Maximize Subscribers at Checkout: A Guide to A/B Testing for Incremental Growth

With customer acquisition costs up 222% over the last decade, the pressure to make every visitor count has never been higher. That's why leading Shopify brands are doubling down on email and SMS opt-ins as core drivers of incremental revenue.

The problem is that most brands still rely on discount popups or top-of-funnel captures to grow contact lists. But in reality, the checkout page is where the majority of your highest-quality contacts are, and getting more customers to opt in has an outsized impact on their LTV.

In this guide, we'll break down why optimizing your checkout consent flow (and A/B testing it to prove incrementality) is the best way to turn more customers into marketing subscribers, drive long term retention, and incremental lift. You'll also learn how to run your own tests and which tools can help.

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Why Collecting Consent at Checkout Drives Incremental Revenue

Checkout is one of the most powerful and underused opportunities to capture marketing consent. Unlike top-of-funnel tactics like popups or quizzes, checkout taps into real purchasers.

When a customer opts in to email or SMS at this stage, they're not just browsing — they're already buying from your brand. And capturing that consent pays off: These subscribers consistently deliver higher lifetime value and better downstream performance than top-of-funnel leads.

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This trend shows up clearly in the data. For Benchmade, subscribed customers were worth significantly more than those who did not opt in. The average CLV of a subscriber was $85, compared to just $34 for a non-subscriber​. This is a meaningful lift that scales as your list grows.

Because the opt-in happens during a transaction, you can tie every subscriber to a purchase. That means you're not just growing your list — you're tracking exactly how much incremental revenue it's adding over time.

SMS takes it further. These messages are short, personal, and hard to ignore, making them ideal for driving urgency through restock alerts, delivery updates, or one-day offers. With high open rates and instant visibility, text updates can drive faster repeat purchases. In fact, brands notice a 2X increase in repurchases from customers who subscribe to SMS and email compared to those who don't.

If your checkout experience isn't built to capture consent, you're missing a high-leverage opportunity to grow a list that drives not just purchases, but repeat purchases and retention. Unlike most list-building tactics, checkout opt-ins are easy to measure, easy to test, and clearly tied to business outcomes.

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Why You Should Test Consent Flows at Checkout

Unlike popups or discounts, which can inflate list growth with lower-intent users, checkout focuses on technical changes that aren't tied to incentives. That makes your results more reliable and your growth more sustainable.

At checkout, you're not testing flashy, incentive-driven promotions that create short-term spikes. You're testing small, technical changes that drive lasting growth, like:

- The wording of the consent flow

- Whether opt-in is pre-selected

- Logic variations, such as region-specific compliance defaults

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Because these changes happen during the purchase and the change to the consent widget is binary, you'll get a super clear picture of the impact on opt-in rates in your data. If a variation lifts consent rates, you know it's not just a fluke. It's the result of the changes you made.

And you don't need massive changes to get started. Just a small change to the consent configuration can make a huge difference. Over time, these tests help refine your approach and steadily grow a higher-quality subscriber list.

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A/B Tests for Checkout Consent You Should Try

Here are a few high-impact A/B tests to improve checkout opt-in rates, which directly contribute to increasing retention and incremental revenue.

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Default Shopify Checkbox vs. Dynamic Consent

For many brands, the default Shopify setup includes a simple checkbox for marketing consent. Depending on the customer's location, the box is either pre-ticked or left blank. While this setup is functional, it often leaves performance on the table. A good starting point for testing is to compare this baseline with a more customized consent experience.

Shopify does allow some location-based behavior (like auto-checking the box for U.S. shoppers and leaving it unchecked elsewhere). But the platform has limits: it doesn't offer flexible consent logic. Shopify also doesn't take legal responsibility for how consent is collected. This means that you can technically select a consent configuration that is non-compliant in the region that you're selling in, and you'd bear full responsibility for that.

Metrics to Track

- Opt-in rate

- Unsubscribe rate (make sure this doesn't go up)

- Checkout completion rate

- Average LTV of subscribers vs. non-subscribers

- Revenue generated by your checkout opt-in list

A more dynamic, localized consent experience can meaningfully increase both opt-in rates and long-term customer value, as well as simplify your marketing compliance.‍

Benchmade tested this hypothesis by swapping their standard Shopify setup for a dynamic, location-aware consent experience. The test ran for three weeks and showed immediate impact:

- 97% email opt-in rate

- 2,448 incremental subscribers added per month

- $124K in incremental LTV unlocked monthly

- Subscribed customers had a projected $51 higher LTV than those who didn't opt in

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You can start with this baseline test, then use the results to guide your next iteration.

