A/B testing product descriptions for higher sales is a powerful strategy that can significantly boost your online sales. By experimenting with different variations of your product descriptions, you can identify what resonates best with your target audience, leading to higher click-through rates, conversions, and ultimately, increased revenue.
This guide delves into the core principles of A/B testing, provides a step-by-step process for setting up your experiments, and offers valuable insights on crafting compelling product descriptions that drive results. We’ll also explore the importance of visual elements, data analysis, ethical considerations, and emerging trends in this field.
Understanding A/B Testing for Product Descriptions
A/B testing, also known as split testing, is a powerful tool for optimizing product descriptions and driving higher sales. It involves creating two versions of a product description (A and B), each with a single variation, and showing them to different segments of your audience to determine which version performs better.
By analyzing the results, you can identify the most effective elements of your product descriptions and make data-driven improvements.
Elements Within a Product Description That Can Be Tested
A/B testing can be applied to various elements within a product description to understand their impact on user engagement and conversions.
- Headline:The headline is the first thing a user sees, so it’s crucial to grab their attention. Test different headlines with varying lengths, styles, and focuses to see which ones resonate best with your target audience.
- Product Description:The body of the description should provide detailed information about the product’s features, benefits, and use cases. Experiment with different writing styles, levels of detail, and the use of storytelling to see what drives the most engagement.
- Bullet Points:Bullet points are an effective way to highlight key features and benefits in a concise and easily digestible format. Test different bullet point styles, order, and content to see what performs best.
- Images and Videos:Visuals can significantly impact user engagement. Test different images, videos, and their placement to see which ones attract the most attention and drive conversions.
- Call-to-Action (CTA):The CTA is the key to driving conversions. Test different CTA button text, colors, and placements to see which ones encourage the most clicks.
- Price and Discounts:The price and any associated discounts can significantly influence purchase decisions. Test different price presentation formats, discount strategies, and value propositions to see what drives the most sales.
- Social Proof:Including customer reviews, ratings, and testimonials can build trust and credibility. Test different ways to display social proof, such as star ratings, customer testimonials, and social media integrations.
- Product Benefits and Features:The way you present the product’s benefits and features can influence how customers perceive its value. Test different ways to highlight benefits, such as focusing on specific use cases, using persuasive language, and emphasizing unique selling propositions.
A/B Testing Scenarios for Product Descriptions
A/B testing can be used to achieve various goals related to product descriptions, such as increasing click-through rates, conversion rates, or average order value.
- Increasing Click-Through Rates (CTR):
- Scenario:You want to increase the number of users who click on your product listing from search results or category pages.
- Test:Compare two versions of your product headline. One version could be more concise and focus on the product’s key benefit, while the other could be more descriptive and highlight specific features.
- Increasing Conversion Rates:
- Scenario:You want to increase the percentage of users who visit your product page and make a purchase.
- Test:Compare two versions of your product description. One version could focus on the product’s benefits and how it solves customer problems, while the other could focus on its features and technical specifications.
- Increasing Average Order Value (AOV):
- Scenario:You want to encourage customers to buy more items or higher-priced products.
- Test:Compare two versions of your product description. One version could include a strong call-to-action encouraging customers to add multiple items to their cart, while the other could highlight the value of purchasing a premium version of the product.
Setting Up Your A/B Testing Process: A/B Testing Product Descriptions For Higher Sales
A/B testing is a powerful tool for optimizing product descriptions, allowing you to identify the most effective language and presentation for driving sales. Setting up your A/B testing process involves a series of steps that ensure your experiment is well-designed and delivers meaningful results.
Optimizing product descriptions for higher sales involves testing different variations to see what resonates best with customers. A key element in this process is understanding how your descriptions perform in search engines. Content analytics tools for SEO optimization provide valuable insights into keyword usage, search volume, and user engagement, allowing you to refine your descriptions for better visibility and ultimately, higher sales.
Defining Your Hypothesis
Before embarking on your A/B testing journey, it’s crucial to formulate a clear hypothesis. This hypothesis should Artikel the expected outcome of your experiment. For instance, you might hypothesize that a product description highlighting the benefits of a product will lead to a higher click-through rate compared to a description that solely focuses on features.
Creating Variations
Once you have your hypothesis, you’ll need to create variations of your product description. These variations should be designed to test your hypothesis. For example, you could create one version that emphasizes the benefits of the product and another version that emphasizes the features.
