The 3 Main Types of Machine Learning

Today, sales teams have access to more data than ever, enabling them to understand their market on a whole new level.

While it can be challenging for sales managers to effectively harness all of that information, machine learning offers a potential solution.

The technology might sound like a complex algorithm but it’s quickly becoming popular among sales teams for transforming large amounts of complex data into actionable insights.

In this article, we’ll look at the three types of machine learning currently in use and how they can improve your sales process.

What is machine learning?

Machine learning (ML) is a subset of artificial intelligence (AI). ML models can learn from data, identify patterns and make decisions with minimal human intervention. As the models analyze more data, they improve their performance over time.

People are already using ML in various industries to solve real-world problems and optimize existing processes.

A common use case for ML in healthcare is analyzing patient records to improve medical diagnoses. In finance, institutions use ML to improve fraud detection systems and combat crime.

From Netflix recommendations and Amazon Alexa to self-driving cars and your photo app’s image recognition, ML is rapidly becoming part of our daily lives.

Machine learning in marketing can help you better understand customers for tailored campaigns. In sales, ML can predict consumer behavior and personalize the customer experience at scale.

ML broadly comprises three categories:

• Supervised learning, using labeled data

• Unsupervised learning, using unlabeled data

• Reinforcement learning, using trial and error

Within these categories, there’s a wide range of algorithms, such as K-means clustering, support vector machines and artificial neural networks.

Each algorithm uses a different approach that makes it suitable for different tasks, from simple classification to complex pattern recognition.

For detailed information on ML, read our guide on deep learning vs. machine learning.

Predicting outcomes with supervised learning

Supervised learning involves training an algorithm on a dataset where you already know the correct answers. Over time the model can work out the patterns between the input data and the outcomes, enabling it to predict the results for new, unseen data.

For example, a simple supervised learning algorithm could analyze images of cars, each labeled with the make and model. Eventually, the model would be able to work out the type of car in images it’s never seen before.

Common supervised learning algorithms include:

• Linear regression algorithms for outcomes that vary across a range (e.g., height, weight, temperature)

• Logistic regression for binary outcomes (e.g., yes/no, win/lose, true/false)

• Decision trees for modeling the outcome of a series of steps or decisions

• Random forests, combining multiple decision trees for improved accuracy

The more high-quality labeled datasets you have, the higher the accuracy. Keep in mind that getting hold of labeled data can be time-consuming and costly.

Using supervised learning in sales

Supervised machine learning is helpful when you already understand how your input variables (e.g., specific customer information) relate to the desired outcome (e.g., whether they’ll buy a product).

You can use this known relationship to teach the model what to look for during its training process.

For example, by showing the model examples of past customers who bought (or didn’t), the model learns to predict future buying behavior. You can then apply the model to sales activities like lead scoring, customer segmentation and sales forecasting optimization.

The right training data is crucial when using supervised learning models in your sales process. Decide your performance objectives and determine what kind of data will help you meet those goals.

Once you’ve identified your data sources, you’ll also need to ensure the data is clean and properly labeled.

Uncovering patterns with unsupervised learning

Unsupervised learning is a type of machine learning method that finds underlying patterns in data without needing any answers upfront.

Unlike supervised learning, where you give the model all the right answers, unsupervised learning explores data on its own. The model can then identify natural groupings or relationships to uncover fresh insights.

Popular unsupervised learning techniques include:

• Clustering, used to find natural groupings within your data

• Association, used to identify items that frequently go together

The power of unsupervised learning lies in its ability to reveal trends and patterns you might not have even thought to look for. The patterns or groups it identifies can be hard to understand right away, so it might take a bit more digging to make sense of the insights.

Using unsupervised learning in sales

Unsupervised machine learning is most useful for exploring data without a specific outcome in mind. Use it to uncover hidden patterns or relationships in your customer information or sales data.

For instance, clustering algorithms can classify your customers based on their demographics, interests or purchasing behaviors without your instructions on how to categorize them.

These naturally formed groups can reveal surprising insights into different types of customers. You can then tailor your marketing strategies and sales pitches so they’re more effective.

These associations can help your sales team spot the relationships between products customers frequently buy together. By understanding customer buying habits, you can improve cross-selling and upselling performance.

To use unsupervised learning algorithms, you’ll need a comprehensive dataset of customer transactions, interactions or behaviors. The data may not be labeled, but you still need clean, comprehensive datasets.

