Sending the same direct mail campaign to every recipient is like fishing with the same bait, regardless of the kind of fish you’re trying to catch or the depth of water. By not taking these factors into account, you’ll likely fail to attract fish. And as a marketer, if you send the same campaign to everyone, your response rates will suffer.
How do you know what types of campaigns to send to different recipients? By using direct mail testing. Isolate aspects of your campaign that you’d like to optimize, and use A/B and multivariate testing to determine which factors resonate with different audience cohorts.
Develop a hypothesis about what a cohort wants to see
Before you can start testing direct mail, you’ll need to evaluate your existing customer data to make an educated guess about different recipients’ mail preferences.
Collect and analyze this information by using a variety of marketing tools.
- A CRM like Salesforce is ideal for storing customers’ personal information, like their location and demographics.
- A marketing platform like Marketo, tracks how prospects and customers interact with your marketing campaigns.
- A product analytics platform like Amplitude monitors how users interact with your product.
All of this data can help you form an informed hypothesis about how you might adjust a direct mail campaign for different groups.
Say your marketing platform identifies a group of customers who used the discount code in your last “20% off” email campaign. These individuals would likely be ideal recipients for future promotional direct mail campaigns. On the other hand, a group of people who unsubscribed from your email list after receiving this discount message shouldn’t receive the promotional mail. Identifying trends can be tricky, though, with customer information coming from so many sources. Consider bringing your customer data into one platform through integrations. Our direct mail platform, Lob, can connect with your CRM and marketing automation platforms, so you can review customer trends at a glance and hypothesize about different groups’ campaign preferences.
Decide what aspects to test
Now that you’ve learned more about customers’ preferences, it’s time to create variations in your campaigns. How will you know which changes resonated with recipients? Include unique QR codes or URLs on your direct mail so you’re able to track which recipients engaged with your campaigns.
Based on your hypothesis about what a cohort wants to see, determine what aspects of direct mail you’ll change in your testing.
- Offers: Although 20% off and $5 could be the same monetary offer, customers may be more incentivized by the larger percentage number.
- Personalization: Based on how customers have responded to past personalized campaigns (whether digital or direct), consider testing whether recipients are more responsive to direct mail that includes information such as their name, imagery based on previous purchases, or even information and imagery based on geographic location.
- Calls-to-action: Test different wording to see if certain language or text lengths resonate with a group of customers. You might experiment with “Start your free trial” against “Start your free 14-day trial today!” to see how length affects engagement.
- Imagery: Image testing works best when you compare images that are similar. If there are too many differences, you won’t know which distinguishing factor drove engagement. So rather than having one direct mail design feature an image of a city and another feature an image of a dog, you might limit your variations to the city image—one campaign is an illustration, while another is a photograph.
- Packaging: Generally, oversized envelopes have the highest response rate compared to letter-sized envelopes with the lowest.
- Style: Consider how customers have responded to different campaign designs in the past (whether direct or digital) to adjust an aspect of your physical mail’s style. Say one customer group engaged with an email campaign that had loud, bold colors. You might send half of these people a postcard with neon colors, while the other half receives one with more subtle tones.
The type and number of variants you decide to test will then inform which testing method you use.
Choose your direct mail testing method
Now that you know the who and what of your testing strategy, it’s time to consider the how. There are two techniques for testing: A/B (or split) testing and multivariate testing. Each method has its pros and cons, depending on the goal of your direct mail campaign.
A/B direct mail testing refers to experimenting with a single change to a direct campaign. Half of a group of recipients will receive the direct mail campaign with the change, while the other half will receive the campaign without any adjustments.
With an automation delivery service like Lob, you can create a direct mail campaign with two versions. One mailer is the control campaign with no adjustments, while the second mailer has a single change. Be sure that the mailers are identical apart from the one element you are testing.
Once you’ve sent the campaign, you can use the custom QR codes and URLs on the mailers to track whether the adjustment led to more engagement.
- Fast. By adding tracking tools such as QR codes to your designs, you can quickly gather results to optimize your next campaign.
- Clear. Because A/B testing compares just one variant against the other, data will reveal which variant gives the best results.
- Limited. Results just provide data on the performance of the two variants, which can be time-consuming if you have multiple elements you want to test.
As its name suggests, multivariate direct mail testing compares multiple variables of a campaign.
A multivariate test might change both the CTA and an image in a piece of direct mail at the same time. Say you have three different CTAs and images—you’ll be left to determine the winner of those six variations.
To conduct multivariate testing, you could have a designer completely redesign your mail each time. Or, you could use a platform like Lob, which has modular HTML templates for easy design changes.
- Informative. By changing multiple elements simultaneously, enterprises can test numerous components to determine what combinations create the highest conversions.
- Saves time. By testing multiple variants at once, you save the time you would otherwise spend conducting multiple A/B tests.
- Not good for testing individual elements. Because multivariate tests change a combination of elements, it can be difficult to parse out which individual elements are most effective.
Conduct direct mail testing regularly to optimize future campaigns
Direct mail testing isn’t something you conduct just once. Recipients’ tastes won’t stay the same forever, so you need to conduct regular tests to gauge their direct mail preferences. Design experiments based on what you concluded from previous tests, and modify your pieces to keep up with the needs of your audience. To learn more get our Direct Mail Tactics Playbook.