What is machine learning (and how will it revolutionize sales)?

Machine learning is a type of artificial intelligence (AI) that can help you streamline almost every business process through better automation, forecasting and insights.

You can use machine learning to better understand your customers, deliver more effective marketing campaigns and improve your sales processes – but to get the most out of AI, you need to understand how it works.

So, what is machine learning?

In this article, we’ll demystify machine learning and explain how you can use it to optimize your sales processes.

What is machine learning?

Machine learning (ML) enables a computer system to learn and improve by itself, without you having to program it for a specific task. It’s often used to develop programs that can make powerful decisions or predictions based on key information.

Imagine you have a large CRM database and you want to know which customers are most likely to purchase a new product. A machine learning algorithm can analyze your customer data (like past purchases, browsing history and so on) to identify purchasing patterns.

Based on these patterns, it then predicts which customers are most likely to make a purchase. As customers do or do not purchase your product, the system learns and improves its predictions even further.

Artificial intelligence vs. deep learning vs. machine learning

AI, machine learning and deep learning are three closely related data science concepts that people often confuse.

AI is the overarching term that includes both machine learning and deep learning. It’s a very broad concept that includes many types of software that work in a similar way to human decision-making.

Machine learning is a form of AI that uses algorithms to learn and improve on certain tasks through experience.

Deep learning is a specific kind of machine learning that uses complex multilayered algorithms (called neural networks) to mimic the learning process of the human brain. The main difference between machine learning vs. deep learning is that deep machine learning algorithms need much larger amounts of data and far less human intervention.

Another way data scientists use deep learning is to analyze customer sentiment from reviews and social media, providing insights into how customers perceive your brand. It’s also commonly used in fraud detection.

Computer vision is another deep learning application that helps AI understand images and videos. Self-driving cars use computer vision to identify objects and pedestrians.

Here’s a summary of these three concepts:

How does machine learning work?

In traditional programming, a computer system follows instructions (an algorithm) and performs a specific task.

In machine learning, you give the system a set of training data and a desired outcome or task to perform. It then works out what to do on its own, improving itself over time as it becomes better at that specific task.

For example, say you wanted to create an image recognition model that could identify dogs in pictures. In traditional programming, you’d tell the software what to look for – four legs, two ears and so on. In machine learning, it learns to identify dogs by itself.

When the algorithm has become good at identifying the dogs in the original dataset, you can show it a random set of new images. If the training was successful, it should now be able to identify dogs in images it has never seen before.

There are four main steps to achieve all this.

Step 1: Data collection

The first step is to collect and prepare the data that will train the machine learning model. This could be any form of data like pictures, statistics, posts (formerly tweets) and so on, depending on what you want to achieve.

The data needs to be relevant to the task and prepared so that the machine learning program can understand (called preprocessing). If you’re developing a tool to identify dogs, you’ll need to provide the model with lots of images, some of dogs and some not.

You’ll also need to format the data into the correct spreadsheet or database for the model you’re using. You should clean your data – remove all duplicates, correct errors and normalize the data according to the model’s requirements.

This step makes sure that your machine learning model can understand the task and interpret the data correctly. Without preparing the data, the model may produce incorrect results.

For instance, if you give it a bunch of images of cats but they’re labeled wrong, and the model thinks they’re dogs, it will train itself to identify the wrong images.

Incorrect predictions can have serious consequences, especially if you use the algorithm to analyze confidential healthcare information or sensitive sales data. Also, if an algorithm is poorly trained, it will need to be retrained and retested, adding to its overall cost.

Step 2: Model selection

Once prepared, you need to choose the right machine learning model for the job at hand.

Take your time to research each model. Each uses a different algorithm to solve different problems in different ways. It’s also important to consider how complex each model is and how much computational power it needs to run. Complex models often require more training time, processing power and memory, increasing the cost of development.

If you want the model to classify and label your data, you might want to choose a linear regression model. Whereas if you want it to cluster certain types of data, you could use a K-means model.

We’ll explain the difference in machine learning algorithms in more detail below.

Step 3: Training

After you’ve chosen a suitable model, you need to train it using your prepared data. In this step, the model will learn how to map your input data to the output it should give.

At first, the model will adjust its parameters randomly. It makes a prediction based on these random parameters and then compares it to the actual output. If there’s a big difference between its prediction and the output, it’s inaccurate.

Returning to the image identification example, the model will analyze each image and set random parameters based on pixel values.

For instance, it might decide that the percentage of brown pixels is a useful parameter and use that along with dozens or hundreds of other parameters to try to predict which images are of dogs.

The predictions are then compared to the real label, whether it’s a dog or not. The parameters will then be adjusted slightly in a direction that makes it more accurate. Perhaps it needs to consider a smaller percentage of brown pixels.

The model will repeat this process hundreds of times until its predictions closely match the output every time.

Step 4: Evaluation

After training, you need to work out how effective the model is in a real-world scenario. To do so, you have to test the model using new data that wasn’t used in the training stage. Once it has generated its predictions, you can use key metrics to measure how accurate the model is.

