the industry think, and how to prepare for the future.
Channels of the Future
Just fifteen years ago, the iPhone didn’t exist. Neither did Twitter. In five years, the landscape of customer service channels may be dramatically different. According to the CCW study, the phone will not disappear as a dominant support channel, with only 17% of respondents expecting its relevance to decline, however a digital transformation may come to fruition.
A whopping 84% of respondents believe chat and messaging bots will become more central to support functions in the next five years, showing the importance of AI to the future of customer service. But instant agent channels will also become more popular, with 81% expecting messaging to rise and 76% saying live chat will be imperative. Lastly, social media and connected devices will have a place in the future, with 68% of respondents saying social will become more important, and 60% saying connected devices are on the rise.
The Customer Service Intelligence Challenge
Did you know only 11% of consumers believe that organizations take their feedback seriously? It’s true, and it’s an issue that businesses are trying to solve.
Collecting great insights from customers is the leading contact center objective that companies want to achieve by 2025. Unfortunately, only 1% of organizations currently believe that their customer intelligence strategy is perfect. Why? Almost half (47%) of organizations have data scattered across various systems, showcasing the importance of a unified data environment to gather meaningful and actionable insights.
Other customer intelligence issues reported by businesses were: not collecting enough data (43%), not using data to personalize the experience (43%) and not doing enough to understand customer sentiment (40%). An additional 40% believe that they don’t even HAVE sufficient data to fully understand their customers.
The contact center of the future will have to leverage systems that unify data and give businesses a seamless way to analyze customer intelligence and take action on that intelligence.
Establishing Customer Service Objectives
Many of the “objectives of the future” are the ones we strive to currently measure, but may have difficulty achieving. In order of importance, customer service experts think the following will be the biggest objectives by 2025: reducing effort, consistency across touch points, proactively resolving customer needs, and collecting great insights.
These objectives highlight the importance of a true omnichannel experience, as opposed to a disjointed multichannel experience, and proactive support.
On the other hand, contact center professionals will not be prioritizing some of the traditional success metrics of days past, such as reducing call volume and reducing handle times. Instead agents will be empowered to focus on providing the best possible experience for customers, no matter how much time that interaction takes.
To read the full report, which includes a plethora of additional data, download here.
Customers have high expectations when it comes to the level of service they demand from brands. While the American Express Customer Service Barometer found that Americans are willing to spend up to 17% more on businesses with excellent customer service, the top reason most customers switch products or services is because they feel unappreciated by the brand. In fact, 33% of Americans are inclined to switch to a different company after a bad experience.
Unfortunately for companies, the cost of human support is high. Introducing artificial intelligence (AI) into operations is one way companies can control costs while improving their service abilities and maintaining the human touch that makes customers feel appreciated and valued.
What Is AI Customer Service?
While AI and machine learning may at first appear to threaten the customer service industry, they actually have the power to make customer service agents’ jobs less time-consuming and more fulfilling.
Integrated AI can instantaneously retrieve the data an agent needs, while the agent or support team deals directly with the human side of customer service. This eliminates the need for human agents to run multiple systems simultaneously to address customer inquiries. Rather than employ agents to work 24/7 in a call center, AI can be used to field and classify calls and messages so human agents are then able to work more reasonable shifts with increased efficiency and reduced physical and mental stress.
Through intuitive machine learning that constantly works to improve itself, AI allows companies to be present to the very best of their abilities along every step of the customer journey.
How Are AI and Machine Learning Being Used in Customer Service?
There are plenty of reasons why AI and automation should be loved, especially when it comes to customer service capabilities. Here are a few ways the technology is already being used:
Everyone has had the experience of needing a simple question answered by a brand, only to dread having to jump through customer service hoops just to get someone on the phone who may or may not have the answer. Conversational chatbots can make these conversations more seamless. Not only do conversational platforms help cut costs, they also can help your customer service scale and enable your agents to have more meaningful and productive conversations. By using chatbots to aid your live chat operations, your business will be able to engage customers in real time without the need for an around-the-clock staff.
Amazon, for instance, uses chatbots that leverage the data the company collects on all of its customers and their past orders. By allowing chatbots to access information about the customer’s past preferences, you can have the chatbot interact with customers up to the point where an agent is needed. Once the conversation is transferred to an agent, they can pick up where the chatbot left off.
Eventually, you can train your chatbot to not only acquire customer information, but also recommend the actions customers and agents should take next. If a customer simply needs a common question answered about a product they already purchased, the chatbot can direct them to a FAQ rather than contact an agent. This saves the human agent’s time and allows them to make better use of it dealing with more complex customer queries. All chatbot interactions can be automatically tagged in your AI system so they’re easy to track and reference, and can be used to improve future recommendations.
