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Companies are often forced to compete fiercely in today's rapidly-changing corporate environment. Artificial intelligence (AI), machine learning (ML), and big data are becoming the new standard for competitive advantage in the age of deep consumer behaviour analysis.
It is now easier than ever to collect large volumes of customer data. The current AI algorithms are powered by this large amount of data, or "big data." Machine learning has been created and adopted as a result of AI advancement.
The corporate world found that machine learning provided cost-effective answers to issues that previously demanded many resources.
Market leaders such as Google and Microsoft have influenced the corporate world.
Data science is a growing field, and businesses worldwide are using it to improve business processes:
- Big data
- Machine Learning (ML)
- Artificial intelligence
Machine learning, for example, is one of the technologies that can help businesses gain valuable insights from raw data.
Machine learning--specifically machine learning algorithms--can be used to iteratively learn from a given data set, and understand patterns, behaviours, etc., all with little to no programming.
The machine learning process is iterative, and the results are constantly changing. This allows businesses to stay up-to-date with consumer and business needs.
All the cloud platforms offer an easy way to integrate machine learning into business processes.
Machine Learning Overview
We need a fundamental understanding of ML before we can look at its benefits. Machine learning is the process of extracting valuable data from large data sets.
Consider, for example, an online retailer that tracks the behavior of users and their purchases on the site. It's just data. Machine learning is essential, allowing the online shop to extract patterns, statistics, information, and stories from the data.
Adaptability is a crucial feature that distinguishes machine learning algorithms from traditional analytical algorithms.
The algorithms used in machine learning are constantly evolving. As ML algorithms consume more data, their predictions and analytics will become more accurate.
Machine learning is a powerful tool that has allowed businesses to:
- Adapt more quickly to changing market conditions.
- Enhance business operations.
- Understand the needs of consumers and businesses.
Machine learning has become commonplace in all sectors, from the stock market to agriculture, medical research, and traffic monitoring.
Machine learning is used in agriculture to perform various tasks, such as crop rotation and predicting weather patterns.
Combining machine learning with artificial intelligence can enhance analytic processes and provide further advantages to businesses.
Azure Machine Learning, Amazon SageMaker, and other cloud-based services allow users to use computing power to incorporate ML into any business requirement.
Machine Learning Use Cases
We now have an understanding of the basics of machine learning. Let's talk about its benefits for businesses and organizations.
User Behavior Analysis
Machine learning is often used to analyze user behavior, especially in retail. Imagine a shopping experience. Businesses collect vast amounts of information about customer purchases, whether online or offline.
This data can be run through machine learning algorithms to help companies to predict customer purchasing patterns, trends in the market, top products, and more. Retailers can base their business decisions on these predictions. For example, ML allows companies to:
- Take accurate stock management decisions.
- Ordering according to consumer and market demand.
- Increased efficiency in the logistics and operational processes.
- Directly market to specific customers using marketing platforms integrated with your platform.
In an online environment, ML can be:
- Analyze user browsing habits.
- Accurately predict user preferences.
- Make targeted suggestions.
Below are a few more examples:
- User behavior analysis is a powerful tool for pharmaceutical companies that run drug trials. It can be used to determine the efficacy of drugs and identify anomalies or outliers.
- A logistics firm in the maritime sector can forecast shipping demand using logistical data, such as route, goods transported, time, etc., to a machine-learning algorithm.
The analysis of user behavior is not restricted to the consumers. In this context, any entity interacting with the business can be viewed as a user.
ML allows enterprises to better understand their business processes by detecting hidden patterns.
Improved Automation
Automating mundane, repetitive tasks has significantly impacted almost every business sector. It saves time and money.
Automation is evolving to include machine learning and automation.
Machine learning is a powerful tool that can improve industrial manufacturing processes. To achieve this, it is necessary to evaluate the existing manufacturing models to identify all of the shortcomings and pain points.
Businesses can then quickly fix problems to keep the production pipeline running smoothly.
ML does not only apply to manufacturing. Combining ML and AI, for example, can create intelligent, constantly evolving robotic workers.
The automated robots that are made will be able to:
- Reduce manufacturing defects to a minimum.
- Scalability and efficiency can be increased.
ML automation is not limited to industrial sectors. It also benefits agriculture, research, and scientific fields.
ML can improve different tasks in agriculture, such as automated farming and analysis, by integrating them with other data sets.
Improved Security
The world is becoming more dependent on the web as web technologies proliferate. It has also led to an easier and more convenient way of life.
