Jeff Sauro - Customer Analytics For Dummies

Customer Analytics For Dummies

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Appendix

In This Appendix

Recognizing relationships

Predicting performance

Predictive analytics comprises several methods to analyze what happened in the past to predict what will most likely happen in the future. You use your historical and transactional customer data to identify risks and opportunities.

Youve almost surely encountered the results of predictive analytics as a customer yourself. Some examples you likely encounter include

• Amazons recommendation: Probably one of the most famous examples of predictive analytics that touch the customer is Amazons recommendation engine. This includes the customers who purchased this book, also purchased this book.
• Facebook and LinkedIn: Social media websites like Facebook and LinkedIn use algorithms to determine both whom you might want to connect to and which stories and updates you want in your timeline based on patterns in your viewing behavior and people with similar behavior to you.
• Netflix: Netflix recommends which movie or TV show youll like based on your past views and matching that to customers with similar behavior.
• Return rates: I worked with a mobile carrier to predict which phones customers would return most often based on the opinion of customers evaluating the phones usability.
• Credit cards: Your credit score and credit report are the results of the banking and credit industry wanting to predict who is more likely to pay on time and those who will more likely default.
• Insurance: Life insurance, car insurance, and health insurance providers notoriously collect a number of data points about customers to predict which customers will more likely get sick and need care, have a higher chance of dying prematurely, or are more likely to get into a car accident.

In all these examples, some past customer data is being used to predict future events. The same principle applies to customer analytics: using past customer behavior to predict future behavior. Throughout this book, Ive covered both what customer analytics to collect and methods to collect them. With these analytics collected to describe the customers current and past experience with products and services, you can also predict the future. This appendix is a primer to help you get started with the skills needed to predict with customer analytics.

Three essential techniques to make predictions with customer analytics include

• Finding similarities: Identify how customers are similar, either based on behavior like purchase history or attitudes like customer satisfaction
• Identifying trends and patterns: Predict when customers will purchase, future revenue, website page views, subscription rates, or same-store sales.
• Detecting differences: Understand how customers differ or respond differently to product features and designs, which allows for customizing products, experiences, and pricing.

Finding similarities and associations with customer analytics data is the most common analysis technique to predict future customer behavior. Some examples of the types of questions based on making associations with customer data include:

• For customers who purchase product A, what other products do they purchase?
• Will coupons increase same-store sales?
• Does a longer time on a website result in more purchases?
• Will a reduced price mean higher sales?
• Is customer loyalty tied to future company growth?
• Does the change in home page design cause higher conversions?

Understanding the relationship between variables, how strong that association is, and ultimately the cause of outcome variables, is a fundamental and useful skill for predicting with customer analytics.

You can visualize the relationship between two variables by graphing them in a scatterplot. Scatterplots are a useful tool to identify associations and examine the strength of the relationship.