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.

 

To Bot or Not to Bot? Neither, Start with a Strategy

By Mark Kersteen from Kustomer and Maggie Lin from Solvvy.

Customer service automation is the hot topic of conversation these days, and more specifically, how bots fit into the mix. While intelligent automation is core to both Solvvy and Kustomer, we encourage our customers to not simply take an automation-centric or bot-centric approach, but to first take a step back and identify what your key goals are.

We’ve seen companies jump the gun and add a bot because they felt like everyone else was doing it, only to find it delivered sub-optimal/disappointing results.

In reality, not everyone is using a bot—but many are experiencing mixed results. In our recent webinar, 67% of participants shared that they aren’t using any bot technology today and 72% of participants who have tried a bot have experienced issues.

So, what can we take away from this? It’s important to see the big picture and identify where automation can add value, rather than implementing point solutions like bots and hoping they make an impact. We’ve all been there–it’s easy to get swept up into adding a piece of technology just because it’s what everyone else is doing.

In this post, we’ll tackle a key goal we’ve frequently seen from our customers: how do we increase agent productivity to improve our overall customer experience? We’ll share how intelligent automation can effectively support this goal in two ways by 1. increasing efficiency for customers and 2. increasing efficiency for agents.

Increasing Efficiency for Customers

Empowering customers to resolve questions on their own means agents handle less repetitive questions (and less tickets). This translates into getting back to customers faster and focusing on high-value questions that require the human touch. Increasing efficiency doesn’t have to be complicated. Depending on where you are in your support team maturity, ways to improve include investing in content, intelligent self-service, and end-to-end automation.

Content
While it seems obvious, when companies are scaling quickly, a lot of focus is given on agent enablement versus customer enablement. But, at the end of the day, customer enablement helps agents at scale. In an organization where customer interactions are often 1:1, investing in content is 1:many and scales with the business. Taking the time to create help center articles can save your support team hours of copy-pasting a macro that should be public to customers. Publishing content that helps customers find answers on their own frees up your agents to deal with more complex questions.

Intelligent Self-Service
Investing in content is step one. Intelligent self-service is the next step to making it easy for customers to discover this information. With intelligent self-service, it’s important to understand the underlying technology used to determine user intent. A lot of bots fall short here because they are keyword-reliant or rules-based and ultimately aren’t able to understand the context of a question and the relationship of words unique to a specific business. Self-service eases the workload for agents, but if a bot is falling short of expectations, it can create friction when a customer reaches an agent and has to repeat their question.

End-to-End Automation
The ability to fully automate repetitive transactions is a huge opportunity in customer service. These could be questions around order lookups, returns, refunds, and subscription changes. By handling these types of questions without an agent, support teams can direct attention to complex questions and take on proactive initiatives that scale. The interface for end-to-end automation can be guided steps, or it could be a bot in a chat window. Whichever way you deem the best customer experience, it should be clear that it’s not a human and that it is an automated experience.

Increasing Efficiency for Agents

We’ve spoken with agents who have expressed anxiety about chatbots or other technologies taking over their roles. It’s totally natural to be wary of new technology, but our answer has always been clear: We don’t think there’s anything to worry about. In fact, there’s a whole list of ways that bots and automation can assist agents and make their lives easier. Bots can take over the boring, repetitive, and mechanical tasks that drive agents up the wall, freeing up their time to focus on the interpersonal connections and more emotionally complex tasks that likely attracted them to the profession in the first place.

Conversational Forms
Just because it looks like a bot and acts like a bot, doesn’t always mean it’s a bot. Conversational forms are robots in disguise. When a customer opens the chat window, they’ll feel like they’re chatting to an immediately available agent. The conversational form will start asking the customer questions. These include important queries for identification—name, email address, phone number, shipping info, and whatever else is necessary—as well as more quantitative questions, like asking them to describe the issue they’re having. This way the customer gets the instant feedback they expect on chat, and the agent can jump into the conversation with all the info they need.

