Media Planning and Buying Strategies: The Importance of Test & Learn
By Eran Goren
With today’s rapidly evolving technology, shifting consumer behavior, and increasing competition, the ability to adapt and optimize media strategies is paramount. Access to new platforms and powerful, new technologies – reigning in the sea of ever-increasing data points – has made it critical to have a "test and learn" approach in modern media planning and buying.
In this paper, we will outline the key elements of test and learn methodologies, their benefits, and provide actionable insights into implementing effective test and learn strategies. Embracing this approach empowers media professionals the ability to measure, analyze, and optimize campaigns to deliver greater efficiency and effectiveness, leading to more successful outcomes.
Additionally, we will examine the challenges companies may encounter in adopting test and learn methodology and provide thoughts on future trends and technologies that will continue to fuel the importance utilizing a test and learn approach.
Defining Test and Learn
Applying a systematic approach to ‘testing’ (running controlled ‘what ifs’) varying aspects of media campaigns and ‘learning’ from the results to refine strategies, drive better results, and maximize marketing ROI. The core principle is to iterate and optimize based on data and insights gathered from continuous testing.
Successful test and learn strategies include setting clear objectives, selecting appropriate key performance indicators (KPIs), allocating resources effectively, conducting A/B testing or multivariate testing, and implementing robust measurement and analysis procedures.
- Setting Clear Objectives
Establishing well-defined goals and measurable objectives will be the foundation of the testing process and will ensure that there is a well-defined roadmap to follow.
- Defining Specific Objectives
The objectives should align with the company’s overall marketing goals and business objectives. Whether it's increasing website traffic, boosting conversions, or enhancing brand awareness, being as specific as possible is key. Vague objectives like "improve online presence" will not provide the focus needed for effective testing.
- Outlining Testing Phase Goals
Breaking down the objectives identified into smaller, actionable goals for each testing phase. For example, if the primary objective is to enhance the performance of paid advertising campaigns, goals could be:
- Improving Click-Through Rates (CTR):
- Optimizing Ad Spend Efficiency: Reducing the cost-per-click (CPC) or cost-per-acquisition (CPA) by experimenting with bidding strategies, targeting options, or ad placements.
In defining these goals, we create a roadmap for testing efforts, affording focus, and ensuring the insights gained from each testing phase contribute directly to achieving the broader marketing objectives.
- Hypothesis Formulation
Developing well-crafted hypotheses is essential as they provide the framework for your experiments and serve as a basis for measuring success.
- Data-Informed Insights and Industry Trends: Analyze past data to draw insights from previous campaigns and industry trends to identify patterns, successes, and areas that need improvement. Consider factors like audience behavior, ad performance, and engagement metrics. These insights will ensure specific pain points and opportunities are addressed.
- Specific, Measurable, and Actionable: First, avoid being vague or using broad statements that do not provide the clarity needed to conduct effective testing. For example, instead of a "Our ad campaign can perform better," use more precise language like "By adjusting ad scheduling to target peak hours, we expect to increase our click-through rate (CTR) by 15% within the next month." Second, ensure there are quantifiable expected outcomes for the testing. In the above example, the ‘15% increase in CTR’ is a measurable metric that allows a hypothesis to be specifically measured. Lastly, make hypotheses actionable within the designated time frame. Big goals are great but avoid setting goals that are unattainable and fit within the available resources, budget, and personnel.
- Clear and Testable Statements: Hypotheses should be clear and testable to provide direction for tests, as well as enable an objective evaluation of success. Abiding by this approach will help to determine if the strategies are on the right track or if adjustments are required to achieve the defined objectives.
Hypothesis formulation is a critical step in a Media Test and Learn Strategy. It transforms abstract goals into actionable plans with specific expectations.
- Testing Design
With the hypotheses in place, we move on to the practical phase – test design. It is here the hypotheses are put to the test in designed, controlled experiments that will isolate the impact of the variables selected to test. Essential steps include:
- Select the Variables to Test: The first step is to identify the specific variables to be tested. These variables can encompass a wide range of elements within the marketing strategy, including ad copy, visuals, targeting criteria, ad placements, and landing page designs. The key is to select variables that directly correlate to the objectives and hypotheses. For instance, if the hypothesis centers around improving ad click-through rates (CTR), we might test variations in ad copy, imagery, or call-to-action buttons as they can precisely measure their impact on the CTR.
- Create a Control Group: To accurately assess the effectiveness of the variables being testing, it's crucial to have a control group for comparison. The control group remains unchanged and represents the baseline performance against which to measure the results of tests. It ensures that any improvements or changes can be attributed directly to the variables being manipulated.