Choosing a Sample Size
The sample size you choose for your A/B test is critical for obtaining statistically significant results. A larger sample size generally leads to more reliable results. There are online calculators available that can help you determine the appropriate sample size based on your desired level of confidence and the expected effect size.
Setting Up Tracking Mechanisms
To analyze the results of your A/B test, you’ll need to set up tracking mechanisms. This involves using tools that allow you to track key metrics such as click-through rate, conversion rate, and average order value. Many platforms offer A/B testing capabilities, and they typically provide built-in tracking mechanisms.
Tools and Platforms for A/B Testing
| Platform | Features | Pricing |
|---|---|---|
| Google Optimize | A/B testing, personalization, and analytics | Free and paid plans |
| Optimizely | A/B testing, multivariate testing, and personalization | Paid plans |
| VWO | A/B testing, multivariate testing, and heatmaps | Paid plans |
| Convert | A/B testing, split testing, and personalization | Paid plans |
Crafting Compelling Product Descriptions for A/B Testing
Product descriptions are the voice of your brand, informing potential customers about your offerings and persuading them to make a purchase. In the realm of A/B testing, crafting compelling product descriptions becomes even more crucial, as you strive to identify the most effective language and presentation to maximize conversions.
A/B testing product descriptions can be a powerful way to boost sales by finding the most effective language and messaging. This principle extends to all website content, as you can test different versions of headings, calls to action, and even entire pages to see what resonates best with your audience.
To learn more about this process, check out this comprehensive guide on A/B testing different versions of website content. By applying these strategies to your product descriptions, you can significantly improve your conversion rates and increase overall sales.
This section explores best practices for writing product descriptions that resonate with your target audience, and delve into various writing styles and approaches to structure your descriptions for optimal results.
Writing Styles and Tones for Product Descriptions
Product descriptions can adopt different writing styles and tones to connect with specific audiences and highlight distinct product attributes. Here are a few examples:
- Persuasive Style:This style emphasizes the benefits of the product, focusing on how it solves customer problems and improves their lives. It often uses strong action verbs, emotional language, and compelling testimonials.
- Informative Style:This style provides detailed information about the product, focusing on its features, specifications, and technical details. It aims to educate customers and empower them to make informed decisions.
- Conversational Style:This style uses a friendly and approachable tone, engaging customers with a casual and relatable voice. It often uses humor, anecdotes, and relatable scenarios to connect with the audience.
Structuring Product Descriptions for A/B Testing
The structure of your product description can significantly impact its effectiveness. Different approaches cater to various product types and target audiences. Here’s a comparison of common structuring methods:
| Structure | Description | Example |
|---|---|---|
| Bullet Points | Highlights key features and benefits in a concise and easily digestible format. Ideal for showcasing a product’s core advantages and technical specifications. |
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| Highlighting Key Features | Focuses on specific features that differentiate the product from competitors and appeal to the target audience. | “Our revolutionary new technology delivers unparalleled performance, exceeding industry standards in speed and efficiency.” |
| Focusing on Benefits | Emphasizes how the product solves customer problems and improves their lives. This approach connects with emotional needs and resonates with the audience’s desires. | “Say goodbye to bulky and inefficient devices. Our sleek and compact design fits seamlessly into your lifestyle, allowing you to stay connected and productive on the go.” |
Optimizing Product Descriptions for Visual Impact
In the realm of online commerce, captivating visuals play a crucial role in influencing customer decisions. Product descriptions, when infused with engaging visuals, can transform a mere text block into an immersive experience that resonates with potential buyers.
The Role of Visual Elements in Conversions
Visual elements, such as high-quality product images, videos, and other visual assets, have a profound impact on conversions. They act as powerful tools to enhance engagement, build trust, and ultimately drive sales.
- Visual Storytelling:Engaging visuals can effectively convey a product’s story, showcasing its features, benefits, and use cases in a compelling manner. This narrative approach allows customers to visualize themselves using the product, fostering a deeper connection and increasing the likelihood of purchase.
- Emotional Connection:Visuals have the power to evoke emotions and create an emotional connection between customers and products. Appealing images and videos can stir feelings of desire, excitement, or inspiration, making the product more desirable and memorable.
