How can you account for external factors in your A/B tests?

This is true in case of email marketing as well. While the traditional approach of email marketing A/B tests involved CTA testing, subject tests etc. But wouldn’t it make sense to weigh which emails actually helped in creating opportunities in form of deals maybe? Or, which emails lead to meetings booked. This way you can never go wrong on what is ACTUALLY working and what is just junk.

Choosing the right metric is like tuning a radio to the right station for crystal-clear reception. - Go for metrics that are directly impacted by what you're testing to minimize noise from external factors. - Steer clear of vanity metrics; they might make you feel good, but they won't necessarily move the needle where it counts. In A/B testing, the right metric acts as your North Star, guiding you toward actionable insights.

Bounce rate is an essential metric for any email campaign. Maintaining a bounce rate between 1 to 2% is an acceptable number.

There’s nothing inherently wrong with A/B testing to improve email click rates and open rates, and I wouldn’t discount these test types as chasing vanity. What is wrong is to create a test hypothesis which attempts to manipulate the audience… click bait is a great example of this (when an ad doesn’t correspond to the content linked behind it) Creating A/B tests must be as scientific as possible, with rationally considered hypotheses… cause (test variable) and effect (key metric) proven over multiple tests against a control, with representative samples. I’d encourage anyone to keep improving an email marketing program by rolling out proven hypothesis to incrementally nudge your open / click rate KPIs in the right direction.

Whenever possible, create "hold out" groups. Depending on the amount of traffic or the size of your database, try and create a way to keep a certain group with the same experience. For example, I typically like to segment out a 10-20% "hold out" group for a majority of my campaigns if it's a newer test or I believe there may be external factors that could influence outcomes. This way, regardless of the external factors, I can compare the new test results with data and performance from a campaign that was "status quo" and had no changes. The one thing to remember with hold out groups, is they need to be statistically significant. Enough data to see if something actually changed.

Not every audience will behave in a similar way. For examples: Based on lifecycle stage: you will not test the same content for your existing client vs your prospects. Or, The emails you are sending to cold leads will not be same as emails you are sending to mql/sql Based on geography: You would not want to send the same email at the same time to 2 different people with different time zones. Here - you can use also leverage SMART content if you wish to showcase the content in one particular language in one par geolocation

Managing A/B tests during seasonal events like Black Friday, Christmas sales, or any other peak periods (depending on your business) requires a bit of extra effort to ensure reliable results. Here are some ways you add control for seasonality in A/B tests: - Baseline Data: Collect pre-event data as a performance reference in non-seasonal times. - Extended Tests: Lengthen tests to include pre-and post-event periods to gauge full impact. - Control Groups: Use them effectively to compare audience behavior with test groups. - Data Segmentation: Segment data for different audience behaviors. By following these strategies, ensure that your A/B tests remain reliable and deliver valuable insights even in the busiest times.

To address any confounding variables that may affect the results of your experiment (A/B test) you have two options. First, control the confounding variable so it does not change across treatment and control groups. Second, if you cannot control the variable, then simply measure it. If you have go measures, then there are statistical options available to parse out any of the variability due to the confounding variables so you get a true estimate of focal independent variable effects. For example, taking into account any macroeconomic trends is common in most marketing research.