Top 10 Machine Learning Applications in Business and Their Real-Life Examples

Machine learning applications in business

From the moment the technology burst onto the scene, machine learning has been making a difference to business ecosystems all over the world. By enabling operational adaptation based on changing conditions, ML in business is improving almost every aspect of function and process automation.

Machine learning applications in business have been ratcheting up significantly. According to the latest research, the machine learning market is expected to grow by $11.16 billion during 2020-2024.

Machine learning applications in business are already infiltrating a broad spectrum of different business processes. Notably, the top artificial intelligence and machine learning application in terms of use case frequency (in 2021) concerned efforts to improve customer experience (57%).

The reason behind such growth lies in the value to be extracted from large amounts of raw data. But that is not the whole picture. So let us dive into the other components and see how machine learning unlocks their worth.

10 machine learning applications in business and examples

Top 10 machine learning applications in business

Machine learning can be used in various ways by a business in any industry to improve workflows and processes. Easy data availability, more affordable and efficient processing, and practical data storage techniques have made it a natural fit for any progressive sector.

Let’s learn more about each of these 10 applications.

1. Text parsing

Machine learning algorithms can also be trained to recognize and process human-generated text. This operation is referred to as text parsing and falls under natural language processing (NLP). By teaching an algorithm the rules of language and grammar, it is possible not only to analyze existing data but also to harvest new data.

Text parsing real-life example

Amazon Kendra is a machine learning-powered intelligent search service.

Amazon Kendra reinvents search for websites and applications so that employees and consumers can access the content they need, even if it is scattered across multiple locations and content repositories within your business.

The solution combines Amazon Textract for document preprocessing and optical character recognition (OCR) and Amazon Kendra for intelligent search. With this combination, you can swiftly automate document processing and take action on the extracted information, whether for loan processing or picking out information from invoices and receipts.

2. Recommendation engines

Machine learning is at the heart of consumer recommendation engines, aiming to improve and tailor the customer experience.

In this use case, algorithms process data points about an individual customer, such as past purchases, as well as other data sets such as a company’s current inventory, demographic trends, and the purchasing histories of other customers, to determine which products and services to recommend.

By evaluating and processing user data effectively, recommendation engines enable businesses to improve their customer service substantially.

Recommendation engine real-life example

Via collaborative filtering, the streaming entertainment service Netflix uses machine learning to analyze a customer’s viewing history, the viewing history of people with similar entertainment interests, information about individual shows, and other data points to deliver personalized recommendations to its customers.

Recommendations are based on the predictions and determine what shows, movies, and videos will display on each user’s homepage and watch-next reel.

3. Customer service through chatbots

Chatbots were one of the first kinds of automation and a landmark in machine learning applications in business, bridging the communication gap between people and technology.

Early generations of chatbots followed predefined rules that instructed the bots on what actions to conduct in response to keywords.

However, machine learning and natural language processing have enabled chatbots to be more involved and productive. As a result, these newer chatbots respond more smoothly to users’ needs and converse increasingly like real humans.

Chatbots real-life example

A good example of a well-known company chatbot would be Watson Assistant, offered by IBM. Marketed as providing “quick, straightforward responses,” Watson was built to recognize when to inquire for clarification and when to route the request to a person.

However, as technology advances, IBM’s customer service speech bots have evolved by training a customer service bot to sound more human. With the help of a voice actor, a dynamic script, and AI voice-conversion technology, researchers have transformed the flat voices of older virtual agents into something more expressive.

Explore our custom chatbot development services and find out how we can build one for your project

4. Decision support

One indisputable fact about machine learning applications in business is that massive amounts of data can be transformed into actionable insights.

Algorithms trained on historical data and other relevant data sets can assess information and run through various possible scenarios at a scale and pace that humans cannot.

Such processes help management anticipate trends, identify problems and speed up decision-making.

Decision support real-life example

An industry that is emphatically affected by the efficiency of these systems is healthcare. Clinical decision support tools incorporating machine learning help clinicians make diagnoses and choose treatment options, improving healthcare worker efficiency and patient outcomes.

For example, PathAI, a provider of pathology research, uses predictive machine learning to help healthcare professionals measure the accuracy of diagnoses and the efficacy of approaches to complex diseases. The company’s technology can also make medicinal solutions more accurate, reproducible and personalized, based on patient history.

5. Image classification

Image classification ML techniques

The past few years have seen huge strides in this field, from autonomous vehicles to biometric identification and even Google image recognition applications, all of which were made possible due to ML recognition algorithms.

To train ML algorithms to classify images, you need massive amounts of data. ML learns in two ways:

  • Supervised: Supervised learning is the more common of the two. This is where a person gives the computer sample data that has been labeled with the correct answers. This teaches the computer to identify correlations and apply the methods to new data.
  • Unsupervised: Unsupervised learning is less prevalent than supervised learning. Unsupervised learning is characterized by unstructured, raw data with no human involvement. However, it uncovers insights that humans haven’t yet identified.

