Analyzing data in order to find patterns and apply these findings to business goals is the most general definition of data analytics. In the past few years, the flow of data into enterprises has grown exponentially, making it difficult to analyze data using traditional statistical methods. Data analytics can now overcome this hurdle by incorporating machine learning, an aspect of artificial intelligence. By building efficient algorithms, machine learning automates the process of data analysis, revealing hidden patterns and insights.
The goal of predictive analytics is to determine the future using several techniques and tools. These days, it uses machine learning techniques as well as data and algorithms to provide the best scientific evaluation of what the future will bring. Even though predictive analytics has been around for years, it's only now that more organizations are turning to it. That's because in order to handle the large amount of data, today we have faster computers, cheaper technology, and easy-to-use software, all of which make predictive analytics deployment easier. Enterprises are slowly turning to predictive analytics to discover new opportunities.
This guide explains the concepts of a B2C Customer data platform (CDP) and predictive analytics, and how businesses, big and small, can benefit from these technologies. How a B2C CDP Customer Data Platform can deliver a customer-centric experience: read a complete guide.
ML algorithms are used to run recommender systems, which filter information based on a customer's ratings or preferences. Recommendation engines are helpful in reducing the challenge of information overload in e-commerce. Amazon and Netflix were first to popularize recommender systems. However, you can now find recommendations in almost every corner of the digital world, from Facebook posts, Instagram stories, YouTube videos, to food delivery services and e-commerce sites. Today, more and more companies are able to develop and deploy recommender systems thanks to cost-effective and easily accessible software.
Building A Recommender System In Machine Learning – A Primer : Part 2 In Part One of this post, you read about what is a recommender system in Machine Learning, the different classes, and the steps involved in building a recommendation engine. In this second and last post,
Part 1 of this post looked at the definition of Big Data and its importance in data analytics. In this post, we will tell you how to go about implementing your Big Data project, and the pitfalls to avoid while doing so. Be it established or new enterprises, all of them ne
How to Set Up and Find Success in Your Big Data analytics Project – A Primer We at Express Analytics often get this question – how do we (a business) go about setting up our Big Data analytics project? Typically, we get this question from two types of businesses – t
What is a Recommendation Engine and How it Works? In today’s digital world, a recommendation engine is one of the most powerful tools for marketing. Recommendation engine is nothing but an information filtering system composed of machine learning algorithms that pr
How to choose CDP | Selecting the Right CDP Platform Software for your business It’s no longer a mere buzzword in marketing and analytics circles. A customer data platform or CDP has today evolved into a sophisticated piece of software, the lure of which few digita