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25 Feb

5 Common Uses for Machine Learning Applications in Business

Machine learning is what makes artificial intelligence intelligent. It’s a process that uses algorithms to make computers learn from data collected and discover patterns in them. AI’s ability to do such a task is what businesses are looking for in computers.

All our daily processes depend on taking in data. However, it’s difficult for humans to handle data in vast amounts. This is where AI can help by providing a range of functions for collecting, sorting, and analyzing data.

In the healthcare industry, doctors are leveraging Machine Learning to conduct accurate diagnoses and provide correct treatments to patients. In the world of marketing, businesses use it to streamline their marketing strategies to recommend the best items to their target audience.

When you buy a product on Amazon or watch a TV series on Netflix, they are designed in such a way that they will show you recommendations similar to what you buy or watch. These recommendations are delivered with the aid of machine learning technology.

According to Fortune Business Insights, the value of the global machine learning market was $8 billion in 2019 and is forecasted to reach $117 billion by the end of 2027. If you’re unsure about the use of machine learning applications in business, here are a couple of use cases that can help to change your mind about machine learning.

1.     Client Recommendation Software

Several businesses have begun to use recommendation software to provide personalized experiences to their customers. The software uses algorithms to process data values related to a customer. These values cover purchasing history, company inventory, and local trends, which help to figure out product recommendations for each customer of your brand.

Some companies that use recommendation software are:

  • E-commerce companies such as Walmart and Amazon use recommendation engines to provide a personalized shopping experience to their customers.
  • Youtube, an online video platform, leverages recommendation software to show videos that match a user’s interests.

2.     Chatbots

Chatbots are of great help in the automation of repetitive tasks in general, such as answering FAQs and helping in navigation through an eCommerce site. Initially, chatbots were designed to execute commands based on specific keywords mentioned by a customer. Now, chatbots have become more interactive due to the use of machine learning and natural language processing, which is another branch of AI technology.

One great benefit of using chatbots is that they can offer customer service to more than 1 customer at a time. At the same time, people can manage to hear only one concern at a time, making the processing time-consuming.

Another good advantage is that chatbots are available 24/7 to guide customers, irrespective of their time zones. They don’t require rest, so they can provide support even out of office hours.  Here are a few examples of chatbot applications.

  • Emirates Vacation uses a conversation bot in their display ads so that customers don’t have to leave the current webpage to search for desired information.
  • WHO created a bot on WhatsApp called WHO Health Alert to convey information related to the pandemic.
  • Software development companies use chatbots for customer servicing.

3.     Customer Defection Modeling

Machine learning and AI can help businesses to detect if customer relationship is going downhill and how it can be repaired. The features of this technology enable businesses to tackle an important problem called customer churn.

The machine learning algorithm identifies trends in a huge amount of location-based, factual, and sales data to recognize why a company is losing its customers. The machine learning capabilities can be used to analyze any changes in the activity of customers so that companies can be warned that companies are at risk of losing those customers. It can also provide them techniques on how these clients can be regained.

Churn rate is a key performance indicator for any brand, especially those that provide subscription-based services. Here are examples of companies that use consumer churn modeling:

  • Music and video streaming service providers such as Netflix, Youtube, Apple Music, Spotify, HBO, Amazon.
  • Software as a service (Saas) companies like Salesforce (CRM software), accounting (Sage 50cloud), e-mail marketing (MailChimp, Zoho campaigns)

4.     Applying Dynamic Pricing

Businesses can analyze their transactions history to discover trends during a specific season, occasion, or section of the day. This shows what the effects of such factors on demands for goods or services are.

Machine learning algorithms can leverage such information, which can be reinforced with additional customer and market data. Based on all of these variables, companies can apply dynamic pricing on their gods to maximize revenue generation.

Many businesses apply such a technique of pricing. Below are a few examples.

Charges placed for Uber taxis:- The fares placed by the company depend on demand. Travelers see the estimated costs for their journey before confirming a ride.

Ticket sales for professional sports events. Organizers that hold competitions use dynamic pricing for seat bookings. For renowned matches, prices will rise.

5.     Detection of Fraud

Machine learning’s capability of recognizing patterns and finding abnormalities that don’t fit in the patterns make it an essential tool for detecting fraudulent activities. Actually, several financial organizations use this technology.

To understand a specific customer’s behaviors, for example, when and where the customer uses their credit card, data analysts use machine learning. The AI can read such information and analyze other data sets in just seconds to process transactions that were authentic and those which indicate that they are false.

Industries that leverage machine learning to handle fraud attempts include:

  • Transportation
  • Financial services
  • Gaming
  • Trade

Wrapping Up

Every business is trying to simplify its processes to increase efficiency and customer satisfaction. To achieve such a goal, companies must keep in check with the latest technological advancements that are occurring at present.

These technological advancements can help organizations to conduct various data handling processes efficiently. Whether you own an eCommerce website or a fast fashion shop, machine learning applications not only provide benefits to your business but can enhance your customer’s experience leading to more long-lasting customer relationships.

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