Machine learning and businesses are mutually connected, as the first one is developed to increase the company’s competitive advantage, and the second requires innovation to get rid of repetitive tasks and keep customers satisfied. The ML market is expected to reach $90.1 billion by 2026, compared to $17.1 billion in 2021, and propel different industries to higher levels of sophistication.
Once considered fiction, artificial intelligence and ML are crawling into our lives, influencing our decisions, and astonishing businesses with futuristic solutions. Today, we will talk about the use of machine learning in business, major use cases for all types of business, and outline the benefits of machine learning.
Insights of the ML in Business
Before we dive deep into machine learning business use cases and define how it can add value to your business processes, let’s find out how machine learning actually works. Its main task is to process vast amounts of data and extract vital insights from this information.
Imagine that you have started an online clothing store and have a customer base, but you still don’t understand the audience’s preferences and can’t predict their future demand for your goods. Machine learning algorithms will gather data about your consumers and process their behavior and purchase history to find patterns. As a result, you can offer users personalized recommendations and predict future demand to optimize logistics. Constantly evolving ML algorithms identify valuable information from raw data sets. The more information algorithms consume, the more precise predictions they will make.
The machine learning is continuously supervised for new pattern identification and more ML applications can be used throughout industries. It is worth mentioning that emerging trends like reinforcement learning, federated learning, and edge computing have a direct impact on business decision-making and customer behavior predictions:
Reinforcement Learning
In simple words, reinforcement learning is a type of ML based on trial and error. As a result, the learning process relies on positive and negative reinforcements, where correct answers will dominate with time.
Federated Learning
Just think of voice recognition while using a Google Assistant, or Siri, or face-scanning applications. That is federated learning. With the federating learning approach, the model is trained locally from the raw data without extracting data from client devices. The most common applications are smartphones, transportation, manufacturing, and healthcare industries.
Edge Computing
The idea behind the invention and development of edge computing is to analyse large quantities of data near the point of its generation and provide immediate responses. The devices like self-driving autos, IoT devices, and automated machines in manufacturing can benefit from this approach.
With lots of features and trends in ML and AI, all types of businesses can find a good solution for better management and decision-making.
Machine Learning for Business: Use Cases
From spam detection by renowned email clients to face recognition by Facebook, we benefit from machine learning models daily. Let’s take a look at the most significant applications of machine learning in business:
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Customer Lifetime Value Prediction
Customer lifetime value is the total income you can expect from a typical consumer from the first to the last purchase. It’s a crucial metric to optimize marketing campaigns and set your budget efficiently. Machine learning prediction models analyze historical data for hidden patterns to help you figure out how customers cooperated with your company and how they will do it in the future. This data will also enable you to find out your consumers’ spending habits and the income you generate from a typical client.
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Behavior Analysis for a Better Experience
User behavior analysis is one of the most common ML use cases, especially in the retail industry. This sector deals with vast amounts of data, so machine learning algorithms can process it to detect consumers’ purchase patterns and purchase history.
Customer behavior analysis will enable you to find data-powered answers to the following questions:
- Which products or services grab your customers’ attention and why?
- How do consumers navigate your website or store?
- Why do customers choose your business over competitors?
- How do they learn about your brand?
- How do they use your goods or services?
- What issues may they face?
Thus, with ML in retail services, you will be able to anticipate the changing demands, provide consumers with offers based on their preferences, and develop powerful marketing strategies to boost sales.
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Data Entry Automation
Machine learning technology is a lifesaver for businesses that need to reduce manual processes and optimize data entry to let employees do more strategic tasks. With ML algorithms, you can extract information from imagery, websites, and documents to pick relevant data and view it in a structured manner.
Data entry automation will help you avoid errors caused by human factors, save time, and analyze large volumes of data. You won’t have to hire more data entry specialists to deal with your corporate information, so it’s a great way to reduce operational costs.
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Personalized Recommendations
If you are looking for a way to enrich your audience with new customers and retain the existing ones, machine learning will help you. By analyzing users’ browsing experience and their interaction with your website, you can offer them the products or services they require.
For example, a user’s recent search query was “middle-priced black leather jacket”. Machine learning algorithms will capture this information to make your website offer personal recommendations and show relevant products.
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Spam Detection
With 333.2 billion emails sent every day, email providers don’t always filter out spam messages properly. Usually, they analyze the email’s content. So, messages will probably be marked as spam if they include:
- mass mailing,
- grammatical and spelling mistakes,
- an anonymous sender,
- a financial information request,
- poor-quality images, etc.
