Why Businesses Should Use Machine Learning in 2023?
In the words of Nick Bostrom, “Machine learning is the last invention that humanity will ever need to make.”
Let’s start etymologically; machine learning (ML) is a subset of artificial intelligence (AI) that trains systems to apply specific solutions rather than providing the solution itself. It learns from previously existing data to detect any recurring patterns or trends in the future.
There are primarily three ways of doing this: unsupervised learning (uses unlabeled data), supervised learning (uses labeled data), and reinforcement learning (uses an agent and an environment to learn without specifying a target value). All these techniques help in generating data-based insights.
The importance of machine learning for business lies in making data-driven decisions, boosting performance, and gaining a competitive edge. Still, if the proper framework is not in place, it risks the exposure of business data to numerous dangers.
ML technology has recently become a significant part of businesses, irrespective of the industries they belong to. Machine learning has opened several undiscovered business paths by generating deeper consumer insights, increasing engagement, and forecasting financial outcomes. Over 76% of companies have prioritized ML automation over other initiatives.
This blog walks you through the impact of machine learning on different areas of businesses and how some real-world companies are leveraging the same.
Table of Contents
- Impact of Machine Learning on Different Functions of a Business
- Machine Learning for Business Operations
- Machine Learning for Customer Insights
- Machine Learning for Marketing
- Machine Learning for Sales
- Machine Learning for Fraud Detection
- Machine Learning for Predictive Analytics
- Benefits of Machine Learning for Businesses
- Challenges of Machine Learning for Business
- Future of Machine Learning for Businesses
- Frequently Asked Questions
Impact of Machine Learning on Different Functions of a Business
Like many other industries, including healthcare, finance, etc., machine learning has also significantly impacted the business domain. It has helped with day-to-day business operations, predictive maintenance, automated decision-making, and reducing human errors. Read on to learn more about the importance of machine learning for businesses that have facilitated each of these tasks/processes.
Machine Learning for Business Operations
1. Streamlining Business Operations Using Machine Learning
The primary role of applications of machine learning in business is to assist personnel in streamlining operations like predictive maintenance, fraud detection, customer segmentation, inventory management, and process automation. Over 41% of retail respondents believe that ML could optimize their internal operations.
2. Automated Decision Making with Machine Learning
As per a report by the world-famous McKinsey and Company, over 45% of work activities could be automated using machine learning, yet again highlighting the importance of machine learning for businesses. Although published in 2015, the report’s findings are much more valid today. Advances in ML algorithms have led businesses to utilize deep learning, a domain of AI that can analyze many factors prevalent in present data and learn its patterns to predict future states and suggest more efficient operations.
3. Predictive Maintenance with Machine Learning
Predictive maintenance is a data analysis technique that uses condition-monitoring tools to track the operational performance of equipment. By predicting when maintenance is needed, businesses can schedule maintenance activities more efficiently, reducing costs and minimizing disruptions to operations. It is evident that ML algorithms can enhance predictive maintenance as companies like GE have come up with AI and ML-based predictive maintenance to reduce jet engine failure by up to 33%!
4. Reducing Human Error with Machine Learning
Reducing human errors by incorporating machine learning and AI for businesses is one of the primary use cases. ML algorithms can automate repetitive tasks, such as data entry, reducing the risk of human error. They can also identify patterns and anomalies in data that humans may miss, allowing businesses to detect and correct errors before they cause problems. For example, a study by McKinsey found that automating tasks using ML can reduce errors by up to 80%.
Machine Learning for Customer Insights
Source: Starlight Analytics
1. Personalized Customer Experiences with Machine Learning
People interested in your business or product are often seeking some or the other personalized experience. Businesses that realize it sooner are more likely to be the center of attention in their target markets. Moreover, sending generalized messages or updates to all your customers won’t help either. Machine learning and AI for businesses have facilitated hyper-personalization, which was earlier deemed unachievable, as seen in companies like Nike, eBay, etc.
As per a Salesforce survey, over 88% of customers agreed that a personalized experience was as necessary as the product/service. Clearly, ML algorithms have a long way to go in doing so.