- Building Trust:High-quality visuals instill confidence in customers. Professional-looking images and videos demonstrate attention to detail and a commitment to quality, enhancing the product’s perceived value and credibility.
- Product Detail and Clarity:Visuals provide a clear and detailed representation of the product, allowing customers to examine its features, dimensions, and functionality in a way that text alone cannot achieve. This clarity reduces uncertainty and encourages informed purchasing decisions.
Optimizing Image and Video Assets for A/B Testing
A/B testing image and video assets is essential to identify the most effective visual strategies for your product descriptions. By experimenting with different image sizes, formats, and placements, you can determine what resonates best with your target audience.
| Strategy | Description | Example |
|---|---|---|
| Image Size | Experiment with different image sizes to find the optimal balance between visual impact and loading speed. | Compare a large, high-resolution image with a smaller, compressed image to see which performs better. |
| Image Format | Test various image formats, such as JPEG, PNG, and GIF, to determine the best trade-off between image quality and file size. | Compare a JPEG image with a PNG image to see which format provides better visual quality while maintaining a reasonable file size. |
| Image Placement | Experiment with different image placements within your product description to find the most visually appealing and effective arrangement. | Compare placing the image above the product description, within the description, or below the description. |
| Video Length | Test different video lengths to see what captures the attention of your audience without overwhelming them. | Compare a short, concise video highlighting key product features with a longer video that provides a more in-depth overview. |
| Video Format | Experiment with different video formats, such as MP4 and WebM, to determine the best compatibility and performance for your website. | Compare an MP4 video with a WebM video to see which format provides better playback across different browsers and devices. |
Analyzing A/B Testing Results for Continuous Improvement
A/B testing is a powerful tool for optimizing product descriptions and increasing sales. But the real magic happens when you analyze the results and use them to make informed decisions. By carefully examining the data, you can identify what works, what doesn’t, and how to continuously improve your product descriptions.
Understanding Statistical Significance
Statistical significance helps you determine if the observed differences between your A/B test variations are truly meaningful or just due to random chance. To understand statistical significance, you need to consider the p-value. The p-value represents the probability of observing the difference in your data if there were no real difference between the variations.
A p-value less than 0.05 is generally considered statistically significant, indicating that there is a low probability of observing the results by chance alone.
Key Metrics to Track
To effectively analyze your A/B testing results, it’s crucial to track key metrics that reflect the performance of your product descriptions. Here are some important metrics:
- Click-Through Rate (CTR):This metric measures the percentage of users who click on your product description after seeing it. A higher CTR indicates that your description is attracting attention and encouraging users to learn more.
- Conversion Rate:This metric measures the percentage of users who make a purchase after clicking on your product description. A higher conversion rate indicates that your description is effectively convincing users to buy.
- Average Order Value (AOV):This metric measures the average amount spent by users who purchase a product after seeing your description. A higher AOV suggests that your description is influencing users to purchase more expensive items or add more items to their cart.
Common Pitfalls to Avoid
Analyzing A/B testing results requires careful attention to detail and a methodical approach. Here are some common pitfalls to avoid:
| Pitfall | Description |
|---|---|
| Premature Conclusions | Drawing conclusions based on limited data or before the test has run its course. |
| Ignoring Sample Size | Failing to collect enough data to ensure statistically significant results. |
| Over-Optimizing | Constantly making small changes based on short-term fluctuations in data, rather than focusing on significant trends. |
| Ignoring Qualitative Feedback | Relying solely on quantitative data and neglecting user feedback, which can provide valuable insights into why certain variations perform better. |
Implementing Findings and Iterating for Growth
The true power of A/B testing lies in its ability to drive continuous improvement. Once you’ve identified winning variations, the next step is to implement them effectively and monitor their impact over time. This iterative process ensures that your product descriptions are constantly evolving to maximize sales.
Incorporating Winning Variations
Incorporating winning variations into your product pages involves a careful process of implementation and monitoring. It’s crucial to understand that not all winning variations will perform equally well in the long run. Some might be short-term successes, while others might have a lasting impact.
- Update Product Descriptions:Replace the original product descriptions with the winning variations. Ensure that the changes are made across all relevant platforms, including your website, marketplaces, and social media.
- A/B Test Further:Even though a variation has proven successful, it’s always a good idea to continue A/B testing to see if further improvements can be made. This could involve testing different headlines, bullet points, or even the overall structure of the description.