- Design Controlled Experiments: Experiments should be structured in a way that isolates the impact of the variables being tested to ensure that any changes in performance can be attributed to the specific elements you're experimenting with. For example, if you're testing different ad visuals, we might design experiments where the only variable to change is the visual element, while other aspects of the ads remain consistent. A controlled approach like this allows for pinpointing the impact of the visuals on user engagement.
- Outline a Plan and Timing: Craft a comprehensive plan and timing for implementing the tests including specifying when and how changes will be rolled, monitor results, and gather data. Depending on your objectives and the complexity of your tests, the timing can vary from days to weeks or even months. Consider factors like seasonality, industry trends, and audience behavior patterns when planning the timing of your tests. Additionally, ensure that you have the necessary tools and resources in place to track and analyze the data effectively.
- Testing Methodologies
A/B testing, also referred to as split testing, is a fundamental method used to compare two versions of a variable in determining which will perform better. In creating two distinct versions of a marketing element, like a web page, email campaign, or ad, and then sharing them with similar groups of users. By analyzing the performance of the versions, we can identify which variant leads to better results. A/B testing can be applied to:
- Email Marketing: Testing different subject lines, email templates, or call-to-action buttons to see which combination yields higher open and click-through rates.
- Website Optimization: Test variations of your landing page layout, headlines, or images to determine which version generates more conversions.
- Advertising: Run A/B tests on ad copy, visuals, or targeting criteria to uncover which elements drive better ad performance, such as click-through or conversion rates.
To ensure reliable results in A/B testing, randomization and sample size are crucial factors:
- Randomization: Assigning users at random to A and B groups will help to eliminate bias by ensuring any differences in performance between the two groups are likely due to (only) the variations being tested (rather than external factors).
- Sample Size: Employing a sufficient sample size is essential to detecting meaningful differences (e.g., larger sample sizes provide more statistically reliable results). The specific sample size required depends on the expected effect size and significance level – often set at 95%.
Where A/B testing focuses on comparing two versions of a single variable, multivariate testing goes a step further by assessing multiple variables in combination. This approach affords marketers the ability to test various elements simultaneously to determine the optimal combination that will maximize performance.
- Variables in Multivariate Testing: Variables can include elements like headlines, images, call-to-action buttons, colors, and the overall page layout. Marketers create multiple versions of the elements to test different combinations and identify the most effective configuration.
- Uncovering Interactions Between Factors: A significant advantage is the ability to uncover interactions between different factors to identify which work best together (e.g., a specific headline with a particular image and call-to-action button).
- Example of Multivariate Testing: Suppose we are optimizing a product page on an e-commerce website. In a multivariate test, we can simultaneously test variations of the product image, product description, pricing, and the placement of customer reviews. To analyze user interactions and conversion rates and pinpoint the combination that drives the highest sales and engagement.
- Test Implementation
The next crucial step is the execution phase where we put hypotheses into action (across media channels and platforms) and gather the data to evaluate their validity. Here are some key considerations:
- Tools and Techniques: For A/B testing, platforms like Google Optimize and Optimizely can help set up experiments seamlessly. For multivariate testing, tools like VWO (Visual Website Optimizer) enable the testing of multiple variables simultaneously. And social media platforms often offer built-in testing features for ad campaigns.
- Consistency: Ensure that the testing environment, audience segments, and other relevant factors remain constant to accurately isolate the impact of the variables being tested.
- Proper Tracking and Measurement: Implement robust systems to capture key metrics using tracking pixels, event tracking, and analytics platforms like Google Analytics to collect data on user interactions, conversions, and other performance indicators.
- Data Collection and Analysis
The heart of any media test and learn strategy lies in the rigorous process of data collection and analysis. In this phase we gather critical information from the tests and transform it into actionable insights. Important aspects of data collection and analysis include:
Gathering Relevant Data
- Comprehensive Data Collection: Includes a wide range of information, such as performance metrics (e.g., click-through rates, conversion rates, revenue generated) and user behavior data (e.g., time spent on page, bounce rates, interactions with specific elements).
- User Feedback: In addition to quantitative data, collecting qualitative insights from user feedback, like user comments, surveys, and direct feedback, can provide valuable context to quantitative findings.
Analyzing the Data
Once you've collected data, the next step is to analyze it to determine the impact of the tested variables on the desired outcomes. Common analysis techniques are:
- Comparative Analysis: Comparing performance metrics of the variations being tested to identify patterns and differences in how each variant performed.
- Statistical Significance: Determines whether observed differences are meaningful or simply due to chance using tools like Google Optimize or statistical software like R or Python.
