The Connected Customer Experience: Leveraging Data to Drive Hyper-Personalized Experiences and Build Trust

To say we’re living in a customer-centric age is an understatement: companies who fail to prioritize the customer experience are outpaced by their CX-leading competitors by nearly 80%. Additionally, more than half of companies have experienced a serious drop in consumer trust, resulting in an estimated missed $180 billion in potential revenues, according to this Accenture study. There are numerous reasons consumers lose trust in brands they once knew, loved, and purchased from frequently, but 71% of consumers say poor customer service contributes to that trust erosion.

Unfortunately, many tactics that once served an organization well in engendering a customer-first culture simply fail to keep up with the enormous increase in both customer data, and use of connected devices. Two and a half quintillion bytes of data are created each day at current pace, and Gartner predicts there will be more than six connected devices per person as early as 2020. This device proliferation and increase in data results in an overwhelming number of touchpoints that must be tracked and connected to the customer’s buying journey. It’s a tall order, but the organizations who will win are those who can use all of this data to scale the customer experience quickly, efficiently, and effectively, and all on the customer’s terms. It’s not just enough to collect data: it needs to be the right data that can be acted on in the moment.

Working with the customer where they’re comfortable

The digital age has changed where, when, and how customers interact with a brand. What was once a simple cycle of seeing an ad, making a purchase, and repeating, has shifted into a looping journey with the potential for numerous friction points that can turn a customer away from a brand all too quickly. McKinsey describes this journey through four critical areas: consideration, evaluation, purchase, and post-purchase experience. Instead of assuming a consumer will immediately be faithful to the previous brand purchased, McKinsey states that today’s buyer continues to consider new brands available to them. McKinsey adds the element of the Loyalty Loop, which fast tracks future purchases, but in order for a brand to effectively qualify for this shortcut, they must have fostered lasting loyalty with the customer. And 95% of consumers say customer service is important in their choice of brand loyalty. In other words, helping a customer find the answer they need quickly is a significant indicator of whether or not a brand has continued ownership of that customer’s wallet share.

An additional complication is the increase in possible touchpoint locations: digital searches, email, social media, website, and more. In fact, 31% of millennial customers looking for help reach out to a company via Twitter. It’s important for an organization to connect all relevant touchpoints to a unified customer profile in the event of a customer service interaction, or they run the risk of further fracturing the experience and the relationship.

Brands must be willing to look critically at their existing systems to evaluate if they’re truly prepared to handle the significant amounts of data, devices, touchpoints, and the unified view necessary to provide a seamless customer experience. Tools driven by AI and machine learning are the only way to ensure a business can scale to keep pace.

The expectations for customer agents have never been higher; below are ways that AI magnifies data to bolster a support team so they can create optimal customer experiences.

Automate processes and tasks

KPMG has estimated that the service cost reduction with Robotic Process Automation (RPA) is as great as 75%. With the average cost of service centers continuing to rise — voice is $12 per contact, and live chat is $5 per contact — shifting resources to self-service through automation and a knowledge base can result in huge savings. Automation tools can decrease costs to just 10¢ per contact.

It isn’t simply the dollars and cents saved, however, that make automation so impactful to an organization. In one use case, automation can vastly improve worldwide organizations needing to route certain language speakers to agents who can communicate in that language. Additionally, by routing common questions and needs to a self-service portal or base that can both quickly and effectively solve a customer’s problems, agents are freed up to more quickly take on the more complex, nuanced issues that customers face.

While skeptics might be concerned about customers valuing human interaction above all else, according to this report from Statista, 88% of US consumers expect an online self-service portal. In fact, bringing numerous types of customer data touchpoints into one place — and from any resource — creates a more seamless, personalized experience for that customer. This method allows for both speed and a personalized approach to be achieved, and on the customer’s terms.

Augment existing agent support

When a customer dials into a service call center, provides significant information regarding who they are and why they’re calling, and is then directed to an agent for further assistance, the worst possible scenario is that customer then having to repeat all of that information…again. When considering a customer may have also reached out through email and even social media, it becomes even more crucial to use data in the right way. Much like being retargeted by an ad for a product you purchased yesterday, today’s customers are smart and expect organizations to be intelligent with their data. If, after interacting with a chatbot and providing all relevant data, a customer’s issue is escalated to a human agent, the customer expects an agent to already have the necessary context to properly manage the issue. That context should include relevant information like shipping number, previous conversations from both online and offline sources, and previous purchases made, combined into a unified customer profile.

Not only does the full customer data view aid with escalating issues directly, it can even be used to provide recommendations to the agents before even interacting with the customer. Through AI technology, an agent can be given an automated recommendation for how to best handle the customer’s request, eliminating both time and mismanagement; thereby improving the quality, time, and ease of service for both the customer and the agent.

When AI is used to capture data for context, the technology and the human agent become critical partners in providing the right customer experience. It empowers an agent to be a true specialist, who can change the customer’s outcome in a way automation cannot. The marriage between the two is what elevates the customer experience to a level that promotes long-term loyalty.

Proactively boost future outcomes

As a part of the new expectations customers have for service-related interactions, customers expect their preferred brands to be proactive in handling potential issues. For an organization this can be as simple as customer communication that informs of impending weather that will impact a shipment, or as sophisticated as predicting volume needed quarters in advance based on real-time interactions. In order to accomplish this, however, all relevant data must be gathered in a location where it can be acted upon quickly.

One use case could even enable leads and managers to get ahead of issues in-the-moment. For example, as a call is happening, the voices can be translated into text, then analyzed and graded in real time to measure key indicators that identify a call going south. Instead of arbitrarily choosing which calls to QA, or to QA all calls after-the-fact (and risk missing the ones requiring assistance), AI and machine learning can alert a team lead exactly when to jump in and improve the customer interaction as it occurs.

Antiquated technology looks reactively at improvement; the best customer experience requires proactive use of data as the touchpoint interaction occurs, rolling it into the most personalized experience possible.

Customers who have a good experience are three and a half times more likely to repurchase, and five times more likely to recommend to friends and relatives than those customers who have a poor experience. And 59% of respondents to the Microsoft State of global customer service report say that customer service expectations are higher than they were last year. In order for an organization to scale to meet the growing demand, they must provide a seamless omnichannel experience that connects all touchpoints, automates tasks and processes for maximum efficiency, and proactively uses real-time customer data to further create the best experience. Doing so will empower your agents, and build the trust your customers need to remain loyal for years to come.

Connecting all the data to relevant touchpoints and driving a hyper-personalized experience will change how your customers experience you and your product. Tune into our webinar with guest speaker from Forrester where we break down how you can create an elevated customer experience.

 

Leveraging Artificial Intelligence for Customer Service Without Losing the Human Touch

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:

Chatbots

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.

Agent Specialization

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.

 

Deliver effortless, personalized customer service.

Request Live DemoStart Interactive Demo