Suggested Responses
The scariest thing about pursuing a chatbot strategy is the lack of control. Once you switch on that feature, there’s nothing standing in between your customers and an algorithm that may not always provide the best experience. Enter suggested responses. This system works like the suggested text feature on your phone, but just for service. A computer processes the conversation and generates answers, but instead of sending them straight to a customer, the agent gets them first. This speeds up their reply time, and the system can also learn from the agent’s choices to become smarter. The more agents use the system, the better it gets at helping them, so you can be certain that automation is helping your experience, not holding it back.

Further Uses for Automation
There is a whole world of automation-enhanced solutions to everyday problems for your organization—and the majority of them work behind the scenes. Using an automated system to suggest tags, categorization, macros, and helpbase articles for your agents can save tons of time, and can be much simpler to set up and operate than a customer-facing chatbot. Assisting your agents’ everyday workflows and reporting may not be glamorous, but it can have a massive knock-on effect towards streamlining your experience and increasing efficiency. As Peter Johnson, Kustomer’s VP of Product, summarized in our recent webinar: “Automation is not just about helping the customer, it’s about helping your support organization scale, and identifying areas the product team can improve.”

Final Thoughts

If we can leave you with one bit of advice to take away, it’s this: before pursuing a bot or automation strategy, do your homework and consider your options. It’s crucial to have a strategy, and that you don’t just jump right in. As Kaan Ersun, Solvvy’s SVP of Marketing, advised on our webinar: “Number one, define a strategy, and figure out where the bot can be useful to you, where it won’t work, then pursue new opportunities. Start with the big picture, then move towards implementation.”

Look at the data available to you, and use that to define your strategy going forward. Take some time with your reporting, and see what the most common issues are and where customers are asking for support. Once you start spotting patterns, those can dictate where you’ll go next. Maybe something as simple as an updated help page or self-service tool can cut your service volume in half? If your agents are constantly doing the same things over and over again, solve for those issues first. If your goal is to increase efficiency, then you should be focusing on finding the best method, not using chatbots for their own sake. By looking at the patterns within your support organization, you can start identifying issues that are holding back your experience to dictate your strategy, which is great practice as a whole. That’s the best way to figure out how automation will fit in.

With the right groundwork, you can be certain that when you do start to explore and use new technologies, your efforts will be a success—and will make a meaningful difference for your customers.

The Truth About Bots and Intelligent Automation

84% of the attendees of our recent webinar, The Truth About Bots and Intelligent Automation, consider Customer Experience Automation a priority for their strategy going forward. What options are available for the automation-minded company, and will a bot deliver amazing service AND make you breakfast? Well, not quite. We got to the bottom of these questions on air, and you can too from the recap below.

Watch the recording here.

Peter Johnson, Kustomer’s VP of Product, and Kaan Ersun, Solvvy’s SVP of Marketing, are both authorities on bots, automation, and using intelligent technologies for better service and support. They discussed the pros and cons of the solutions out there, and made some suggestions for picking and enabling more intelligent service.

Is Automation a Priority?

To kick things off, we started with a poll to take the temperature of the audience. We asked, “Is adopting a customer experience automation solution, such as bots, a priority for you / your organization?” The results were surprising—the majority of respondents were actively pursuing an automation strategy. Here’s the breakdown:

Yes, this year: 40%

Yes, next year: 32%

Yes, within 2 years: 12%

Not a priority: 16%

Terms You Need to Know

To level-set, PJ and Kaan laid out an overview of the terminology they’ll be using when discussing this complicated technology.

While “intelligent” technologies have existed since Roman times, the term “Artificial Intelligence” came into use in the 1950s—though truly intelligent products just started becoming widely available over the last handful of years. Machine Learning is a more specific application, referring to the ability of machines to advance their program and “learn” from their mistakes without additional programming. A good example is the recent Google AI that beat a world champion at Go. Deep Learning is an even more advanced subset, describing computers that use algorithms that mimic the neural networks of the human brain—meaning they can learn on multiple levels without human supervision.

