Deep Learning vs Machine Learning: AI Superpowers for Customer Service

Artificial intelligence (AI) is rapidly transforming the world of customer service. Two key AI technologies at the forefront of this revolution are deep learning and machine learning. By 2024, it‘s estimated that 75% of enterprises will shift from piloting to operationalizing AI, driving a 5x increase in streaming data and analytics infrastructures.

As a customer service leader, it‘s critical to understand how deep learning and machine learning work and the opportunities they present for delivering faster, more intelligent, and more personalized service experiences. In this post, we‘ll break down the differences between deep learning and machine learning and explore how leading brands are leveraging these AI superpowers across the customer journey.

What is Machine Learning?

Machine learning is a subset of AI that involves training algorithms to learn patterns from data in order to make predictions or decisions without being explicitly programmed. With machine learning, the algorithms are "trained" on large datasets, and then learn and improve over time as they are exposed to more data.

Machine learning requires some level of human input to label and categorize the training data at the outset. For example, to train a machine learning model to automatically tag and route incoming customer support tickets, you would first need to manually label a dataset of existing tickets by topic, sentiment, priority level, etc. The model would then learn from those labels to start making its own classifications and predictions.

Some common applications of machine learning in customer service include:

• Ticket classification and routing
• Churn prediction
• Sentiment analysis
• Upsell/cross-sell recommendations
• Fraud detection

What is Deep Learning?

Deep learning is a more advanced subfield of machine learning that utilizes artificial neural networks to enable systems to learn and make intelligent decisions on their own. Deep learning algorithms, known as deep neural networks, have multiple layers that progressively extract higher level features from raw input.

Inspired by the structure and function of the human brain, each layer in a deep learning model contains units called "neurons" that process and pass information to subsequent layers. With enough training data and computing power, deep learning enables systems to automatically discover hidden patterns and representations within unstructured data such as images, video, audio, and text.

Unlike traditional machine learning, deep learning does not require human input to identify and extract features from the data. However it does require massive amounts of labeled data and substantial computing power to train the models.

Some emerging applications of deep learning in customer service include:

• Conversational AI chatbots and voice assistants
• Real-time language translation
• Image and video analysis for visual search or authentication
• Hyper-personalized recommendations
• Predictive customer analytics

Key Differences Between Deep Learning and Machine Learning

While deep learning is a subset of machine learning, there are some key differences between the two in terms of their data requirements, need for human input, and complexity:

Data volume – Deep learning models require extremely large amounts of labeled training data, often in the millions of data points, whereas machine learning can work with smaller datasets in the thousands or tens of thousands.

Feature engineering – With machine learning, relevant features and representations need to be identified and extracted from the raw data by human experts before being fed into the algorithms. Deep learning removes this manual step by automatically discovering the proper representations from raw, unstructured data.

Hardware – Due to their complexity and the volume of data involved, deep learning models require more computing power to train and run, typically using GPUs or TPUs for parallel processing. Machine learning can be done with standard CPUs.

Interpretability – The inner workings of deep learning models, with their many hidden layers, are less transparent and harder to interpret than classic machine learning models. So there are potential tradeoffs between performance and explainability.

Time to train – Given the large number of parameters and volume of training data involved, deep learning models often take a long time (days to weeks) to train compared to machine learning models.

The choice between deep learning and machine learning depends on the specific customer service use case, the nature and volume of data available, and the level of prediction accuracy and automation desired. In some cases, hybrid approaches utilizing both machine learning and deep learning may deliver the best results.

How Deep Learning and Machine Learning are Transforming Customer Service

Now let‘s look at some of the key ways deep learning and machine learning are being applied across the customer service landscape:

Intelligent Self-Service

One of the most transformative applications is the use of deep learning-powered conversational AI to enable more natural, human-like interactions via chatbots, voice assistants, and messaging apps. Intelligent virtual agents can engage in contextual dialogue, answer questions, troubleshoot issues, and even detect customer sentiment – all without human intervention.

For example, Interactions uses deep learning to power its conversational AI for contact centers. Its IVAs can understand complex intents, manage context switching, and engage customers in human-friendly dialogue to resolve issues quickly.

Hyper-Personalization

Another key area where deep learning shines is in delivering hyper-personalized customer experiences. By continuously analyzing large volumes of customer interaction and behavior data, deep learning models can identify patterns to determine individual customer preferences, intents, emotions, and next-best-actions.

Amazon has been a pioneer in using deep learning to power its personalized product recommendations, which drive 35% of the company‘s sales. By training its models on trillions of data points, Amazon can predict which products a customer is most likely to purchase based on factors like past browsing, purchase history, and real-time behavior.

Agent Assistance

While AI can automate many routine customer service tasks, human agents are still essential for handling more complex and nuanced customer issues. Machine learning can help augment human intelligence by serving up relevant knowledge articles, suggesting responses, and recommending next-best-actions for agents to take.

