Machine Learning is all about making machines more human-like in their behaviour and decisions by giving them the ability to learn and develop their own programs. This is done with minimum human intervention, i.e., no explicit programming. The learning process is automated and improved based on the experiences of the machines throughout the process. Good quality data is fed to the machinesand different algorithms are used to build ML models to train the machines on this data. The choice of algorithm depends on the type of data at handand the type of activity that needs to be automated.
Machine learning has applications in all types of industries.
- Manufacturing. Predictive maintenance and condition monitoring
- Retail. Upselling and cross-channel marketing
- Healthcare and life sciences. Disease identification and risk satisfaction
- Travel and hospitality. Dynamic pricing
- Financial services. Risk analytics and regulation
- Energy. Energy demand and supply optimization
Machine learning leverages algorithms to automatically model and find patterns in data, usually with the goal of predicting some target output or response. These algorithms are heavily based on statistics and mathematical optimization.
- Clustering (Unsupervised)
- Two-class and multi-class classification (Supervised)
- Regression: Univariate, Multivariate, etc. (Supervised)
- Anomaly detection (Unsupervised and Supervised)
- Recommendation systems (aka recommendation engine)
A recommendation engine filters the data using different algorithms and recommends the most relevant items to users. It first captures the past behaviour of a customer and based on that, recommends products which the users might be likely to buy.
We can recommend items to a user which are most popular among all the users.
We can divide the users into multiple segments based on their preferences (user features) and recommend items to them based on the segment they belong to Companies across many different areas of enterprise are beginning to implement recommendation systems in an attempt to enhance their customer’s online purchasing experience, increase sales and retain customers. Business owners are recognizing potential in the fact that recommendation systems allow the collection of a huge amount of information relating to user’s behaviour and their transactions within an enterprise. This information can then be systematically stored within user profiles to be used for future interactions.
As well as improving customer experience, the information gathered from a recommendation system can also be used as an ad targeting tool. By integrating a recommendation system with ad exchanges, a business may have the ability to target other website users with products they have liked on the company’s site.
Revenues can be increased using simple strategies such as:
- Adding matching product recommendations to your purchase confirmation.
- Collecting information about abandoned shopping carts.
- Sharing “what customers are buying now”.
- Sharing other customer’s views and purchases.
- Making personalized recommendations.
The next generation of recommendation systems may include the following improvements:
More relevant recommendations: By digging deeper into customers’ interests and preferences, recommendation systems will be able to present users with more-relevant, predictive recommendations.
Incorporate item profitability: Instead of having recommendation based solely on a customer’s browsing history and past purchases, this would allow businesses to control how much a profit-based recommendation differs from the traditional recommendation and to set a balance so that customer trust would not be compromised.
Increase product reach: Each retailer has an individual catalogue of products, improved recommendation systems would be able to access a broader range of merchandise in order to include new or niche items in shoppers’ recommendations.
shoppers through multiple channels: Next generation recommendation systems should be able to reach customers across a range of channels including email, social media, on an off-site shopping widgets, mobile apps and the retail customer service centres.