This article compiles a comprehensive, curated list of the most common use cases and applications of Machine Learning in Business from a predictive analytics perspective.
56% of organizations today are using machine learning in at least one business function, according to a recent McKinsey survey. That means that AI (Artificial Intelligence) and ML (Machine Learning) will benefit more than half of companies in 2022.
The AI market will grow from USD 86.9 billion in 2022 to USD 407.0 billion by 2027 - at a fantastic Compound Annual Growth Rate (CAGR) of 36.2% (Markets and Markets).
Only 29% of SMEs (small and medium enterprises) said they had adopted AI technology (Forbes) - and this article is for them.
Based on our own research, the best way to incorporate AI into your business and to increase adoption is to identify how AI can help you. The essential part is to recognize the possible use cases of AI in your industry.
We cover marketing, sales, customer services, products, technology, finance, and other processes.
But first, a gentle introduction to the field and some basic expressions.
How Do Machines Learn?
Traffic prediction, face recognition, product recommendations, self-driving cars—machine learning is at the core of the future of technology.
With machine learning, machines can match and even transcend human information processing and learning capabilities to solve complex problems.
Machines and their learning accuracy determine how good they are at solving everyday problems. Understanding how machines learn should be a priority for organizations worldwide, so they can leverage their ability and take their operations to the next level.
What Is Machine Learning?
Machine learning is a discipline of artificial intelligence that helps computers learn, identify patterns, and make decisions without human influence.
Simply put, machine learning is the process of computer learning with the help of data sets. Through the process, computers learn how to perform specific tasks without being explicitly programmed to do so.
This is achieved through sophisticated algorithms that teach computers how to process data. Each time the computer is fed new data, its algorithms recognize patterns and make accurate predictions.
But that’s not all. Depending on the algorithms fed to the computer, it may perform various tasks beyond recognizing patterns and making predictions, which are widely applied in modern life and tech products. Their functional accuracy is what sets machine learning algorithms apart from the rest.
Are Deep Learning and Machine Learning the Same?
No. Deep learning is a subfield of algorithms under machine learning.
Machine learning involves teaching computers to analyze data and perform tasks without human intervention. Deep learning involves teaching computers to think using structures modeled on the human brain.
Machine learning aims to solve complex problems, while deep learning aims to mimic the human brain’s processing ability.
Machine learning models work with smaller or medium data sets, and deep learning models often involve large data sets.
Machine learning is the parent category of all algorithms, and deep learning is just one of them.
Machine Learning Algorithms
Algorithms are sets of rules created to help computers perform problem-solving operations. Machine learning algorithms are systems that run the machine learning process. They are the key to how computers interact with data to learn how to interpret it and put it to use.
Interacting with, interpreting, and using data can happen in many different ways. Machine learning algorithms must be trained to learn how to perform any task. This training often requires the machine to be fed large volumes of data multiple times over.
The machine learns the specific representation after applying an algorithm to a dataset is referred to as a machine learning model or hypothesis.
Upon multiple instances of using algorithms, computers get better at achieving desirable results. This type of zero interference model makes up the core of all machine learning processes.
Therefore, all machine learning relies on the availability of data to train the machines. This data must be well-organized and labeled to ensure the outcomes are accurate.
There are many types of machine learning algorithms based on how the machine interacts with the data and what it needs to accomplish.
Supervised machine learning centers on using labeled or categorized data to train algorithms. It is the most common machine learning algorithm used today, especially in commercial applications.
In such a model, humans first label data and then feed it to computers that will learn how to identify it. This process teaches the computer how to identify similar data from a new dataset with no human intervention.
The supplied training dataset includes two sets of data: the input and the output.
The input is the data set that needs to be identified or learned.
The output is the data that teaches the machine what the correct outcome should look like.
This type of learning model is helpful in tasks that require a higher level of accuracy and reliability. Popular applications include automation, where a large amount of data must be processed correctly. These are tasks that machines can perform faster than humans and with much more accuracy, too.
Here are two everyday use cases in which supervised learning is best applied:
Regression analysis or learning use cases are used to evaluate the relationship between two types of variables, a dependent one (a number) and changing ones.
This type of use case is often required in sales or revenue forecasting, trend identification, or prediction-based problem-solving tasks.
The classification use case provides the machine with datasets divided into two or more classes. These classes identify the data within them and are learned by the machine.
The machine is then fed a set of data without classes, which it must work to divide without human intervention.
A real-world example of classification is the process of predicting sales lead conversion or customer churn.
Unsupervised machine learning involves using unlabeled data to detect patterns or find solutions. It is most helpful in solving problems that we can’t predict the result for or when we want to see previously unrecognized patterns in the data.
In this model, the machine only receives the input, not the desired output.
Such a machine learning model is applied in real-life situations where you must segment a large dataset based on similarities or differences. This segmented dataset may be used as it is or further processed to solve more complex problems.
Here are two common types of uses cases for unsupervised learning:
This involves providing the machine with a dataset that the machine must identify and sort into groups or clusters. The sorting is done based on similarities within the units in the dataset.