An example is Zendesk‘s Answer Bot, which uses machine learning to suggest relevant help center articles to customers before they submit a support request. If the articles don‘t resolve the issue, Answer Bot can then intelligently route the ticket to a human agent for follow-up.

Analytics & Insights

Perhaps the greatest potential for machine learning and deep learning in customer service is in uncovering hidden insights from the vast volumes of customer interaction data. By analyzing data across channels – from phone calls and live chat to social media posts and product reviews – AI can help identify common issues, trends, and opportunities to optimize the customer experience.

Google‘s Contact Center AI integrates machine learning across a variety of interfaces to help streamline customer service operations. It includes an Insights dashboard that performs AI-driven quality management, with features like sentiment analysis and smart topic tagging to identify trending customer issues and surface coaching opportunities.

Examples of Brands Leveraging AI in Customer Service

Here are a few more examples of how leading brands are applying deep learning and machine learning to reimagine their customer service:

• The North Face uses IBM Watson‘s visual recognition and natural language processing capabilities to power its AI shopping assistant. Customers can upload an image of the jacket they want and the AI will analyze the attributes (e.g. material, fit, occasion) to recommend similar products from The North Face‘s catalog.

• Persado uses deep learning to generate personalized email subject lines, social media ad copy, and landing page headlines that drive customer engagement and conversion. Chase Bank used Persado‘s "cognitive content" for its mortgage email campaigns and saw a 625% lift in click-through rates.

• Opus Research found that for simple transactions, chat and voice bots powered by conversational AI now have an average error rate of just 5% — putting them on par with human agents. Companies using AI-powered bots have seen reductions of 60-70% in call, chat and/or email inquiries, saving 4-5 minutes per inquiry.

As these examples illustrate, AI is no longer just hype but a proven tool for transforming customer care and delivering real business results. By 2024, Servion predicts that AI will power 95% of all customer interactions, including live telephone and online conversations.

The Future of AI in Customer Service

As deep learning and machine learning continue to mature, we can expect to see even more exciting developments in AI for customer service. Some key trends on the horizon include:

Foundation models – The emergence of massive, pre-trained language models like GPT-3 that can be fine-tuned for a variety of natural language tasks with minimal additional training data. These foundation models will accelerate the development of more advanced conversational AI agents that can engage in open-ended dialogue.

Multimodal learning – The ability to train AI models on multiple modalities of data (text, voice, images, video) will enable richer, more contextual interactions with customers across touchpoints. An example is visual search, where a customer could take a picture of a broken part and the AI could identify it and walk them through the repair process.

Affective computing – The field of affective computing focuses on creating systems that can recognize, interpret, and simulate human affect (e.g. emotions, moods, personality). As affective computing matures, we‘ll see more AI-powered agents that can detect customer emotions in real-time and respond with appropriate empathy and EQ.

Augmented intelligence – Despite the sci-fi fears of AI displacing humans, the future of customer service will be one of augmented intelligence – humans and AI working together in seamless collaboration. AI will handle the simple, repetitive tasks so that human agents can focus on higher-value work that requires creativity, strategy and relationship-building.

Importantly, as the use of AI becomes more pervasive in customer service, brands will need to prioritize the responsible and ethical development of these systems. This includes being transparent with customers about when they are interacting with an AI, providing opportunities to opt out, and ensuring that AI models are free from bias and discrimination.

Getting Started with AI in Your Customer Service Organization

The AI revolution in customer service is well underway. To stay competitive in this new era, customer service leaders need to start developing their AI strategies and roadmaps now. Some key steps to get started include:

1. Identify high-impact use cases for AI based on your customer service goals and pain points. This could include automating routine inquiries, personalizing agent-customer interactions, or uncovering insights from interaction data.

2. Assess your data readiness to support AI. Deep learning and machine learning models require large volumes of high-quality, labeled data to train on. You may need to invest in data collection, integration, and tagging to get your data AI-ready.

3. Choose the right AI platform and partners to work with based on your use case requirements and existing technology stack. Key considerations include ease of use, scalability, pre-built vs custom models, and integration capabilities.

4. Develop a plan for human-AI collaboration. Identify which tasks will be automated by AI and which will remain in the hands of human agents. Provide training for agents on how to work alongside AI and interpret its outputs.

5. Establish governance and ethical frameworks for your AI systems. This includes developing policies for data privacy and security, bias testing and monitoring, and explainable AI.

6. Measure and iterate based on results. Put clear metrics in place to track the performance of your AI systems and their impact on customer satisfaction, efficiency, and revenue. Continuously monitor and refine your models based on feedback loops.

By following these steps, customer service organizations can tap into the game-changing potential of deep learning and machine learning to deliver faster, smarter, and more personalized experiences. The organizations that move quickly to adopt and scale AI will be the ones that thrive in the future of customer service.