A Shopify merchant’s guide to A/B Testing on Pack: clean data, no flickers

Pack makes it easy to deliver flicker-free A/B test experiences on landing pages or your Hydrogen storefront. Instead of replacing your data and analytics platforms, Pack works alongside your existing tech stack to deliver the experience side of testing while your analytics tools handle measurement. Let's explore what you can do with Pack's A/B testing feature, and how you can set it up for success.

Prerequisites: Is your data foundation ready?

Before you launch your first A/B test experience on Pack, you'll need solid data infrastructure in place. Pack handles A/B testing experience delivery—your analytics stack handles measurement.

Pack works best when you have:

• Clean, standardized data collection via partners like Elevar, Fueled, Blotout, or Littledata

• Data warehouse infrastructure (BigQuery, Snowflake, Databricks, etc.)

• Established analytics governance and clear KPI definitions

• Server-side event tracking with accurate attribution

Signs you might need to build your data foundation first:

• No existing data infrastructure or unclear measurement strategy

• Expecting Pack to resolve analytics discrepancies or own attribution

• Looking for Pack to replace your analytics stack

Don't worry if you're not there yet! Pack's team works with data partners regularly and can help you evaluate your setup and connect you with the right tools to get your foundation ready. Book some time with us to discuss your specific situation.

Set your A/B test experiences up for success

Once your data foundation is solid, here are our recommendations and best-practices to make your test experiences as successful as possible.

Step 1: Understand what you can test with Pack

Pack makes it easy to deliver:

• Content changes—ex. Custom layouts, imagery, text etc. (anything that you can control in the Pack Customizer)

• 3rd party apps or built-in site features—ex. What's the impact of providing product recommendations on our product page?

• Cart elements—ex. What's the impact of having a package protection widget at checkout?

• Code-based changes—ex. Webhooks and packages so you can create your own unique test experiences

Pack's biggest strengths:

Out of the box, Pack's optimized for:

1. Super-fast page loads during tests

2. Dynamic content delivery

3. No test variant flickering

4. Minimizing privacy regulation

That means they're going to give you cleaner test experiences than most platforms, since the shopper experience is top-notch. Pack can deliver this smooth experience because it can fully compile a test experience before it even hits your shopper's device (that's thanks to server-side rendering—the new technology that powers Shopify Hydrogen!).

Pack's testing is particularly powerful and useful when you're experimenting with personalization. Soon you'll be able to use data from your existing "CDP" or anywhere you have access to your shoppers information or user behavior (I.e. location, page visits, segment IDs, etc) to change content dynamically on the page without requiring the shopper to do a refresh to see the personalized content. Because all the experience data is compiled on the server, it can also help avoid some of the client-side data privacy challenges.

When Pack won't work for you:

Pack does not enable tests for data that is not stored within Pack (ex. product pricing), which means you'll want to integrate other tools for more intricate or complex tests (ex. Multi-step content activation).

Step 2: Establish Your Testing Strategy

Before you deliver any test experiences, you'll want to clearly define your:

• Process or framework for ideating and prioritizing high-impact optimizations (Ex: scoring ideas based on reach, impact, confidence, effort (RICE))

• Business objective (Ex: Boost AOV)

• Hypothesis (Ex: by adding a product recommendations slider to the product page, we can boost AOV x%)

• Key metrics (Ex: AOV with do-no-harm for conversion rate)

• Plan for data analysis and interpretation (Ex: Fueled for data collection, Blotout for analysis)

• Process for documenting and sharing learnings

With a documented testing strategy, you're much more likely to end up with test experiences that deliver both accurate and actionable results.

Step 3: Connect Pack to your analytics stack

Pack delivers A/B test experiences and surfaces event data that your existing analytics platforms consume for measurement. Pack's dashboard provides baseline insights like total users, engagement metrics, CVR, AOV, and revenue for your control and variant groups.

To connect your data: today you can integrate with Google Analytics and/or BigQuery, and Pack uses Bayesian statistics to calculate a winner. For more advanced analysis—like custom confidence levels, Frequentist testing, or complex event funnels—export your results to your data warehouse and visualization tools.

Quick tip: If you don't have historical benchmarks, run an A/A test first to establish a baseline for interpreting future results.

Step 4: Set up your first test experience

To start delivering your first A/B test experience, you'll want to pull in a developer / Pack's support team to help you install some packages on your storefront (see our A/B testing developer documentation which covers all the technical prerequisites and setup steps in detail).

Once you've run through setup, it's easy to create your test experience in Pack's visual editor and analyze the results through your connected analytics platforms:

Step 5: Iterate + apply your learnings

Wins: For winning test experiences, you may want to deliver the same experience across multiple pages on your website. With Pack, you can create a template to add / modify sections that appear on multiple pages at once, and assign them to specific pages with tags.

In-between: For experiences that aren't a huge win or loss, it might be worth an iteration if you have a strong hypothesis as to why it didn't have the impact you predicted—qualitative user research can be a powerful tool to double click on the "why" behind a test result. You may also decide to scrap the test in favor of bigger needle movers.

Loss: You'll want to check in with your test experiences frequently to monitor for massive drops in core metrics. Once you've identified a test as a loss, you can turn it off with a simple click.

Common Pitfalls to Avoid

1. **Starting Without Proper Data Infrastructure
   **Without clean data collection and analytics governance, you'll waste time on unreliable results.

2. **Not Connecting All Data Sources
   **Make sure Pack's test event data flows properly to all your analysis tools and data warehouse.

3. **Skipping Strategy
   **Testing random elements without clear objectives wastes resources.

4. **Understanding Pack's Role
   **Pack delivers experiences—your analytics stack measures performance. This partnership approach ensures you get both great user experiences and reliable measurement.

Getting Help: Who to Contact When

Since Pack focuses on experience delivery while your data partners handle measurement, here's when to reach out to whom:

Contact Pack support for:

• Experience delivery issues or bugs

• Setting up test variants in the dashboard

• Questions about Pack's capabilities and features

• Connecting with recommended data partners

Work with your analytics providers for:

• Data discrepancies between platforms

• Attribution model questions

• Event tracking accuracy issues

• Statistical analysis and interpretation

Contact your data infrastructure team for:

• Data warehouse connection problems

• Schema or pipeline issues

• Integration troubleshooting

Not sure who to contact? Start with Pack and we'll help direct you to the right resource.

Ready to Start Testing?

Before launching your first test experience with Pack, ensure you have:

1. Clean, reliable event tracking via partners like Elevar, Fueled, Blotout, or Littledata

2. Server-side tracking solution with accurate attribution

3. Data warehouse infrastructure (BigQuery, Snowflake, etc.)

4. Connected analytics platforms

5. Clear testing strategy

6. Analysis plan and measurement framework

Need help evaluating your testing readiness or connecting with data partners? Book some time with our team.

Remember: The key to successful testing isn't just having the right experience delivery tools—it's having the right data foundation and measurement strategy in place first. Pack maximizes the value of your analytics investments by delivering better experiences, but we don't replace your analytics stack.