Why Artificial Intelligence is an Agent’s New Best Friend

Let’s face it. The job of a customer service agent is not an easy job. In a typical contact center, agents are on the phone or communicating through other channels; email, text, web chat, Facebook Messenger, WeChat… When customers reach out to an agent, they need help and are often upset. They may be asking about a lost bag or debit card, a flight that was canceled or a delayed package.

What’s important to remember is customer service agents dedicate their daily professional career to helping people solve these problems. As a company’s brand ambassadors, they are the guardians of a business’s customer relationships. A customer’s impression of a company is directly related to their experience with the contact center and its agents. And since customer experience directly affects revenue, it’s about time an agent’s job got a little easier and more enjoyable so they can better serve the customer.

Unfortunately, in many contacts centers, customer service agents are asked to do more with less and do without the best technology. Imagine taking a calculator away from an accountant? They couldn’t do their job to the best of their ability. When we don’t provide agents with the best technology, they can’t possibly meet customer expectations. The result? Poor CSAT and agent attrition, which is very costly and part of what gives contact centers the black eye of being a cost center. It’s time things changed for both the agent and Customer Service departments.

Enter Artificial Intelligence (AI.) There’s a lot of hype about how AI is going to shape the future. It’s often portrayed in Hollywood as robots taking over the world and at the very least, replacing jobs. The truth about AI and machine learning is that when it’s implemented responsibly it actually allows humans to focus on the type of work they actually enjoy. Take for instance the factory floor. During the Industrial Revolution, automated machines began doing the repetitive tasks human get bored with. There are still many people working in factories, but with automation, people can focus on performing critical jobs where their intelligence is required.

In the Fourth Industrial Revolution, where technologies like AI are driving change, the trend for contact centers is similar. Rather than taking jobs away, AI is blending the best of human intelligence with AI, to provide improved, seamless experiences and at the same time improve agent’s job satisfaction.

How does AI help customer service agents? First, AI bots take the burden off agents with intelligent, satisfying self-service by resolving routine customer requests. And when a customer wants to talk to an agent, the hand-off is easy. The interaction with the bot is transferred into the agent console so the customers don’t have to repeat what they did with the bot. This reduces agent’s average handle time because all the agent has all the customer interaction history. And first contact resolution is increased because the agent can quickly resolve the issue with the proper information and context. All of this adds up to increased agent job satisfaction and morale by off-loading routine, boring requests to the AI bot.

Now let’s say a customer wants to talk directly to an agent without interacting with an AI bot. AI prioritizes and classifies cases to help your agents quickly understand what the customer needs to provide a more effective and efficient experience. AI collects information about the customer and the context of their request resulting in a more connected, personalized experience. And AI increases agent productivity with a world-class, omnichannel service experience with full context to customer’s request.

As a result, agents have a sense of accomplishment by working on more challenging customer issues because the AI bots handle the routine questions. And when agents directly help customers, they are able to build loyal relationships because they are enabled by the best technology possible. And at the end of the day, the customer, their needs and exceeding their expectations is what great customer service is about. That’s all a customer service agent wants to do and it’s time contact centers gave the best possible technology to make this real.

@DrNatalie, Service Cloud, Salesforce

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Five Steps to Strategically Choose AI For Customer Service

AI and customer service; it’s a hot topic! But when I talk to customers, they say, “I get there’s something to AI and customer service, but how do I deliver business results and value with AI?” If you are wondering how AI can move the needle on the metrics you get measured on every day, below is a short guide on how to get the best business results when considering AI as part of your Customer Service strategy.

Step 1: Focus AI on Metrics that Matter
Implementing technology without strategy makes us do the wrong things faster! Or at the very least, spend a lot of money without getting business results. Studies show while 80% of executives want to use AI in their business, only 20% have an actual strategy. (1) First step? List the strategic metrics you are measured on every day and then think about how AI can help you to:

    • Reduce call volume (call deflection via satisfying self-service)
    • Increase First Contact Resolution (FCR)
    • Reduce Average Handle Time (AHT)
    • Increase agent productivity, morale, retention (reduce attrition costs)
    • Improve CSAT, NPS, customer lifetime value (CLV)…

With that information, you can begin to strategically decide how to use AI to transform your customer service operations and KPIs, positively impact customer and agent experiences and directly affect your bottom-line.