There are some downsides to it:
- Phishing Attacks
- Identity theft
- Ransomware
- Breach of Data
- Privacy Concerns
To ensure security for users and businesses, companies use various prevention and control measures. Many of these include firewalls, intrusion prevention software, threat management apps, strict policies on data storage, etc.
Security teams in big companies constantly monitor, update and fix online application vulnerabilities.
The use of machine learning can complement the existing security team by automating some monitoring tasks and vulnerability assessments.
Consider a spam filter. By integrating ML into the spam filter, businesses can reduce the amount of spam and risky emails in employees' inboxes.
The ML algorithm is constantly learning, and the more emails it considers, the better the filtering accuracy.
A threat assessment is another example in which most online applications are subjected to different types of attacks every day.
By analyzing past episodes and pinpointing vulnerabilities in the application, machine learning can predict attack vectors for the future. Taking it a step further, the development team can incorporate ML into an application testing phase to assess application vulnerabilities before releasing them to a production setting.
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Financial Management
Financial analytics can benefit from machine learning algorithms:
- Tasks that are easy to perform, such as predicting expenses for business and performing a cost analysis
- Assisting with complex tasks like algorithmic trading, fraud detection, and more
These use cases all rely on the analysis of historical data to accurately predict future outcomes. These predictions are subject to fluctuations in accuracy depending on both the algorithm used and the data provided.
A small set of data and a simple ML algorithm can be used to predict the costs for a company. For algorithmic trading, however, ML algorithms must be revised, modified, and tested over decades before they are ready for production.
Stockbrokers and investors rely heavily on ML for accurate market predictions before they enter the market. This accurate and timely prediction helps businesses manage expenses while also increasing profits. This, combined with automation, will result in significant savings on OpEx.
Cognitive Services
The use of machine learning to improve cognitive services, such as computer vision (image recognition) and natural-language processing.
For example, improved image recognition technology will allow businesses to develop more convenient and secure authentication methods and product identification for autonomous retail services, such as cashier-less checkout. Amazon Go is one of the innovative retail experiences that have resulted from this.
Businesses can cater to various customers from diverse backgrounds, including ethnic, cultural, and geographic ones, with the help of natural language processing.
The ability to offer services and experiences in the native language of the customer will also lead to an increased number of customers interacting with the company.
Machine Learning Applications for Business
Thanks to its versatility, the corporate world has many uses for machine learning. It is a perfect fit for the needs of a growing market.
Intelligent automation allows businesses to deploy AI and machine learning solutions that are low-cost but high-accuracy to replace workers with low skills.
AI is also evolving to perform more complicated and narrower tasks. This will eventually replace highly paid professional positions.
AI has taken over more complex job roles by assisting humans to achieve their jobs faster and more effectively.
In either case, companies adopting AI to solve a specific problem will improve their efficiency and lower costs.
Cloud service providers such as Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure offer plug-and-play AI solutions.
These factors have led to the acceptance of ML in enterprises. Its disruptive potential has also contributed to its approval.
The business application of ML is diverse. Please take a closer look at a few of them.
1. Image Classification
Image classification is the training of algorithms to identify images. It may seem easy to humans to examine a picture and place its contents.
Still, specialized AI algorithms are needed for computers to analyze images. Image classification algorithms are now on the same level as human abilities.
Companies can use AI-based algorithms designed to analyze photos to deploy image classification software that increases efficiency and reduces errors.
Companies can use these solutions to perform tasks such as know-your-customer and identity verification.
Machine learning solutions for image classification have become very popular in business. This is mainly because they can disrupt systems created to achieve the same goal.
Human labor was needed to label vast quantities of data. Today's giants, such as Facebook, Twitter, and Google, use image classification to prevent unwelcome content from going viral.
2. Text Parsing
AI algorithms can be taught to process and understand text generated by humans. Text parsing is a part of natural language processing.
It is possible to reduce the time it takes to process large amounts of data by teaching AI grammar and language rules. The text parsing technique is helpful for both analyzing data already available and obtaining new information, whether from the content of users or that of competitors.
This has several benefits for businesses that work with large amounts of text. Text parsing can replace low-skilled employees in such industries.
Text parsing can benefit companies by allowing them to process vast payments of text using AI quickly.
Text parsing allows computers to interpret vast amounts of text like humans. This will enable companies to choose between a simple search engine and an algorithm that is more complicated for complex needs, such as a bibliography.
It reduces the requirement for low-skilled laborers to parse the text. This improves the bottom line.
3. Engines of Recommendation
Collecting data from users and using deep learning, neural networks, and other techniques to train algorithms to make recommendations to them.
