How To A/B Test and a Real-World Example

A/B testing is a crucial tool for modern businesses seeking data-driven decision-making. Whether it’s testing different marketing messages, product features, or landing page designs, A/B testing enables companies to experiment with variations and discover what truly resonates with their customers. However, effective A/B testing goes beyond simple metrics like clicks or conversions. The real value comes from understanding the underlying behavior and preferences (and data) that drive these actions.

In this blog, we’ll dive into how businesses can extract deeper insights from A/B tests, moving past the surface-level results to uncover more strategic learnings. Instead of just focusing on which version performed better, it’s essential to ask why it performed better, which can shape not only future tests but also broader business and marketing strategies.

Identifying Preferences

At its core, A/B testing is about learning what works best for your audience. But it's not just about which button color or headline garners the most clicks; it's about uncovering user preferences at a deeper level. By testing different variations, such as formal vs. casual language or bold vs. subtle calls-to-action (CTA), you can gain insights into how different segments of your audience respond.

For example, you may find that a more casual tone in marketing emails resonates better with younger customers, while a formal approach works best with professionals in certain industries. These insights extend beyond the campaign being tested and can inform future decisions about brand voice, messaging strategies, and user experience design.

Customer Behavior Analysis

One of the key benefits of A/B testing is the ability to segment customer behavior. The data you gather reveals how different groups—such as new vs. returning customers—interact with your content or product.

Consider a scenario where an online retailer is running two marketing campaigns: an email promotion and a digital ad campaign. By analyzing A/B test data, you may uncover that returning customers respond more strongly to email promotions, while new visitors are drawn in by ads. These behavioral insights allow you to personalize the customer experience by catering to the unique needs and preferences of each group, improving engagement and conversion rates.

Understanding Customer Decision Making

A/B testing doesn’t just tell you what works—it offers a glimpse into how customers make decisions. When you test variations of pricing, messaging, or even the positioning of trust elements like reviews or security badges, you start to understand the psychology behind user actions.

For instance, a test could reveal that adding scarcity messaging (e.g., “Only 5 left in stock”) leads to a higher sense of urgency and, in turn, a higher conversion rate. Or you might find that social proof elements like customer testimonials have a greater impact on conversion than technical product details. These insights into customer psychology help refine not just individual campaigns but overall product positioning and marketing strategies.

This foundational understanding of A/B testing’s potential to uncover user preferences, segment behavior, and provide psychological insights sets the stage for the practical application of these tests in real-world business scenarios, which we’ll explore in the next section.

A/B Testing in Action: Real-World Scenario

The Problem

Imagine an online retailer running two different marketing campaigns—a Google Ads promotion and an email marketing promotion. The goal is to determine which of these campaigns generated more sales, but there’s a catch: the data is messy. It's scattered across multiple tables, with missing values, misaligned fields, and inconsistent formats. In situations like this, businesses typically turn to business intelligence (BI) tools like Power BI or Tableau. However, these tools often require a lot of manual intervention from data teams to clean up the data first.

In a real-world example from a Gigasheet customer, they faced a similar problem when trying to analyze which country had the most sales during a promotional period and which of the two campaigns performed better. Rather than going back and forth with the data team to fix issues, Gigasheet’s no-code solution enabled business users to quickly manipulate and clean the data themselves. Let's dive in to some post hoc analysis.

Check out this video where we walk through this analysis in Gigasheet:
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The Solution

Using Gigasheet, the customer imported data directly from their Snowflake database into Gigasheet's big data spreadsheet interface. Gigasheet allows users to handle millions of rows of data without needing to write SQL or Python queries. In this scenario, the user imported tables related to transaction data, customer cohorts, and promotional campaigns. Here's how they approached the analysis:

1. Data Import & Cleanup:
   • The data was initially inconsistent, with missing country names and customer cohort labels. Gigasheet’s cross-file lookup feature allowed the user to map ISO country codes from a lookup table to the transaction data, resolving these inconsistencies.
   • The customer cohort column, which identified which group of customers received the Google Ads promotion vs. the email promotion, was also unclear. The user used Gigasheet’s if-then logic to label the cohorts properly, making the data easier to interpret.
2. Data Aggregation & Grouping:
   • With the data cleaned and labeled, the user then grouped the transactions by country to determine which country had the highest sales during the promotional period. Gigasheet’s grouping and aggregation features made this easy to do, without needing to touch any code.
   • Next, they segmented the data by campaign (Google Ads vs. email) and by time period (May vs. June) to see which campaign performed better over time.
3. Results & Insights:
   • The analysis showed that Germany had the highest overall sales during the promotional period, with Google Ads generating the most sales in May but declining in June. On the other hand, the email campaign saw a significant increase in sales in June, suggesting that the email promotion was more effective at driving conversions later in the campaign cycle.
   • With Gigasheet’s visualization capabilities, the user could quickly create charts to compare the performance of each campaign over time, although more advanced visualizations were easily exported to tools like Power BI or Tableau if needed.

By enabling non-technical users to perform this type of analysis, Gigasheet made it possible to get actionable insights without the bottleneck of relying on data teams to clean and prepare the data.

Scaling A/B Test Analysis

Handling Large-Scale Data

One of the major challenges businesses face with A/B testing is scaling the analysis to handle large datasets. When working with millions of rows of data, typical spreadsheet tools like Excel struggle to keep up. This is where Gigasheet shines, allowing users to work with vast amounts of data—whether it’s 8 million rows or 80 million rows—without sacrificing performance.

In the scenario described earlier, the user had to analyze over 9 million rows of sales data to determine which campaign performed better. Instead of relying on SQL queries, which could be time-consuming and error-prone, Gigasheet’s intuitive spreadsheet interface allowed the user to work with the data in a familiar format. Features like cross-file lookups, if-then logic, and grouping and filtering make it easy to perform complex operations on large datasets without needing advanced technical skills.