After running the data analysis, you’ll have groups or associations. Work with your team to understand these insights. Ask questions like:

As your business and customer base evolve, regularly update your models with new data to capture the latest sales trends and patterns.

Semi-supervised learning

Semi-supervised learning combines the guidance of supervised learning and the exploration of unsupervised learning. You have some labeled data with the correct answers, but you mostly use unlabeled data.

A semi-supervised approach is useful when you have substantial customer data but labeling it all would be too time-consuming or costly.

To illustrate how semi-supervised learning works, imagine you’re teaching a new rep to overcome sales objections. If you only have a few examples to show them (labeled data), they’ll likely struggle when dealing with new objections.

However, if you also let them read through many previous customer interactions on their own (unlabeled data), they’ll start to notice patterns. The sales rep can then apply what they learn from the few examples across a much broader set.

Semi-supervised learning works in a similar way. You use a small amount of labeled data to guide the learning process while also letting the model draw insights from a larger pool of unlabeled data.

Make smarter decisions with reinforcement learning

Reinforcement learning teaches the ML model to make better decisions by rewarding it for correct actions. Interaction and feedback, rather than existing data categorizations, drive the learning process.

Models learn to achieve a goal in an uncertain, potentially complex environment by trying different actions. This trial and error helps them understand which actions get the best results.

In sales, the environment could be the market. The actions might include different customer interactions. If a particular sequence results in a sale, the model gets a reward encouraging it to repeat those actions.

The model’s strategy evolves and becomes more sophisticated as it learns from each interaction.

Setting up a reinforcement learning system can be more complex compared to other types of machine learning. The model requires a comprehensive understanding of the environment and a clear definition of rewards. The process also requires a lot of trial and error, which might only suit certain sales tasks.

Despite these challenges, the ability to adapt to changing environments makes reinforcement learning algorithms powerful tools for sales and marketing.

Using reinforcement learning in sales

Reinforcement learning works best for tasks involving sequential decision-making. The model is also ideal for dynamic environments where strategies change regularly.

For instance, you can use reinforcement learning to automatically adjust pricing based on customer behavior and market conditions.

Similarly, a reinforcement learning model can optimize your communication strategies. The algorithm could determine the best times and channels to contact potential leads based on the likelihood of a positive response.

To benefit from reinforcement learning, you need a clearly defined environment where sales-related actions – such as sending a sales follow-up email – lead to feedback signals – like a sale, no response or a negative response.

With the right feedback, the model can learn which actions are most likely to lead to your desired outcome, continuously refining its strategies.

If you want to use reinforcement learning in your sales process, focus on defining your goals and their value. Goals can range from short-term targets like increasing click-through rates on sales campaigns, to long-term objectives like enhancing customer lifetime value.

Continuously monitor the model’s performance and adjust your environment definitions, your goals, your reward signals or even the model itself to improve outcomes.

Getting started with machine learning in sales

The idea of using AI and ML in your sales processes might seem overwhelming at first, but it doesn’t have to be.

Here’s a straightforward guide to getting started.

1. Identify your top opportunities for improvement

Think about what you want to achieve and review your current sales process to identify improvement opportunities. Are there any areas that could benefit from ML, such as lead qualification, customer segmentation or sales forecasting?

Understand where ML can make the biggest impact to help you prioritize your efforts.

Include your sales team in this process from the start. They may be feeling apprehensive about using ML in their work or dismissive about its potential. Offer them access to resources and training on ML basics to demystify the technology and increase the chances of a successful outcome.

2. Prepare your data

Data is the foundation of any ML project. Compile historical sales data, customer interaction logs, market research and any other relevant information related to your objectives.

Ensure your data is accurate, organized and consistent.

For instance, you might need to remove duplicate entries, correct errors or standardize your data points so they’re all in the same format. Clean data is essential for developing accurate ML models.

3. Choose the right tools and technologies

There’s a wide range of ML tools available, from sophisticated platforms requiring data science expertise to more user-friendly software with pre-built models.

Customer relationship management (CRM) software with built-in AI capabilities or cloud-based ML platforms are great starting points.

For example, Pipedrive’s AI sales assistant looks through your sales data to draw valuable insights. It then suggests ways that can significantly improve your sales success.

Unless you have in-house data science expertise, consider partnering with ML experts or vendors. Professional help from a data scientist will make it easier to choose the best model for the task and tailor your ML solution to your needs.