The algorithms are called recommendation engines, which collect user preference data. Knowing what a user prefers or dislikes allows you to build a model that reflects what they want to purchase. The model can be used to make customized recommendations for the user.
Recommendation engines are now the standard industry practice for curating and personalizing content for users. Amazon recommends products based on previous purchases.
Netflix also uses this technology to make movies and show recommendations based on a person's tastes. Recommendation engines improve customer experience.
As evidenced by their adoption in different industries, recommendation engines represent an essential step forward for customer service.
Recommendation engines allow companies to improve customer service by accurately analyzing and processing data. Amazon, for example, offers millions of customers a personalized home page every day. Although this may seem like a minor change, the recommended product placement on your homepage increases conversion rates.
4. Predictive Modeling
Predictive models are a class of machine-learning solutions that mine large quantities of data to forecast the outcome of possible scenarios.
The predictions made can be used to inform business decisions. The predictive modeling algorithms provide an accurate forecast of future events based on historical data. This allows companies to plan and make informed business decisions.
Companies with large amounts of data can use machine learning to identify patterns. The patterns in these data can help identify pain points or profitable opportunities within the business.
Predictive modeling algorithms are also used in this way because they allow for informed decision-making and improve processes by identifying patterns.
Predictive modeling algorithms, for example, can be used to determine the demand for a product within a retail setting.
The prediction is then used to determine the best inventory to send, thereby reducing overhead. It is an essential competitive advantage that can help the business save money and avoid shortages while increasing sales.
5. Customer Service
Using natural language processing, algorithms can be taught to handle common complaints from customers. AI is transforming the customer service industry with the availability of chatbots, natural language processing, and other AI solutions.
As they can respond almost instantly, chatbots can take on the role of customer service executives. After being trained on a list of customer complaints with their respective solutions, the chatbot can offer quick and easy solutions efficiently.
A customer service representative contacts the customer if the complaint still needs to be resolved.
NLP can also be heavily utilized to assess large volumes of support and training material to produce a knowledge base accessible to human workers to resolve problems quickly.
B2C firms used to spend a great deal of money on this area. Once AI was introduced, the costs of this segment were significantly reduced.
Read More: What Is Machine Learning? Different Fields Of Application For ML
List of Companies Using Machine Learning in Smart Ways
Machine learning and artificial intelligence are not limited to the applications mentioned above. Companies have used AI and machine learning in various innovative ways to solve different problems.
Cost savings and competitive advantages are two of the most significant incentives companies have to develop innovative solutions. Take a look at the list of innovative companies that are using machine-learning solutions.
1. Go-Jek
Go-Jek is an Indonesian startup that uses AI to solve problems. Go-Jek can be described as an ecosystem of apps that includes elements from the hyper-local and payments verticals, along with social media, services, and other aspects.
The company aims to offer a wide range of solutions for everyday problems.
This app has 18 services in one application, so the company must process over 5TB of data daily. They have used AI to reduce human errors and optimize delivery routes.
To save resources and time, the company uses machine-learning solutions to assign tasks to drivers and determine which way is best for them to follow to complete an order.
Jaeger, a solution developed in-house by the company to manage multiple drivers simultaneously, was also deployed.
Since its deployment, the answer has helped complete more than 1 million trips for the company.
2. Pinterest
Pinterest is a platform that allows users to discover and save new project ideas. The platform recommends new ideas using AI and deep learning.
Deep learning determines the user's likes and dislikes and suggests new ideas.
Pinterest has been able to optimize its user experience by deploying deep learning. The solution allows Pinterest to customize each user's newsfeed with tailored ideas based on their preferences.
Pinterest has also deployed deep learning to its search function, allowing it to understand the users' intent better and provide more relevant search results.
The company has grown from being a primary internet platform to a company based on data and artificial intelligence.
3. Paypal
PayPal is synonymous with online payment convenience, particularly in this age of the gig economy. The company's global reach made it difficult to prevent fraud.
The company's situation changed after learning about cloud AI's benefits.
These technologies were used to create PayPal OneTouch - a service allowing users to pay with a tap. This may alarm some users.
However, AI and ML have enabled PayPal to make safe transactions for millions.
The company uses AI and ML in addition to OneTouch to identify fraudulent transactions and assess the risks of doing business with a particular user.
PayPal solutions can predict the validity of a trade by analyzing millions of data points collected during the company's operation. This provides greater security for all users around the globe.
4. T-Mobile
T-Mobile is one of America's largest mobile network providers. Over 83,000,000 customers can use its data and calling services.