2. Understanding Customer Behavior with Machine Learning
The importance of machine learning for businesses is also because it helps understand customer behavior. A customer is the one who decides how your product/service will perform in the market. Inevitably, their behavior and interaction with the product will determine whether something should be changed. ML algorithms can analyze customer data to identify different segments of customers with similar behavior or preferences.
For instance, the famous music streaming service Spotify uses an ML-based recommendation system to recommend new songs and artists based on a user’s listening history. More than 70% of these recommendations come from this system.
3. Customer Segmentation with Machine Learning
To find trends and combine consumers with similar behavior or preferences, machine learning (ML) based customer segmentation analyzes vast volumes of customer data using algorithms and statistical models. Compared to conventional segmentation techniques, which frequently rely on manual analysis and expert judgment, this approach is more data-driven and automated.
The e-commerce giant Amazon uses this machine learning approach to segment its users based on their purchase history, browsing patterns, demographics, and other factors.
4. Sentiment Analysis with Machine Learning
A machine learning approach called sentiment analysis examines texts for polarity, ranging from positive to negative. Machine learning technologies can automatically learn how to recognize sentiment without human input by being trained with samples of emotions in text. Simply put, the design of sentiment analysis models is such that they can read beyond definitions and understand contexts, sarcasm, etc. This machine learning approach is widely used in business intelligence. For instance, Intel utilizes machine learning software from Kanjoya Inc to identify emotions in writing.
Machine Learning for Marketing
Source: Digital School of Marketing
Machine learning and AI for businesses also have applications in the marketing industry. They help in optimizing marketing campaigns, real-time analytics, targeting ads, and giving personalized recommendations.
1. Marketing Campaign Optimization with Machine Learning
There is more to marketing efforts than just the message you convey. It’s equally crucial when and how you deliver that message. Generally, marketing campaigns can only achieve traction with a data-driven, analytical strategy or miss essential chances. This is because social media analysis, customer behavior analysis, etc., are vital aspects of marketing, and the automation of these tasks happen using machine learning. Moreover, ML algorithms also help in email marketing, landing page optimization, and a lot more.
DoorDash, the renowned company connecting people with retailers, uses machine learning to optimize its marketing campaigns.
2. Ad Targeting with Machine Learning
Ad targeting is locating and presenting advertisements to selected audiences most likely interested in the promoted good or service. The social media giant Facebook uses this ML-based approach to target ads, maximizing user and advertisers’ value. By analyzing vast amounts of data and applying the resulting knowledge to forecast customer behavior, ML and AI for businesses in marketing can improve this process’s effectiveness and efficiency.
3. Real-time Analytics with Machine Learning
Using machine learning and AI for businesses involved in marketing is also meant to facilitate real-time analytics, making it easier for marketers to develop targeted marketing content and campaigns. With these technologies, marketing teams can accumulate information on customer behavior, forecast future needs, and customize marketing initiatives to match those needs using a combination of AI, analytics, and machine learning capabilities. Because of this use case, many AI-focused companies provide specially designed marketing platforms like Strong Analytics to help marketers curate tailored campaigns.
4. Personalized Recommendations with Machine Learning
Personalized recommendations are one of the most used applications of machine learning in businesses in marketing. It can analyze large amounts of data, identify patterns in consumer behavior, and create personalized recommendations based on them. This further helps improve the customer experience and increase sales by suggesting products that may more likely interest the customer.
One real-life example of personalized recommendations using ML is Amazon’s recommendation engine. It employs machine learning (ML) algorithms to examine customers’ past purchases, browsing habits, and data from similar customers to make product recommendations.
Machine Learning for Sales
Lately, businesses have realized the importance of machine learning in increasing their sales by many folds.
1. Sales Forecasting with Machine Learning
The technique of estimating future sales based on past data and patterns is known as sales forecasting. Large-scale data analysis using machine learning algorithms can reveal patterns and trends that can be used to produce precise sales estimates. As a result, operational efficiency, customer satisfaction, and income can all be improved. Many businesses use ML-based platforms/tools like Salesforce’s Einstein to predict better sales forecasts.