However, spammers are becoming more cunning, and it’s crucial to benefit from machine learning algorithms to predict whether an email is spam.
To teach ML models to identify unwanted messages, you have to provide them with a sequence of examples of “spam” and “non-spam” emails. Then, they will have to find relevant patterns in your data and divide messages into 2 categories. It’s a great way to detect and eliminate phishing messages and junk emails.
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Financial Analysis and Prediction
From predicting business expenses to fraud detection, financial institutions can benefit from machine learning technology to forecast outcomes more precisely. Advanced algorithms need less time to capture data, conduct a data analysis process, and come up with accurate financial predictions.
After processing your current policies and goals, market research data, economic surveys, and other essential records, a predictive model will offer you informative reports about your company’s performance. It will help you make data-driven decisions, build strategic business plans, and run an effective budgeting process.
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Enhanced Cybersecurity
Machine learning for businesses enables companies to minimize concerns associated with privacy issues, phishing attacks, and data breaches. Using ML algorithms, you can empower your current security system and predict future cyber attacks by analyzing historical data and identifying anomalies.
Enhanced cybersecurity applies to many industries, like finance and e-commerce. If you need to strengthen payment security, machine learning algorithms can make it happen. Based on the previous data, they will identify suspicious transactions and actions to notify users of fraudulent activity.
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Image Recognition
The image recognition market is expected to reach $53 billion by 2025, compared to $26.2 billion in 2020, with neural networks trending in the healthcare industry and retail. With empowered image recognition, businesses can provide customers with a checkout-free shopping experience. A robust combination of computer vision and deep learning controls when goods are taken from shelves and monitors these products in a virtual cart. When customers leave a store, they will get a receipt via email. In the case of healthcare organizations, image analysis is used to predict diseases by identifying normal or abnormal patterns.
In transportation, Airbus is using TensorFlow, an open-source platform for machine learning, to get and analyze the data from satellite images to deliver real-time information to its clients. This platform can be used for multiple tasks like natural language processing, image and handwriting recognition, and other simulations.
Benefits of Machine Learning in Business
If you are still wondering whether you need machine learning for your business, let’s consider the next benefits of machine learning:
- Enhanced customer experience and precise sales forecasts. With ML, retail companies can understand their consumers’ behavioral patterns, predict market demand accurately, and provide their audience with personalized offers tailored to their preferences.
- Accurate medical predictions. The healthcare industry can use ML to detect high-risk patients, suggest the most suitable treatments or procedures, and predict readmissions. It’s also possible to analyze current symptoms and genetic history to detect diseases in the initial stages.
- Efficient predictive maintenance. Manufacturing companies need to avoid equipment breakdowns. ML in manufacturing can predict possible issues before they occur to increase the lifetime of your equipment.
- Data-driven decision-making. ML algorithms acquire valuable insights from data as rapidly as possible and transform this information into structured reports. A fast and data-powered decision-making process will enable you to build more powerful strategies and accelerate business operations.
Although developers constantly are searching for ways to make AI and ML technology work for humankind in different industries, there is a definite scope of challenges and limitations to overcome.
Data Privacy Concern. Data security has always been a number one concern in dealing with information and block threats. In ML processes, any type of information leak can lead to the whole algorithm crash and influence both business performance and customer experience.
ML Algorithm Bias. Machine learning only starts to give great results and it takes time to train algorithms and get the final product. In some cases, it may take a year to implement a machine learning algorithm to achieve the desired model and businesses are not eager to bet on it.
Lack of Specialised Expertise. As AI and ML are relevantly new in career development, fewer developers have in-depth skills and expertise to implement complex ML algorithms for your business, except Python, which is used by a large stake of IT specialists. The journey to the ML career leaders is still ahead, yet, for large businesses with big project ideas, it is recommended to collaborate with development agencies that already have similar projects in the portfolio.
Yet, the benefits of machine learning in business are far better than the limitations. Any type of business can start with a simple ML model and learn its potential. Moreover, with the tools like TensorBoard, developers now can visualize metrics like data loss, biases, inaccuracies, and more.
Empower Your Business with Advanced Machine Learning Solutions
The future of deep learning algorithms is here to come and reinforce the current ML applications in businesses. But now, with image recognition, automation, and spam detection features, machine learning will enable you to benefit from cybersecurity to stay on the safe side, conduct behavior analysis to understand your customers, and eliminate manual data entry to let specialists focus on more essential tasks. LITSLINK is always happy to build innovative ML solutions for your business and help you thrive!