Here’s an example of data validation. Say you’ve trained a model to classify images as either “dog” or “not dog,” you might test its performance by measuring the number of:

• True positives (dog images correctly labeled as dog images)

• False positives (non-dog images wrongly labeled as dogs)

• True negatives (non-dog images correctly labeled as non-dog)

• False negatives (dog images wrongly labeled as non-dog)

Different problems need different metrics, so it’s crucial to choose those that reflect your specific task.

If the model works well and meets your requirements, you can start to use it. However, it’s important to watch its performance and check for biases or incorrect predictions. It may become less effective over time due to evolving data trends.

What are the 3 main types of machine learning?

There are dozens of machine learning algorithms and each type has its unique strengths and weaknesses. Here are the three main machine learning methods:

1. Supervised machine learning

In a supervised learning model, you give the algorithm both a labeled dataset and the correct answer. It then tries again and again to get closer to predicting the correct answer, changing its approach each time.

First, you label the data you’re going to feed into the model. If you were creating a spam filter, your labeled data would consist of both spam emails and normal emails. The model then practices identifying spam in this dataset until it’s very good at it.

Examples of supervised learning algorithms include:

• Linear regression models. A learning method for relationships goes in a straight line (e.g., house price predictions going up when the number of bedrooms increases).

• Logistic regression models. A model for when relationships are binary (e.g., outcomes like yes/no or true/false).

• Decision trees. A learning method that makes predictions by learning simple decision rules gleaned from the data features, similar to a flowchart. Random forest models, for example, use multiple decision trees to get a more accurate prediction.

• Support vector machines (SVM). A machine learning model that splits data into classes.

2. Unsupervised machine learning

In unsupervised learning, the machine learning system is only provided with the input data. It isn’t given a correct prediction to compare its answers with. Instead, the goal of this type of machine learning is to use pattern recognition to uncover hidden trends in the data.

For example, it might discover hidden patterns or cluster groups of data that have similar traits. You could give it a dataset and a prompt like, “What do these profiles have in common?” and the program will find patterns that may have taken hours or even days for humans to discover.

This can provide valuable insights in areas like customer segmentation and personalization.

Examples of unsupervised learning algorithms include:

• K-means clustering. A clustering algorithm that groups related data points based on their similarity.

• Hierarchical clustering. A model that groups related data points together by combining the most similar clusters.

• Self-organizing maps. A type of model that uses a process called “dimensionality reduction” to provide a visual representation of complex data.

3. Reinforcement learning

In reinforcement learning, the model interacts with unlabeled data, makes decisions and learns from the results. It isn’t given the correct answer ahead of time. Instead, it finds the correct answer through trial and error.

They learn through feedback. Each good prediction gets a positive signal and vice versa. Over time, it’ll learn the best strategy to achieve the task at hand.

Businesses often use these algorithms for dynamic pricing and product recommendation systems.

Examples of reinforcement learning algorithms include:

• Q-Learning. A type of model that learns how to take the best action based on the potential future rewards it might receive.

• Policy gradients. A model that learns how to create “decision policies” that lead to better rewards.

• Deep Q network. An algorithm that combines Q-learning with neural networks, helping the model to learn optimal strategies in complex environments.

How is machine learning used in sales?

Thanks to improvements in computer science and AI, the number of machine learning applications is increasing exponentially.

Here are five key use cases that show how machine learning can improve your sales processes:

1. It helps create more accurate sales forecasts

Sales forecasting involves predicting future sales based on past sales data. Using these accurate forecasts, businesses can anticipate demand, manage inventory and make strategic decisions.

Without machine learning, companies need to analyze sales data manually, which takes a lot of time and effort.

Machine learning algorithms can enhance sales forecasts in the following ways:

• Historical data analysis. Machine learning algorithms can analyze enormous volumes of sales data. From this, they can identify trends that might go unseen by the human eye.

• External variables. Machine learning models aren’t limited to historical sales data. They can also use weather forecasts, market indicators and more to make better predictions.

• Real-time forecasting. Machine learning algorithms can adapt to data in real time, helping you respond to changes quickly.

Let’s consider a fictional company that sells sunglasses. Before machine learning, they predicted sales using historical data points and trends. For example, they might usually sell more sunglasses in summer.

However, they’ve now invested in a machine learning algorithm and fed it a wealth of data. The information might include sales data, website traffic, social media trends, weather forecasts and economic indicators.

The model might notice that there are increased web visitors and a heatwave is on its way. It predicts that there’ll be a surge in demand and the company increases stocks to meet the demand.

Likewise, if the model predicts a dip in sales, they could launch a promotional email campaign to counter it.

2. It can automate and enhance lead scoring

Lead qualification enables companies to rank leads according to their readiness to buy. It helps salespeople focus on the leads that are most likely to convert, making it easier to land sales.

Machine learning can help by creating predictive lead-scoring algorithms. Analyzing successful conversions means they can build a model that predicts how likely each lead is to convert into a customer.

Such a model can analyze a much broader range of factors for each sales lead. It could include demographic, behavioral and market data, leading to more accurate predictions.

