Deep Learning vs. Machine Learning and How Brands Use Both in Customer Service

How can artificial intelligence make life better for human customers? Glad you asked! Actually, cold, unfeeling machines can make your customers feel all warm and fuzzy inside.

Using AI, customer service reps can improve and scale their customer service efforts. But you should also be aware that AI is a reflection of how the customer service game is changing before our eyes.

在这篇文章中,我了解人工智能的应用n customer service, deep learning and machine learning CS processes, and examples of brands that use technology to improve the customer experience.

AI in Customer Service

Artificial intelligence is becoming a prominent part of customer service operations. Processes like machine learning, natural language processing, and speech recognition are proving to be assets in customer service — enabling seamless customer experiences and taking stress off customer support reps.

As time goes on, artificial intelligence will continue to become more prevalent in the context of digital customer service. These kinds of resources are becoming ubiquitous in any aspect of business that relies on modern technology, and customer service is no exception.

人工智能有不同的子集，我们将在下面讨论它们。

什么是机器学习？

Machine learning applies artificial intelligence with algorithms that sort through sets of data and learn from data to make predictions. Algorithms improve at tasks with experience but usually need initial human input to begin sorting through data.

What is deep learning?

深度学习是一个使用称为神经网络的算法的过程，该算法模仿人脑从数据中学习并做出明智的决策和预测。神经网络依靠大量数据开始学习，并且不依赖人类的投入来开始学习过程。

What is a neural network?

如上所述，深度学习取决于神经网络。在人的大脑中，这些网络是相互联系的神经元，可以处理输入，从输入中学习并可以基于数百个神经连接做出决定。

In computers, neural networks mimic the connections between neurons in a human brain and learn from hundreds of different data points to begin making connections and making decisions based on what they’ve learned.

Deep learning and machine learning are sometimes used interchangeably, but there are critical differences between each model.

Deep Learning vs. Machine Learning

深度学习是机器学习的一种形式，但它们是不同的过程。最重要的是，机器学习通常始于人类的输入，该输入有助于算法学习数据点之间的区别。随着时间的流逝，机器在没有人类投入的情况下识别差异方面变得越来越有经验。

On the other hand, deep learning does not need human input and learns from data on its own, which is why it requires significantly more data to begin learning and processing and takes longer than machine learning. A great way to understand the difference between deep learning and machine learning is image processing.

Say you’re hoping to teach a machine the difference between four different animals so it can learn to make the distinction on its own. With machine learning, you’d need to teach the computer about the distinguishing features that differentiate each animal. The computer then uses that human input to begin learning the difference and becomes better at identifying each animal over time.

借助深度学习，计算机不需要您告诉它具有区别功能，因为它可以对不同的数据点进行分类并自行学习差异。但是，机器将需要更多的数据点才能开始理解差异。

If you’re anything like me, understanding these concepts is rather challenging, especially when it comes to applying them to customer service teams, especially since Having that understanding might mean the difference between your customer service efforts keeping pace with digital transformation or becoming outdated and insufficient.

下面，我们将更好地了解深度学习和机器学习过程如何改变客户服务的格局。

AI如何改变客户服务

语音识别

今天，您的客户服务操作可能会生成大量数据。音频电话，这些呼叫的文本转录，文本聊天，实时聊天 - 您将其命名。最近McKinsey studysees this as rich material for AI systems to process. Done right, this can produce some profitable machine-enabled customer service outcomes.

The study notes:

“在呼叫中心管理中改善了语音识别和通过应用AI技术来改善呼叫路由，可以为客户提供更无缝的体验，并更有效地处理，”

而且它不止于此。使用称为深度学习的人工智能，客户服务运营变得越来越复杂。

“例如，对音频的深度学习分析允许系统评估客户的情感语气；如果客户对自动化系统的反应负面响应，则可以将呼叫重新安排给人类操作员和经理。”

个性化

Emotion recognition is one area where AI can help. Another ispersonalization.

In this way, AI is pushing the boundaries of what customer service is. It's not just about customer satisfaction after the sale (though that's important). It's about creating incredible experiences and offers — time and time again.