Bots—Are They All They’re Cracked Up to Be?

But how are these advancements being used on a practical level today? Bots are already taking on a variety of service and service-adjacent tasks within the enterprise, from Digital Marketing and DIY Service, to use cases involving virtual assistants. However, these experiments are still in their early stages. While they may help scale your service, they require a lot of effort to build, and lack customer understanding and the ability to deliver a quality, memorable experience. When you look at the cost and effort to build one versus the level of experience they provide, the math is a bit off.

As PJ put it:

“You’ve probably contacted or been contacted by a support system that tried to act like a human being, but clearly is not. One of our best practices is not to try to seem human, because it can really hurt your brand image and experience.”

On top of that, they aren’t exactly plug-and-play. Service teams have to create replies for every possible input, and they need to be customized for the relevant terminology and details of your business. Actually integrating them with your existing data systems can be a headache, plus they need ongoing maintenance every time you add a new feature or product.

Who’s Using Bots?

Bots may not be the tech overlords they’ve been billed as (yet), but other applications of automation and intelligent systems can supe up your support. And, it’s probably not too late. In our second poll, we learned that most attendees haven’t started using bots yet:

We asked, “What has been your experience with traditional bot technology in your CX operations?” and these were the results:

We use a bot today and love it: 9%

We use a bot today and have encountered some issues: 12%

We use a bot today and have encountered many issues: 12%

I don’t use any bot technology today: 67%

From Bots to Conversational Experience

Before you start experimenting with bots, it’s good to know your options. As Kaan recommended:

“It’s key to have an overarching, holistic automation strategy first—then you can deploy bots as point solutions.”

Bots are a part of this strategy, but not the only focus. Instead, you can also use automation in conjunction with other integrations and platforms to create a stronger experience. Conversational forms look like a chat, but can be used to gather customer info and issues before handing off to a more capable agent to handle the issue. A system that automatically suggests responses to agents works the same as a bot, but uses the added layer of human oversight to learn the right way to respond by tracking your agents’ decisions. And automation is useful for suggesting tags, categorization, macros, helpbase articles, and assisting workflow and reporting—all things that can speed up your experience and make it more efficient, without directly interacting with customers.

As PJ summarized: “Automation is not just about helping the customer, it’s about helping your support organization scale, and identifying areas the product team can improve.”

Kutomer and Solvvy work together to make conversational experience a reality. If you submit a question to Solvvy and can’t find the answer, you can choose to instantly open up a chat in Kustomer and get the answer. Kustomer’s conversational form then collects your personal information, then connects you with an agent who knows your whole customer history.

Where to Begin?

Where do you start the process of using automation or bots strategy if you haven’t already? Kaan had some advice: “Number one, define a strategy, and figure out where the bot can be useful to you, where it won’t work, then pursue new opportunities. Start with the big picture, then move towards implementation.”

Adding to Kaan’s advice, PJ suggested going straight to the data: “First thing: Look at your reporting, and see where you have the highest level of support volume. Look for patterns, see which questions your customers are asking, and what the most repetitive tasks are for your agents?”

If you’re taking a wide-angle approach and carefully planning your strategy, instead of leaping head first into messing around with a bot, your initiatives are much more likely to be a success.

You can always watch the recording HERE, and for more great insights into service, experience, and technology, follow Solvvy and Kustomer.

Live Chat: What Does a Modern Solution Look Like?

When organizations are considering a chat strategy, there’s a common debate over whether live chat or a messenger app is the right method to use for client communication. Both models have pros and cons, but technologies have evolved to make a hybrid approach not just possible, but effective. By blending both models together, you can test, collect feedback, and grow—and new tools make it easier than ever to take the best from each approach.

Read about Kustomer Chat’s new features here.

But before we define the benefits and drawbacks of each, it’s important to define the difference between “Synchronous” and “Asynchronous” messaging.

Synchronous Messaging:

This is commonly associated with “Live Chat”, where a customer can only maintain one chat “session” at a time with an Agent. These conversations only exist for as long as the customer is active or at least one agent is online.