- User Behavior Insights: Understanding how users interacted with the tested elements. For instance, did users spend more time on a webpage with the new layout, or did they quickly bounce off?
Benefits of Data Visualization
A powerful tool that can greatly enhance analysis and the presentation of results.
- Clarity: Visual representations, such as charts, graphs, and dashboards, provide a clear and concise way to convey complex data.
- Pattern Recognition: Visualizations can help quickly identify patterns, trends, and outliers within your data.
- Storytelling: Well-crafted visualizations can tell a compelling story about test results, making it easier for stakeholders to grasp the key takeaways.
Tools for Data Visualization
Tools that can help deliver effective data visualization include:
- Excel: Provides a range of charting and graphing options suitable for basic data visualization.
- Google Data Studio: A free tool creating interactive and customizable dashboards to present test results.
- Tableau: A powerful data visualization tool providing extensive capabilities for creating insightful and interactive visualizations.
- Power BI: Another robust data visualization tool allowing the creation of dynamic reports and dashboards.
Data collection and analysis are the linchpin of media test and learn strategies. Gathering relevant data and meticulously analyzing it enables meaningful insights that inform better decisions. Incorporating data visualization techniques and leveraging appropriate tools can convey findings effectively and drive data-driven optimization quickly and efficiently.
- Insights Generation
Interpreting test results and deriving actionable insights is the goal for media test and learn strategies. A simplified framework would include four key steps:
- Interpret Test Results: Analyze data to determine the impact of the tested variables on the desired outcomes. Identify which versions of variables performed better and why. For instance, did a particular ad copy resonate more with a specific audience segment? What contributed to the improved performance?
- Guidelines for Action: Based on the analysis, develop guidelines for action and determine what changes or optimizations are necessary to improve performance. These guidelines should be specific and actionable to efficiently guide efforts toward the desired future state.
- Identify What Worked and What Needs Improvement: Clearly differentiate between what worked well, what needs improvement based on data-driven conclusions, and what to actions to prioritize based on potential impact and feasibility of implementation.
- Short-Term vs. Long-Term Actions: Some insights may lead to immediate adjustments in ongoing campaigns, while others may inform longer-term strategic decisions, such as changes to your overall content strategy or target audience segmentation.
Successful test implementation, data collection, analysis, and insights generation are essential components of any test and learn strategy. By executing tests effectively, gathering and analyzing data rigorously, and drawing actionable insights, we can continuously refine and optimize efforts – ultimately, driving better results and achieving defined objectives.
- Iterative Optimization
This is the phase where we put insights to work by implementing changes to marketing strategies to provide continuous improvement. Important considerations when beginning to optimize marketing strategies include:
- Examples and Tools: Implementing changes could involve refining ad copy, adjusting targeting criteria, or revamping your website based on the insights gained from testing. For example, if you discover that a specific ad headline resonates better with your audience, make that headline a standard practice in your ad creation.
- Tools Used: Marketing automation platforms like HubSpot or Marketo can help streamline the process of implementing changes across various channels. Additionally, project management tools like Trello or Asana can assist in tracking and coordinating optimizations.
A media strategy should be a living, breathing entity that is continuously adjusting to incorporate the learnings from tests. Monitor performance closely and be ready to adapt in response to changing trends and consumer behavior.
- Scaling Successful Strategies
After identifying variations that demonstrated positive results, it's time to consider scaling them up. Scaling involves expanding successful strategies to reach a broader audience or enhance overall marketing efforts. Scaling up is appropriate when you have a high level of confidence, based on robust data and consistent positive outcomes, in the effectiveness of a particular strategy or variation. For example, if A/B testing consistently shows a specific email subject line results in higher open rates, consider using it as the default subject line for email campaigns.
Additionally, incorporate the lessons learned from testing into ongoing media efforts to ensure the successful variations are integrated into your standard operating procedures. However, be cautious not to become complacent and to continue testing and refining to stay ahead of changing market dynamics, while also fostering a culture of continuous improvement and innovation.
- Knowledge Sharing
Vital components of any successful media test and learn strategy is effective knowledge sharing and collaboration to foster a culture of shared insights.
- Share Insights and Outcomes: Regularly communicate reports, conduct team meetings, or use collaboration tools like Slack or Microsoft Teams to disseminate information.
- Encourage Collaboration: Foster an environment where team members from different departments can contribute insights and expertise. Cross-functional learning ensures that knowledge is shared across an organization, leading to more informed decision-making.
By implementing the above iterative and collaborative approach enables an organization to adapt to changes and drive continuous improvement.?
- Documentation and Tracking
To ensure organizations benefit from immediate improvements, as well as from long-term learnings, effective documentation and tracking are key to maintaining a structured record of test and learn activities.