2. Sales Pipeline Management with Machine Learning
Sales pipeline management is the method of monitoring and controlling the steps in the sales process. Data from each pipeline step can be analyzed using machine learning to pinpoint areas where the procedure can be more efficient. For instance, ML algorithms can pinpoint which pipeline steps take the longest to finish and offer suggestions for streamlining the procedure. It is yet another area where the majority witnesses the importance of machine learning for businesses.
3. Lead Scoring with Machine Learning
Lead scoring gives each lead a numerical value depending on how likely they will become clients. The traits and behaviors of high-quality leads can be determined using ML to examine client data and behavior, assisting firms in allocating their resources more wisely and prioritizing the most promising sales opportunities. Along similar lines, businesses can also use these algorithms to predict when a customer is likely to stop using your product (customer churn).
4. Automated Customer Interactions with Machine Learning
Face-to-face encounters are more crucial than ever in a digital world. Making appointments is a business tactic that benefits the client and the organization. Thanks to machine learning, even this process can be automated and free-up human time. Automated customer interactions use ML algorithms to personalize and automate routine tasks.
This is another application that Facebook widely uses in its Messenger app. Any developer can make and submit a chatbot to be added to Facebook Messenger. This implies that chatbots can be used by businesses that place a high priority on client satisfaction and retention.
Machine Learning for Fraud Detection
Fraud detection is one of the most vital applications of machine learning in businesses because:
- It is super fast, even if you are simultaneously working with hundreds of queries.
- It is highly scalable even if your business has a vast transaction volume.
- Needless to say, it is very efficient. It can handle the workload of hundreds of people.
Read on to learn more about the importance of machine learning in business for fraud detection.
1. Fraud Detection and Prevention with Machine Learning
Fraud detection and prevention is a critical area where machine learning (ML) can be applied to prevent financial losses and maintain customer trust. ML algorithms can be trained on historical data to identify patterns and anomalies that may indicate fraud. For example, ML algorithms can be used to detect unusual transactions, identify suspicious user behavior, and flag potentially fraudulent accounts.
For instance, PayPal analyzes user activity and spots fraudulent transactions using ML algorithms. The company’s fraud detection system combines supervised and unsupervised ML algorithms to spot fraudulent activity in real-time.
2. Anomaly Detection with Machine Learning
Finding unusual occurrences, observations, or patterns in a dataset that deviates from expected behavior is known as anomaly detection. Anomalies can take many forms, such as unexpected spikes or dips in data, unusual values or distributions, or unusual patterns of behavior. ML is a powerful instrument for anomaly detection because it can find patterns and anomalies that human analysts might miss.
As a matter of fact, before volcanologists requested that NASA send a satellite to image an unusual volcanic event in Ethiopia, an AI system created by the Jet Propulsion Laboratory of NASA could locate and direct the satellite to do so.
3. Risk Management with Machine Learning
Traditional risk management has become dormant and outdated. Newer methods of managing risks are far more effective and widely used by all businesses. ML algorithms for risk management are used to achieve various aims, including meeting legal requirements, achieving commercial objectives, and establishing institutional risk cultures. The key benefit of machine learning algorithms is their ability to find patterns in data without using rules-based programming.
4. Security Enhancement with Machine Learning
The importance of machine learning for businesses has increased in recent years, especially in the security industry. ML algorithms help in:
- Intrusion detection,
- User authentication,
- Threat intelligence,
- Malware detection.
However, it is essential to note that machine learning is not a silver bullet and should be used with other security measures, such as access controls, encryption, and regular security audits.
Machine Learning for Predictive Analytics
Predictive analytics, strictly speaking, does not forecast the future; instead, it uses probability theories to ascertain what is most likely to occur based on patterns and trends found in the analysis of previous data. Here are some ways where machine learning-based predictive analytics can help businesses.
1. Predictive Analytics for Better Decision-Making
To actualize business growth, it is vital to realize the importance of machine learning for businesses to make better data-driven decisions. And to make these choices, predictive analytics plays an important part. predictive analytics is the process of using historical data to predict future states via statistical and predictive modeling techniques. Previously, this process was altogether different from the standard machine learning process. However, the increasing demand for predictive analytics has necessitated ML-based automation.
