Longitudinal: Customer analytics involves looking at customer behavior over time.

calculating the CLV of different customer segments helps orient your marketing strategy.

Breaking the personas into primary and secondary groups helps website developers decide on the prominence

As a general approximation, you need to quadruple your sample size in order to cut your margin of error in half.

Base your persona on actual data. Collecting, aggregating and analyzing customer data from customer databases and social media helps provide input into a data-driven persona process.

Quantitative data falls into two categories: Discrete (countable items) Continuous (measurements)

You can measure the amount of error with a confidence interval. Confidence intervals tell you how much you can expect sample estimates to fluctuate based on sample size. They provide you with the most likely range for the unknown customer population numbers you’re trying to predict.

Some of the most important insights companies gain from their customer analytics comes from merging survey data with transactional data.

understanding the geographic diversity or concentration of your customers.