| Criteria | Machine learning (ML) | Deep learning (DL) |
|---|---|---|
| Goal | To create predictive models based on features engineered from data, making it more suitable for structured data. | To automatically learn and extract features from data, making it suitable for unstructured data like images, text, and speech. |
| Definition | It employs various machine learning algorithms and techniques to make predictions or classifications. | It uses deep learning algorithms – deep neural networks with multiple layers to perform tasks. |
| Data feeding | Machine learning can work with smaller datasets, and the quality of features is more important. | Deep learning requires a vast amount of labeled input data to train neural networks effectively. |
| Data processing | Requires manual feature engineering to select and create relevant features from the data. | Automatically extracts hierarchical features from raw or new data, eliminating the need for extensive feature engineering. |
| Data volume | Can work with smaller datasets but may require extensive preprocessing. | Thrives with large datasets, as it can effectively utilize big data for training. |
| Complexity | Less complex models, making it more suitable for simpler tasks. | Complex neural networks with multiple layers that are capable of handling intricate tasks. |
| Hardware necessities | Less demanding in terms of hardware, making it more accessible. | Requires high computational power and GPUs or TPUs to train deep neural networks. |
| Feature engineering and learning process | Relies heavily on manual feature engineering to create relevant attributes. | Automatically extracts hierarchical features from data - complex and unstructured data |
| Interpretation | Provides intricate insights, difficult to interpret | Provides straightforward and interpretable, with the model's decision-making process |
| Computation time | Faster training and can work with smaller datasets, leading to quicker results. | Time-consuming for training due to the deep structure and large datasets. |
| Technology cost | More cost-effective as it can work with standard hardware and does not require significant resources. | Can be more expensive due to the need for powerful hardware and specialized resources. |
| Approaches | Uses algorithms like linear regression, decision trees, SVM, k-means, etc. | Utilizes artificial neural networks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. |
blog
Deep learning vs machine learning: Their roles in customer service
- What are the differences between deep learning and machine learning?
- What’s the significance of machine learning and deep learning? And why are they important today?
- How do machine learning and deep learning impact customer experience?
- 3 Real-life examples of machine learning and deep learning in customer service
- Build an intelligent customer support system with Artificial Intelligence (AI)
Imagine navigating a website, delving into a product, or searching for help when suddenly a chatbot emerges, proposing to assist with your needs.
Will you feel amazed?
Well, you should.
Such chatbots are powered by advanced technologies like deep learning and machine learning. Deep learning and machine learning, while part of the same AI family, are often compared due to their distinct mechanisms.
In this blog, we will cover –
- The differences – and what do they mean for your company?
- The significance – and why are they important today?
- Their roles in customer service – and how can you improve your CX?
- Case Studies – and the insights they provide.
Let’s explore.
What are the differences between deep learning and machine learning?
Machine learning is the stepping stone towards Artificial Intelligence (AI).
And deep learning is the next in machine learning.
While they share common goals and concepts, they have fundamental differences in terms of architecture, performance, and applications.
Let’s put them in the perspective of AI –
Machine learning uses algorithms to learn from data to identify patterns and make predictions essentially. It can be supervised and unsupervised learning, which often requires data scientists to check the prediction accuracy.
As the name suggests, it is the science of creating algorithms. Personalization in e-commerce is the best example to understand how it works – for example, Amazon’s recommendation system.
Deep learning, on the other hand, uses advanced computing and its deep neural networks to perform tasks that are complex and extensive in nature. It requires a large amount of data in comparison with ML. At the same time, it can also determine the prediction accuracy without human intervention.
Ultimately, the goal is to learn from data so that businesses can make informed decisions about customer support, investments, risk analysis, and more. However, the key differences between ML and DL lie in the level of abstraction and automation.
Deep learning is more about training algorithms. Self-driving cars are the best example – for example, Tesla’s autopilot system.
More in detail –
1️⃣ Data and complexity
Action - Evaluate the scale and nature of your data.
Direction - When you have access to extensive, unstructured data such as images, text, and audio, consider deep learning for complex tasks like image and speech recognition and natural language processing. It significantly enhances automation and communication applications such as AI for elevating customer experience.