Detailed Breakdown

In practical terms, this means that even non-technical team members can take raw, unprocessed data and turn it into actionable insights. For example:

• Grouping by Campaign: The user can quickly group the data by the different promotional campaigns (e.g., Google Ads vs. email) to compare performance.
• Filtering by Date: Gigasheet’s filters allow users to zero in on specific time periods, such as comparing May’s performance to June’s.
• Handling Data Type Issues: In large datasets, it’s common to encounter problems with data types (e.g., prices stored as text instead of numbers). Gigasheet’s data type conversion tools make it easy to fix these issues on the fly, without needing to request help from the data team.

Once the data is cleaned and organized, users can then calculate key performance metrics, such as the total sales for each campaign or the average order value for a particular customer cohort. These insights can guide future marketing strategies, helping teams optimize campaigns based on real-world data.

With Gigasheet, analyzing A/B test data at scale becomes manageable for any business user, not just data engineers or analysts. This approach opens up a wealth of opportunities for companies to quickly iterate on their marketing strategies and improve performance based on data-driven insights.

Avoiding Common Pitfalls in A/B Testing

Confirmation Bias

One of the most significant risks when running A/B tests is falling into the trap of confirmation bias—interpreting the results in a way that confirms your preconceived notions or desired outcomes. A/B testing, by nature, should be an unbiased exploration of what works best for your customers. To avoid confirmation bias, it’s essential to let the data tell the story, even when the results don’t align with your expectations.

For example, if you expect a bold CTA to perform better and the data shows otherwise, it’s crucial to resist the urge to discount the results. Instead, focus on why the more subtle CTA might have resonated better with your audience. Gigasheet’s tools make this process easier by enabling users to clearly visualize and aggregate data without manually manipulating or cherry-picking the results. The ability to view the raw data and easily share it with stakeholders helps ensure transparency and keeps teams grounded in factual insights.

Overlooking Data Quality

Messy data is a common problem in A/B testing. Incorrect or missing values, inconsistent formats, and incomplete records can all skew your results if left unchecked. In fact, many tests fail to produce useful insights simply because the underlying data wasn’t cleaned properly. With Gigasheet, data cleaning is built into the workflow, allowing users to detect and resolve data issues quickly.

For example, in the real-world scenario described earlier, dates were incorrectly stored in price columns, making it impossible to calculate accurate sales figures. Gigasheet's data type conversion features allowed users to clean up these columns and ensure that only valid data was being analyzed. This process is essential for getting trustworthy results, especially when working with large datasets.

Statistical Significance and Reliability

Another common pitfall in A/B testing is misinterpreting statistical significance. It’s easy to look at two sets of results and declare a winner based on a slight difference in metrics, but without sufficient sample size or a proper testing period, those results might not be reliable.

In Gigasheet, users can work with large datasets to ensure that they have enough data to reach statistically significant conclusions. By filtering, grouping, and segmenting the data, users can analyze subsets of the population (e.g., by region, age group, or customer type) to ensure that any observed differences are meaningful and not the result of random fluctuations.

Gigasheet’s Role in A/B Testing Success

Empowering Non-Technical Users

A key challenge in A/B testing is that it often requires specialized technical skills to clean and analyze the data – especially when you're dealing with massive datasets with from different sources and different formats. With traditional BI tools or databases, users typically need SQL or Python expertise to manipulate the data and perform analyses. Gigasheet changes this dynamic by providing a spreadsheet-like interface that’s familiar and easy to use, even for non-technical users.

For example, business users can import large datasets directly from sources like Snowflake, HubSpot, or Salesforce, clean the data using cross-file lookups and if-then logic, and quickly filter and group the data to find the insights they need. All of this can be done without writing a single line of code, making A/B testing accessible to teams across the organization.

Handling Big Data

Gigasheet excels at handling large-scale datasets that would overwhelm traditional spreadsheet tools like Excel. With Gigasheet, users can analyze millions of rows of data, whether they are looking at transaction data, customer segments, or campaign performance metrics. This ability to scale is particularly important for companies running multiple A/B tests simultaneously or for those working with high volumes of transactional data.

For instance, in the scenario where a retailer wanted to compare the performance of their Google Ads and email campaigns, the user was able to work with over 9 million rows of sales data. Gigasheet’s grouping and aggregation features allowed them to analyze the data at scale, uncovering insights about which campaigns performed better over time and in specific regions.

Sharing and Collaboration

Collaboration is another area where Gigasheet shines. Once the analysis is complete, users can save their custom views and share them with colleagues for further review. Whether it’s saving a filtered view of Germany’s sales data during a promotion or sharing insights with the executive team, Gigasheet makes it easy to share insights and collaborate on data-driven decisions.

With comments on individual cells or columns, teams can discuss data issues directly within the spreadsheet, reducing the need for endless email threads or meetings. This collaborative feature is especially helpful when working with large, complex datasets, as it keeps all relevant conversations tied to the data itself.

Conclusion

A/B testing is a powerful method for understanding what drives customer behavior, but its true potential lies in the ability to dig deeper into the data, analyze trends, and derive actionable insights. Tools like Gigasheet simplify the process by making it easy for business users to clean, manipulate, and analyze large datasets without needing technical expertise.

Whether you’re a marketer trying to figure out which campaign drives more conversions or a product manager testing different features, Gigasheet provides the tools to scale A/B test analysis and gain valuable insights that guide better decision-making.

Take your A/B testing to the next level with Gigasheet. With the ability to work with millions to billions of rows of data, perform complex calculations, and collaborate seamlessly with your team, Gigasheet makes it easy to unlock the insights hidden within your A/B tests. Get started today!