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Reply Y vs. Onsite Verification (Email + SMS)

If SMS is part of your growth strategy, you know getting permission to text your customers (especially in the U.S.) is difficult. Checkout is one of the more overlooked capture points, and there is certainly a lot of ground to cover by testing your approach there.

Most brands do have SMS opt-in as an option at checkout, but the standard Reply Y process has dismally low success rates (<1%). This is because only a small percentage of shoppers are going to check the SMS box to begin with, and an even lower percentage are going to bother replying to an automated text after they’ve finished the purchase.

You can test replacing the Reply Y text with a verification code text that appears to the user while they're at checkout, much like the experience you're used to with Shop Pay or any other SMS-based verification system. Opt-in rates for this approach can get as high as 10% (vs the sub 1% for Reply Y). In our customers' experience, this does not increase checkout dropoff in any way whatsoever.

Metrics to Track

- SMS opt-in rate (email + SMS combined)

- Checkout completion rate

- Channel-specific opt-ins (email vs. SMS)

- Engagement and LTV by channel

If you see strong results from email opt-in testing, it's a smart time to layer in a new approach to SMS. By verifying SMS opt-in during checkout, you can drive immediate engagement and long-term revenue lift.

For example, IQBAR tested their switch from a post-purchase "Reply Y" SMS method to an in-checkout verification step. As a result, SMS opt-in rates jumped from 0.36% to 6%, email opt-ins rose from 60% to 96%, and the change generated over 19,000 new contacts.

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How to Design Tests That Can Detect Statistical Significance

Now you've got your test ideas for lifting opt-in rates — but to translate those lifts into real, incremental growth, you need to know the results are statistically sound. Even the best hypothesis won't help if your data's too thin or your test ends too early. We'll walk you through how to design your tests so you can detect meaningful changes with confidence.

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Start with the Right Sample Size and Timeline

To detect meaningful results, aim for at least 2,000 completed orders per variation in your test. This sample size gives your experiment enough power to pick up on a real difference (if one exists) and helps you avoid drawing conclusions from random noise.

If your store sees lower volume, you can still run valuable tests — you might just accept a lower sample size, for example, 50% of your average monthly order volume. High-traffic brands might reach significance in a few days, but if your baseline is smaller, plan for a timeline of two weeks.

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Define What Counts as a Meaningful Difference

Before launching your test, get clear on what kind of change would move the needle for your business. This is often called your minimum detectable effect — the smallest uplift you'd feel confident acting on.

You won't need as much data if you're aiming for a significant change, like a 10-point increase in opt-in rate. But the smaller the lift you try to detect, the more orders you'll need to reach reliable conclusions. This approach is important because smaller shifts are harder to separate from natural fluctuations.

A good starting point is reviewing your historical data. For example, if your opt-in rate has been stable around 40%, ask: What increase would be worth rolling out? If three percentage points would justify the change, design your test to detect that level of impact. Then, calculate how many orders you'll need to reach statistical confidence.

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End the Test When Results Are Stable

End your test only once conversion rates — whether for opt-ins or purchases — have settled. A short-term spike might look promising, but it could fade as more data comes in. Make sure your control is performing at its best so you can clearly measure the lift from your variation.

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Action on Your Results and Track Long-Term Impact

Once a test shows a statistically valid lift in consent rates and conversion remains stable, the next step is implementing the winning variation. But the process doesn't end there. To understand the true value of the change, you'll need to measure its long-term impact.

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Analyze Your Cohorts

After rollout, segment your customer base by test variation. Track how each group behaves over time — not just in the first week, but across several months. This is where the real lift in retention and incremental revenue tends to show up.

Dataships supports this kind of analysis with detailed reporting. These reports show:

- How many additional subscribers were generated by each version

- How many subscribers are compliantly/non-compliantly marketable

- Your incremental lift to LTV and revenue

This view helps connect short-term wins to long-term value.