Robotic Process Automation
Robotic process automation (RPA) can be used to handle the necessary, but routine tasks that keep support agents from interacting with customers in meaningful ways. By taking care of low-priority, mundane tasks, RPA helps customer service agents reclaim time in their days that would be better spent handling high-value customers or fully addressing complex questions without feeling rushed.
RPA works across multiple systems to track user actions within an application to complete and perform tasks ranging from automatically replying to emails to routing conversations. The improved efficiency from saved time on menial tasks also saves companies money. Aside from cutting costs, RPA has the power to increase revenue by speeding up the rate at which customers are able to make purchases through your company.
In the past, automated phone systems performed data dips, moving customers through a phone tree where they were asked to “press 1 for a current reservation,” “press 2 for reception,” “press 3 to make a new appointment” or something similar. The flaw in this system is that the information collected was never handed off to the agent, and the customer would have to repeat themself once they were connected with a human. AI eliminates this unnecessary process — if a customer is calling about a product that’s discontinued, for example, there might not be a need for a human agent to talk to the customer only to relay that same information. This saves time for both parties by supporting your human customer service agent and saving the customer from exasperation.
Using AI to capture information about the customers and pass along only the absolutely necessary parts of that information allows agents to have more meaningful conversations and become more knowledgeable about the areas of the business that matter.
If a customer still wants to talk to a human even after discovering their product is discontinued, the agent can immediately begin the conversation by offering recommendations for other products the customer may like. AI doesn’t eliminate the need for humans, as many people incorrectly assume when they hear talk of using AI in customer service. Instead, it augments the human team and allows them to be better at their jobs.
Monitor Support Operations
When you use AI to monitor support operations, you can predict when conversations may start to turn from positive to negative. This insight allows managers to intercede accordingly, and no longer requires them to randomly audit customer service calls to regulate quality.
AI can also help monitor which responses result in reopened tickets. If response A, for instance, tends to resolve inquiries quickly, but response B results in the ticket repeatedly being opened, the system can recommend you eliminate response B in order to set your agents up for success. Managers and executives can use the data generated by AI to oversee customer service operations in a more clear, efficient way, improving day to day operations for everyone involved.
What Are the Advantages of Automated Customer Service?
Customer satisfaction is directly linked to the service experience, and so it’s important to make sure the customer journey is as seamless as possible. Integrating AI into your customer service isn’t about replacing humans. Rather, it is about arming your customer service agents with the information they need to have purposeful conversations with your customers, and using data to personalize your customers’ experience with your brand.
Incorporating AI customer service not only improves your relationships with your customers, it builds trust and increases brand loyalty. This means more repeat customers, and more word of mouth referrals for your business.
When you build an incremental strategy to roll out AI in your organization and optimize according to data collected, success is sure to follow. Using AI to build a more complete view of a customer’s relationship with the brand helps companies meet high expectations for exemplary service, and come across as anything but artificial.
Kustomer Offers AI Business Solutions
The Kustomer platform stands out among customer service solutions for the comprehensiveness of available customer data and its business process automation that is driven by branchable, multi-step workflows and custom business logic. Kustomer IQ is a groundbreaking new service that integrates machine learning models and other advanced AI capabilities with the Kustomer platform’s powerful data, workflow and rules engines to enable companies to provide smarter, more personalized, automated customer experiences with increased efficiency.
Kustomer IQ integrates machine learning, natural language processing, predictive analytics, deep learning and multi-dimensional neural network mappings as a part of its AI suite. Natural language processing involves the interactions between computer and human language, and dictates the extent to which computers are able to process and analyze large amounts of natural language data. Natural language processing is used along with text analysis, computational linguistics, and biometrics in sentiment analysis, also known as opinion mining, which helps companies keep a finger on the pulse of their target audience’s interests and values.
Companies that employ the AI suite are then able to use their own data to train Kustomer IQ’s predictive machine learning models, automatically customizing them to address their own business needs. With each new interaction and piece of data, these models learn and self-tune increasing their predictive accuracy and improving the decision making of both the models themselves and the customer service organizations using Kustomer.
Through Kustomer IQ, companies will be able to automate manual, repetitive tasks and essential processes of their customer service experiences. In addition, Kustomer IQ changes the way companies manage knowledge during a service inquiry by surfacing relevant insights and predicting future outcomes to enhance customer self-service, facilitate real time intervention through recommendations, and streamline proactive outreach. By automating everything and providing the right information at the right time, Kustomer IQ frees up agents to focus on more complex and emotional customer interactions, resulting in reduced costs and faster resolution of calls.
Features of Kustomer IQ include automated conversation classification, queues and routing, customer sentiment analysis, automatic language detection, suggested agent shortcuts, customer self-service, conversation deflection and workforce management. If you’re interested in learning more about Kustomer IQ and how it can help elevate your business’s customer service capabilities, download our ebook, explore our website and get in touch today.
