Welcome to our comprehensive guide to digital ad testing and optimization. In today’s competitive landscape of digital advertising, it is crucial to constantly evaluate and optimize your ad campaigns to achieve maximum results. Whether you are new to digital advertising or looking to improve your existing strategies, this guide will provide you with the knowledge and tools to maximize the performance of your digital ads.
Throughout this guide, we will explore the fundamentals of digital ad testing, the strategic importance of A/B testing, setting clear objectives for your ad campaigns, creating effective ad variations, implementing A/B testing, tracking and measuring ad performance, iterating and scaling your campaigns, expert guidelines for A/B testing, addressing mobile optimization, and the role of consumer trends. We will also delve into the exciting world of artificial intelligence (AI) and its impact on ad testing.
By the end of this guide, you will have a comprehensive understanding of how to optimize your digital ad performance and achieve better results through data-driven testing and optimization strategies. Let’s dive in!
Key Takeaways:
- Digital ad testing and optimization are essential in the competitive landscape of digital advertising.
- A/B testing is a powerful tool for comparing and optimizing different ad variations.
- Setting clear objectives for your ad campaigns is crucial for aligning your testing efforts and maximizing performance.
- Effective ad variations can significantly impact the success of your ad campaigns.
- Implementing A/B testing requires the right tools and a step-by-step implementation process.
Understanding the Fundamentals of Digital Ad Testing
In the world of digital advertising, testing the effectiveness of your ads is essential for optimizing ad performance and achieving better results. To get started with digital ad testing, it’s important to understand the fundamental concepts that form the foundation of this practice. In this section, we will explore the key elements of digital ad testing, including A/B testing, testing variables, and statistical significance.
A/B testing: A/B testing, also known as split testing, is a method used to compare two versions of an ad to determine which performs better. It involves creating two or more ad variations and showing them to different segments of your target audience. By measuring the performance of each ad variant, you can identify the version that resonates most with your audience and drives the desired outcomes.
Testing variables: When conducting digital ad testing, it’s important to test specific variables that can impact the performance of your ads. Variables can include elements such as headlines, images, ad copy, colors, and calls-to-action. By systematically testing these variables one at a time, you can gather valuable insights into which elements have the greatest impact on ad performance.
Statistical significance: In digital ad testing, statistical significance refers to the level of confidence you can have in the results of your tests. It helps determine whether the observed differences in performance between ad variations are due to chance or if they are statistically significant. Understanding statistical significance is crucial for making informed decisions based on test results and optimizing ad performance effectively.
By mastering the fundamentals of digital ad testing, you can conduct meaningful tests, gain valuable insights, and optimize your ad performance. In the next section, we will explore the strategic importance of A/B testing in digital advertising, delving into its methodologies and the importance of isolating variables to gain clearer insights from test results.
The Strategic Importance of A/B Testing in Digital Advertising
In the realm of digital advertising, A/B testing holds strategic significance for optimizing ad performance. By comparing different ad variations and measuring their effectiveness, marketers can make data-driven decisions to achieve better results. A/B testing entails splitting an audience into two groups and exposing each group to a different version of an ad. The performance of each variation is then measured to determine which yields the best results. This methodology allows marketers to experiment with different elements, such as headlines, images, and calls-to-action, and identify the most impactful combinations.
One crucial aspect of A/B testing is isolating variables for clearer insights. By focusing on one variable at a time, marketers can accurately attribute the impact on ad performance to that specific element. This approach avoids confounding factors and provides valuable insights to refine future ad campaigns.
Ensuring statistical significance in A/B test results is another key component of effective ad testing. Statistical significance helps marketers determine whether the observed differences between variations are due to chance or are truly meaningful. By achieving statistical significance, marketers can have confidence in the reliability of their A/B test results and make informed decisions that drive successful ad optimization.
Understanding the strategic importance of A/B testing, the process of isolating variables, and the significance of statistical significance are vital for maximizing the effectiveness of digital advertising campaigns. By employing these methodologies, marketers can identify winning ad variations, optimize their ad performance, and achieve better results in the competitive landscape of digital advertising.
Setting Clear Objectives for Your Ad Campaigns
In order to maximize the effectiveness of your ad campaigns, it is crucial to set clear objectives. By defining specific goals and objectives, you can align your ad testing efforts and optimize your campaigns based on key performance indicators (KPIs). Setting clear objectives provides a roadmap for success and enables you to measure the impact of your ad campaigns.