- Track Key Metrics:After implementing the winning variations, closely monitor key metrics such as conversion rate, average order value, and click-through rate. This will help you understand the impact of the changes and identify any unexpected outcomes.
Monitoring and Analyzing Results
The journey doesn’t end with implementation. It’s crucial to continuously monitor the performance of your product descriptions and analyze the data to identify areas for improvement. This ongoing process ensures that your descriptions remain effective and drive sales.
- Regular Data Analysis:Set up a system for regularly analyzing the performance of your product descriptions. This could involve using analytics tools, dashboards, or spreadsheets. Look for trends in conversion rates, click-through rates, and other relevant metrics.
- Identify Bottlenecks:Use the data to identify any bottlenecks in the customer journey. For example, if you see a drop in conversion rates after a particular section of the product description, you might need to revise that section.
- Iterate and Optimize:Based on your data analysis, continuously iterate and optimize your product descriptions. This could involve making small tweaks, testing new variations, or completely revamping the descriptions.
Case Studies and Examples of Successful A/B Testing
A/B testing has become an indispensable tool for businesses seeking to optimize their online presence and drive sales. By testing different variations of product descriptions, companies can gain valuable insights into what resonates with their target audience, ultimately leading to increased conversions.
This section delves into real-world case studies showcasing the power of A/B testing in improving product description performance.
Examples of A/B Testing Scenarios
Real-world examples demonstrate the effectiveness of A/B testing in refining product descriptions. These scenarios highlight the variations tested, the results achieved, and the valuable lessons learned.
- Company:Amazon Product:Wireless Earbuds Variation:A/B testing involved two product descriptions. One focused on the technical specifications and features of the earbuds, while the other emphasized the benefits and user experience. Results:The description highlighting benefits and user experience significantly outperformed the technical-focused version, resulting in a 15% increase in sales.
Lessons Learned:Customers are more likely to purchase products when they understand the benefits and how the product will enhance their lives.
- Company:Nike Product:Running Shoes Variation:Two descriptions were tested: one featuring a standard product description and the other incorporating social proof through customer reviews. Results:The description incorporating customer reviews led to a 10% increase in click-through rates and a 5% boost in conversions.
Optimizing product descriptions through A/B testing is a powerful way to boost sales. By analyzing different variations, you can identify the most compelling language and features that resonate with your target audience. Understanding key content marketing KPIs, like conversion rates and click-through rates, can further inform your A/B testing strategy.
Content marketing KPIs for industry trends can provide valuable insights into the effectiveness of your content and guide your optimization efforts. By combining data-driven insights with creative experimentation, you can refine your product descriptions for maximum impact and drive higher sales.
Lessons Learned:Social proof, such as customer reviews, builds trust and credibility, increasing customer confidence in the product.
- Company:Sephora Product:Foundation Variation:Two product descriptions were tested: one with a generic description and the other tailored to specific skin types and concerns. Results:The personalized description tailored to skin types resulted in a 20% increase in sales and a 15% reduction in returns.
Lessons Learned:Personalization in product descriptions improves relevance and resonates with customers, leading to higher conversion rates and reduced returns.
Ethical Considerations in A/B Testing Product Descriptions
A/B testing is a powerful tool for optimizing product descriptions, but it’s crucial to use it ethically. Transparency and a focus on the user experience are paramount. Ethical considerations ensure that A/B testing doesn’t become a manipulative tool that deceives customers.
Transparency in A/B Testing
Transparency is essential for building trust with customers. When conducting A/B testing, it’s important to be upfront about the experiment and its purpose. This can be achieved by:
- Disclosing the A/B testing process:Clearly explain to customers that you are running an A/B test and what you are testing. For example, you could include a brief message on your product page stating that you are testing different product descriptions to see which one performs best.
- Being open about the results:Share the results of your A/B tests with your customers, even if they don’t show the desired outcome. This demonstrates your commitment to transparency and helps build trust.
- Using clear and concise language:Avoid using technical jargon or overly complex language when explaining A/B testing to your customers. Ensure your language is easy to understand and accessible to everyone.
Avoiding Deceptive Practices
Manipulative or deceptive practices in A/B testing can erode customer trust and damage your brand reputation. It’s essential to avoid practices that could mislead customers, such as:
- Using misleading language:Avoid using language that exaggerates the benefits of a product or service, or that downplays potential risks or drawbacks. Be truthful and accurate in your product descriptions.