Kustomer offers real-time, actionable views of customers, continuous omnichannel conversations, and intelligence that automates repetitive, manual tasks to make personalized, efficient and effortless customer service a reality.
When the conversation turns to AI, there’s often a Sci-Fi novel’s worth of terminology and jargon that the uninitiated reader has to decode. If you’re looking at using automation for service, then here’s a quick guide to the difference between AI, Machine Learning, and Deep Learning.
Artificial Intelligence as a concept has been around since at least the ancient Greeks, who designed some mechanical devices that could be loosely-termed as intelligent. However the term itself is around 60 years old, and the first applicable AI technologies have only just started coming to market in the last few years.
Machine Learning is a more specific subset of AI. It describes machines’ ability to learn from their mistakes and improve over time. A good example of Machine Learning in practice example is the recent Google AI that beat a world champion at Go. The more the AI plays, the better it becomes at spotting patterns and predicting its opponents’ moves.
Deep Learning is a further iteration of machine learning. It describes machine learning algorithms that run on multiple layers, mirroring how our own neurons function. A now common example of deep learning is the way that smart assistants like Alexa or Siri process speech.
Also important is Natural Language Processing. NLP is the ability for a computer program to understand human speech, regardless of slang or dialect. By being able to make sense of written or spoken language in the messy and error-filled ways humans normally express it, AI capabilities become much more applicable to everyday life.
What does this mean for service? Artificial intelligence and intelligent automation can take over existing tasks and create new efficiencies that your organization couldn’t dream of previously. Machine Learning is just one example. By suggesting responses agents can use to common customer queries, a partially-automated system could learn the most effective replies and language for your customer base. Deep learning capabilities should extend to IVR trees, and put an end to the common “Sorry, I didn’t get” response from many systems that currently rely on processing speech. And NLP is crucial for chatbots, and for analytics that look at all of the conversations your agents have across chat, social, and any other text-driven medium.
It’s important to build a solid understanding of these exciting technologies as they become more prevalent and relevant to the service and customer experience sphere. To learn more, listen to our webinar with Solvvy: The Truth About Bots and Intelligent Automation.
There are a lot of buzzwords gaining traction as we settle into 2018, but probably none are bigger than “Bot”. Particularly in the customer support arena, as companies look to further reduce the cost of serving customers. This has resulted in the rise of chatbots. However, no matter how good the technology, bots aren’t going to be able to resolve every situation or interaction anytime soon. That means that transferring from bot to agent will remain a crucial part of the chatbot experience. What do agents and customers expect when the time comes for them to be connected?
Chatbots are undoubtedly improving and becoming better at seeming human while collecting crucial customer information—their name, address, and description of the problem—and based on that they may be able to produce some initial solutions.
However, the risk companies face is that they give their customers flashbacks to the 90’s. That means an experience that’s identical to the Interactive Voice Response phone trees that end up connecting them to an agent who needs to ask all the same questions over again. How can brands prevent this? Here are a few suggestions:
Put Agents in the Driver’s Seat: Empower agents to select the right channel to engage with the customer and best resolve the issue.
Deliver Complete Context: The agent of the future will be a critical thinker. If you provide them with all the information they need about the customer story so they are well-informed of their profile and history, agents can craft some of their own dialogue based off of talking points from reference scripts. While this creates a more natural customer interaction, it also means that agents must be able to think on their feet and deal with possibly tense situations. Adapting to every situation and keeping calm and focused under fire is thus crucial for great customer service.
Give Agents a Heads-Up: Automate agent alerts based on changes to the customer’s status, order updates, or snoozes so they’re always aware and ready to connect.
Enable Empathy: It’s always great for agents to show empathy, but empathy is hard for any human being to deliver if they don’t understand the gravity of the situation. Brands can use tools like NLP (Natural Language Processing) to provide some insight into how the customer is feeling at the time of engagement, and know whether their outlook is positive, negative, or neutral.
Streamline Connectivity: Efficiency is still critical at the point of contact, but not at the detriment of communication skills. Create personalized shortcuts that don’t just display simplistic customer information like name and email, but provide details of their relationship such as recent items they’ve viewed, their current sentiment, their order’s delivery status, etc.
For brands to be successful in the future, the hand-off between bot and human needs to promote a differentiated experience. If your customers have to start the process all over again when they switch to an agent, then they’re better off just connecting with one in the first place.
However, if your customer can go from speaking with a bot to an informed and empowered agent, that’s a game-changer. If your agents are equipped with all the context and transaction information they need, then they’re well-placed to deliver a meaningful experience. Combining chatbots, automation, sentiment analysis, and a full view of the customer is what it takes to turn your agents into heroes and deliver next-level service. Instead of going from a bot to a human who’s asking mundane questions, doesn’t know anything about the customer, and is powerless to make a decision, they can be connected to a CX superhero.
Vikas Bhambri is Kustomer’s VP of Global Sales and Customer Success