8 Ways Businesses Can Benefit from Machine Learning
With all the buzz around big data, artificial intelligence, and machine learning (ML), enterprises are now becoming curious about the applications and benefits of machine learning in business. A lot of people have probably heard of ML, but do not really know what exactly it is, what business-related problems it can solve, or the value it can add to their business. ML is a data analysis process which leverages ML algorithms to iteratively learn from the existing data and help computers find hidden insights without being programmed for.
With Google, Amazon, and Microsoft Azure launching their Cloud Machine learning platforms, we have seen artificial intelligence and ML gaining prominence in the recent years. Surprisingly, we all have witnessed ML without actually knowing it. Some of the most common instances are 'Spam' detection by your email provider, and 'Image' or 'Face' tagging done by Facebook. While Gmail recognizes the selected words or the pattern to filter out spam, Facebook automatically tags uploaded images using image (face) recognition technique. Business benefits of AI and ML are numerous.
Top 8 Business Benefits of Machine Learning
Let us look at some of the most significant ML and artificial business benefits, starting with the sales and marketing sector.
Simplifies Product Marketing and Assists in Accurate Sales Forecasts
ML helps enterprises in multiple ways to promote their products better and make accurate sales forecasts. ML offers huge advantages to sales and marketing sector, with the major ones being -
Massive Data Consumption from Unlimited Sources
ML virtually consumes unlimited amount of comprehensive data. The consumed data can then be used to constantly review and modify your sales and marketing strategies based on the customer behavioral patterns. Once your model is trained, it will be able to identify highly relevant variables. Consequently, you will be able to get focused data feeds by foregoing long and complicated integrations.
Rapid Analysis Prediction and Processing
The rate at which ML consumes data and identifies relevant data makes it possible for you to take appropriate actions at the right time. For instance, ML will optimize the best subsequent offer for your customer. Consequently, the customer will be able to see the right offer at a given point of time, without you actually investing time to plan and make the right ad visible for your customers.
Interpret Past Customer Behaviors
ML will let you analyze the data related to past behaviors or outcomes and interpret them. Therefore, based on the new and different data you will be able make better predictions of customer behaviors.
Facilitates Accurate Medical Predictions and Diagnoses
In healthcare industry, ML helps in easy identification of high-risk patients, make near perfect diagnoses, recommend best possible medicines, and predict readmissions. These are predominantly based on the available datasets of anonymous patient records as well as the symptoms exhibited by them. Near accurate diagnoses and better medicine recommendations will facilitate faster patient recovery without the need for extraneous medications. In this way, ML makes it possible to improve patient health at minimal costs in the medical sector.
Read an article on Machine Learning and Big Data in Healthcare.
Simplifies Time-Intensive Documentation in Data Entry
Data duplication and inaccuracy are the major issues confronted by organizations wanting to automate their data entry process. Well, this situation can be significantly improved by predictive modeling and machine learning algorithms. With this, machines can perform time-intensive data entry tasks, leaving your skilled resources free to focus on other value-adding duties.
Improves Precision of Financial Rules and Models
ML also has a significant impact on the finance sector. Some of the common machine learning benefits in Finance include portfolio management, algorithmic trading, loan underwriting and most importantly fraud detection. In addition, according to a report on ‘The Future of Underwriting’ published by Ernst and Young, ML facilitates continual data assessments for detecting and analyzing anomalies and nuances. This helps in improving the precision of financial models and rules.
Easy Spam Detection
Spam detection was one of the earliest problems solved by ML. Few years ago email providers made use of rule-based techniques to filter out spam. However, with the advent of ML, spam filters are making new rules using brain-like neural networks to eliminate spam mails. The neural networks recognize phishing messages and junk mail by evaluating the rules across a huge network of computers.
Increases the Efficiency of Predictive Maintenance in the Manufacturing Industry
Manufacturing firms have corrective as well as preventive maintenance practices in place. However, these are often costly and inefficient. This is exactly where ML can be of great help. ML helps in the creation of highly efficient predictive maintenance plans. Following such predictive maintenance plans will minimize the chances of unexpected failures, thereby reducing unnecessary preventive maintenance activities.
Better Customer Segmentation and Accurate Lifetime Value Prediction
Customer segmentation and lifetime value prediction are the major challenges faced by marketers today. Sales and marketing units will have enormous amounts of relevant data sourced from various channels, such as lead data, website visitors and email campaigns. However, accurate predictions for incentives and individual marketing offers can be easily achieved with ML. Savvy marketers now use ML to eliminate guess work associated with data-driven marketing. For instance, using the data representing the behavioral pattern of a particular set of users during a trial period will help businesses in predicting the probability of conversion to paid version. Such a model triggers customer interventions to better engage the customers in the trial and also persuade customers to convert early.
Recommending the Right Product
Product recommendation is an important aspect of any sales and marketing strategy including upselling and cross-selling. ML models will analyze the purchase history of a customer and based on that they identify those products from your product inventory in which a customer is interested in. The algorithm will identify hidden patterns among the items and will then group similar products into clusters. This process is known as unsupervised learning, which is a specific type of ML algorithm. Such a model will enable businesses to make better product recommendations for their customers, thereby motivating product purchase. In this way, unsupervised learning helps in creating a superior product-based recommendation system.
All these applications make machine learning a top value-producing digital innovation trend. Furthermore, ML enables businesses to effortlessly discover new trends and patterns from large and diverse data sets. Businesses can now automate analysis to interpret business interactions, which were traditionally done by humans, to take evidence-based actions. This empowers enterprises to deliver new, personalized or differentiated products and services. Therefore, considering ML as a strategic initiative can be a lucrative decision. However, deployment might carry certain business risk. Therefore, it is better to approach investment decisions with utmost care.
Read our well-researched article to know All about machine learning - its applications, how it works, and who uses it.
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