In the era of big data, companies are continually looking for innovative usage of technology as a tool to gain an advantage over their competitors. A technological development that is now not only prevalent but also widely accepted is Machine Learning (ML). Machine learning, a part of AI, is a tool for companies to find the exact information within the huge amounts of data which help with the decision-making process and result in growth. From predictive analysis to the personalized customer experience, machine learning applications in business are many and varied.
Machine Learning Applications in Business
1. Predictive Analytics- Machine Learning Applications
Predictive analytics is the most popular application of machine learning technology in business. As per the MarketsandMarkets report, the global market size of predictive analytics is expected to grow from $7.2 billion in 2020 to $21.5 billion in 2025, with a compound annual growth rate (CAGR) of 24.5%. This significant growth demonstrates the increasing use of predictive analytics solutions among different industry sectors.
Likewise, in retail, machine learning algorithms utilize historical sales data, customer demographics, and external factors like seasonality and economic trends to predict future sales demand for products. McKinsey & Company cited in a study that companies that incorporate efficiently predictive analytics into their operations are 2.2 times more likely to outperform the industry average profitability.
2. Customer Segmentation and Targeting
Machine learning algorithms are especially good at creating segments of customers according to different attributes such as demographics, purchasing behavior, and preferences. An investigation by Accenture revealed that 91% of consumers are more likely to shop with brands that deliver personalized offers and recommendations. This underlines the fact that personalized marketing strategies enabled by machine learning are important.
Besides that, machine learning delivers dynamic segmentation which is a process of updating customer segments based on current data in real time. Gartner’s report states that for organizations that can personalize digital commerce using machine learning algorithms, the profits can be increased by up to 15%.
Also Read: 10 Ways to Navigate and Overcome Challenges in a Competitive Online Marketplace
3. Personalized Recommendations- Machine Learning Applications
Personalization now is the key element of almost any business strategy, especially in e-commerce, media, and content platforms. Research by Evergage says that about 88% of marketing executives have recorded visible improvements in their digital marketing due to personalization, and more than half of these gains have surpassed 10%.
Streaming services such as Netflix and Spotify use artificial intelligence algorithms to suggest films, TV series, and music that match users’ history of viewing or listening, ratings, and preferences. Netflix spends $1 billion every year on customer retention costs because of its recommendation system powered by machine learning.
Also Read: How to Protect Your Online Marketplace from Fraud and Scams?
4. Fraud Detection and Risk Management
Cyber fraud poses a serious threat to businesses in banking, insurance, and e-commerce. One of the key roles that machine learning algorithms play in detecting and fighting fraud is analyzing the massive transactional data volumes and outlier patterns or behaviors that are indicative of fraud.
Considering banking as an example, machine learning models look for anomalies in customers’ transaction history, spending patterns, and geographical locations to detect suspicious transactions in real-time. As per a report published by the Association of Certified Fraud Examiners, companies lose an annual 5% of their income to fraud which proves the criticality of strong fraud detection mechanisms that leverage machine learning.
5. Supply Chain Optimization- Machine Learning Applications
In addition to predictive maintenance and demand forecasting, managed testing services powered by machine learning can further optimize supply chain operations by ensuring the reliability and functionality of critical systems, minimizing disruptions, and ultimately contributing to the seamless flow of goods and services.
For instance, logistics companies use machine learning algorithms to predict equipment failures before they occur, allowing for proactive maintenance and minimizing downtime. Additionally, machine learning models analyze historical demand data and external factors such as weather patterns and geopolitical events to forecast future demand accurately, enabling businesses to optimize inventory levels and transportation routes.
Conclusion on Machine Learning Applications
Machine learning continues to revolutionize the way businesses operate, enabling them to extract actionable insights from data, enhance decision-making, and deliver personalized experiences to customers. From predictive analytics to personalized recommendations, the applications of machine learning in business are vast and multifaceted. As organizations embrace machine learning technologies, they gain a competitive advantage, drive innovation, and unlock new opportunities for growth in an increasingly dynamic and data-driven marketplace.
Interesting Reads:
The Impact of AI and Machine Learning on Online Marketplaces
Understanding And Preventing Friendly Fraud In eCommerce
Are My Ad Campaigns Affected By Fraud? Here’s How To Find Out
