Artificial Intelligence and Machine learning are advanced technologies that could solve the most critical problems faced by organizations. Apart from facilitating automation of critical business processes in an organization, these technologies can also greatly enhance the quality of final deliverable that could establish brand reputation. For e.g., Facebook.com is using these technologies for filtering spam emails and fake posts, thereby improving the quality of user experience.
Below are some of the usecases for building Artificial intelligence and Machine learning applications:
Data is the backbone of any business to do future predictions. Collecting, sorting and filtering huge data manually is a herculean task. Artificial intelligence and machine learning applications can easily process large volumes of data and can extract meaningful information.
The digital commerce product recommendation system is the process of collecting data from past behavior of users, storing the data, analyzing the data and providing product recommendations to the users. To do product recommendation, first the system has to collect sufficient data about the users and later analyze the data to know user tastes and preferences by learning the user’s behavior from the data collected and finally, provide product recommendations to the users.
- The product recommendation system encourages customers to revisit the website more
- The user browser history can generate more valuable data to analyze the interests of users
- Encourages visitors to generate more sales.
The product recommendation system is most useful for e-commerce industries to enhance customer’s online search experience and thereby get good returns on investment.
Many e-commerce companies get challenged in communicating with customers who have purchased their products. In such scenarios, Artificial intelligence and Machine learning applications can play a major role in enhancing the quality of post-sales service provided to the customers.
Below are some of the salient features of implementing AI based solutions in the post-sales communications:
- Automated emails after purchasing the products like delivery tracking
- Automated emails regarding product reviews
- Automated emails regarding past purchased product offers
- Automated emails regarding complaints, returns, and refunds
- Automated emails regarding cart abundant to attract the customer to purchase the products
Machine learning algorithms can collect data from past customer behavior and predict future outcomes.
ML in predictive analytics for digital commerce: Using machine learning applications in digital commerce, organizations can predict customer churn, which products customers are clicking/navigating, and identifying fraudulent transactions etc..
ML in predictive analytics for marketing: Using machine learning applications in B2B, organizations can predict the accurate leads who are likely to convert into sales.
ML in predictive analytics for diagnosis: Machine learning algorithms can store data from previous medical records from the patients, analyze the records data and predict the illness for a particular patient.
Image recognition using Artificial Intelligence can detect objects, classify and recognize them for later use.
Image detection is the process of scanning the images to identify the right object and also identify fake images.
Image classification is the process of labeling the images.
For example, AI in face recognition: An everyday example of using AI for face recognition is unlocking your smartphone, which is done by scanning your face using the Smartphone camera. In this case, AI can detect the face, classify it as a human face and further recognize it as the owner of the smartphone.
Artificial Intelligence and Machine learning applications can also help in an image search. Today it is possible to capture images and do image search in the e-commerce store instead of a keyword search.
Benefits of AI image search technology:
- Suggests a close match of product using the image being searched
- Automatic mail engagement based on the image search history
- Identify customer interests and dislikes on image search behavior
Artificial Intelligence and Machine learning can detect spam emails and filter them. For example email spam. Daily some of the customers get spam mails like asking personal bank details, fake offers by using famous brands and it leads financial loss to the customers. To rectify this spam mail, AI classification algorithms are being used as spam filters. A classic example is how Gmail filters the spam.
Artificial intelligence and machine learning applications can improve patient care, generate more revenue for the healthcare industry, diagnose diseases faster, engage with the patients through virtual assistants more effectively when compared traditionally managed healthcare.
Virtual learning is a new trend in online education. Artificial intelligence can automate administrative tasks like grading, providing responses to the queries, and evaluating homework of students.
Digital content is going to play a major role in the education sector than traditional textbooks like the electronic curriculum. Artificial Intelligence and Machine learning techniques can help in providing digital content for the students.
Artificial Intelligence can also help the students outside of their classrooms, like tutoring. For e.g., outside of the classrooms, it is challenging for parents to spend time with their wards in helping them complete the homework. In such cases, AI tutors could be the best source for parents to help their children in their homework and also, to facilitate a better understanding of the concepts.
According to the recent study of researchandmarkets.com, the AI market in the US education sector will grow at a CAGR of 47.5% from 2017 to 2022. Further, Artificial Intelligence can help in removing boundaries and facilitate online education to people anywhere at any time in the global.
The marketers are getting challenged in segmenting customers, analyzing customer churn, and predicting customer lifetime value. The businesses have a huge amount of data collected from various sources like website visits, email campaigns, social media channels, referral sites, and online ads etc.. The marketers are getting challenged with these huge volumes of data to analyze. In such scenarios, Machine learning and Artificial intelligence applications can help marketers in analyzing this huge amount of data and thereafter, predicting future outcomes. Marketers can use this information to do effective marketing.