The Web is but a canvas to our imagination - ideas and digital strategies to finding gold at the end of every Web journey

Wednesday, June 13, 2018

From the mundane to hidden gems - AI is here to help


 So let's dive into four areas that the customer experience can be improved with a variety of different AI / machine learning technologies.


Free the customer service team from the mundane
AI can automate some time consuming tasks such as data entry and directing a caller to the right person to allow customer service representatives to focus on the customer’s problem rather than gathering mundane information such as their name, address and date of birth or answering simple FAQ questions. This is where chatbots and virtual assistants can be deployed for basic automatic data entry.

Chatbots are designed to simulate human interactions and provide immediate, personalized responses 24/7. This is particularly useful managing customer questions or complaints. Virtual assistants on the other hand can engage customers in simple conversations to check on orders or find recommendations based on querying large databases of information, past responses or predictive next best actions.

By automating the mundane parts of customer service, the opportunity is to give the customer service team more complex tasks to deal with.

Predict next-best actions
Brands have made a seismic shift in the last decade away from telling customers what they want (okay, they’re still guilty of that somewhat) to creating a brand experience that feels like it was tailored for them. AI tools are enabling brands to accomplish this by sifting through customer data and personalizing the information to offer relevant products.

These technologies help identify the consumer's needs before they even know it (which in the past, visionaries like Steve Jobs have done this with products such as the iPod and iPhone).  In the world of marketing, an insurance company could use a recent doctor visit to offer a coupon for a medical device, or retailers could use smart shopping carts to understand where the buyer is in his or her purchase decisions to recommend a product, such as if they are buying ingredients to make a cake, it would recommend eggs, flour and milk.

Discover  hidden high-impact decisions
Delving deeper into the customer journey, data mining looks at the bigger opportunities in terms of what future brand engagements or actions could be, such as the banking example above with a customer inquiring about a wealth management product and subsequently ending up with a college fund. Whatever the challenge is, the key is that AI is being used to handle that particular co-relation over more traditional, manual methods used in the past.

Content that creates itself
Another way AI can be used to better the customer experience is training it on business-specific metadata to dynamically generate webpages, mobile alerts or other content-centric communications. For example, a professional sports association wanting to drive more attention to its games and get fans more engaged,they can use AI to deliver live statistics on each game and provide fans a unique display of their favorite player stats on their mobile device.   

Investing in customer experience and AI should be a high priority no matter what industry. And it’s not just about adhering to compliance and governance that should be the reason for doing so – the customer expectation is changing and if businesses aren’t appealing to them and making changes – the customer is going to find a different company to align their brand loyalty.

Wednesday, June 6, 2018

Art of the Possible - Artificial Intelligence and the Customer Experience


There is a craze right now about Artificial Intelligence ( AI  - or more likely machine learning) and its different applications in both technology and our culture.    I have spent the last few years in helping various industries to improve their customer experience or digital experience initiatives.   Now the conversation has turned to imagine the possibilities of adding AI to their mix of CX/DX activities.  In order to deliver the best experience, organizations need to know as much about their customers as possible. While there's no shortage of customer data available to them today, the problem lies in analyzing that overwhelming amount of data in an augmented fashion.

This is where the benefits of AI technologies in a customer-centric setting comes in. 

Those who see the value in connected end-to-end customer journeys, and are using machine learning to predict future customer behavior, can deliver intelligent experiences. Using a bank as an example, a customer visits its banking website to learn more about its wealth management products.  AI-generated insights based on the customer’s information are then used to optimize the content to help them make future decisions (and to help the bank keep the customer happy).  The customer can then contact a branch agent to talk about wealth management options and even go into a brick-and-mortar location to discuss the products further in person.  

Next, with the ability to analyze current and future needs, the wealth manager can be alerted that their client has excess funds that could be used to set up a college fund for their grandchildren. The data that the branch employee initially provided for wealth management online uncovered a hidden relationship to a new product – a college fund. 

In other cases, AI can be used to anticipate customer behaviors. While it is still in the early stages, contact centers are experimenting with voice recognition technologies that can detect sentiment in a customer’s voice. For example, if a customer calls into the bank and is upset, the technology can recognize that emotion and immediately direct them to an agent that is adept in dealing with emotional customers.


 These are just a couple of examples of how AI can improve the customer experience.  Over the next few posts, I will delve into more detail on four potential ways that AI can help in this regard.  We will look at the customer support center, predicting customer journeys, uncovering high impact decisions and augmented or automated information.