Step Two: Closely Compare AI Solutions
Artificial Intelligence isn’t new, so what is? Exponential advances in AI capabilities, as well as, integrated and packaged AI solutions. These new capabilities are allowing AI to become a real business asset, but not all AI solutions are created equal. A classical approach to AI requires specialized teams of:

    • Data Scientists (to sample data, build the models, tune them for accuracy)
    • App Developers (to build it into the customer service application)
    • UX Designers (to make the interface to the AI user-friendly)

These dedicated resources are expensive and difficult to find. And AI development is not the core business focus of a contact center. The result? Wasted time and money and longer time-to-value. When choosing an AI solution, look to see how much work is required on your end to implement a solution. To make AI intelligent, all systems require some work. The question to consider is how business/user-friendly is the application? Do you need to hire a team of AI and UX specialists to get benefits in customer service? Or can you deploy the AI quickly and get back to focusing your efforts on delivering amazing agent/customer engagement?

Step 3: Consider an Integrated Solution
Data. Data. Data. AI is all about the data. So, consider where the data in customer service resides. The customer record data lives inside the CRM platform. And the customer interaction history lives inside the customer service application. Since AI is only as good as the data it interacts with, look for an “AI-inside” solution; one where the AI is built into the CRM platform and the customer service application.

And it’s even better if the same AI solution is also integrated into other applications that Sales, Marketing, E-commerce and other parts of your business. The more contextual, historical information about that customer across all your departments, the more intelligent the AI is and the better business results it can provide. With an integrated AI solution that’s built for business users, there is no need for specialized implementation teams because the:

      • Data is already prepped
      • Models are automatically built and
      • AI is already integrated into the CRM platform and the customer service application.

What does this mean to you? Faster time-to-value. With an integrated solution, AI can easily learn from the customer data to deliver contextual customer/agent answers. So, whether you are delivering self-service or agent-assisted service, intelligent AI service is satisfying service.

Step 4: Select an Agile Solution
How many times have you wanted to change something in your customer service application, but don’t because it’s so difficult — even though it would transform the agent/customer experience? This is where using an agile platform and application leads to faster, better business results.

As you are considering adding AI, ask yourself, “How quickly/easy is it to make changes to the AI solution, as well as to the agent and self-service application?” Look for a drag and drop integration layer that allows for easy configuration of process flows to set up customer service AI. This way AI isn’t some futuristic “ideal” that sounds good when you say it fast but in reality, it takes forever to drive business results. Consider choosing an integrated AI customer service platform/application that is agile – so you can make changes on the fly to quickly deliver on the brand promise of great customer experiences.

Step 5: Use a Solution with a Pre-built UI/UX
And while this step is listed last, it may be one of the most important. Why? New technology often becomes “shelfware,” i.e., technology that is owned or licensed but not utilized because it’s difficult to implement, use or change.

A solution with a prebuilt UI/UX interface makes the customer-facing interaction intuitive, which builds trust, so customers use it. The last thing you want is a throwback to “bots of yesteryear” that didn’t have the advantage of AI or a poor user-interface. If customers can get their answers using a bot, call and email volume will be reduced, while delivering a great experience.

And for the agent? Key to great service is empowering agents with the best possible tools. AI integrated into the agent desktop and console classifies cases and identifies key information needed to serve the customer even before the agent gets the case. And then enables the agent with the best possible next actions. AI is not meant to replace agents, it’s meant to empower them to deliver exceptional customer service.

The most important thing to take away? When considering AI for customer service, focus on what you do best, delivering great experiences. Avoid choosing systems that require you to become or hire a bunch of rocket scientists. Choose a solution designed to allow customers to easily get the answers they need on their own, your agents to intelligently engage with customers and for you to deliver business results that matter.

References:
Is Your Business Ready for Artificial Intelligence? MIT Sloan and BCG Study, 2017
https://www.bcg.com/Images/Reshaping%20Business%20with%20Artificial%20Intelligence_tcm30-177882.pdf

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Guest Post and Infographic: How AI Changed Customer Service

You can make or break your business’s future by how well, or how poorly, you treat your clients. But you have a lot on your plate, too—even more as you manage the endless technology needs that seem to be developing overnight.


    That’s why artificial intelligence (AI) aspects of customer service are so crucial to how well (or how badly) we do business in the future. You may not even realize how many of these AI elements are already in place in your interaction with companies—and that number is only going to increase.


    Right now, about 2 out of 5 enterprises in the United States are already using AI in some form; that number is going to grow to about 3 out of 5 just this year alone. The human touch still matters, but the bots are already here. What does that mean for you and your business? This graphic explains it.