Asynchronous Messaging:

This is commonly associated with email, social media, or SMS messaging. Within these channels, neither the customer nor the agent communicate in real time. This means customers can start a chat and come back to it an hour later without worrying about ending “sessions”.

What’s wrong with Live Chat?

Chat used to be confined to a website, where customers would wait for an agent to become available. If they got disconnected or refreshed the page, the session would end. To keep customers from waiting after sending their chat message, many organizations would disable the chat experience on their site whenever agents weren’t available. Once connected to an agent, customers would have to stay confined to their desk chairs chatting back and forth until they resolved their issue.

The Old Version of Live Chat: Pros and Cons

  • PRO: Customers get instant replies and immediate feedback, which sets that expectation going forward.
  • CON: The “session” philosophy means a customer can’t message you from their computer, and then respond to you from their mobile phone.
  • CON: Normally works based on “agent availability” meaning that if agents are maxed out or not available chat is removed, and you are asked to leave a message or worse, the website hides chat completely.
  • CON: Missed/Dropped Chats immediately stop a conversation and require everyone to start over.

Why Have Messaging Apps Replaced Live Chat?

With the introduction of smartphones, app-based communication shifted customer expectations. They could open an app, click “contact support”, and start a conversation, but didn’t have to wait around for a reply. When a reply did come, they’d get a notification to check it and keep the conversation going. This allowed customers to move freely from a desktop to their mobile app if they needed to get up and grab a coffee, for example. The ease of use across any device lead to a natural shift from the need to be “live” to customers becoming accustomed to asynchronous messaging within third-party apps.

Asynchronous Messaging App: Pros and Cons

  • PRO: Customers can start a chat from their computer and finish it from their smartphone.
  • PRO: The app is always available as a means to collect and store customer issues while “offline”, which agents can follow up on later.
  • PRO: Past chat conversations can be stored and replied to for context.
  • PRO: Customers don’t expect instant replies.
  • CON: Conversations are never “closed”, making it hard to measure agents on that metric.
  • CON: Conversations with customers are dragged out over a longer period of time, slowing down resolution times.
  • CON: Customer can always reply to old conversations, which can make it harder to follow up and provide timely or quality support.

While asynchronous messaging has become more popular, there are some great concepts that underlie Live Chat functionality, like using Agent Availability to set expectations. Instead of completely removing the experience of chat from your site when agents aren’t available, you can collect customers’ info and issue, and then pass them to another channel for follow-up—setting the expectation that a reply will not be live.

Modern Chat Gives You the Best of Both Worlds

Ideally, you can bridge the gap between these kinds of synchronous and asynchronous messaging by providing a customer the ability to chat live with an agent, but maintain an asynchronous state when agents are not available or over-capacity by shifting the conversation to channels like email or text messaging or setting expectations about your reply times.

Customers need a fast response to get an answer or complete a sale—like asking about clothing sizes on a retail site—but you can’t always provide 24/7 communication. That’s why your chat tool needs to evolve to combine the best features of synchronous Live Chat and an asynchronous Messaging App. Kustomer chat is always on, allowing you to set business hours so that customers have the right expectations. That makes it easy to provide synchronous chat when agents are available, and asynchronous when they’re not. The history of every conversation is saved across platforms, so it’s easy for agents and customers to move from platform to platform for a fully omnichannel chat experience. The option to close conversations makes chat support more efficient and easier to manage and measure, and because everything is tied to the customer, agents have all the necessary conversation when they start a new one. Modern chat solutions meet the expectations of your customers and the needs of your business—and with Kustomer Chat, you can deliver the best possible chat and messaging experience.

Kustomer’s Chat makes it easy to deliver the experience that’s right for your team and organization. To learn more about our latest additions to our chat offering, read our product update here.

Product Update: Making Live Chat Scalable

Online chat is everywhere these days, but many companies are still figuring out how best to manage chat conversations at scale. This is why Kustomer’s Chat function—available over both web and mobile—now includes a few features that makes life easier for support teams.

