Machine learning has quickly become a business’s most prevalent and practical technology tool. ML can make sense of unstructured data and quickly identify patterns, making it easy to integrate into existing business processes.
Companies use ML to detect fraud in credit card transactions, insurance claims, and tax filings. It also helps companies improve the customer service experience with personalization like personalized recommendations.
So, what is machine learning used for? When ML algorithms sift through data sets, they discover patterns and relationships hidden from human eyes. This makes them ideal for tasks that require a large amount of data analysis, such as market basket analysis.
For instance, the coffeehouse mentioned above might collect the time of each purchase and which products are purchased to determine the perfect line-up. Similarly, financial institutions use ML to assess the creditworthiness of loan applicants.
Companies are also using ML to improve their logistics and transportation networks. By forecasting demand, ML helps optimize routes and reduce shipping costs and CO2 emissions. In addition, ML enables businesses to adjust product pricing dynamically in near real-time.
Finally, ML is helping businesses increase sales and customer loyalty. For example, a bank might use ML to identify which customers will likely default on their loans and offer them better terms. This way, customers are more satisfied and return to the business for more services. In addition, a chatbot built with generative AI can cut down on customer service agent labor costs and provide personalized customer support.
Predictive modeling uses existing data to forecast trends, behaviors, or outcomes with a high degree of accuracy. Businesses choose the appropriate model type based on their business objectives and available data. Standard predictive models include decision trees, regression, and random forests.
Data preparation is critical for this process, with some estimates stating that this can take up to 80% of a machine learning project’s time. This includes collecting, organizing, and cleaning historical data for analysis.
Once a dataset is prepared, it can be fed into a machine learning algorithm to identify patterns and predict future outcomes. The model will be trained on the data set and then tested on a test dataset to evaluate its performance.
By using predictive modeling, organizations can better understand potential risks and opportunities to help inform their decision-making processes. For example, predictive analytics models can help determine whether a sales lead will convert and prioritize the best tips, identify when a customer service case will likely be escalated and assigned accordingly, or optimize accounts receivable workflows by predicting payment patterns.
Natural Language Processing (NLP)
NLP helps to process and extract vast amounts of data without human intervention. Suppose your business processes require a lot of human input, like data entry, reporting, or cost management. In that case, NLP can help automate these tasks, which saves time and allows employees to focus on other, more important activities.
You’ve probably experienced NLP before – for example, when your smartphone autocorrects or finishes a sentence for you or when the search bar in your favorite e-commerce site suggests relevant results based on your previous searches. The underlying technology behind these is NLP, which uses predictive text and artificial intelligence to understand the semantic meaning of words and suggest them.
Businesses can use NLP to analyze text documents, such as social media comments, customer support tickets, survey responses, and news reports. This can then provide actionable insights in a fraction of the time it takes for humans to analyze them.
NLP is also used to identify trends in customer feedback, such as the sentiment toward a specific product or service. This information can enhance and improve products, services, or advertising campaigns.
Computer vision uses algorithms to detect, identify, and recognize objects and conditions in images or videos. It is useful in industries such as retail, healthcare, and security, where the ability to analyze visual data allows companies to optimize operations.
For example, a company can use computer vision to track warehouse inventory or monitor shipments for damage automatically. The information captured is then used to adjust pricing, which can help maximize profit. Similarly, companies can use predictive inventory management to anticipate what products or services will sell best in specific locations.
Using computer vision also helps businesses streamline operations and improve customer service. For instance, a company can automate inventory tracking and alert staff to replenish supplies before they run out.
This technology can also reduce medical costs in the healthcare industry by ensuring that staff follow infection control guidelines. This can reduce hospital-associated infections, which are the most common cause of death and cost the US more than $28 billion annually.
Additionally, a healthcare system can monitor patient and employee behavior using computer vision. This can alert nurses to signs of high temperatures or trigger reminders for routine handwashing, lowering the spread of germs and improving patient safety and comfort.
When businesses use predictive analytics, they can predict their customers’ wants and needs. For example, it analyzes customer buying habits to provide personalized recommendations, which has led to 80% of its customers being fully loyal to the brand.
And a company like Capital One uses machine learning to perform credit risk assessments, allowing them to understand their customers better and improve overall business intelligence.
Airliners, farmers, and mining companies also rely on ML to help with predictive maintenance by constantly monitoring equipment and data for deviations from normal parameters that may indicate a problem that needs immediate attention. Companies can then take the right preventative action to save money on costly repairs or downtime.
Additionally, ML is often used for repetitive tasks like verifying applicants’ resumes or matching invoices in finance departments to free up employees to focus on more complex work and maximize their time.
Rackspace found that improved decision-making was the fourth most crucial benefit of their machine learning programs, with the technology helping to reduce errors and increase efficiency.