Machine learning vs. deep learning for sales teams

As artificial intelligence (AI) continues to make headlines, the debate around which is most useful between machine learning vs. deep learning becomes increasingly relevant to sales organizations.

While the terms “machine learning” and “deep learning” are often used interchangeably, there are some important differences.

For sales managers who want to embrace AI to optimize their sales processes, understanding the distinction helps them make smarter decisions about using this new technology.

In this article, you’ll learn what machine learning and deep learning mean, the key differences and how you can use them to boost sales performance.

What is machine learning?

Machine learning (ML) is a subset of AI that mimics how humans absorb information. Essentially, computer systems learn from data and improve their accuracy over time without being explicitly programmed for each task.

ML uses different algorithms and statistical models to analyze vast amounts of information and identify complex patterns. As a result, it can classify new data, predict future outcomes and uncover new insights.

As children, we learned what a cat or a dog looked like. At first, we needed someone to show us, but by seeing more examples we became better at identifying cats and dogs for ourselves. We can now easily classify them without any external guidance – and without seeing every dog and cat in the world.

ML works in a similar way by learning iteratively from existing data. The more data points available, the better it will perform.

Thanks to other technological advances, such as the ability to store more data and process it faster, ML models can operate at a scale and speed far beyond human capabilities.

Machine learning algorithms in sales

Businesses are already using ML models to improve performance in many areas including sales.

For example, traditional lead qualification typically relied on your marketing and sales team to manually score leads based on metrics such as recent email engagement metrics (open rate, click-through rate, etc.), company contact and website behavior (page visits, downloading white papers, etc.). Sales leaders set scoring rules in advance and manually update them.

ML algorithms assess vast historical data (including demographics, lead behavior and conversions) to identify complex relationships. By considering a wider selection of information, ML can spot subtle patterns that humans might miss.

ML lead scoring uses these insights to offer more accurate assessments that adapt to changing trends and behaviors. As new data comes in, the model updates its scoring criteria based on what behaviors and characteristics currently indicate a high likelihood of conversion.

ML can also group customers into highly personalized segments by analyzing past purchase history, social media interactions and other behavioral data.

Sales and marketing teams can then precisely tailor strategies and communications to each segment, significantly increasing their effectiveness.

Types of machine learning

Broadly speaking, there are three approaches used in ML:

• Supervised learning

• Unsupervised learning

• Reinforcement learning

Supervised learning

One of the most common types of ML is supervised learning, where the algorithm learns from labeled data.

You give the algorithm a series of inputs matched up with the right outputs so it can understand the relationship between the two. The algorithm then uses that information to predict the outputs for new, unseen inputs.

Common supervised learning tasks include classification (grouping input data into predefined categories) and regression (predicting numerical values based on input data).

For example, you can use a classification task to categorize prospects as low-, mid- or high-value for lead scoring.

Similarly, you might group customers based on whether they’re likely to churn or not, enabling your sales team to prioritize their customer retention efforts more effectively.

Regression tasks are ideal for sales forecasting through machine learning based on historical data such as past sales, marketing spend, seasonality and other factors.

Unsupervised learning

In unsupervised learning, you give the algorithm data without explicit instructions. The goal is for the algorithm to identify patterns and relationships on its own.

Supervised learning techniques require predefined labels for customer segmentation. However, unsupervised learning methods can identify new categories you haven’t considered yet.

Common unsupervised learning tasks include:

• Clustering to group unlabelled data according to similarities, differences and patterns

• Association to discover relationships between different variables

• Dimensionality reduction to simplify complex data down to its most influential attributes and metrics

For example, the Pipedrive team assesses our users’ different behavioral personas with cluster analysis. We use that information to analyze sales feature usage, target users for product research and more.

Retailers often use association rule mining to perform a market basket analysis, which identifies different items customers often purchase together. You can then review this data to make more relevant product recommendations to prospective buyers.

Dimensionality reduction can determine which sales metrics have the biggest impact on your revenue. Your sales teams can use those metrics as key performance indicators (KPIs) to focus on what matters most.

Reinforcement learning

Reinforcement learning algorithms learn to make decisions by performing actions and receiving feedback. The model continuously tries new ideas, identifies the most rewarding ones and adjusts its approach accordingly.

