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

Chapter 3: Understanding How to Predict Industry-Wide Trends Using Big Data

Forecasting is a tricky business; no one is sure why it is that some forecasts are right and others are wrong, but two main factors contribute to forecast accuracy:

  • Which data and models are used
  • What assumptions are made about the variables being forecasted

Unfortunately, as this chapter will show, most traditional methods of forecasting suffer from low predictive accuracy because they do not take these important factors into account properly. Here, we will explore how big data changes all that by enabling better predictions.

Our goal is not to present you with yet another prediction tool (though we are going to discuss a few). Instead, we want to share some insights about why conventional methods fail and how we might harness big data to make better predictions ourselves. These insights
will set product teams up for success since accurate forecasts of market demand and sentiment...