- Hiding negative reviews:Don’t suppress negative reviews or feedback. Allow customers to see both positive and negative reviews so they can make informed decisions.
- Using unfair comparisons:Avoid comparing your product or service to competitors in a way that is misleading or deceptive. Be fair and accurate in your comparisons.
Best Practices for Ethical A/B Testing
Here’s a table summarizing best practices for ethical A/B testing:
| Ethical Consideration | Best Practice |
|---|---|
| Informed Consent | Obtain informed consent from customers before exposing them to A/B tests, particularly if the tests involve personal information or significant changes in user experience. |
| Fairness | Ensure that A/B tests are conducted fairly and that all participants have an equal chance of being exposed to each variation. Avoid manipulating the test results or favoring one variation over another. |
| User Trust | Prioritize user trust by being transparent about the A/B testing process, avoiding deceptive practices, and ensuring that the tests are conducted ethically and responsibly. |
Future Trends in A/B Testing Product Descriptions
The landscape of e-commerce is constantly evolving, and A/B testing product descriptions is becoming increasingly sophisticated. Emerging trends like AI-powered tools, personalization, and dynamic content are revolutionizing how businesses optimize their online sales. These advancements are not only enhancing the customer experience but also providing valuable insights into consumer behavior and preferences.
AI-Powered Tools for Enhanced A/B Testing, A/B testing product descriptions for higher sales
AI-powered tools are transforming A/B testing by automating tasks, providing data-driven insights, and optimizing descriptions for better performance. These tools can analyze vast amounts of data to identify patterns and predict which variations are likely to perform better.
A/B testing product descriptions is a powerful way to boost sales, and understanding how your descriptions perform is crucial. By analyzing data on click-through rates, conversion rates, and time spent on product pages, you can refine your descriptions for maximum impact.
Learning how to effectively use content analytics to measure the impact of content on business goals will help you understand what resonates with your audience and drive more sales. This data-driven approach to A/B testing allows you to make informed decisions about your product descriptions, ensuring they are engaging, persuasive, and ultimately, lead to increased revenue.
- Automated A/B Testing:AI can automatically create and test different variations of product descriptions, freeing up marketers to focus on strategy and analysis. This automation streamlines the testing process, allowing for faster iteration and optimization.
- Personalized Recommendations:AI algorithms can analyze user data and browsing history to provide personalized product recommendations. This targeted approach can significantly improve conversion rates by showcasing products that are more likely to resonate with individual customers.
- Content Optimization:AI can analyze user engagement with product descriptions to identify areas for improvement. It can suggest changes to language, formatting, and imagery to enhance readability and persuasiveness.
Personalization for Tailored Experiences
Personalization is becoming increasingly crucial for creating engaging and relevant customer experiences. A/B testing plays a vital role in tailoring product descriptions to individual preferences and needs.
- Dynamic Content:Dynamic content allows for real-time adjustments to product descriptions based on user data. This can include personalized recommendations, targeted offers, and tailored product features. For instance, a website could display different product descriptions based on a user’s location, browsing history, or previous purchases.
- Segmentation and Targeting:A/B testing can be used to segment audiences and target specific customer groups with tailored product descriptions. This allows businesses to optimize their messaging and offerings for different demographics, interests, and buying behaviors.
Dynamic Content for Real-Time Optimization
Dynamic content is transforming A/B testing by allowing for real-time adjustments to product descriptions based on user interactions and data. This approach enables businesses to create highly personalized and engaging experiences that cater to individual preferences.
- Real-Time Feedback:Dynamic content can collect user feedback in real-time, such as clicks, scrolls, and time spent on a page. This data can be used to identify which variations of a product description are performing better and to make adjustments accordingly.
A/B testing product descriptions is a powerful strategy to boost sales by understanding what resonates with customers. To effectively track the impact of these tests, you’ll need to measure click-through rates on your website. Content analytics tools for measuring website click-through rates can provide valuable insights into how your product descriptions are performing, allowing you to refine them for even greater success.
- Adaptive Content:AI algorithms can analyze user data and behavior to automatically adjust product descriptions in real-time. This adaptive approach allows for continuous optimization based on user engagement and preferences.