Begin by documenting the results of each test, encompassing both quantitative metrics, such as conversion rates, click-through rates, and revenue generated, as well as qualitative insights derived from user feedback or observations. Then outline the insights gained from each test to explain what worked, what didn't, and most importantly, why. This step is vital for understanding the underlying factors contributing to the test outcomes and identifying any unexpected findings or trends that emerged during the testing phase.
Lastly, document the specific changes made, the rationale behind each, and the expected impact based on the insights gained. This will serve as a historical record and a valuable resource for making better decisions in the future.
Maintain a Structured Record
- Central Repository: Create a repository or database (e.g., a shared drive, cloud-based platform, or project management tool) where all test and learn documentation is stored.
- Consistent Format: Apply to documenting results, insights, and changes to makes it easier to review and compare findings across different tests and time periods.
- Version Control: Implement for documentation to ensure changes are tracked over time and can be referred to if needed.
- Annotations: Consider using within your documentation to highlight noteworthy insights or trends to make it easier for team members to quickly grasp key takeaways.
Tools for Documentation
- Excel or Google Sheets: Spreadsheet tools are versatile for creating structured documentation templates to record test details, results, and insights.
- Project Management Software: Tools like Trello, Asana, or Jira can help organize and track test and learn activities, with the added benefit of task assignment and scheduling.
- Document Collaboration Platforms: Google Docs or Microsoft SharePoint are excellent for collaborative documentation allowing team members to contribute insights and findings in real-time.
- Data Visualization Tools: Tableau or Power BI can be used to create dynamic dashboards that display key test results and insights visually.
By maintaining a structured record of your test and learn activities, you prioritize that the knowledge gained from each experiment becomes a valuable resource for future decision-making. Documentation that helps in tracking progress and aids in sharing insights with team members and stakeholders, ultimately contributing to the long-term success efforts.
- Risk Management
Given how dynamic and unpredictable marketing is, risk management is an integral part of any successful test and learn strategy to address potential risks and challenges associated with testing and ensure effective contingency plans are in place.
Addressing Potential Risks and Challenges
- Example – Short-Term Fluctuations in Performance: One common risk in testing is experiencing short-term fluctuations in performance. For instance, if you're A/B testing a new website layout, the new version might initially see lower conversion rates compared to the old one due to user unfamiliarity with the changes.
- How to Address: It's important not to jump to hasty conclusions but to address the risk by recognizing that some changes take time to settle in with users. Keep a close eye on performance metrics and consider monitoring them over an extended period to differentiate between short-term fluctuations and a sustained decline.
Contingency Plans to Mitigate Negative Outcomes
- Plan for Reversion: Have a clear process for reverting to the previous version or strategy if a test results in significantly negative outcomes. This "exit strategy" ensures that you can quickly mitigate any potential damage to efforts.
- Continuous Monitoring: Don't just set and forget your tests, but regularly monitor performance metrics and user feedback throughout the testing phase. If there are signs of significant negative impacts, take proactive measures to address them promptly.
- Adjustment and Refinement: Sometimes, tests may not yield the expected results, but they don't necessarily result in a catastrophe. In such cases, having plans for adjustments and refinements (options to tweak the variables being tested or altering the strategy mid-course to steer it back on track) can be valuable.
- Learning from Setbacks: When a test doesn't go as planned, gather insights from what went wrong and apply those lessons to improve future tests. In the long run, these setbacks can contribute to a more refined and successful media test and learn strategies.
Risk management is not about avoiding risks, but about being prepared to handle them effectively. By addressing potential risks and challenges, and having well-thought-out contingency plans in place, we can navigate unpredictable terrains with confidence.
Media test and learn strategies is a powerful approach to being armed with the right tools to continuously improve business. At its core, it represents a commitment to data-driven decision-making. It is the embodiment of the age-old adage "knowledge is power."
By testing hypotheses, analyzing results, and refining strategies based on insights, organizations gain a profound understanding of what works and what doesn't. The benefits of this approach are an empowered business that can optimize media efforts, maximize ROI, foster a culture of experimentation, and adapt to the changing tides of consumer behavior and market dynamics.
The various stages – setting clear objectives, continuous optimization, resource allocation, and risk management – demonstrate this should never be a one-time solution. It's a continuous cycle, a commitment to ongoing learning, and a dedication to staying ahead of the curve.
Media test and learn strategies serve as both compass and guide, illuminating a path forward, helping organizations harness the power of data, refining approaches, and ultimately, achieving marketing objectives.
With each test conducted, every insight gained, and every optimization made, we improve efforts and position ourselves for success. Keep testing, keep learning, and keep thriving.