This entry-level class offers something for designers and business leaders who want to apply Big Data to solve real-world problems, and for data wizards who want to understand the practical applications for their abstract algorithms and insights.
A recent survey showed that 89% of companies want to use predictive analytics to improve their business. One of the biggest problems in the realm of Big Data is now that organizations have built teams to generate all these analytics - they’re struggling to figure out what to do with it.
In this course, Using Predictive Analytics to Improve the Customer Journey, you'll learn how to address this problem by harnessing Big Data in a way that leads to real-world benefits.
First, you'll learn what predictive customer analytics are, how they are generated, and why they are valuable for making sure your products, customer experience, and websites don’t fall behind. Next, you’ll explore how to use customer journey mapping to spot hidden opportunities (as well as “pain points”) that need to be addressed. Finally, you’ll meld predictive analytics with journey mapping to reveal how you can improve your products and increase customer satisfaction.
At the end of this course, you not only will be able to build data-informed visualizations that improve UX and boost sales, but you’ll also gain insights that will help you better manage teams comprised of both data geeks and design nerds.
Course Overview Hi everyone. My name is Dave LaFontaine, and welcome to my course, Using Predictive Analytics to Improve the customer journey. I'm a UX researcher and designer, but I think a better description of what I do is creative data scientist, and you're welcome to steal that title if you'd like. I use it to describe how I combine my experience in UX design, analytics, and content and stories that bring data to life. In this class, I hope to help you learn practical ways to use predictive analytics. This is a beginner‑level course, so no previous experience with either predictive analytics or user experience design is necessary. Predictive analytics are on the rise because they offer organizations ways to realize the promise of big data. In this course, we're going to learn how to combine these abstract analytics with user journey maps to solve the point points of your users and unlock hidden value in ways that will boost the bottom line. Some of the major topics we'll cover include the phases and steps necessary to start generating predictive analytics, how to acquire and extract relevant data, how to use that data to build realistic user personas that give you insights into who your customers are and what they really want, what kind of journey maps to use and how to start filling in the blanks, and finally, combining predictive analytics with those journey maps to spot the areas where critical failures are taking place and then start to improve them. By the end of this course, you'll be able to use data to help your users get over their pain points, making their customer journey smoother, faster, and as a result, more efficient, effective, and most importantly, more profitable for you and your organization. From here, you can continue learning more about predictive analytics with this course for data analysts working with Python, or if you prefer, you can learn more about crafting solid user journeys by following these design learning paths. I hope you'll join me on this journey to learn how to practically apply predictive analytics with the Using Predictive Analytics to Improve the Customer Journey course here at Pluralsight.