When setting ad campaign objectives, consider the following:
- Ad Campaign Goals: Clearly define the desired outcomes of your ad campaigns. Whether your goal is to increase click-through rates, improve conversion rates, or boost overall engagement, setting specific goals will help drive your ad testing and optimization strategies.
- Measuring Ad Campaign Success: It is important to establish measurable KPIs to track the performance of your ad campaigns. Identify the metrics that are most relevant to your objectives, such as impressions, reach, click-through rates, conversion rates, or return on ad spend (ROAS).
- Key Performance Indicators (KPIs): Select the most relevant KPIs that align with your ad campaign goals. These KPIs will serve as benchmarks to measure the success of your campaigns and guide your optimization efforts.
- Ad Campaign Optimization: Use the data collected from your ad campaign performance to optimize your future campaigns. By analyzing the results and making data-driven adjustments, you can continuously improve the effectiveness of your ad campaigns.
By setting clear objectives for your ad campaigns and measuring their success through key performance indicators, you can refine your strategies and optimize your ad performance. It is essential to continually assess and adjust your campaigns based on the insights gained from your ad testing efforts. The journey to achieving better results starts with clear objectives and a data-driven approach to optimization.
Identifying and Creating Effective Ad Variations
In order to optimize ad performance, it is crucial to identify and create effective ad variations. By testing different variables, such as headlines, images, ad copy, colors, and calls-to-action, marketers can uncover insights and make data-driven decisions for their ad campaigns.
Prioritizing Variables to Test
When identifying ad variations to test, it is important to prioritize the variables based on your ad campaign objectives. Consider which elements are critical to the success of your campaign and focus on testing those first. By prioritizing the variables, you can streamline your testing process and allocate your resources effectively.
Designing Impactful Ad Variants
Designing impactful ad variants involves creating compelling content that resonates with your target audience. Pay attention to the visual elements, such as images and colors, as well as the messaging and calls-to-action. Experiment with different combinations to find the most effective ad variants.
By testing and optimizing your ad variations, you can refine your advertising strategy and improve your ad performance. Utilize creative testing and ad copy testing methodologies to understand which ad variants are most impactful and drive better results. Continuous testing and optimization are key to staying competitive in the digital advertising landscape.
Ad Variant Prioritization
| Variable | Prioritization |
|---|---|
| Headline | High |
| Image | Medium |
| Ad Copy | High |
| Colors | Medium |
| Calls-to-Action | High |
In the example table above, we showcase the prioritization of variables for ad testing. By assigning a priority level, you can focus your testing efforts on the most impactful variables first. This allows you to gather valuable insights and optimize your ad performance efficiently.
Crucial Elements for Constructive A/B Testing Implementation
In order to effectively implement A/B testing and optimize ad performance, there are several crucial elements that must be considered. These elements include the use of ad testing tools, a structured test implementation process, and adherence to best practices for A/B testing. By understanding and implementing these key elements, marketers can ensure accurate results and make data-driven decisions for better ad performance.
Using Ad Testing Tools
Ad testing tools play a vital role in streamlining the entire testing process. These tools provide marketers with the necessary capabilities to design and execute A/B tests, track performance metrics, and analyze results. By utilizing ad testing tools, marketers can save time and effort while obtaining accurate and reliable data for optimization.
The Test Implementation Process
The successful implementation of A/B testing requires a well-defined process. This process involves several key steps:
- Defining test objectives: Clearly define the objectives of the A/B test, such as improving click-through rates or increasing conversion rates.
- Developing test variations: Create multiple versions of the ad with varying elements, such as headlines, visuals, or calls-to-action.
- Splitting traffic: Divert the audience traffic evenly between the different ad variations to ensure an unbiased comparison.
- Running the test: Deploy the ad variations and run the test for a specified duration, allowing sufficient time to gather statistically significant data.
- Collecting and analyzing data: Gather data on key performance metrics, such as engagement or conversions, and analyze the results to determine the most effective ad variation.
- Implementing the winning variation: Apply the insights gained from the test to optimize the ad campaign further.
Best Practices for A/B Testing
Adhering to best practices is essential for conducting accurate and meaningful A/B tests. Some of the best practices include:
- Maintaining test consistency and control: Ensure that the test conditions, including the target audience, ad placement, and testing duration, remain consistent throughout the experiment.