These experiences and offers are then highly personalized using the power of AI. The more personalized the offer, the better chance a customer walks away delighted — and the better chance your brand scores a sale.

That means AI can turn your actual sales process into a valuable customer service tool by giving consumers even more opportunities to spend money on what they already like.

On one hand, AI can make your current customer service operations better, faster, and more effective at scale. On the other, it can personalize your marketing content so well that it delights customers. As a result, content becomes a vehicle for offering consumers the best offer for them at the best time.

自动票证标签

AI也可能是您内部客户服务基础架构的资产。例如，如果您的业务使用ticketing system, your customer service department is probably inundated with a massive volume of support inquiries every day. Those tickets must be read, analyzed, tagged, and ultimately routed to an appropriate representative or team.

Without AI, the process is tedious and time-consuming. Frankly, it can be a waste of your support team's effort and resources. AI tools — specifically text analysis ones — take the stress, personal effort, and monotony out of that process.

他们可以分析文本并自动标记支持门票 - 将长达一个小时的过程减少到几秒钟。

Chatbots

另一种方式客户年代ervice departments have been leveraging AI to improve customer experiences is throughchatbots— bots companies place on web pages to address basic customer support inquiries at any time of day. The efficiency and accessibility these bots offer are redefining customer support.

Chatbots leverage AI and machine learning to understand the fundamentals behind a company's product or service. As a result, they’re able to answer common questions customers might have well beyond operating hours — while actual support reps are offline.

它们使客户服务对客户和服务代表都更加简单。借助聊天机器人，有基本问题的客户可以在需要时轻松解决他们的查询。销售代表并不负担不断的，单调的，简单的问题 - 给他们更多时间来解决更紧迫的重大问题。

数字助手

Yext's Duane Forrester，语音搜索专家说，

"A digital agent will be a game-changing moment in a customer's life, and each company knows they have a small opportunity to get it on the bullseye, and a large opportunity to miss the mark and drive consumers away from their platform. This means these products will be much more advanced than the digital assistants we now live with when introduced."

人工智能助手和服务工具提供了巨大的机会，使客户服务正确。但是，他们做错了，然后将消费者带入竞争品牌的怀抱中。这一切都发生了，因为消费者的偏好正在发生变化。

Let’s go over some examples of how machine learning, deep learning, and AI are used by businesses to supplement their customer service practices.

How Brands Use Machine Learning in Customer Service

1.亚马逊

亚马逊uses machine learning to give customers a personalized experience.

Its algorithm learns from customers browsing history and past orders to recommend products that they are likely to enjoy, contributing to a delightful experience where the customer feels as though the brand knows who they are, what they want, and exactly how to help them.

2. Walgreens

沃尔格林使用深度学习的虚拟助手来帮助向商店拨打电话的客户。当您调用该号码时，语音助手会接听电话，提供者呼叫者列出了客户在与商店联系时经常采取的操作。

It usually begins by asking, “How can I help you today?” and, based on customer responses, the virtual assistant can reply with adequate solutions to customer queries. For example, if you speak into the phone and say “Pharmacy,” it knows to respond with options related to Pharmacy needs, like connecting you to a pharmacist or getting the pharmacy hours of operation.

Optimum

Optimumis an internet, tv, and mobile provider that uses a chatbot for customer service. Customers can text the chatbot via mobile phone and explain their issue, as shown in the image below. The chatbot can process the words you’ve sent and extracts key markers that help it understand how to best help you. For example, in the image below, the keyword likely was “reset my password.”

The Changing Landscape of Customer Service in the Age of AI

We're moving to a contextual world, where consumers search online for personally relevant results in real-time. Voice is ascendant, as consumers make more on-the-fly searches, decisions, and purchases. Online reviews generate tons of data that can tell us much about customers if only we had the time and ability to analyze these reviews.

In a world of almost limitless data, AI is helping us leverage that data to improve our existing customer service operations. But AI is also being adopted to help brands cope with a fundamentally changed customer service landscape, where everyone expects one-to-one attention — at scale.

One thing is evident in this brave new world; effective customer service is no longer a job humans can do alone.