Five Tips to Help You Revamp Your A/B Split & Multivariate Test Designs
All email marketers are more than aware of the email AB testing and multivariate testing methods and their importance, but how do you go a level further and ensure that these tests give you the most accurate results?
Well, there is no single answer to this question. Thus, the monks have prepared a listicle that will guide you through revamping your test designs to ensure there results are far more precise and applicable. There are a hundred intricacies that can be checked for, but these basic 5 will help you sail through quite a lot and arrive at meaningful data to crunch.
Here’s the list of the chosen five:
1. Reach Out to the Right Crowd
You need to match your email AB testing & multivariate testing sample audience to your target audience, to receive accurate testing results. For instance, if you are women’s health organization and you are running a test for data on nursing mothers or expecting mothers make sure you send the test emails to such women and their partners and not people of all demographics, as this would result into skewed data. Also ensure that before you begin with the test you have a former database to serve as a base for comparison.
2. Categorize your sample
Categorize your sample based on the funnel stage they belong to. This will help track behavior of different groups in different categories precisely. Let’s take the example of a triggered cart abandonment mail, there could be two categories of people, one who left your site with products in their cart and other who left the site after trying to make a payment for the cart items because they couldn’t process the payment for some reason. In such a case you must test both of them separately and not compare the results of one with the other.
3. Adequate sample size
Now that you have fixed upon the demographic and other parameters of sample selection, make sure that your sample size is big enough. Send the test emails to an adequate number of people to ensure that the test results are a majority based opinion that will hold true for a larger population base.
Let’s consider a case where a news channel wants to conduct an email opinion poll on the general elections of a country which, has a population of 1,000,000 people and they decide to send the email to a sample audience of 50 people, do you think such a poll will reflect the majority’s opinion? No. The reason for this is the inadequate sample size. Similar is the case for email AB testing or multivariate testing in the email marketing domain.
4. Adequate test duration
You need to conduct the test within a time frame that allows sufficient space for the sample audience to respond. Also conducting the test within an adequate time frame will help you understand behavior within different times of the day and week. This will also help average out the extremes and give you a data that considers various time bands and industry cycles.
For instance, if you are an online apparel store and you are running a test to know the buying pattern of a seasonal product like winter-wear, you should conduct the test throughout the season and not just the peak winter time to ensure that you do not obtain extreme values.
5. Eliminate extremes
Eliminate all those dates that are posed to give you an extreme data value.
This is fine in the case where you want to draw out the difference between a specific occasion and the other days, otherwise you need to exclude such occasions to receive normalized values.
Consider the case of a candy store that is trying to define the sales pattern of various candies, such a store must exclude the days around the Halloween holiday for sending out the test emails, unless they want to measure the impact of the holiday on candy sales.
To sum it up
Email AB testing or multivariate testing, a test that is designed well is more likely to reflect the right data, such a test can be the source of some great insights and can help you form your email marketing strategy in a well-informed manner. Make sure that you compile and save the data you receive to form a rich data bank, and do not stop at this, dive deep into this data to gain deeper insights to help your brand perform better.
Any more divine tips on making testing designs more effective? Do post them in our comment section. Any issue designing and coding emails, contact the expert Monks.