- Testing one variable at a time: Isolate variables when creating ad variations to obtain clear insights on the impact of each element.
- Implementing a statistically valid sample size: Use statistical significance calculations to ensure that you have gathered enough data to draw reliable conclusions from the test results.
- Iterating and scaling: Continuously iterate on winning ad variations and scale successful campaigns to maximize results.
To gain a better understanding of the implementation process, refer to the table below:
| Implementation Steps | Description |
|---|---|
| 1 | Defining test objectives |
| 2 | Developing test variations |
| 3 | Splitting traffic |
| 4 | Running the test |
| 5 | Collecting and analyzing data |
| 6 | Implementing the winning variation |
Implementing these crucial elements and following the outlined process and best practices will help marketers optimize their ad performance through constructive A/B testing. By using ad testing tools, structuring the test implementation process, and incorporating best practices, marketers can make informed decisions that drive better results and overall campaign success.
Tracking and Measuring Digital Ad Performance
In the world of digital advertising, tracking and measuring ad performance is crucial for understanding the effectiveness of your campaigns and making data-driven decisions for optimization. By analyzing key performance indicators (KPIs) and incorporating statistical tools for data analysis, you can gain meaningful insights into your ad performance and drive better results.
Analyzing Key Performance Indicators (KPIs)
Key performance indicators (KPIs) serve as measurable metrics that help evaluate the success of your ad campaigns. These metrics provide valuable information about various aspects of your ad performance, such as reach, engagement, conversion rates, and return on investment (ROI). By tracking KPIs, you can assess the effectiveness of your ads and identify areas for improvement.
Some common KPIs used in digital ad performance tracking include:
- Click-through rate (CTR) – measures the percentage of users who click on your ad after viewing it
- Conversion rate – calculates the percentage of users who complete a desired action, such as making a purchase or filling out a form
- Return on ad spend (ROAS) – measures the revenue generated from ad campaigns compared to the amount spent on advertising
- Cost per acquisition (CPA) – determines the average cost of acquiring a new customer through ad campaigns
- Engagement metrics – tracks user interactions with your ads, such as likes, comments, shares, and video views
By analyzing these KPIs in relation to your campaign objectives, you can gain insights into the strengths and weaknesses of your ads, and make informed decisions to optimize their performance.
Incorporating Statistical Tools for Data Analysis
Statistical tools play a vital role in analyzing and interpreting the data collected from your ad campaigns. They help uncover patterns, trends, and correlations that can provide actionable insights for ad optimization. Here are some commonly used statistical tools for data analysis in digital ad tracking:
- A/B testing: This methodology enables you to compare the performance of two or more ad variations, helping you understand which elements drive better results. By conducting controlled experiments and measuring statistical significance, you can make data-driven decisions to improve your ad performance.
- Data visualization: Visual representations of your ad performance data can help you quickly grasp trends and patterns. Heatmaps, line graphs, bar charts, and scatter plots are just a few examples of data visualization techniques that can provide valuable insights into your ad performance.
- Regression analysis: This statistical technique helps identify relationships between variables and can be useful for determining which factors are driving ad performance. By analyzing the impact of different variables on your KPIs, you can refine your ad strategies for maximum effectiveness.
By incorporating these statistical tools into your data analysis process, you can uncover hidden insights and optimize your ad performance based on empirical evidence.
The Iterative Nature of A/B Testing and Campaign Scaling
In the world of digital advertising, A/B testing plays a critical role in optimizing ad performance. However, it’s important to recognize that A/B testing is not a one-time endeavor but an iterative process that requires ongoing analysis and refinement. By constantly gleaning insights from A/B test outcomes, marketers can refine winning ads to resonate with broader audiences and scale their campaigns effectively.
Gleaning Insights from A/B Test Outcomes
When conducting A/B tests, it’s essential to carefully analyze the results to extract meaningful insights. This involves comparing the performance of different ad variations and identifying patterns, trends, and areas of improvement. By understanding which elements of your ads drive better results, you can make data-driven decisions to optimize their performance.
For example, if a particular ad variation consistently outperforms others in terms of click-through rates or conversion rates, it’s crucial to identify the factors contributing to its success. Is it the compelling headline, the eye-catching visuals, or the persuasive call-to-action? By pinpointing the winning elements, you can refine your ads accordingly and amplify their impact on a larger scale.