End Chats

Kustomer Chat now includes the option to “End Chats”. Now agents can permanently close a chat conversation once it’s over. This will happen when an agent marks a conversation as Done—locking the ability for a customer to type a reply back to the chat (they can always open a new chat, of course).

This also adds an “end chat” button to the customer experience, allowing the customer to end a conversation when they are done communicating and notifying the agent. In addition, it sets customer expectations regarding agent availability, so customers aren’t replying to chat messages when agents are not available.

Single Chat Sessions

Another option available to companies is to allow only one chat conversation from a customer at a time. This feature ensures that every customer is matched with one agent, and limits the number of teams the customer may work with at once.

Chat Reporting

Support teams that choose to activate the above feature will also be able to better track open and closed conversations and more accurately analyze their chat volume. For example, if you collect the contact reason, every conversation will (usually) have a single reason for contact (unlike chat conversations that are opened again and again, for a different reason each time), so you can look at a definitive number of closed chat conversations per specific topics.

Kustomer Chat is always evolving. Our latest updates to Kustomer Chat include:

  • Conversational Assistant: a pre-conversational feature that helps your team collect information from customers automatically, before reaching the agent.
  • Chat Availability: a feature that incorporates Business Hours into the chat experience, allowing admins to determine what the after-hours experience will be for chat users.
  • Chat Deflection: helps to set proper expectations for your customers with estimated reply times, and diverts traffic when your Chat team doesn’t respond by directing your customers to other channels.

Kustomer Chat is used by online marketplaces and direct-to-consumer brands like Slice, Zeal, UNTUCKit, LOLA, PetcareRX and more. Schedule a demo below to see how Kustomer Chat can work for your business:

Rie Yano and Randi Zuckerberg on What’s Really Next for Retail

Material World is exactly the kind of brand that’s shaping the future of retail. As millennials move towards acquiring more pre-owned, high quality goods and away from Fast Fashion, smart resellers and digital-first marketplaces are snapping up market share. This shifting landscape was a big topic at our Future of Retail Summit, but we got to continue the conversation with one of our panelists—Material World’s co-founder Rie Yano—on Randi Zuckerberg’s Dot Complicated radio show on Sirius XM’s Wharton Business channel.

Randi opened the conversation with some stats, “Despite the press on store closings, data shows a net increase in store openings of over 4,000 in 2017 and sales have increased more than 3% per year since 2008. More and more hyper-customized concierge and on-demand services like ultra-fast delivery, digital dressing rooms, and robot customer service are becoming part of the CRM norm.”

There’s no doubt that customer behavior is driving the future of retail and relationships. Rie and Alon discussed the implications of these big picture changes. Find some of the highlights from Alon and Rie’s conversation with Randi below.

The Future of Brick and Mortar

While there’s so much focus on etail, traditional store-centric retail still has a huge place in the market. However, brick and mortar is changing, and needs to account for the digital experience. To make sure that the buyer journey continues and that the experience is relevant and consistent, you need the total customer view:

The Future of Subscriptions

Subscription business models are supplementing or even replacing both brick and mortar and etail channels. Customers love the convenience, and it’s a unique way for retailers to build loyalty. Material World has recently started offering a Material Box: the service delivers a pre-owned outfit styled by a designer straight to your doorstep, which you can also use to donate items of your own when you send it back. Subscription is proving to be both a great way to provide an easier shopping experience to your customers, and to learn more about them and build a deeper relationship. 

The Future of Bots

Automation is a useful way to cut down on the number of basic inquiries coming through to your customer service representatives, giving customers the fast responses they expect. However, the experience can only be taken so far. Instead, bots have to amplify your agents’ abilities, not replace them.

What’s Next?

We can’t be sure of what the future holds, but from our conversation with Rie and Randi, we’re pretty sure it’s going to include way better experiences for customers—whether they’re in-store, online, or somewhere in between. That, and dancing. Lots of dancing.

To listen to the full episode, head over to SiriusXM.

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