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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.

From Bots to Superheroes: Empowering Agents to Deliver Amazing Service with the Help of Chatbots

There are a lot of buzzwords gaining traction as we settle into 2018, but probably none are bigger than “Bot”. Particularly in the customer support arena, as companies look to further reduce the cost of serving customers. This has resulted in the rise of chatbots. However, no matter how good the technology, bots aren’t going to be able to resolve every situation or interaction anytime soon. That means that transferring from bot to agent will remain a crucial part of the chatbot experience. What do agents and customers expect when the time comes for them to be connected?

Chatbots are undoubtedly improving and becoming better at seeming human while collecting crucial customer information—their name, address, and description of the problem—and based on that they may be able to produce some initial solutions.

However, the risk companies face is that they give their customers flashbacks to the 90’s. That means an experience that’s identical to the Interactive Voice Response phone trees that end up connecting them to an agent who needs to ask all the same questions over again. How can brands prevent this? Here are a few suggestions:

  1. Put Agents in the Driver’s Seat: Empower agents to select the right channel to engage with the customer and best resolve the issue.
  2. Deliver Complete Context: The agent of the future will be a critical thinker. If you provide them with all the information they need about the customer story so they are well-informed of their profile and history, agents can craft some of their own dialogue based off of talking points from reference scripts. While this creates a more natural customer interaction, it also means that agents must be able to think on their feet and deal with possibly tense situations. Adapting to every situation and keeping calm and focused under fire is thus crucial for great customer service.
  3. Give Agents a Heads-Up: Automate agent alerts based on changes to the customer’s status, order updates, or snoozes so they’re always aware and ready to connect.
  4. Enable Empathy: It’s always great for agents to show empathy, but empathy is hard for any human being to deliver if they don’t understand the gravity of the situation. Brands can use tools like NLP (Natural Language Processing) to provide some insight into how the customer is feeling at the time of engagement, and know whether their outlook is positive, negative, or neutral.
  5. Streamline Connectivity: Efficiency is still critical at the point of contact, but not at the detriment of communication skills. Create personalized shortcuts that don’t just display simplistic customer information like name and email, but provide details of their relationship such as recent items they’ve viewed, their current sentiment, their order’s delivery status, etc.

For brands to be successful in the future, the hand-off between bot and human needs to promote a differentiated experience. If your customers have to start the process all over again when they switch to an agent, then they’re better off just connecting with one in the first place.

However, if your customer can go from speaking with a bot to an informed and empowered agent, that’s a game-changer. If your agents are equipped with all the context and transaction information they need, then they’re well-placed to deliver a meaningful experience. Combining chatbots, automation, sentiment analysis, and a full view of the customer is what it takes to turn your agents into heroes and deliver next-level service. Instead of going from a bot to a human who’s asking mundane questions, doesn’t know anything about the customer, and is powerless to make a decision, they can be connected to a CX superhero.

Vikas Bhambri is Kustomer’s VP of Global Sales and Customer Success

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