It becomes impossible to offer customer service with a large customer base. T-Mobile decided to use human agents instead of a standard chatbot.
The company uses NLP to interpret text messages and understand customer problems. T-Mobile's algorithms could learn to read text accurately thanks to the vast amount of data it received from its customers.
The most common issues are in a database, which the algorithm then parses to determine the solution. The information is passed to customer service representatives, enabling them to solve problems more quickly.
5. Expedia
Expedia, a hotel and travel company, provides users with a comparison platform to compare prices for flights and hotels.
Expedia also partners with hotels to offer better holiday deals to their customers.
They developed a solution based on deep learning to enhance the booking experience. The answer was implemented in three phases, beginning with adding new properties to Expedia's lineup.
Next, they sorted them according to users' preferences in real time. Finally, a recommendation API interface brought new customers to relevant properties.
Deep learning is used in each of these steps to customize a travel plan. Platforms will, for example, suggest more significant properties for users who typically travel with their families.
In contrast, smaller options are indicated for travelers who travel solo.
6. Lenovo
Lenovo, the world's largest computer manufacturer, uses artificial intelligence (AI) to predict supply and demand in specific markets.
Lenovo Brazil, for example, has been deploying an advanced ML-based solution to identify a lucrative location in the South American Market.
The Lenovo laptop division needed help to predict potential sales volume accurately. The ability to control the retail supply chain and logistics by knowing the quantity allows for a better competitive edge.
Lenovo has been able to predict accurately the number of products it should produce by using a cloud-based modeling service.
The company developed product-launch strategies based on this information. This solution, according to reportsOpens a New Window, was able to predict sales for several weeks into the future.
7. Adobe
Adobe's powerful Creative Cloud software suite includes software such as Photoshop Illustrator and Premiere Pro.
Adobe's Creative Cloud software suite, which creative professionals around the globe use, shares a lot of data. Adobe uses this information to create complex AI algorithms.
Adobe Sensei is a solution that was created for Creative Cloud. Most professionals spend a significant amount of time on tasks that are difficult to perform manually.
Adobe has applied intelligent automation for these tasks with a growing set of AI-enabled tools that help users optimize their workflow.
Adobe Sensei can be found in many other Adobe products. Adobe Experience Cloud offers predictive analysis, personalization, and form field recognition features.
Adobe Document Cloud creates digital versions of paper documents.
8. HP
Hewlett Packard, or HP, manufactures computers, printers, and peripherals. It is a leader in enterprise, with over 600,000,000 technical support contracts handled in one year.
HP took advantage of the availability and ease of using cloud-based services for AI in customer service.
HP developed a virtual customer service agent using the Microsoft Azure platform. This solution, which was installed as the "HP Support Assistant" in each computer sold by HP, not only interacted with customers but also helped them solve their problems.
This solution has a dialogue with customers and attempts to resolve their issues by consulting its manual support database.
9. Twitter
Twitter has over 126,000,000 active users daily, making it one of the world's most popular social media sites. To prevent the platform from being misused by antisocial elements, any company of this size must keep an eye on all posted content.
The platform announced in 2017 that it would use AI to combat inappropriate content regarding hate speech, racism, and terrorism.
The platform realized that neural networks could be used to improve its platform. Twitter redesigned both its timeline and the way tweets are displayed to users. The venue, powered by neural networks, suggests content relevant to each user, creating a customized experience.
10. Facebook
Facebook is the largest social media platform in the world. They have used artificial intelligence and machine learning for so long that they've developed frameworks and platforms to create AI.
The company also announced its intention to create an AI chip specialized for AI.
AI is used for all aspects of the platform, including serving up the most popular viral videos to the users according to their preferences, flagging antisocial or inappropriate content, and recognizing the contents in pictures uploaded by users.
The site collects different types of data from users to improve its algorithm's accuracy, speed, and power.
Facebook uses AI as well to match advertisers with their targeted audience. Facebook can find out what a user wants by analyzing comments, photos, posts shared, text, etc.
The conversion rate will increase as the advert is highly relevant to its audience.
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Conclusion
Artificial intelligence, machine learning, and deep-learning solutions are used to solve real-world problems. AI is being used to solve problems in a variety of industries.
Machine learning has become a key technology in all sectors of business.
It is used to improve the efficiency and scalability of organizations while solving complex problems. Even though ML is a complex and time-consuming process, it offers tangible benefits that are superior to traditional analytical methods.
Some companies have adopted AI. However, the newer ones today adopt a data-first approach. AI and ML will be part of their DNA, which gives them a competitive edge in the market.
