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 is using these technologies for filtering spam emails and fake posts, thereby improving the quality of user experience.
Below are some of the use-cases for building Artificial intelligence and Machine learning applications:
AI and ML in data collection:
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.
AI and ML in digital commerce product suggestions:
The digital commerce product recommendation system is the process of collecting data from past behaviour of users, storing the data, analysing 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 analyse the data to know user tastes and preferences by learning the user’s behaviour 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 analyse the interests of users
- Encourages visitors to generate more sales.
- 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
- 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 behaviour
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.
AI in post-sales support:
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:
AI and ML in predictive analytics:
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.
AI and ML in image recognition:
Image recognition using Artificial Intelligence can detect objects, classify and recognise 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 labelling 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 recognise 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:
AI and ML in spam and error detection:
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.
AI and ML in healthcare:
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.
AI and ML in the education sector:
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.
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.
AI and ML in the prediction of customer lifetime value:
The marketers are getting challenged in segmenting customers, analysing 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 analyse. In such scenarios, Machine learning and Artificial intelligence applications can help marketers in analysing this huge amount of data and thereafter, predicting future outcomes. Marketers can use this information to do effective marketing.