How to Bring an Intelligent Customer Experience to Your Organization

How to Bring an Intelligent Customer Experience to Your Organization TW

I was beyond excited. I had the perfect gift for my wife for our anniversary planned out. After doing some initial research I had an ad pop up on my Instagram feed that provided exactly what I wanted — a personalized canvas with our wedding song on it. I pictured my wife opening up the package on the day of our anniversary and being overcome with emotion. I was sure that I had “husband of the year” in the bag. Unfortunately, it didn’t work out as I had planned.

The order process for this personalized canvas was very straightforward. I specified how I wanted the canvas to look and provided the exact wording, the canvas size, and the design. It was three weeks until our anniversary so I believed I had plenty of time. I put in the order and they sent me an email that said it would take them 1-2 days to provide me a proof and then 1-2 days to complete the canvas before shipping it. It was exactly what I saw on their website before I ordered. I knew I was cutting things a little tight but wasn’t worried. After four business days, I approved the proof they sent me, I kept waiting to get the confirmation that my order was shipped. After four more days I emailed them on a Friday asking where my order was. I started to freak out as I was down to a week before our anniversary.

I finally heard back from them on the following Monday (as they don’t work on the weekends): “We are a little backed up on our orders. We had more orders come in that we weren’t prepared for “. While they were extremely apologetic in their response they were putting my “husband of the year” award in jeopardy. Two days later I emailed them again asking when my order would be shipped. They responded quickly that it would be shipped the next day and to my relief, it was. It’s too bad that it was shipped on the same day as our anniversary. My wife is very understanding and wasn’t upset. I was disappointed though as this whole situation could have been avoided. Organizations need to consider how they can be more proactive in their approach to the customer experience so they don’t let down their customers and create lifelong customers. This is at the core of becoming an intelligent customer experience (CX) organization.

What Is an Intelligent Customer Experience?

Intelligent CX involves leveraging the technology and data that exists today to create a better overall customer experience. This includes sharing data between the different teams such as marketing and customer service, creating new roles to act on the data, and leveraging new technology such as AI.

Eliminating the Silos

Too often, organizations suffer from a lack of communication between different functions such as marketing, customer service, sales, and manufacturing. The loser in all of this is the customer, and ultimately the business, as companies will lose potential revenue and customers.

Intelligent CX organizations have more open communication and data transparency which creates a more fluid transition between the discovery and buying customer journey stages. As an example, the manufacturing team at the customized canvas company should have informed the marketing and support teams that orders would be delayed. They then should have updated their website and order emails so I would be aware of any delays and sent proactive communication of these delays while I anxiously waited for updates. Instead, I was the one that had to reach out to their customer service team a few times for updates. The friction points that existed in my customer journey could have been avoided by breaking down the silos within this organization.

Use Data to Provide a Differentiated Experience

The second component of an intelligent CX organization is leveraging the data you have about the customer to provide a better customer experience. This was the first canvas that I was purchasing from this company, yet there didn’t seem to be an acknowledgment of that. I felt like any of their other customers. If this data was appropriately used they could have:

  • Proactively reached out when they realized that my order was going to be delayed
  • Routed my issue immediately to the next available agent
  • Provided me with an exclusive and personalized offer as a first-time buyer to help drive repeat business.

We’re seeing organizations with an intelligent CX mindset collect more data at each touchpoint. They are also creating new roles that combine CX and analytics to help deliver on an organizations’ CX vision.

Embedding Artificial Intelligence

The last component of an intelligent CX organization is applying AI to inject automation and machine learning into the customer experience. AI takes advantage of the data that you have and helps organizations act on it in ways that could never be done before. This not only generates additional revenue but can result in significant cost savings.

During the purchase of my customized canvas, AI powered technology could have detected a delay in the processing of my order and proactively sent me an email without having to reach out to the customer service team. Another example is having an AI-powered chatbot on their website that could have provided me with an updated status so I didn’t need to wait until Monday to receive a response. These examples are just a small slice of what AI can do. Smoothing out these areas of the customer journey by leveraging an intelligent CX mindset is what transforms a good customer experience into a great one.

The Time for Intelligent CX Is Now

We need to go beyond providing a great customer experience — customers are expecting more. Intelligent CX organizations break down the silos that exist between different departments, they collect more data and better leverage existing data, and they embed AI into their CX processes. This ultimately creates an extraordinary, frictionless experience for your customers that will result in brand loyalty and ultimately drive a more profitable business.

PS: While it was late, the canvas has a special place in our home and reminds my wife and me of our wonderful wedding.

How to Bring an Intelligent Customer Experience to Your Organization Inline

 

Keep AI From Feeling Like Sci-Fi With Our Terminology Guide

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.

Watch Our Webinar with Solvvy here – The Truth About Bots and Intelligent Automation

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.

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.

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