When you have smaller datasets or simpler problems, machine learning can be a more efficient and cost-effective choice. It is less complex and more interpretable – making it a perfect choice for personalized customer experience, automation, and operation management.
57% of respondents state that improving customer experience represents the top artificial intelligence and machine learning use cases. - Statista Global Report”
2️⃣ Feature engineering
Action - Analyze the characteristics of your dataset and the quality of features.
Direction - When you have more data and can allocate computational resources, deep learning's automatic feature extraction capabilities become an attractive option.
When you have structured data or limited computational resources, machine learning may be the preferable choice. Focus on identifying and engineering relevant features, improving data quality, and considering dimensionality reduction techniques to enhance model performance.
3️⃣ Interpretability and transparency
Action - Assess the importance of model transparency and interpretability in your business context.
Direction - For departments that require interpretable AI systems, such as customer support or legal compliance, opt for machine learning models. These models offer transparency and allow for easy auditing of decisions, ensuring compliance with industry standards and regulations.
In departments focused on innovation and cutting-edge technology, like research and development, deep learning may be more suitable due to its raw predictive power.
56 percent of all respondents report AI adoption in at least one function. - McKinsey Global Survey”
Selecting between deep learning and machine learning isn't a matter of favoritism; it's about choosing the right model for specific tasks and requirements. Align these models with your business goals and data resources to optimize your AI strategy for successful outcomes.
What’s the significance of machine learning and deep learning? And why are they important today?
Machine learning and deep learning are pivotal for modern businesses because they can help them take leaps into the future. Some of their strengths include –
- Automate tasks
- Personalize user experiences
- Drive automation
- Enhance security and more.
Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.” - Mark Cuban, Entrepreneur, and American billionaire businessman.”
There are many more tech innovators and business leaders emphasizing AI (deep learning and machine learning) is the future.
And there are a number of survey results to support these claims.
Let’s look at a few statistics.
By 2025, nearly 100% of enterprises will be implementing some form of AI.
Source: Forrester
And here are the top reasons – why businesses are adopting artificial intelligence and machine learning.
Source: IBM Global Adoption Index 2022
And here are the top ways – how businesses are using AI.
Source: Forrester
Businesses have realized and experienced all the benefits ML and DL bring to their businesses. And the rapid shift is taking place across the globe. Here are the AI adoption rates around the globe.
Are you keeping pace with the global adoption of artificial intelligence? If so, congratulations on your transformation journey! If not, now is the time to explore the potential of AI for your business, starting with enhancing your customer service.
Let’s explore one function of your business – customer service and support management. And understand the role of deep learning and machine learning in CX.
How do machine learning and deep learning impact customer experience?
Today’s customers expect rapid, personalized, and efficient solutions to their problems. Businesses that can meet these expectations enjoy repeat business, referrals, and positive feedback.
However, delivering exceptional customer service and fostering loyalty can be challenging for multiple reasons, such as –
- Multitude of choices
- High expectations
- Shorter attention spans
That’s where artificial intelligence comes in. Machine learning and deep learning models can help companies scale CX by improving the following functions –
1️⃣ Customer service and support
Machine learning and deep learning enhance customer service by enabling chatbots and virtual assistants to provide instant responses – 24/7 customer support.
👉 Complex issue resolution
The deep learning systems can provide recommendations and solutions for more intricate customer issues, freeing up human agents to solve pressing issues, all the while improving issue resolution times and accuracy.
👉 Predictive analysis
Predictive analysis of customer behavior empowers businesses to anticipate needs, deliver personalized service, and proactively retain customers by identifying potential churn risks.
👉 Knowledge management
Machine learning and deep learning can assist in building and maintaining extensive knowledge bases for self-help services. They can support customers to resolve common queries by themselves and agents to deliver accurate and up-to-date information to customers.
DevRev, an AI-native OneCRM, enables companies to build knowledge bases that are comprehensive, user-friendly, and customer-centric. Here’s your guide to building a powerful knowledge base.
👉 Real-time insights
AI can analyze customer interactions in real time, offering immediate feedback to agents on how to optimize their responses and enhancing the quality of customer support.
You can use an AI-based tool to build an intelligent and robust knowledge base.