4. Start small

A good pilot project should be manageable in scope, have a clear objective and offer measurable outcomes. This way, you can quickly gauge ML’s effectiveness and impact on your sales.

For example, rather than attempting to fully automate your customer interactions across all channels, choose a simpler project – such as improving your lead scoring or sales forecasting accuracy.

Continuously monitor your ML model’s performance using sales metrics relevant to your goals.

Based on the learnings and outcomes from your pilot, you can evaluate success and refine your approach. Then, gradually scale your ML initiatives to other areas.

5. Keep learning

Keeping abreast of the latest developments in the rapidly evolving fields of AI and ML can provide new opportunities to enhance your sales processes.

Online platforms like edX, Coursera and Khan Academy offer tutorials in ML and computer science fundamentals. Many courses are tailored to beginners, so they’re a good starting point for sales teams.

There are also many online groups, like the Pipedrive community, where you can connect with other sales professionals to help you in your journey.

The future of machine learning in sales

ML has quickly evolved from a niche topic to an intrinsic part of everyday life, including businesses and sales organizations.

As we look toward the future, several advancements will likely further redefine how sales teams manage operations and engage with customers.

Enhanced predictive analytics

The rise of big data means businesses can access more information about their sales, customer behaviors and market activities.

Going beyond current datasets and traditional metrics, ML models will use a broader range of data sources, including real-time market trends, social media sentiment and global economic indicators.

As a result, sales prediction using machine learning will become more accurate over time. Sales teams will be able to use models trained on the latest data to anticipate market shifts with greater precision and agility.

Advanced personalization

The demand for personalized customer experiences is growing, driven by both consumer expectations and the competitive advantages it offers sales teams.

With the ability to analyze detailed data on customer preferences and behaviors, deep machine learning algorithms are evolving to enable highly tailored interactions.

As ML models become more sophisticated, they’ll enable sales and marketing teams to craft hyper-personalized communication using conversational AI and individually tailored offers.

For example, recommendations will go from the surface level (e.g., which batteries go with a customer’s new electrical device) to far more personalized suggestions (e.g., clothing ideas based on previous purchases and personal interests).

Automation and efficiency

Sales reps often spend hours on repetitive admin tasks each day. In Pipedrive’s State of Sales and Marketing 2021/22, only 54% of respondents said they spent most of their working day selling. A significant 19% reported spending the most time on admin support.

Managers are already using sales automation to help their teams reduce their time on low-value tasks.

Related technologies such as natural language processing (NLP) are further extending the range of tasks you can automate.

For example, sales managers and reps can use AI to analyze hours of sales calls in minutes and quickly identify winning patterns. They can then include those patterns in sales training and coaching to improve sales performance.

Chatbots can handle basic initial inquiries, while sales reps can use generative AI to create the first draft of sales collateral for each prospect.

By easily handling these kinds of tasks, ML models will increasingly enable sales professionals to concentrate on more high-value activities.

Augmented decision-making

While some salespeople are concerned about AI and ML replacing them, it’s likely that the technologies will assist human teams and work alongside them.

In the future, ML’s capacity to swiftly analyze complex datasets and simulate potential outcomes could empower businesses of all sizes to quickly make informed decisions at strategic levels. The insights uncovered by ML models will help inform product development, sales strategies and more.

Humans will remain an indispensable part of the process, using their expertise to review insights, decide on the best course of action and execute it effectively.

Ethical and responsible use of ML

There’s an expected increased focus on ethics as ML becomes more ingrained in sales processes. Companies will need to verify that any algorithms are fair, transparent and unbiased.

Ethical practices cover more than just compliance. They’re about building trust and ensuring long-term sustainability in the use of ML and other AI technologies.

UNESCO has already produced a global standard on AI ethics offering guidelines for the safe use of AI and privacy protection. Meanwhile, companies like Microsoft and Apple are facing requests for more transparency on AI’s potential risks.

Developments in explainable AI (XAI) and regulations like GDPR indicate that the ethical use of ML will become a more important trend in the future.

Final thoughts

The impact of AI and machine learning technologies is only going to grow. For businesses and sales teams, embracing the potential of ML is no longer an option – it’s a necessity.

Integrating ML into your sales processes helps you uncover unexpected insights into your customers, better personalize their experiences and streamline your operations more efficiently.

By continuing to learn about the different types of ML and their optimized applications, you can drive innovation and gain a significant competitive advantage in your sales strategies.