For example, a car manufacturer might use reinforcement learning to teach a self-driving car to park. The developer would issue a “reward” whenever the car parks safely within the lines and a “negative reward” when it crosses a line or crashes.

The car’s goal is to gain as many rewards and as few negative rewards as possible.

Machine learning in marketing can involve reinforcement learning systems to test different campaigns. For instance, try out different combinations of content, channels and offers to see which works best.

On the sales side, you can optimize your chatbot’s interactions through ongoing customer conversations and feedback to improve its recommendations and overall customer satisfaction.

What is deep learning?

Deep learning (DL) is a specialized subset of machine learning composed of neural networks with multiple layers.

The human brain’s architecture inspires these networks, with its series of interconnected nodes (or “neurons”) that process information in a hierarchical manner.

Artificial neural networks consist of input layers, hidden layers and output layers.

As the layers develop and increase, the neural network can analyze more intricate relationships between input data attributes and improve the output models. Each layer builds on the previous one, progressively moving from a broad understanding to more precise distinctions.

The layered structure enables neural network models to learn and improve automatically. As a result, DL algorithms can handle sophisticated tasks like:

• Computer vision

• Speech recognition

• Natural language understanding

By allowing customers to express themselves in their own words and not limiting them to a preselected list of questions, you can reduce response times and enhance the customer experience.

Neural networks are also useful for predictive sales analytics. DL models can analyze your customer data to accurately predict future buying behaviors, enabling your sales teams to tailor strategies to individual customer needs.

Types of deep learning

Different types of neural networks suit different tasks. Here are two worth considering in a sales context.

Convolutional neural networks (CNNs)

Analysts use CNNs for processing visual information, with multiple layers filtering images for specific features. The CNN model is ideal for tasks such as image recognition and classification.

For example, sales teams could use CNNs to quickly categorize products based on images or analyze a prospect’s sentiment during a sales demo.

Recurrent neural networks (RNNs)

RNNs can handle sequential data, which makes them suitable for analyzing sales trends over time and understanding customer attitudes from reviews or feedback.

Because RNNs remember previous inputs in a sequence, you can use them to predict future behaviors and refine personalized marketing strategies.

Deep learning vs. machine learning: similarities and differences

As we’ve explained, machine learning is a subset of AI, while deep learning is a subset of machine learning.

Both models can identify patterns and relationships in data that you couldn’t achieve with traditional programming alone. Both build on experience to improve automatically over time, with more reliable data leading to better results.

However, there are important differences to consider when choosing what type of model to use to reach your sales goals.

Learning depth

Basic ML algorithms typically work with data linearly, going straight from input to output. These models are ideal for tasks that require straightforward pattern recognition.

In contrast, DL’s neural networks recognize patterns through a hierarchy. They start with the simplest elements and gradually get more intricate.

DL models have greater depth as they use multiple hidden layers to process more data. Algorithms can handle more complex problems, like understanding verbal requests or recognizing objects in images.

Deep machine learning models operate like a “black box”, so their decisions can be complex to understand. Simpler machine learning models are generally straightforward to interpret.

Data requirements

Both systems are more effective when fed more data. However, some ML techniques can still operate effectively with smaller data samples.

DL models require large datasets to train on, especially when dealing with unstructured data like images, video, audio and natural language.

Resources and performance

Deep neural networks perform intricate calculations across potentially millions of parameters. DL models, therefore, require significant computational power and, often, high-performance graphics processing units (GPUs) for training.

ML models are generally less resource-intensive. You can train them on standard computers without specialized hardware.

Greater access to training data and computing power gives neural network models higher accuracy for complex tasks like speech recognition and image classification. Traditional ML models are better suited to simpler tasks.

Human intervention

The neural network architectures supporting DL models generally require greater expertise and more time to develop than basic ML equivalents.

Still, ML models often require human assistance for feature engineering. Also known as feature extraction, this time-consuming process extracts useful characteristics from the data to further train and refine the model.

DL models carry out feature engineering by themselves, minimizing the need for human intervention.

Here’s a table summarizing the key differences between ML and DL.

