Why Is Machine Learning Important?

Machine learning is a process of teaching computers to learn from data without being programmed. It is a subfield of artificial intelligence that enables computers to learn from experience and understand natural language. This blog post will discuss what kind of data you need to run machine learning models in Graphite.

A branch of data science, machine learning involves constructing and studying algorithms that learn from data and use that knowledge to make predictions by detecting patterns in the data. 

In layman's language, using artificial intelligence (AI), machine learning empowers software applications to predict outcomes more accurately and automatically without being programmed to do so, while also enabling systems to learn and improve from experience.

According to a 2021 PWC survey, 86% of respondents agreed that AI had become an integral part of their business. Over 52% of respondents in companies and workplaces across the globe also acknowledged the impact of the pandemic in accelerating plans to adopt machine learning and artificial intelligence. 

 Why is it so important?

There is a lot of excitement around machine learning models these days. And for a good reason — machine learning models have the potential to revolutionize the way we do business and make decisions. But what are machine learning models, and how do they work?

Simply put, machine learning models are algorithms that learn from data. By 'learn,' we mean that the algorithms adjust themselves automatically to improve their performance on a given task. 

You can do this in a supervised or unsupervised manner. 

In supervised learning, the algorithm is given a training data set containing the correct answers for a given task. The algorithm then adjusts itself to produce the right solutions for new data points. Unsupervised learning is more complex as the algorithm lacks training data. Instead, it must learn from the data by finding patterns and relationships.

Machine learning models have been used for 

• facial recognition, 
• weather forecasting, 
• forecast revenues
• predict sales conversion
• and fraud detection. 

The possibilities are endless, and we are only just beginning to scratch the surface of what machine learning can do.

Lead Scoring Model

A lead scoring model is a machine learning model that helps businesses predict which leads are most likely to convert into customers. In order to build a lead scoring model, businesses need to have access to 

• demographic data, 
• behavior data, 
• purchase history, 
• and other data points from their CRM system.

Once the model is built, businesses can use it to score new leads and prioritize them for sales reps. 

This allows businesses to focus their sales efforts on the leads most likely to convert, resulting in more closed deals and increased revenue.

Customer Churn Model

In machine learning, the churn model is a binary classification that predicts whether a customer will cancel their subscription or not. 

The model is trained on a labeled dataset of past customers, where each customer is classified as either "canceled" or "not canceled." After the model is built, a client can use it to predict which new customers are most likely to cancel in the future.

The customer success team can then use this information to reach out and prevent churn before it happens. For example, they may offer a discount or additional services to customers at risk of canceling.

A churn model is an essential tool for subscription-based businesses. However, it's also used for other business models such as retail, e-commerce, and SaaS. 

How does it work?

Machine learning is widely used in various applications, including email filtering, detecting network intruders, bank fraud detection, stock price prediction, medical diagnosis, and robot control. In general, there are two types of machine learning:

●       Supervised Learning - where the training data includes labels or other information that indicate the correct output for each instance

●       Unsupervised Learning - where the training data does not include labels or additional information about desired outcomes.

Various hybrid approaches combine elements of both supervised and unsupervised learning. 

Below, we've outlined some examples of how machine learning is applied in businesses.

Real-Time Chatbot Systems

Real-time chatbot systems use machine learning to monitor conversations and constantly identify improvement opportunities. For example, if a customer service chatbot is having difficulty understanding a customer's question, the system can flag this issue and send it to a human agent for review.

The agent can then provide feedback to the chatbot, which can help improve the chatbot's understanding of the customer's needs. This feedback loop ensures the chatbot is constantly learning and improving, making it more effective at delivering excellent customer service.

Decision Support

Machine learning algorithms can automatically identify patterns and correlations in data, making it possible to generate insights that would otherwise be difficult or impossible to find. 

For example, a retail company might use machine learning to analyze customer purchase history and identify behavior patterns that could indicate future needs or preferences. 

By understanding these patterns, the company can make better decisions about product stocking, marketing, and sales strategies. In this way, machine learning can give businesses a significant competitive advantage, helping them make better decisions.

Customer Recommendation Engines

Customer recommendation engines are machine learning algorithms predicting what products or services a customer is likely interested in. Many businesses, from online retailers to streaming services, use recommendation engines to personalize user experience and increase sales. Customer recommendation engines use a variety of data sources, including purchase history, web browsing data, and social media activity, to create accurate recommendations. 

The algorithms analyze this data to identify patterns and correlations. Finally, the recommendation engine will suggest to the customer what they might want to buy or watch next. Some businesses develop customer recommendation engines, while others offer many off-shelf solutions. In either case, these algorithms are constantly evolving.

Learn more about the benefits of using machine learning

Machine learning is a relatively new field, but it has significantly impacted various industries, including retail, healthcare, finance, pharmaceuticals, and manufacturing. We hope you understand now why is machine learning important. 

Machine learning is also increasingly used to power applications such as search engines and recommender systems. As data becomes more plentiful and computational power continues to increase, machine learning will play a crucial role in several strategic areas, allowing businesses to gain insights from data and automate the data-driven decision-making process. 

Contact us today if you're interested in learning more about how machine learning can help your business grow, or explore Graphite Note Use Cases.