Refining Winning Ads for Broader Audiences
Once you’ve identified the winning ad variations, it’s time to refine them for broader audiences. This involves optimizing the ads to appeal to a larger segment of your target market, meeting their specific needs and preferences. By taking the insights gained from A/B testing and applying them to your ad creatives, you can create more compelling and engaging experiences for your audience.
Refining winning ads can involve tweaking elements such as the ad copy, visuals, colors, or calls-to-action. The goal is to ensure that these ads resonate with a broader range of potential customers and drive better results across different segments. This iterative approach allows you to continuously improve your ad performance and achieve greater scalability in your campaigns.
Furthermore, as market dynamics and consumer preferences evolve, it’s crucial to adapt your A/B testing strategy accordingly. By staying informed about industry trends and consumer behavior, you can tailor your ad testing efforts to remain relevant and impactful. Campaign scaling goes beyond simply increasing ad spend or reaching a wider audience; it involves strategically optimizing your A/B testing approach to align with the changing landscape.
By embracing the iterative nature of A/B testing and scaling your campaigns based on refined winning ads, you can continuously improve your ad performance and drive better results. This ongoing optimization process is essential in a dynamic digital advertising landscape, where adapting to change and staying ahead of the competition is crucial.
Expert Guidelines to A/B Testing for Digital Ads
In order to maximize the effectiveness of your digital ads and optimize ad performance, it is crucial to follow expert guidelines for A/B testing. By implementing best practices, strategies, and ad optimization tips, you can ensure that your ad testing efforts yield meaningful insights and drive better results. Here are some expert guidelines to consider:
1. Define Clear Testing Objectives
Before conducting A/B tests, it is important to clearly define your testing objectives. Identify specific goals for your ad campaigns, such as increasing click-through rates or improving conversion rates. The more specific and measurable your objectives, the better you can evaluate the impact of your ad variations.
2. Test One Variable at a Time
In order to gain valuable insights from your A/B tests, it is recommended to test one variable at a time. This allows you to isolate the impact of each variable and determine its effect on ad performance. By testing variables individually, you can make data-driven decisions and optimize your ads based on reliable results.
3. Collect Sufficient Data for Statistical Significance
When conducting A/B tests, it is important to collect a sufficient amount of data to achieve statistical significance. Statistical significance helps determine the reliability and validity of your test results. By ensuring that your sample size is large enough, you can make confident conclusions about the performance of your ad variations.
4. Continuously Monitor and Track Performance
Once your A/B tests are running, it is crucial to continuously monitor and track the performance of your ad variations. Use analytics tools to measure key performance indicators (KPIs) such as click-through rates, conversion rates, and engagement metrics. By closely monitoring performance, you can identify winning ads and refine them for broader audiences.
5. Iterate and Scale Successful Ad Variations
As you gather insights from your A/B tests, it is important to iterate and scale successful ad variations. Continuously refine the winning ads based on performance data and adapt them to changing market dynamics and consumer preferences. By iterating and scaling successful ad variations, you can optimize your ad campaigns and drive improved results.
6. Leverage AI-Powered Ad Testing Tools
Incorporating AI-powered ad testing tools can greatly enhance your A/B testing efforts. These tools utilize advanced algorithms and machine learning capabilities to analyze large datasets and provide actionable insights. By leveraging AI-powered tools, you can optimize your ad testing strategies and make data-driven decisions to improve ad performance.
These expert guidelines provide valuable insights and strategies for conducting A/B testing for digital ads. By following these best practices, you can optimize your ad testing efforts, improve ad performance, and achieve better results.
Addressing Mobile Optimization in Ad Testing
This section focuses on the importance of mobile optimization in ad testing. With the increasing use of mobile devices for consuming digital content, it is crucial for advertisers to design ads that are optimized for various devices, especially mobile devices. By addressing mobile optimization, advertisers can reach and engage a wider audience and maximize their ad performance.
Designing for Various Devices
When designing mobile ads, it is essential to consider the unique characteristics and constraints of different devices. Mobile ads should be visually appealing, responsive, and functional across various screen sizes and resolutions. Advertisers should prioritize mobile-friendly designs, ensuring that the ad elements are easily viewable, clickable, and interactive on mobile devices. By designing mobile ads that are user-friendly and visually engaging, advertisers can enhance the overall ad experience and improve mobile ad performance.