Similar to clustering, this use case detects abnormalities in a given dataset.
Some problems may also require a combination of two or more learning models for computers to find solutions. Such problems are best tackled using a semi-supervised machine learning model.
This type of learning involves feeding the machine with both labeled and unlabeled data. The machine then learns how to label unlabeled data based on what it learns from the labeled dataset.
Reinforced learning is based on reward maximization. It is a type of unsupervised learning process that guides the computer to make optimized decisions by rewarding positive results and punishing negative results.
These algorithms explore possibilities from new data to evaluate the results of these possibilities. The algorithm then chooses the possibility with the optimal result.
Machine learning involves many other models and algorithms that apply these and a combination of them to the process. These are just some of the most common ones that are widely used for various applications today.
How Does the Machine Learning Process Work?
It is crucial to grasp how do machines learn to make the most of their ability to do so. This begins by first decoding how machine learning happens.
Selecting the Training Data Set
Training datasets contain the data input that is fed into the computer. It often includes labeled data that the machine will be taught to identify. Input data may also be unlabeled for the machine to learn how to identify patterns from the given dataset.
The first step involves selecting an appropriate and accurate dataset. The quality and relevance of the data will significantly influence the quality of the result, so it is crucial to choose data carefully.
Preparing the Training Data Set
The preparation process involves cleaning, distributing, and sorting the data that will be fed to the machine for training and testing.
Training datasets teach the computer how to perform a certain action, while testing datasets evaluate how well the action is performed.
How to Choose Machine Learning Model?
The next part of the process involves choosing an algorithm that will best suit the kind of result you want to get and the problem you want to solve.
The quality of a machine learning model depends on the quality of input data and the choice of model. Hence, choosing a suitable machine learning model is crucial to ensure you achieve accurate results.
An important factor in practical applications of machine learning is the process pf building algorithms and software to fulfill these desired tasks accurately and precisely.
Choosing the Right Machine Learning Model
The choice of which machine learning model to select depends on various factors:
Results to be derived
Nature of the problem
Training the Model
Training the model to predict results better does not involve human intervention.
Depending on the model selected, the algorithm works on multiple optimization methods to learn how to predict results accurately. Over time, this process trains the algorithm to understand and improve the model too.
Feeding New Data to the Model
Once the model is built, the machine is ready to be fed new data. This involves putting the machine to the test.
Old datasets should not be included as the machine is already used to the new outcomes and will simply replicate them. It would be best if you supplied the machine with a new dataset.
The machine applies the model to this new data and learns from its outcomes. Hence, the more data is supplied to the machine, the better it becomes at completing tasks accurately and efficiently.
The last step involves some human intervention in the form of parameter tuning. This involves streamlining the parameters in a particular model to improve the accuracy of the process - and the result.
Data scientists set and change parameters—they control the behavior of the learning model. Model parameters are more likely to make errors and deliver inaccurate, suboptimal results without correct tuning.
Importance of Machine Learning
Traditional models involving data analysis used a trial-and-error-based approach. Those models have long lost relevance and are no longer practical when complex problems involving huge data sets need to be solved.
Machine learning has made it possible to perform specific tasks more quickly and accurately than humans using traditional models. It also enabled lightning-fast analysis of large chunks of data to generate results and solve problems in real time.
Another crucial contribution of machine learning is how it can detect specific details that humans are unlikely to notice.
The most significant benefit and contribution of machine learning in practical applications are that it doesn’t require human intervention. So much time is saved so humans can use their time and energy on more productive tasks.
Here are some popular applications of machine learning in business across various industries:
Popular Applications of Machine Learning in Business
Predict Lead Conversion
Sales and marketing teams find it challenging to predict their conversion rates, especially when they have multiple lead pipelines that generate different sales levels.
Generating leads is the lifeblood of your business.
Your ability to prioritize and follow up on the right ones can significantly impact your bottom line.
When done manually, gathering data on lead scoring or categorizing lead attributes and activities can be a tremendously tedious task.
Sales can fluctuate wildly depending on the time of year, too. Some businesses also face significant shifts in demand throughout the year based on weather, seasonal events, industry trends, and more. These fluctuations affect what products or services people want and their willingness to pay for them.
So how can businesses increase their sales lead conversion and profits and make more accurate sales forecasts that reflect these factors?
Revenue forecasting is crucial for a robust, effective business plan that will lead to long-term success.
It helps business owners develop and streamline strategies for growth and answers questions like how much they intend to grow and how much capital is needed for a healthy cash flow.
Estimating revenue over a given period is crucial for correctly pricing products and services. A sophisticated pricing strategy and cash flow can help the business thrive regardless of demand fluctuations, emerging trends, and product seasonality.
However, accurate revenue forecasting can be difficult to achieve. There are many challenges, such as finding and collecting the best data and choosing the proper forecasting methods to suit the business.
Knowing how much demand a business will generate within a certain period is essential. It helps companies evaluate how much they should spend on production, allocation of resources, and even marketing.
Demand forecasting enables businesses to estimate and predict customer demand for a particular product or service. An accurate forecast can significantly reduce risk and help sales and product teams make intelligent decisions that will have an impact on revenue and profit margins.
