Book Image

AI-Powered Commerce

By : Andy Pandharikar, Frederik Bussler
Book Image

AI-Powered Commerce

By: Andy Pandharikar, Frederik Bussler

Overview of this book

Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must. Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends. By the end of this artificial intelligence book, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions.
Table of Contents (17 chapters)
Section 1:Benefits of AI-Powered Commerce
Section 2:How Top Brands Use Artificial Intelligence
Section 3:How to Use Commerce.AI for Product Ideation, Trend Analysis, and Predictions

Why traditional forecasts fail

Traditional methods of generating forecasts are based on the idea that you need expert knowledge and intuition of different products and services to model their future behavior. However, this approach has fundamental limitations, as follows:

  • It's impossible to know everything about all products and services.
  • Knowing how products perform today is not a good guide for predicting how they will perform tomorrow.
  • The behaviors of many products are highly correlated and can be difficult to disentangle.
  • Traditional models get overwhelmed by today's big data.
  • The data itself keeps changing.

To address these challenges, we need new forecasting methods that can handle large amounts of heterogeneous data while producing forecasts that are more reliable, more accurate, easier to interpret and explain, and more useful for decision makers.

Let's explore these limitations in detail to lay the groundwork for why new forecasting...