Assessing Mobile User Engagement
Understanding mobile user engagement is critical for optimizing mobile ads. Advertisers should track and analyze metrics such as click-through rates, conversions, bounce rates, and time spent on page to assess the effectiveness of their mobile ads. By gaining insights into mobile user behavior and preferences, advertisers can make data-driven decisions to improve their mobile ad performance. Additionally, user feedback and testing can provide valuable insights to refine and optimize mobile ads for better engagement and conversion rates.
How Consumer Trends Influence Digital Ad Testing
This section explores the impact of consumer trends on digital ad testing and the importance of adapting ad testing strategies in response to changing consumer behaviors and preferences. Keeping up with consumer trends is crucial for staying relevant and effective in your advertising efforts. By understanding and incorporating these trends into your ad testing approach, you can optimize your ad performance and achieve better results. Let’s delve into the ways consumer trends influence digital ad testing:
- Ad Testing Adaptation: Consumer trends shape the way people engage with digital content and advertisements. As consumer preferences evolve, it’s essential to adapt your ad testing strategies accordingly. This entails testing different ad formats, creative elements, messaging styles, and targeting options to align with current consumer trends.
- Ad Testing Strategies: Consumer trends provide valuable insights into what resonates with your target audience. By analyzing these trends, you can identify new testing strategies to explore and capitalize on emerging opportunities. For example, if there’s a surge in mobile usage, you may focus on optimizing your ads for mobile devices and testing mobile-specific features.
- Ad Optimization in Response to Trends: Ad testing should be an iterative process that responds to changing consumer trends. By monitoring and analyzing these trends, you can refine and optimize your ad campaigns accordingly. This might involve updating ad designs, adjusting messaging to align with current social or cultural trends, or incorporating new features that cater to evolving consumer preferences.
To illustrate the impact of consumer trends on ad testing, let’s take a look at a case study:
“Brand X, a leading e-commerce company, noticed a significant increase in consumer interest for sustainability and eco-friendly products. To capitalize on this trend, they implemented ad testing strategies to align their messaging and visual elements with sustainability values. By testing different ad variations with eco-friendly messaging and imagery, they were able to identify the most resonant approach. As a result, Brand X experienced a significant improvement in click-through rates and conversion rates, demonstrating the importance of ad optimization in response to consumer trends.”
By understanding and adapting to consumer trends, you can optimize your ad testing efforts, maximize your ad performance, and deliver more compelling and relevant advertisements to your target audience. Stay tuned for further insights and strategies to enhance your digital ad testing and optimization.
Best Practices for A/B Testing in Digital Advertising
This section outlines the best practices for conducting A/B testing in the digital advertising realm. To ensure accurate and reliable results, it is crucial to maintain test consistency and control throughout the testing process. Additionally, applying knowledge and insights gained from A/B testing case studies can significantly optimize your own ad testing efforts and drive better ad performance.
Maintaining Test Consistency and Control
Consistency and control are vital when conducting A/B testing. To achieve accurate and reliable results, it is essential to:
- Use consistent testing methodologies to minimize variables that can skew test results.
- Ensure a sufficient sample size to achieve statistical significance.
- Randomize test groups and control groups to eliminate bias and ensure a fair comparison.
By maintaining test consistency and control, you can confidently analyze the effectiveness of different ad variations and make data-driven decisions to optimize your campaigns.
Applying Knowledge from A/B Testing Case Studies
Case studies provide valuable insights and learning opportunities for A/B testing. By studying and applying the knowledge gained from successful case studies, you can enhance your own ad testing strategies. Consider the following when leveraging case study insights:
- Identify industry-specific case studies that align with your target audience and objectives.
- Analyze the variables and testing methodologies used in successful ad campaigns.
- Adapt and apply these proven strategies to your own testing efforts, taking into account your unique goals and audience.
By incorporating insights from A/B testing case studies, you can refine your testing approach, optimize ad performance, and achieve better results.
Embracing Technology: Artificial Intelligence in Ad Testing
This section explores the role of technology, specifically artificial intelligence (AI), in ad testing. With AI’s advanced capabilities, advertisers can leverage its power to optimize ad performance and enhance personalized ad experiences.