Businesses use ML-based predictive analytics for
- Churn prediction,
- Customer segmentation,
- Customer lifetime value prediction,
- Next best offer prediction, etc.
2. Future Trend Predictions with Machine Learning
As markets expand, their volatility increases, making it more challenging to predict future states or outcomes manually. This is another area where machine learning comes in handy. Machine learning facilitates data collection, cleaning, feature engineering, and the creation of models that predict future outcomes. The most recent application of ML-based future trend prediction was witnessed during the COVID-19 pandemic, wherein scientists relied heavily on machine learning to predict occurrences of virus outbreaks.
3. Forecasting Business Outcomes with Machine Learning
Machine learning is also pivotal to forecasting business outcomes. After proving its mettle in predicting COVID-19 outbreaks and in the stock market, many businesses have adopted machine learning to predict future outcomes in their business models. This may include predicting the revenue/losses based on sales, customer attrition based on their purchasing history, etc. E-commerce giants such as Walmart, Hopper, etc., utilize machine learning to predict future sales and profit.
4. Predictive Analytics for Supply Chain Management
Another use of machine learning driven predictive analytics is for managing the supply chain. Supply chain managers use ML to forecast demand, develop pricing strategies, and manage inventory and logistics. More recently, this technology has been used for implementing real-time tracking via IoT and RFID devices to monitor shipments.
Many companies, like Google, Netflix, LinkedIn, and Amazon, use ML-based predictive analytics to gain a higher return and downsize inventory by up to 30% because of more accurate forecasting.
Benefits of Machine Learning for Businesses
There are numerous business benefits of incorporating machine learning in operations.
1. Increased Efficiency and Productivity
Machine learning and AI for businesses also officiate higher productivity and efficiency. One significant benefit of ML is that it can automate time-consuming and repetitive processes, freeing staff members to concentrate on more strategic work. For instance, it can automate data cleaning and entry, freeing staff members to perform more challenging and vital analytics tasks.
It can also streamline corporate processes like supply chain management by anticipating demand and managing inventory levels. This can aid companies in cutting expenses and boosting productivity.
2. Better Decision Making
Machine learning algorithms can use large-scale data analysis to spot patterns humans might overlook. Using which businesses can make smarter, data-driven decisions by utilizing this analysis. For instance, ML can examine consumer information and behavior in marketing to pinpoint the most successful marketing approaches and the proper customers for targeted advertising. Moreover, machine learning algorithms can also analyze financial data to forecast market trends and spot investment possibilities.
3. Personalized Customer Experiences
Machine learning algorithms can analyze customer data to create personalized marketing campaigns, tailored product recommendations, and more. A real-world scenario of ML benefiting personalization is Brinks, a 163-year-old business that previously savored only 2% of the market share. However, the situation turned completely upside when the company collaborated with OfferFit, an AI startup that enabled the personalization of customer experiences. Consequently, the overall revenue collection saw a growth of 9.5%!
Source: Industry Wired
4. Competitive Advantage
There are several channels through which machine learning and AI for businesses bring them closer to having a competitive advantage over their competitors. These technologies help in better talent acquisition & management, better quality of work, reduced human errors, improved monitoring, business model expansion, and much more. It’s crucial to remember that businesses must also cope with difficulties as they look for methods to leverage AI for a competitive advantage.
Challenges of Machine Learning for Business
As we have said before, businesses need to cope with many challenges while leveraging machine learning and AI for businesses. They need to ensure quality data is being used, professionals responsible for handling the machine learning models are skilled, the process is ethical, and ML systems are integrable with existing systems. Read on to learn about these challenges in detail.
1. Data Quality and Accessibility
Machine learning has two significant challenges: data quality and accessibility. The quality of the training data strongly influences the accuracy and dependability of machine learning models. Unreliable data might produce incorrect predictions and misguided insights.
Another significant issue in machine learning is accessibility. Many businesses need help getting hold of vast amounts of high-quality data, which can make it more difficult for them to develop reliable models.