2️⃣ Customer feedback and analysis
Machine learning and deep learning models can revolutionize how businesses engage with their customers and improve the overall customer experience.
👉 Sentiment analysis
Machine learning algorithms can analyze customer feedback across various channels to determine whether the feedback is positive, negative, or neutral – enabling companies to identify issues or areas of improvement promptly.
👉 Issue prioritization
Deep learning systems can help prioritize customer feedback and complaints based on sentiment and content. As per the urgency, businesses can allocate resources effectively and address high-impact concerns promptly – achieving higher customer satisfaction.
Imagine this: A tech company providing a project management platform wants to improve customer satisfaction. They have applied a machine learning model to their customer helpdesk and support platform to prioritize customer service tickets based on urgency and complexity automatically.
👉 Personalized responses
Predictive analysis anticipates customer needs through feedback patterns and customer behavior – enabling tailored responses and recommendations.
For example, if a customer consistently provides positive feedback about a particular product, the company can proactively offer them tailored recommendations or promotions related to that product.
3️⃣ Customer retention and loyalty
Customer retention and loyalty are paramount in sustaining a successful business. Leveraging various strategies and technologies, companies can enhance customer loyalty and cultivate lasting relationships.
👉 Loyalty programs and incentives
Deep learning can help identify potential churn risks in customer relationships. Businesses can act on it – loyalty programs, discounts, or exclusive incentives to retain customers.
👉 Omnichannel engagement
Machine learning and deep learning can help businesses create a seamless omnichannel experience for customers. This ensures consistent and quality interactions across various touchpoints, reinforcing customer loyalty and trust.
👉 Real-time insights for continuous improvement
Real-time insights help companies optimize strategies and adapt to changing customer preferences swiftly, enhancing their efforts to build and maintain customer loyalty.
DevRev, an AI-native platform, helps businesses extend superior customer service. From integrating a chatbot to managing support tickets intelligently, the OneCRM uses a deep learning technology with natural language processing capabilities to make the customer experience smoother and more efficient.
The system analyzes the content of incoming tickets, the history of the customer, and the context of the issues. Then it assigns priority levels and routes tickets to the most appropriate support agents – saving time, enhancing efficiency, fastening the resolution process, and, as a result, improving the customer experience.
3 Real-life examples of machine learning and deep learning in customer service
👉 Amazon’s recommendation system
Amazon’s recommendation system is a classic example of machine learning in customer service. It uses collaborative filtering and deep learning algorithms to analyze a customer’s browsing and purchase history, as well as the behavior of similar customers, to recommend products that the customer is likely to be interested in. This personalized recommendation system enhances the customer experience and drives increased sales on the platform.
Takeaway: Know your customer preferences and personalize their experience.
👉 Slack’s workflow automation
Slack, a popular collaboration platform, incorporates workflow automation tools that allow companies to create custom automated processes. These workflows can assist in routing customer inquiries, managing support tickets, and streamlining communication within customer support teams.
Takeaway: Understand your customer requirements and make their job easier, faster, and better.
👉 Shopify’s e-commerce chat support
Shopify offers integrated chat support for its e-commerce platform. Online store owners can use this feature to engage with customers, answer product-related questions, and assist with checkout processes in real time. This technology helps businesses offer exceptional customer support during the shopping journey.
Takeaway: Become available to your customers and help them make the right decision fast.
Build an intelligent customer support system with Artificial Intelligence (AI)
Statista conducted a survey on ‘Circumstances under which customers expected better personalization worldwide in 2023’ and found that –
- Around three-quarters of surveyed global customers expect improved personalization when they share more personal data with companies
- 73 percent anticipate a more personalized experience as technology advances
- 64 percent expected the same when increasing their spending
Customers have high expectations for personalization, along with quality, accuracy, and faster services. Achieving these expectations requires businesses to adopt AI to enhance their customer support and services, all while saving costs and time.
And that is only possible when companies embrace AI to improve their customer support and service, all the while saving costs and time.
DevRev, an AI-native platform, plays a crucial role in transforming customer experience through the power of generative AI. It enables the creation of a robust and easily scalable customer support system through deep learning and machine learning, ultimately leading to improved customer satisfaction and sustainable growth.