Using machine learning and deep learning to improve sales

As we’ve already seen, both ML and DL models are useful for improving marketing and sales performance. Algorithms can help with tasks like scoring potential leads, segmentation and sales forecasting.

For example, Pipedrive’s AI Sales Assistant uses ML to optimize sales tasks and:

• Offer performance-based tips

• Track team performance

• Advise on sales tools

• Predict deals’ win probability

• Recommend next actions

If you’re looking to create your own custom AI application, choosing between ML and DL depends on several factors, including the specific task and available resources.

ML may be sufficient for analyzing customer demographics and past purchase histories. DL would be the go-to for more sophisticated tasks like understanding customer sentiments from social media interactions.

Other factors to consider include:

• Data quality and availability. The success of both ML and DL models heavily depends on the quality and quantity of data. Ensure your data is clean, well-organized and sufficiently comprehensive to train effective models.

• Choosing the right tools and technologies. There’s a vast selection of tools and platforms available for developing ML and DL models, such as TensorFlow, PyTorch and scikit-learn. Check which is suitable for your specific requirements.

• Ethical and privacy concerns. When using ML and DL to analyze customer data, be aware of the ethical implications and comply with relevant data protection regulations to maintain customer trust.

• Skillsets and training. Implementing ML and DL requires specialist skills. You’ll likely need to train your existing staff, outsource the development or hire new talent with data science and AI expertise.

If you’re interested in learning more about ML and DL, check out educational platforms like Coursera, Udacity and edX. Courses range from beginner to advanced levels and are often tailored to specific industries or applications.

The future of deep learning and machine learning in sales

AI already impacts the way we sell, with new developments announced every day. Greater computational capabilities will allow sales teams of all shapes and sizes to process more data, recognize new patterns and improve the way they work.

Here are some key innovations expected to shape your sales in the years ahead.

AI systems will be able to learn from smaller datasets.

While the amount of data needed varies between traditional machine learning systems and advanced deep learning models, it’s still a case of more is better.

Sales teams’ access to big data necessary for effective modeling is often limited. But according to AI expert Kevin Kelly, co-founder of Wired and author of The Inevitable, that may soon change.

The ability to train complex neural networks without large amounts of data means that every sales team will be able to benefit from advanced ML models.

AI-driven sales optimization

An AI sales assistant can already help sales teams make smarter decisions and that’s likely to become far more efficient in the near future. ML and DL models will facilitate sales intelligence to enable real-time decision-making and problem-solving.

For example, sales teams will be able to adapt strategies and adjust pricing in line with the latest data.

In many cases, AI will handle more time-consuming tasks that sales teams deal with. Sales automations already manage a lot of the mundane work but we expect to see automation helping with more advanced use cases.

From lead generation to closing deals, ML and DL models will automate and optimize various sales processes. Sales teams will then focus their time on building relationships and strategizing.

Generative AI will become more popular for video production

2023 was a big year for generative AI (a subset of DL) as apps like ChatGPT made headlines around the world. Software companies have released countless tools to help sales teams with everything from outreach emails to analyzing call transcripts.

While people are now used to the idea of generating text and images in seconds, the technology has yet to reach the same level for video. However, you can expect that soon according to Will Douglas Heaven, senior editor for AI at MIT Technology Review.

Will Douglas points to software like Synthesia. The AI video generator already creates multiple deepfake avatars from an actor’s single performance using different scripts.

Generative video could provide sales teams with new ways to scale their engagement, such as recording personalized videos for prospects at the touch of a button.

The role of human intelligence

Amid the leaps in AI, the human element will remain integral to sales. ML and DL models will continue to be their most effective as tools that augment sales team productivity, rather than as replacements.

The insights and intuition of experienced salespeople, combined with AI’s analytical power, will form the backbone of successful sales strategies.

Staff training and development will shift towards working alongside AI, ensuring that sales teams use the technologies effectively.

Final thoughts

In recent years, AI has gone from the subject of science fiction to becoming part of our daily lives – including how we sell.

By understanding how businesses already operate ML and DL models, as well as how they might apply them in the future, you can use AI to add value to your sales team.

Continue learning about different types of AI and using AI-powered tools to ensure that your sales team always performs at its very best.