2. Lack of Skilled Professionals
A primary commercial concern for small businesses that rely on these technologies is the need for more machine-learning experts. A thorough understanding of mathematics, statistics, and computer science is necessary for complicated and specialized machine learning. Experience and knowledge are also needed to design and implement efficient machine-learning models.
As a result, businesses without machine-learning specialists may find it challenging to create and apply efficient machine-learning solutions. Ineffective processes may result in lost chances for growth, decreased competitiveness, and increased costs.
3. Ethical Considerations
As AI and machine learning continue to develop and become more widely used, ethical issues have become more significant.
Bias is one of the primary ethical issues with AI and machine learning. Because machine learning models can only be as objective as the data they are trained on, if the data has biases, so will the models. Due to the potential for discrimination and unfair treatment of particular people or groups, this raises serious ethical issues.
Another ethical issue with AI and machine learning is privacy. The large volumes of personal data these technologies frequently gather and analyze may be misused or compromised. There have been many phishing scams that compromise business data, as even scammers are now ingesting stolen data with AI to attack companies.
4. Integration with Existing Systems
The integration of ML systems with existing systems is a significant challenge for businesses that want to leverage machine learning. Data integration is one of the main difficulties in integrating machine learning with current systems. Machine learning models need a lot of data to train and make predictions, which can be stored in many methods and forms.
Infrastructure compatibility is also a problem when integrating machine learning with current systems. For machine learning to function effectively, necessary hardware and software infrastructure may be absent in current systems.
Future of Machine Learning for Businesses
The machine learning landscape is evolving now more than ever as the $14.1B industry is ingesting and utilizing tonnes of business data. The particular reason for this development is machine learning’s usefulness in enabling better decision-making, helping developers with automated analysis, and making numerous manual tasks more efficient and accurate by automating them. Consequently, the future of machine learning is exceedingly promising.
As per a study published by Helomics, this industry is expected to grow to $20B in the following two years, setting a base for a huge growth window for businesses. This growth will also include several other advances like Big Data, NLP (natural language processing), Image Recognition, Computer Vision, Cloud Computing, and especially Quantum Computing. Although a quantum machine learning model has yet to be ready for commercial use, tech companies like Google, IBM, Xanadu, etc., are actively investing in this technology to accelerate, scale, and enhance complex business operations.
The coming years can also expect multi-purpose machine learning models and algorithms that undertake several tasks at once, making individual frameworks lesser relevant while serving multiple domains via a single one.
Machine learning has changed business operations, supporting data-driven decision-making and giving organizations a competitive edge. It is becoming a vital tool for companies in various industries because of the expansion of data availability. According to a recent report by Grand View Research, the global machine-learning market size is expected to reach $6 billion by 2027, growing at a CAGR of 43.8% from 2020 to 2025.
Alongside machine learning, its superset technological domain, artificial intelligence (AI), has also been a significant contributor to helping businesses adopt automation. Developments like Microsoft’s Copilot, an AI coding assistant, and ChatGPT, a conversational AI, have made a lot of tasks more convenient for developers and non-technical users.
AI and ML can revolutionize the way businesses work, even the smaller ones. You can expect to see further advancements and breakthroughs in both domains that will transform the way we do business.
Frequently Asked Questions
A. Machine learning is crucial for business analytics as it takes over the labor-intensive task of processing and working with data. ML algorithms can analyze tonnes of data and identify patterns and different elements that represent business performance. For example, ML can work with historical sales data to forecast future sales.
A. Numerous business applications of machine learning make it a natural fit for improving efficiency across all industries. Primarily, ML allows the automation of numerous day-to-day operations, lowers cost, and increases accuracy. Moreover, its incorporation has opened doors for several highly-skilled professional opportunities. Some other specific applications of ML in business include image recognition, customer churn prediction, employee attrition detection, and much more. It enables organizations to make better decisions, aids developers in solving challenges creatively, and provides insights continuously at superhuman speeds and accuracy.
A. Some benefits of machine learning for businesses include enhanced decision-making, improved efficiency, personalized marketing, fraud detection, customer service management, predictive maintenance, etc.