When I was in college, I took a class on complex analysis and after all the lectures, studying and nerve-racking exams, I learned one important thing about customer behavior – some characteristics of a person will likely contribute to their future behavior.
In other words, my grandparents are not likely to start buying iPods, but at the same time my younger sister and her friends are not going to go out and start buying rotary telephones either.
Many times, variables such as gender, age, income, education and geographic location will likely play a role in why your customers say yes to your offers. This brings me to my point that selecting a test methodology robust enough to explore statistical relationships among variables is more important than ever to your marketing efforts.
In today’s MarketingExperiments blog post, we will simplify three basic testing models you can use to build an advanced customer theory.
Our goal is not to give you a Ph.D. in statistics, but rather, we want to provide you with a few test methods simplified and free of as much mathspeak as possible you can use to aid your team’s next discussion on test selection.
Test Method #1. ANOVA (Analysis of Variance)
Marketers can use the ANOVA testing method to understand if a statistical significance exists within or between groups. Landing page optimization is a good example of how ANOVA testing can used to analyze a customer’s response to different treatments based on variables of interest.
For example, suppose you’re testing landing pages and you want to determine if the income or education level of new and return customers has any statistical significance on the probability of conversion on the landing pages you’re testing, then ANOVA would be the optimal test method to consider using.
Test Method #2. Logistic regressions
Logistics is a testing method for prediction analysis. In other words, a logistic regression test can help you to discover the statistical likelihood of a conversion for customers in demographic A versus customers in demographic B.
With logistic regressions however there is only one catch …
Customers in both demographic groups A and B have to be known as significant contributors to the likelihood of conversions.
Test Method #3. Time series analysis
Time series analysis is a test method similar to logistical regressions in that’s it has a basis in predictive analysis, but time series analysis is focused on what you can learn from historical data trends.
Understanding the seasonality behavior of your website traffic is a perfect example of when you would use a time series analysis.
These are just a few of the testing methods available to help you learn more about your customers, but ultimately no marketer is an island. So, if you have a testing method that you use to build your customer theory, feel free to share it with in the comments below.
Related Resources:
Marketing Optimization: How to design split tests and multi-factorial tests
Marketing Metrics: Why all numbers aren’t created equal
How to Predict, with 90% Accuracy, Who Your Best Customers Will Be
Online Marketing Tests: A data analyst’s view of balancing risk and reward
We have definitely tried several variances of ANOVA testing with tracking dedicated phone numbers and info capture forms on our site. We advertise in different places with different numbers so we know where our customers are coming in from.