Why You Should A/B Test Your Direct Mail Postcards
Your direct mail postcards are generating leads and sales for your business, but are they as good as they could be? Unless you test your postcards using A/B testing, you may never know which headline, image or copy converts the best.
In this guide, we’ll cover one of the most important topics for getting the most from your postcard marketing company: split testing your direct mail postcards to learn which offers the best return on investment for your business.
What is A/B testing?
For decades, A/B testing has been a staple of direct marketing. From brochures to emails, tens of thousands of campaigns have been optimized using basic A/B tests that pit one variation of an advertisement against another.
A/B testing involves sending out two variations of your direct mail postcards, each with different design elements and copy. Some A/B tests involve subtle variations, while others may involve radically different postcard designs.
Why is A/B testing important?
Unless you test one postcard against another, it’s impossible to know whether your direct mail campaign is performing at its best. Many direct mail campaigns aren’t as optimized as they could be – something that only A/B testing can reveal.
With the right sample size, A/B testing can reveal serious opportunities to enhance your direct mail campaign and improve your return on investment. Tiny changes to headlines, for example, can often double or triple your response rate.
Over time, A/B testing can be used to massively increase the return on investment your direct mail postcards produce. By testing headlines, subheadings, images and copy, you can double, triple or quadruple your response rate and profit margin.
How to A/B test your postcards
Testing two variations of a direct mail postcard is simple. If your campaign involves customers mailing in a voucher or redeeming a coupon, add a small identifier to the coupon so that you can see which is redeemed by a larger audience.
If customers need to call your business to redeem your offer, you can use two phone numbers to test different postcards. Alternatively, you can ask people to reference a specific code or voucher number and track which one appears more frequently.
Understanding statistical significance
You’ve run your first A/B test and the results are in. Postcard 1 resulted in 43 calls and 22 sales, while Postcard 2 resulted in 56 calls and 26 sales. Clearly, the second postcard was the top performer, but was it due to its design or just due to chance?
In order for an A/B test to be relevant to your campaign, it needs to be statistically significant. The larger your audience and the difference between one postcard and the other, the more confident you can be that your testing results are accurate.
Calculating statistical significance is simple: you can either calculate it manually using confidence intervals, or online using an A/B testing calculator. Before you assess the results of any A/B test, make sure they’re statistically significant.
Start A/B testing your postcards today
From titles to images, sales copy to captions, every element of your postcards can be modified and tested to improve your response rate. Split your next postcard mailing into two groups and start A/B testing your direct mail postcard campaigns today.