Infographic source: https://www.salesforce.com/hub/service/how-ai-changed-customer-service/

AUTHOR:

Amanda DiSilvestro gives small business and entrepreneurs SEO advice ranging from keyword research to recovering from Google Algorithm updates and changes. She writes for the nationally recognized SEO Company HigherVisibility that offers online marketing services to a wide range of companies across the country.

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Want to Know More About Machine Learning and AI?

Wondering whether you should invest in AI and Machine Learning? That’s a question that the most innovative companies are considering. Why consider it? One good reason is because your competitors have already started. If that doesn’t give you some reason to get motivated, I hope you get started before you are put out of business. To make sure that doesn’t happen, there are  a few things to consider to help you start to explore an investment in machine learning.

It’s the Data, Stupid

Of course, as with any business initiative, you’ll want to create value. And this can be done using machine learning systems. But for those systems to provide value, companies will need to begin by evaluating their organization’s data maturity, but more importantly their readiness to accomplish its data-driven goals. Company’s need to start with an audit of their data warehousing, data scientific research capabilities, data governance and data hygiene. In addition, it’s important to look at the sources, uses, volume, and veracity of all your date, meaning your first-, second-, and third-party data.

Garbage in, Garbage Out

Why is making sure your data so clean? Machine learning is basically taking a computer and making it smart enough to learn from the data it’s fed. We are essentially programming machines to learn. The goal is that after a certain point of time, the computer is able to predict further data. How so? Let’s pretend you want to make your computer predict the weather. So to begin, you might feed the computer weather reports of every hour of every over the past year. What you might end up with is– because the temperature (z) depends on day of the year (x) as well as the time of the day (y), more than two-dimensional curve. In fact, weather is random, so the equation generated by the computer won’t just have 3 variables (x, y, z), it may also have higher powers. So depending on the number of factors in a prediction and the randomness of the outcome, the complexity of the curve can increasingly get more complicated.

So back to the data… And I know you know the story about data: garbage in, garbage out. So hopefully, now you see can why good, clean data is so important to prediction. As the computer is taking the data you feed it to make future predictions, those predictions dependent on the data you are feeding it. So you want the very best data possible. And it takes super computers which are capable of handling large volumes of data, as well as the ability to learn fast and to make fast decisions based on the learning it under goes.

AI and ML Are Not The Same

Often times Artificial Intelligence (AI) and Machine Learning (ML) are used interchangeably. But they are actually different. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart.” Machine Learning is the application of AI based on the idea that we should be able to give machines access to data and let them learn for themselves. Artificial Intelligence devices (devices designed to act intelligently), are often classified into one of two groups: 1) applied and 2) general.

Applied AI is far more common. Applied AI is about systems designed to intelligently trade stocks and shares or drive an autonomous vehicle. Generalized AI is may up of systems or devices that, in theory, can handle any task. And are less common. However, this is where some of the most exciting advancements are happening today.

Deep Learning is A New Area of Machine Learning Research

It was introduced with the objective of moving Machine Learning closer to one of its original goals: that of being Artificial Intelligence. So essentially Deep Learning is a subfield of machine learning concerned with the algorithms inspired by the structure and function of the brain called artificial neural networks. Deep learning has worked it’s way into business language via Artificial Intelligence (AI), Big Data and analytics. Deep learning is an approach to AI which shows great promise when it comes to developing the autonomous, self-teaching systems which are revolutionizing many industries.

The Two Big Ideas: It May Be Possible To Teach Computers to Learn and The Internet is a Source of a Ton of Data

Arthur Samuel, in 1959 is credited as the one who came up with the big idea that it might be possible to teach computers to learn for themselves. That would be in contrast to teaching computers everything they need to know about the world and how to carry out tasks. The second big idea was that the Internet, with huge increase in the amount of digital information being generated, stored and could be used for analysis. So the scientists and engineers realized it would be far more efficient to code computers to think like human beings, and then plug them into the internet to give them access to all of the information in the world.

Neural Networks Are Algorithms

Neural networks are a set of algorithms, modeled loosely after the human brain and designed to recognize patterns. The development of neural networks has been key to teaching computers to think and understand the world in the way we do, in addition to the innate advantages they hold over people such as speed, accuracy and lack of bias. So a Neural Network is a computer system that classifies information in the same way a human brain does. It can be taught to recognize, for example, images, and classify them according to elements they contain. It works on a system of probability – which means that based on data it’s fed, it is able to make statements, decisions or predictions with a degree of certainty. The addition of a feedback loop enables “learning” – by sensing or being told whether its decisions are right or wrong and then can modify the approach it takes in the future.

