Have you ever thought about how your mail inbox is so smart that it can filter Spams, label important mails or conversations and segregate Promotional, Social and Primary messages? There is complex algorithm in place for this kind of prediction and this algorithm comes under the wide umbrella of Machine Learning.
Have you ever thought about how your mail inbox is so smart that it can filter Spams, label important emails or conversations and segregate Promotional, Social and Primary messages? There is a complex algorithm in place for this kind of prediction and this algorithm comes under the wide umbrella of Machine Learning.
The formula looks at the words in the subject line, the links included in the mail and/or patterns in the recipient's list. Now, this method is definitely helping the business of email providers and such predictive, as well as prescriptive algorithms, can help all kinds of businesses. But first, let’s define what exactly is Machine Learning (ML)…
Simply put, ML is all about understanding mostly hidden, data and statistics and then mining meaningful insights from this raw dataset. The analytical method that uses algorithms can help solve intricate data-rich business problems.
Moreover, ML models are pretty adaptive in the way they continuously keep learning as when new data is introduced. This also makes them increasingly accurate in their predictions the longer they operate.
As far as businesses go ML algorithms driven by new computing technologies can help enhance business scalability and improve business operations. Combined with artificial intelligence and business analytics, ML can be a solution to a variety of business complexities. Today, ML models are being used to predict everything from spikes in web traffics and hardware failures to traffic patterns, outbreaks of disease and stocks and commodities.
Benefits to Business
1. Predict customer behavior: Machine Learning is being used by companies all over the world to predict customer behavior and covert the predictive insights into prescriptive insights to increase customer base or offer them better services. By looking at purchasing patterns and browsing through purchase histories, retail companies can offer the best customized product or service to individual customers and improve demand forecasts. This gets us to the next point…
2. Product Recommendations: In e-commerce, ML algorithms can be used to motivate product purchase. Matching with a large product inventory, ML can be used to identify hidden patterns and group similar things together. These products can then be suggested to customers.
3. Improving marketing strategy: ML can churn massive amount of data in real-time to make it more relevant and useful. The data received from customer behavior analysis can be used to make appropriate changes to a company’s marketing and sales strategy involving upselling as well as cross-selling. ML models equipped with image recognition software in retail companies can be extended to customers so that they can find the right product from across a scanned inventory of thousands of products. Moreover, record sales can be reached via recommendation engine deployment and real-time targeted advertising can also be generated on websites.
4. Data Entry Assistance: Predictive modeling and machine learning algorithms can help streamline a company’s documentation process eradicating the risks involved with manual data entry. The formula can be used to automate data entry process and eventually let the skilled resources focus on important and creative tasks.
5. Financial analysis: Fraud detection proves to be a major hindrance in the finance sector today. Companies involve a huge skilled team of humans to find frauds in their company and their process is not just costly but also time-consuming. ML can help not just find but also predict fraud in a large volume of transactions by applying cognitive computing technologies to raw data. In the monetary portfolio, ML can also help in risk management, investment predictions, improve customer service and deploy digital assistants, loan management and security measures among other things.
6. Medical prediction and treatment: The healthcare sector is like goldmine of data and more the data better the machine learning model. If applied well in the pharma and medicine sector ML could lead to better diagnosis of diseases, personalized treatment, improved efficiency of research and clinical trials, smart health records, outbreak prediction and better control measures.
7. Detect network intrusions: Besides, predicting complex customer behaviors, data mining can also be used to predict patterns in network intrusions and accordingly eliminating them. An intrusion detection systems screens the network traffic while looking for any malicious activity in the form of an attack or unauthorized access. Analysis of this traffic can bring out patterns to be better equipped in the future to catch hold of the intrusions. Since they will be based on analysis, these detections will be more accurate and speedy.