Automatic reporting: Look for ways to output key dependent variables to executive and team dashboards or scorecards that provide real-time insights into customers’ experiences.

Patterns emerge from data analysis. It’s fine to have preconceived ideas about what to expect, but don’t let that dictate how you look for patterns. Let the data do the talking.

Customer analytics largely involves turning customer actions and attitudes into data.

If you want a more precise confidence interval, use the online calculator available at www.measuringu.com/wald.htm.

By far the most common and fundamental measure of customer attitudes is customer satisfaction. Customer satisfaction is a measure of how well a product or service experience meets customer expectations. It’s considered a staple of customer analytics scorecards as a barometer of how well a product or company is performing.

understanding the geographic diversity or concentration of your customers.

Interval: This is data that has equally split intervals between each value. The most common example is temperature in degrees Fahrenheit.

Customer analytics involves gathering data about your customers at various stages of the buying experience, detecting patterns from that data, predicting actions your customers will take, and then making decisions about how to improve your business to attract more customers and keep the customers you already have.

The basic framework is to define what you want to do, find the right ways to measure it, do something about the measures, and put processes in place to continue using customer analytics to make better business decisions.