Here’s how manual lead scoring and machine learning compare:

• Manual lead scoring. You use traditional scoring methods based on demographics and actions the leads take. You weigh each factor manually and assign each lead a score. The process is time-consuming and not always accurate.

• Machine learning lead scoring. You feed your machine learning model with sales engagement data. The model trains itself to find patterns that show whether a lead is likely to convert. It discovers hidden insights and scores future leads more accurately.

A machine learning algorithm might find insights that would be almost impossible for a human to uncover. For example, it might find that someone from a small tech company who has spent five minutes on your pricing page is five times more likely to convert than someone who only spends one minute on it.

The model can also keep adjusting and improving its lead-scoring capabilities as more data flows in. Essentially, machine learning can help automate lead scoring, make it more accurate, save sales reps time and drive more conversions.

3. You can use conversational chatbots to nurture customers

Without AI, marketing chatbots are rules-based, meaning you have to give them responses to specific questions ahead of time. They struggle with complex requests and can’t learn from their experiences.

Here’s how machine learning can enhance the capabilities of chatbots:

• Natural language processing (NLP). Machine learning algorithms help chatbots understand human language. More powerful speech recognition models can even understand slang and bad spelling, helping the chatbot respond to more kinds of questions.

• Contextual awareness. Machine learning can help chatbots understand the context of a conversation. For instance, if someone asks, “How much is it?” after discussing a specific product, the chatbot will know that they’re asking about that product’s price.

• Personalized interactions. Chatbots can remember previous interactions and provide personalized responses. If someone previously had a different problem, the chatbot could follow up and make sure that was resolved.

• Sentiment analysis. Some machine learning algorithms can interpret emotions from text (like whether a customer is satisfied or frustrated). Using this information, the chatbot can adjust its responses to provide a better customer experience.

Above all, machine learning ensures that chatbots and virtual assistants (like Siri and Alexa) are capable of learning from each interaction. If it fails to assist someone, it can improve itself for future conversations.

4. It can personalize recommendations for your customers

Recommendation engines can predict your preferences and recommend content that you’re likely to enjoy. Companies like Netflix and Amazon use these algorithms to recommend new shows and products based on your habits and history.

There are two main ways machine learning can help provide better, personalized recommendations:

• Content-based filtering. The algorithm analyzes content-based factors to find out what you like. For instance, if someone watches science fiction and fantasy movies, it could recommend more films from that genre.

• Collaborative filtering. The algorithm predicts a user’s interests based on other people’s interests and behaviors. If a bunch of people who watch similar things to you have watched another show that you haven’t seen, the algorithm might recommend it to you.

Many systems will combine these two approaches and even add other insights, like demographics and user feedback, into the mix.

Recommendation engines are becoming far more accurate. Many companies use them to offer relevant content, products and services to their customers.

In fact, according to a report from Grand View Research, the recommendation engine market is expected to be worth over $17 billion by 2028.

5. It can help create more effective dynamic pricing strategies

Dynamic pricing, also known as surge pricing, is where businesses set flexible prices based on real-time market demand and other factors.

Ride-sharing apps like Uber and Lyft use machine learning for their dynamic pricing at times when demand is high. Airlines, hotels and e-commerce platforms also use dynamic pricing models to adjust prices in real time, often resulting in higher profits.

Two of the main ways machine learning can help with dynamic pricing are:

• Price-optimization models. Machine learning models learn complex patterns and cause-effect relationships. These models predict how price changes will affect demand at different points, helping companies optimize their prices according to trends.

• Real-time pricing. Models change their prices based on real-time factors like demand fluctuations. For example, there may be more people searching for rides on a rideshare app. They can also compare competitor prices and inventory levels, adjusting prices accordingly.

Without machine learning, it’s difficult to pull off dynamic pricing. It would take too long for a human to analyze the data and make the required price changes.

Furthermore, machine learning marketing capabilities make it possible to change prices in real time, which isn’t possible in traditional static pricing strategies.

Other use cases for machine learning in sales

We’ve barely scratched the surface of what machine learning projects can do to boost sales processes.

It’s beyond the scope of this article to go into more detail, but here are other capabilities that have been (and are being) developed:

• Segmenting customers into key groups

• Predicting customer churn and pinpointing its causes

• Discovering trends and improving sales data analysis

• Identifying opportunities to sell additional products to existing customers

• Calculating how much revenue a customer will create in their lifetime

• Discovering associations between different products and suggesting bundle offers

• Automating sales reports and dashboards

• Detecting fraudulent activities in sales transactions

• Getting better sales predictions using machine learning

Whatever part of the sales process you want to streamline or automate, there’s a high chance someone has already created a tool to help or that it’s coming soon.

Final thoughts

As the capabilities of big data and artificial intelligence continue to increase, so do its possible uses. Machine learning helps you forecast sales, mine customer data for powerful insights, implement real-time pricing strategies and more – the possibilities are almost endless.

Integrating machine learning tools into your sales and marketing workflow is an effective way to boost sales, improve customer relationships and skyrocket your ROI.