What Can Machine Learning Applications Do?

Machine Learning applications can read text and work out whether the person who wrote it is making a complaint or offering congratulations. They can also listen to a piece of music, decide whether it is likely to make someone happy or sad, and find other pieces of music to match the mood. They can even compose their own music expressing the same themes, or which they know is likely to be appreciated by the admirers of the original piece.

These are all possibilities offered by systems based around ML and neural networks. The idea is that we should be able to communicate and interact with electronic devices and digital information, as naturally as we would with another human being. And another field of AI – Natural Language Processing (NLP) – has become an exciting area of innovation in recent years, and one which is heavily reliant on machine learning. (And yes, my initials just happen to be NLP, but that doesn’t really mean anything… just a happy coincidence…)

Where is Used?

Take Google for instance. Google is using it in its voice and image recognition algorithms. It is also used by Netflix and Amazon to decide what you want to watch or buy next. And it is also being by researchers at MIT to predict the future.  While Machine Learning is often described as a sub-discipline of AI, we might look at Machine Learning as the state-of-the-art of AI. Why? Perhaps because it is showing the greatest promise to provide tools that industry and society can use to drive change.

More on the practical uses of AI and ML in the future. For now, noodle on that!

@drnatalie

VP, Program Executive, Innovation and Transformation Center

 

 

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Facebook’s Artificial Intelligence Has The Ability to Search Photos by Content

The term artificial intelligence was coined 60 year ago. But now its starting to deliver. Lumos’s computer vision platform was initially used to improve the experience for visually impaired members of the Facebook community. Lumos is now powering image content search for all users. What does this means to you? You can now search for images on Facebook with key words that describe the contents of a photo, rather than being limited by tags and captions.

How does this work? It starts with the huge task of computational training. For the object recognition used in Facebook’s image search, the artificial intelligence (AI) system started with a small set of 130,000 public photos shared on Facebook. Using the annotated photos the system could learn which pixel patterns correspond to particular subjects. It then went on to use the tens of millions of photos on Facebook. So what this means is that the caption-reading technology trained a deep neural network though public photos shared on Facebook. The model essentially matches search descriptors to features pulled from photos with some degree of probability. You can now search for photos based on Facebook AI’s assessment of their content, not just based on how humans happened to describe the photos with text when they posted them.

How could this be used? Say you were searching on a dress you really liked in a video. Using the search it could be related back to something on Marketplace or even connect you directly with an ad-partner to improve customer experiences while keeping revenue growth afloat. So it seems it can help both customers, customer experience and companies selling things as well as ad partners.

What else is new? Facebook released the text-to-speech tool last April for visually impaired users so they could use the tools to understand the contents of photos. Then, the system could tell you that a photo involved a stage and lights, but it wasn’t very good at relating actions to objects. But now the Facebook team has improved that painstakingly labeling 130,000 photos pulled from the platform. Facebook trained a computer vision model to identify 12 actions happening in the photos. So for instance, instead of just hearing it was “a stage,” the blind person would hear “people playing instruments” or “people dancing on a stage” or “people walking” or “people riding horses.” This provides contextually relevancy that was before not possible.

You could imagine one day being able to upload a photo of your morning bagel and this technology could identify the nutritional value of that bagel because we were able to detect, segment, and identify what was in the picture.

So it seems the race is on for services not just for image recognition, but speech recognition, machine-driven translation, natural language understanding, and more. What’s your favorite AI vendor?

@Drnatalie, VP, Program Executive, Salesforce ITC

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Amazon Go – A Retailer Using AI, ML and Vision Technology

The idea of Amazon Go is to weave into the shopping experience the capabilities of deep learning algorithms, Artificial Intelligence (AI), Machine Learning (ML) and sensor vision. A practical application of AI and ML is Amazon Go via advanced shopping experiences. The ability to walk into a store, grab what you want and walk out, never having to wait in-line: no checkout lanes, no registers. For many customers, especially after work when they are tired and just want to get home or during the holidays could be a much better customer experience.

So how does this work? A customer opens up the Amazon Go app on their smart phone and scans their personalized bar code as they walk into a store. The phone goes into your pocket or purse and the customer begins their shopping. As the customer picks up a product, it’s added to their total. If a customer decides they don’t want an item, replacing back on the shelf removes it from their total.  Amazon Go calls it “just walk out technology” for the modern shopper. Once you are done shopping and leave the store, the total is calculated and charged to the customer’s amazon.com card.