Leveraging AI for Ad Optimization
AI offers valuable insights and automation that can revolutionize ad optimization. By analyzing large datasets and identifying patterns, AI algorithms can make data-driven decisions to improve ad performance. It can automatically test various ad elements, such as headlines, images, and calls-to-action, to identify the most effective combinations. With AI-powered ad optimization, advertisers can achieve better results and increase the return on their ad investments.
The Role of AI in Personalizing Ad Experiences
Personalization is a key driver of successful advertising. AI enables advertisers to deliver personalized ad experiences to individual users based on their preferences, behaviors, and demographics. By leveraging AI algorithms, advertisers can create dynamic ad content that adapts to the specific needs and interests of each user, increasing engagement and conversion rates. AI-powered personalization in ad testing empowers advertisers to deliver more relevancy and effectiveness in their campaigns.
Embracing technology, specifically AI, can amplify the effectiveness of your ad testing efforts and drive improved ad performance. By leveraging AI for ad optimization and personalizing ad experiences, advertisers can stay ahead in the ever-evolving digital advertising landscape.
Conclusion
Throughout this comprehensive guide on digital ad testing and optimization, we have delved into the fundamental concepts, strategic importance, and best practices of A/B testing in digital advertising. By understanding the fundamentals of A/B testing, setting clear objectives for your ad campaigns, and identifying effective ad variations, you can optimize your ad performance and achieve better results.
Implementing the expert guidelines and following the best practices outlined in this guide is crucial for conducting meaningful ad tests and making data-driven decisions. Tracking and measuring ad performance, embracing the iterative nature of A/B testing, and addressing mobile optimization are essential elements in optimizing your digital ad campaigns.
Furthermore, as consumer trends continue to shape the advertising landscape, it is important to adapt your ad testing strategies and stay up-to-date with the latest industry developments. Additionally, leveraging artificial intelligence (AI) technology in ad testing can revolutionize your efforts and personalize ad experiences for your target audience.
In conclusion, by applying the strategies and insights shared in this guide, you can optimize your digital ad performance and achieve better results. Continuously testing, analyzing, and refining your ad campaigns based on data-driven insights will help you stay ahead in the competitive landscape of digital advertising.
FAQ
What is digital ad testing?
Digital ad testing is the process of comparing and evaluating the performance of different versions of an ad to determine which version is more effective in achieving specific marketing objectives.
What is A/B testing?
A/B testing, also known as split testing, is a method used in digital ad testing where two versions of an ad (A and B) are shown to different segments of the target audience to measure their effectiveness in achieving predetermined goals.
Why is A/B testing important in digital advertising?
A/B testing is crucial in digital advertising because it allows advertisers to identify which ad variation performs better, providing insights to optimize future campaigns and maximize ad performance.
How do I set clear objectives for my ad campaigns?
To set clear objectives for your ad campaigns, you need to define specific goals and metrics that align with your overall marketing objectives. These can include increasing click-through rates, improving conversion rates, or enhancing brand awareness.
What are ad variations?
Ad variations are different versions of an ad that include changes in elements such as headlines, images, ad copy, colors, or calls-to-action. Testing these variations allows advertisers to identify which elements work best in achieving their objectives.
What are the crucial elements for successful A/B testing implementation?
Successful A/B testing implementation requires the use of ad testing tools, the consistent implementation of tests, and control over variables to ensure accurate and reliable results.
How can I track and measure digital ad performance?
To track and measure digital ad performance, you can use key performance indicators (KPIs) such as click-through rates, conversion rates, impressions, and engagement metrics. Utilizing statistical tools for data analysis can help derive meaningful insights from the collected data.
How can I refine winning ads for broader audiences?
To refine winning ads for broader audiences, analyze the results of your A/B tests to identify the elements that contributed to their success. Then, adapt these elements to resonate with your target audience to maximize their impact.
What are the best practices for A/B testing in digital advertising?
The best practices for A/B testing in digital advertising include maintaining test consistency and control, using insights from A/B testing case studies, and conducting ongoing testing to optimize ad performance.
How does artificial intelligence (AI) impact ad testing?
Artificial intelligence (AI) can revolutionize ad testing by automating the testing process, providing personalization opportunities, and analyzing large volumes of data to make data-driven decisions for ad optimization.