A recommendation engine is a model that filters or suggests items by predicting how a customer or user might rate them. It solves a problem of connecting customers with items in your inventory (e.g., content, products). Example recommendation engines include Facebook's or LinkedIn's "people you might know", Netflix's movie recommendations, Amazon's product recommendations, or Google Search's autocomplete.
A prediction engine learns patterns or features within data to make predictions. There are numerous ways prediction engines can be used, such as predicting equipment failure, sales relative to marketing spend, customer churn, loan defaults, product uptake, or predict employee needs based upon their role. Basically, if you'd like to predict something, prediction engines help to do so.
Machine learning models are great at spotting behavioral patterns used in detecting fraud. Through the use of pattern recognition, behavior profiling, and classification, attempts can be made to identify probable fraud.
Sentiment analysis is the use of natural language processing to identify useful information from source material, typically text. It is often used to classify customer feedback or identify opinions regarding products, allowing you to proactively identify issues before they become larger problems.
Understanding customers is a key to successful business. What differentiates profitable from unprofitable customers? Machine learning aids in understanding customer features and provides the insight needed to successfully retain and serve customers.
The human brain is wired, through eons of evolution, to look for and identify patterns. Unfortunately, humans are generally pretty poor at differentiating between useful and useless patterns, benefitting from the additional, objective pattern matching capabilities of machine learning. Useful patterns can be used in myriad ways, from predicting inventory needs to timing the effectiveness of sales.
Machine learning can help you price effectively, whether it is accurately forecasting the effects of price changes upon sales, or determining regional pricing structures. The value of machine learning is to add objectivity and probability calculations to pricing decisions.
Effectively forecasting and predicting inventory needs can save an organization money and help it meet the desires of its customers. Machine learning can help predict inventory patterns and needs, helping to ensure the right product is in the right place at the right time.