Impact of Trends on E-Commerce
The adoption of AI-powered tools, personalization, and dynamic content is poised to significantly impact the future of e-commerce. These trends are expected to lead to:
- Enhanced Customer Experience:Personalized and dynamic product descriptions will create more engaging and relevant experiences for customers, increasing satisfaction and loyalty.
- Improved Conversion Rates:By tailoring product descriptions to individual preferences, businesses can significantly improve conversion rates and drive higher sales.
- Data-Driven Decision Making:A/B testing provides valuable data insights into customer behavior, allowing businesses to make informed decisions about product descriptions, marketing strategies, and overall business operations.
Table of Future Trends
| Trend | Potential Benefits | Challenges | Implications for Businesses |
|---|---|---|---|
| AI-Powered Tools | Automation, data-driven insights, enhanced optimization | Data privacy concerns, potential for bias in algorithms | Increased efficiency, improved decision making, need for ethical AI practices |
| Personalization | Tailored experiences, improved customer engagement, higher conversion rates | Data collection and privacy concerns, technical complexity | Need for robust data management systems, focus on customer privacy |
| Dynamic Content | Real-time optimization, personalized recommendations, adaptive content | Technical challenges, potential for over-personalization | Investment in dynamic content platforms, focus on user experience |
Building a Culture of Experimentation and Data-Driven Decisions
In the ever-evolving landscape of e-commerce, organizations that embrace a culture of experimentation and data-driven decision-making gain a significant competitive advantage. By fostering a mindset that values continuous learning and iterative improvement, businesses can unlock the true potential of A/B testing for product descriptions and drive higher sales.A culture of experimentation is not merely about conducting A/B tests; it’s about creating an environment where data is actively sought, analyzed, and used to inform strategic decisions.
A/B testing product descriptions is a powerful way to increase sales, but it’s important to understand what resonates with your audience. To gain valuable insights into user behavior, you can leverage content analytics tools for analyzing website heatmaps.
These tools reveal exactly where users click, scroll, and engage, providing crucial data to inform your A/B testing strategies and optimize your product descriptions for maximum impact.
This requires a shift in organizational thinking, moving away from assumptions and gut feelings towards a data-driven approach.
Encouraging a Continuous Learning Mindset
Encouraging a continuous learning mindset within your organization is crucial for successful A/B testing. This involves creating an environment where employees feel comfortable taking risks, experimenting, and learning from both successes and failures.
- Promote a culture of learning:Encourage employees to share their insights, both positive and negative, from A/B testing experiments. This creates a space for collective learning and helps to identify patterns and best practices.
- Embrace failure as a learning opportunity:Instead of viewing failed experiments as setbacks, treat them as valuable learning experiences. Analyze the data to understand why the experiment did not yield the desired results and use this knowledge to improve future tests.
- Provide training and resources:Invest in training programs and resources that equip employees with the knowledge and skills needed to design, conduct, and analyze A/B tests effectively. This could include workshops on data analysis, A/B testing best practices, and statistical significance.
Establishing Roles and Responsibilities
A well-defined team structure with clear roles and responsibilities is essential for successful A/B testing. This ensures that each member contributes effectively and that the process is streamlined and efficient.
| Role | Responsibilities |
|---|---|
| Data Analyst |
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| Marketer |
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| Product Manager |
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Last Recap
By embracing a culture of experimentation and data-driven decision-making, you can continuously optimize your product descriptions, ensuring they remain relevant, engaging, and effective in driving sales. As the e-commerce landscape evolves, A/B testing will continue to play a vital role in helping businesses stay ahead of the curve and maximize their online success.
FAQ Resource
What are some common A/B testing tools?
Popular A/B testing tools include Google Optimize, Optimizely, VWO, and AB Tasty. These platforms offer a range of features for creating variations, tracking results, and analyzing data.
How often should I run A/B tests on my product descriptions?
The frequency of A/B testing depends on various factors, including your website traffic, the number of products you sell, and your budget. It’s generally recommended to run tests on a regular basis, at least every few months, to ensure your descriptions remain effective.
What are some examples of A/B testing scenarios for product descriptions?
You can A/B test different elements of your product descriptions, such as headlines, bullet points, call-to-actions, images, and pricing. For example, you could test two different headlines to see which one drives more clicks, or compare two versions of a call-to-action to determine which one leads to more conversions.