Image classification real-life example

Yelp, an online review service, hosts tens of millions of photos uploaded by Yelpers worldwide. The categories defined for the image classification task for the labeling of the images are Food, Drinks, Menu, Inside and Outside.

On a test set of 2,500 photos, their current classification model yielded an overall precision of 94% and recall of 70%.

6.Content moderation and generation

Human-based content moderation alone cannot scale to meet safety, regulatory, and operational needs without leading to a poor user experience, high moderation costs, and brand risk. ML-powered content moderation, on the other hand, can help organizations moderate large and complex volumes of user-generated content (UGC).

Content moderation and generation real-life example

CoStar is a global leader in commercial real estate information, analytics, technology and news. It has one of the most extensive data platforms on the market, processing over 150,000 photos daily. CoStar was able to quickly construct a solution to automatically evaluate all uploaded photographs using Amazon Rekognition’s Content Moderation API.

As a result, the company succeeded in offering high-value products to its consumers and innovative solutions to the real estate market.

7. Fraud detection

Machine learning’s ability to identify patterns and discover anomalies that deviate from these patterns make it a great tool for spotting fraudulent activities.

To this end, machine learning analyzes an individual customer’s typical behavior, such as when and where the customer uses a credit card. Next, this information and other data sets are used to reliably assess in milliseconds whether transactions are within the normal range and hence legal, and also spot which transactions are outside expected norms and therefore potentially fraudulent.

Fraud detection real-life example

Financial institution Capital One uses machine learning to detect, diagnose and rectify anomalous app behavior in real-time. To build this model, they first create several hundred features, based on customer and transaction attributes, that might relate to money laundering activity. Then, by applying these features and running the data through a random forest model trained on over a hundred thousand past investigations, they can recognize whether similar transactions were reckoned to be suspicious.

Learn more about the pros, cons and use cases of ML for fraud detection

8. Customer segmentation and market research

Machine learning tools not only assist businesses in setting prices. They also help in delivering the right products and services to the right regions at the right time via predictive inventory planning and customer segmentation. Put simply, they enable a better understanding of specific segments within their broader customer base.

Customer segmentation and market research real-life example

Retailers, for example, make use of machine learning to anticipate what inventory will sell best in which stores, based on seasonal factors affecting a particular store, demographics in that region, and other data points such as social media trends.

ASOS, a fashion retailer, employs machine learning to calculate client lifetime value (CLTV). This measure calculates the net profit a company generates from a specific customer over time. Machine learning assists ASOS in evaluating which consumers are likely to continue purchasing its products and which are likely to represent poor CLTV, a finding which may affect ASOS’ provision of free shipping or other promotions for specific individuals.

9. Customer churn modeling

Machine learning capabilities help companies deal with one of the oldest business problems: customer churn.

In this case, algorithms find and analyze trends within massive amounts of historical, demographic, and sales data, in order to identify and understand why a company is losing clients. The organization can then evaluate existing customer behaviors to spot which customers are at risk of shifting their business elsewhere, work out why those customers are leaving, and determine what steps the company should take to keep them.

Churn rate is an important performance measure for any company, but it is crucial for subscription-based and service businesses.

Customer churn modeling real-life example

Software-as-a-service company Salesforce (CRM software) is betting heavily on its proprietary Einstein machine learning technology. Salesforce Einstein allows businesses that use Salesforce’s CRM software to analyze every aspect of a customer’s relationship, from initial contact to continuous engagement touchpoints, in order to develop much more detailed profiles of customers and identify crucial points in the sales process.

10. Lead conversion and revenue prediction

Predictive modeling is a category of machine learning business applications that mines large amounts of data to predict the outcomes of potential scenarios. These predictions can identify a pain point in the company’s procedures or, in contrast, a potentially profitable undertaking.

This brings a significant competitive advantage which can save the company a considerable amount of resources while maximizing sales and avoiding shortages.

Conversion and revenue prediction real-life example

As ride-hailing services become more popular, precisely estimating demand can help operators efficiently allocate drivers to clients, and in doing so reduce idle time, improve traffic congestion, and enhance the passenger experience.

For example, Uber has incorporated machine learning approaches as part of the company’s forecaster toolkit. Uber harvests its historical pricing data and data sets on a host of variables to understand how exogenous variables (such as weather or concerts) might impact demand for their services.

Finally, machine learning algorithms can learn from the information gleaned and integrate the insights with additional market and consumer data to help companies dynamically price their goods or services.

Explore the portfolio of AI and ML-powered apps developed by PixelPlex

Closing thoughts

Machine learning applications in business ensure accuracy and improve performance across a host of tasks. However, the amounts of structured and unstructured data piling up within enterprises can badly hamper progress.

The good news is that PixelPlex ML team can relieve you from anything to do with machine learning. Paying due respect to your systems’ integrity and compatibility, we build a 100% personalized model for each of our clients. From ideation and business analysis to data contextualization, strategizing, and tech architecture planning, we look after your enterprise.

Get in touch with us and let’s start working on your project today!


Anastasiya Haritonova

Technical Writer

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