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Flip the Switch

Once a test reaches statistical significance, retire the underperforming version. Set the higher-performing consent experience as your new default and continue monitoring its performance.

Keep an eye on core KPIs:

- Opt-in rate

- Unsubscribe rate

- Checkout completion

- Revenue contribution over time

Tracking these metrics consistently helps you understand how list growth translates into recurring value.

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Witness the Compounding Effect

Even modest improvements at checkout can have a meaningful impact. An increase of just one to two percentage points in opt-in rate can drive a significant lift in downstream metrics like repeat purchase rate and customer LTV.

IQBAR's experience shows this well. After testing and optimizing both SMS and email consent at checkout, they achieved:

- 17X increase in SMS opt-ins

- 1.6X boost in email opt-ins

- 36% repeat purchase rate

Improvements in opt-in rates often lead to more than proportional revenue gains. This is because subscribers are more likely to return, buy again, and stay engaged across multiple order cycles.

If your test shows a statistically significant lift in consent rates (without hurting conversions), roll it out with Dataships — then measure the long-term impact.

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How To Set Up Your A/B Tests with Dataships

You can run A/B tests manually by piecing together spreadsheets and dashboards across different platforms. But that process can be time-consuming and hard to maintain. Dataships simplifies your consent testing by offering a streamlined, guided approach that’s built right into our onboarding.

We help Shopify Plus brands stay compliant and grow their lists by automatically applying the right consent strategy based on each shopper's location. That means you collect more email and SMS opt-ins without adding any legal risk that comes from non-compliance.

You can launch an A/B test through Dataships in about 10 minutes. Once you're onboarded, someone from the team will walk you through the entire setup.

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What You'll Need First

Make sure you have these essentials ready to help speed up onboarding:

- Access to your Shopify admin

- A private API key from Klaviyo

- Your store's Privacy Policy URL

- A privacy contact email

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Setup Process

Here's how to get your first test live:

- Install the Dataships App. Once you install the app from the Shopify App Store, the Dataships team will assist with the remaining setup steps below.

- Connect Klaviyo. Generate a private API key and paste it into the setup window. This connects your subscriber data for syncing and analysis.

- Submit Privacy Policy and Contact Info. Provide a URL for your Privacy Policy and an email address for privacy-related queries.

- Add Dataships to Checkout. Drag the app block into the Information, Thank You, and Order Status pages in your Shopify checkout editor.

- Share Shopify Cohorts. To segment your test correctly, Dataships will request access to your customer cohort data.

- Confirm Consent Settings. Let the team know whether your default consent box should be pre-ticked or unticked, and what copy should appear in your consent block.

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How the A/B Test Works

Once everything is in place:

- Half of your customers will see your current setup (control)

- The other half will see the Dataships version, which includes dynamic logic, improved UX, and region-specific compliance settings.

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Start Your A/B Testing Journey with Dataships

For fast-moving DTC brands, checkout isn't just where purchases happen — it's where you have the highest leverage to turn buyers into long-term subscribers. With the right approach, it becomes one of the best places to grow your marketing list with high repurchase potential, fully compliant opt-ins.

If you sell in categories like apparel, wellness, beauty, or food and beverage and rely on Shopify and Klaviyo, improving consent collection at checkout can quickly boost your numbers. Dataships customers regularly see 3–4X more email opt-ins and up to 10X more SMS opt-ins, all while staying fully compliant with regional privacy laws​.

Plus, you don't need to write a line of code. Dataships installs in minutes, integrates with your existing stack, and starts delivering measurable results fast.

That's not mere list growth — it's more customers you can actually market to. You'll see more repeat purchases, retention, and revenue without increasing ad spend.

And it's cost-effective. While discount popups sacrifice actual revenue for every signup and interrupt the customer experience, Dataships charges a flat fee per incremental opt-in, delivering a 23X+ ROI on average for every brand that has gone through our A/B test.

Instead of cobbling together multiple consent tools, Dataships offers a fully managed system that scales with you. It's designed to launch in minutes with minimal lift from your team. And we handle all the data and impact reporting for you to make sure you're getting good value from the tool.

If your goal is to reduce acquisition costs, expand internationally with confidence, or grow a more engaged list at checkout, Dataships gives you a faster, easier way to get there.

Ready to drive incremental growth? Start A/B testing with Dataships.