From the customer’s point of view, while on-line shopping has increased, some customers still like the idea of going to a store and touching / seeing the merchandise. To help ensure that brick and mortar stores don’t turn into showrooms (where customers go to look at merchandise and then search on their phones for a better online deal (from that or other retailers) and buy it online while standing in their store, technologies like Amazon Go provide convenience. Perhaps the thought and the hope is that the convenience will be more important than searching for a cheaper price and buying it online.

Showrooming can be very frustrating for brick and mortar stores and put some of them out of business. It’s interesting that the online and offline shopping worlds are colliding. Fresh goods have a short shelf-life and often thought of as poor candidates for online shopping because of their perishable nature. However, it’s a high margin area that Amazon wants to tackle by using brick and mortar stores and the convenience of shop and go. Younger generals don’t have the tolerance for standing in line.

The future of shopping is just getting more and more interesting as the new technologies get implemented.

@DrNatalie Petouhoff, VP and Principal Analyst, www.Constellationr.com

Covering Customer-Facing Applications

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Capgemini Collaborates with Celaton on Artificial Intelligence in the Cloud

What’s the Partnership Between Capgemini And Celaton Mean to Your Company? Capgemini, consulting, technology and outsourcing services, has announced a new global collaboration with Celaton, a specialist Artificial Intelligence (AI) company, to license and use its inSTREAM, cognitive learning technology. The 3 year contract, signed between Capgemini and Celaton, will extend Capgemini’s already strong automation capabilities, help to drive further efficiencies and add Artificial Intelligence to Capgemini’s Business Services solution portfolio.

What Does Celaton’s inSTREAM Software Do? It streamlines the handling of unstructured unpredictable (and structured) content such as correspondence, claims, complaints and invoices that organizations receive by email, social media, fax and paper. This minimizes the need for human intervention and ensures that only accurate, relevant and structured data enters business systems. Unique to inSTREAM is its ability to learn through the natural consequence of processing information and collaborating with people. Capgemini’s extensive knowledge and experience in business process services will also enable Celaton to accelerate and improve inSTREAM’s capabilities.

What Will The Partnership Provide For Clients? The cooperation will enable Capgemini to increase efficiency, shorten turnaround times and enhance quality in areas where incoming documents and queries need to be processed, improving overall customer satisfaction. At a time when more and more customers expect the use of AI and modern automation tools, the alliance will help Capgemini’s Business Services advance their market leading use of automation and AI for its core business. Earlier this year, Capgemini introduced an Autonomic Platform-as-a-Service (PaaS) offering founded on best of breed technologies to deliver intelligent automation solutions on-demand for enterprises. The Autonomic PaaS aims to improve the predictability of organizations’ operations across their infrastructure, applications and business processes. The Celaton agreement is a further commitment from Capgemini to develop advanced client solutions using intelligent automation, cognitive and AI technologies.

Is This Offered in a SaaS or Cloud Mode? The addition of Celaton inSTREAM expands Capgemini’s Business Services’ extensive Software-as-a-Service (SaaS) portfolio with an artificial intelligence-based processing solution for incoming unstructured content –which is driven by its global automation Centers of Excellence. It is an important element in ensuring the delivery of maximum value to its customers.

Notes From The Executives: Lee Beardmore, VP and Capgemini’s Business Services Chief Technology Officer said, “There is significant industry debate on how cognitive computing and artificial intelligence will impact the BPO market. We are taking our delivery from debate to global implementation and are proud to partner with Celaton as a leading vendor in the business process AI space. Building on the introduction of Capgemini’s Autonomic Platform-as-a-Service, Celaton’s technology extends the penetration of cognitive computing into our delivery of business process services.”

Andrew Anderson, CEO of Celaton said, “I am delighted that Celaton and Capgemini have committed to this global partnership. The transformational impact of AI has been proven with many organizations and yet this emerging technology is often greeted with scepticism. Capgemini’s global reach and credibility will have an impact on the perception and adoption of AI and I’m very excited that Capgemini’s customers will soon be able to realize its significant benefits.”

My POV: AI is very important to the emerging capabilities of company’s to add cognitive computing into the delivery of business processes of discerning unstructured content. And with social and digital content abounding, there is no storage of unstructured content. And there is unlimited potential in the value of this unstructured content if it can be harnessed. This duo will give brands that opportunity.

@DrNatalie Petouhoff, VP and Principal Analyst, Constellation Research

Covering Customer-facing Applications That Drive Better Business Results
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