However, arriving at an accurate demand forecast can be difficult. Approximating demand can bleed a company’s resources, increase losses, and disappoint customers.
Poor demand forecasting also results in a significant loss in market share to competitors or even a company’s loyal market base if they cannot satisfy customer demand.
How can AI help businesses generate an accurate product demand forecast?
The one challenge businesses always face - is customer churn. A churn is when customers stop buying a product or opt-out of service. Often, this entails the need to find new leads and pipelines so you can still meet revenue targets.
The ideal solution, however, is to map out reasons for customer churn to prevent customers from turning to a competitor.
Understanding customer churn goes together with customer retention analysis. Knowing why customers are leaving can improve your customer retention rates and help you understand the weaknesses of your product and strategy.
Monitoring churn helps businesses stay in touch with their customer base. Though it might seem straightforward, many companies find it challenging to address churn.
Churn can be challenging to predict as the raw data needed for an accurate analysis can be messy and might require data to be engineered into a more usable format. Collecting data can be labor-intensive and requires removing outliers and identifying potentially essential features.
Is there an easier way to gather and sift through data to predict churn?
For long-term growth and success, businesses must generate consistent revenues and high-profit margins. Cross-selling is one way to do this, especially if the company has several complementary products or services.
However, cross-selling has its challenges, even if customer demand is high.
Power your business with machine learning, without writing code.
Marketing and sales teams may not have the knowledge to sell many different products to various customer segments. They may be unable to address the correct type of customer poised to purchase a specific product or service.
Furthermore, some customers may not engage with cross-selling strategies, especially if they are not interested in the offered bundles.
How can companies overcome these challenges and arrive at an accurate cross-selling prediction rate?
Companies must have robust inventory management systems to track and monitor every movement of their items throughout the logistics process. Crucial data such as product descriptions and the stock numbers for each product type must be readily available and up-to-date.
Certain products generate more revenue than others. It comes down to customer demand, production costs, and risk calculations. Businesses must identify which of their products or services can impact their total sales to make improvements and plan for growth.
ABC product analysis helps companies manage their inventory and highlight essential products. However, proper research requires extensive data collection: inventory data, product value, percentage value, and the total value of the inventory.
How can companies arrive at an accurate product analysis?
Customer or product segmentation helps companies laser focus their marketing strategies on their target audience. Businesses can allocate their marketing and sales budgets in the most efficient and productive ways because they can target the right audience segment with a specific product they will likely purchase.
Good segmentation gives companies a deeper understanding of their markets. And once they understand their audience, they will gain an edge over the competition.
However, effective segmentation largely depends on data quality and sources. To properly assess customer and product segmentation, specific parameters are required regarding demographics, psychographics, and behavioral and geographical data.
This data can be collected through surveys, transaction histories, and customer information.
However, relying solely on past historical behavior with only limited data will result in inaccurate segmentation that will not benefit the business.
So what is the best way to segment products and customers in a way that helps companies meet their goals and gain a competitive edge?
Businesses want to close the most significant possible deals to boost their revenue stream significantly. However, tracking and cultivating such leads requires time and resources.
Often, companies take complicated steps to find new pipelines and leads, and there is no direct effort to predict the value of a single lead. But if businesses direct some of their resources into assessing what each client could bring and the deal they can close, growth can seem more effortless than ever.
Furthermore, one big deal can be enough to upend an existing sales forecast and impact a business’s bottom line.
It can be difficult for companies to determine which leads can be converted into big sales and which are worthy of the sales and marketing team’s attention.
One of the biggest challenges for any business is retaining existing customers. Understanding why customers stop supporting businesses and what triggers churn is essential. Customer retention is a crucial factor in gaining revenue without increasing costs.
Customer retention analysis uses data to identify behaviors that affect churn and strategies that need improvement. Direct customer feedback and surveys help companies understand why and how customers lose interest in a service or product.
However, analyzing churn can be challenging because it requires accurate data to predict customer behavior. Transaction history, customer information, and other metrics must be collected and analyzed efficiently.
Raw data for churn is often incredibly complex and requires much work to be beneficial.
Is there a way to quantify customer churn and produce an accurate customer retention analysis?
Machine learning is just one of the few ways in which machines learn. The parent field of artificial intelligence has yet to be fully explored, and there is tremendous potential for further advancements when applied to many industries.
Today SMEs can use no-code machine learning platforms like Graphite Note to fill the gap if they don't have in-house AI talent. No-code machine learning is a suitable option for non-technical people because it is less intimidating. This new technology allows everyday business users to create fantastic Machine Learning applications without writing a single line of code.
Machine learning can help make manual and traditional processes much more efficient while reducing the cost required to put them to practice. Used to its best potential, machine learning holds power to provide more insight where humans couldn’t.
All that, however, is only possible after we solve the equation in answer to, ‘how do machines learn?’
We hope this guide to the popular applications of machine learning in business, use cases, and their importance will be helpful.
Understanding machine learning processes and applications is crucial in shaping their application for your business or personal use.
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