The Brand CustomTees is a successful online clothing brand, with annual revenue exceeding $50M. Despite their success, the company's marketing team grappled with one  critical question: How effective was their substantial investment in Facebook? Was there an opportunity to optimize Facebook spend to increase profitability while not impacting revenue?   The Challenge The brand had historically relied heavily on Facebook for growth, allocating north of 40% of their marketing budget to the platform. With quarterly Facebook spend reaching $600,000, the stakes were high. Facebook's prospecting platform reported an 2.8 ROAS (Return on Ad Spend) on a 7-day click one-day view basis, but the client’s marketing team suspected this might not tell the whole story despite the previous success of the channel.    Digging Deeper The initial analysis that was done, using a 7-day click only attribution benchmark which painted a very different picture. This method, which applies a discount to platform-reported results, suggested the brand was merely breaking even on Facebook ads. The UTM attribution calculated off of URL parameters reported an even lower ROAS. If this were true, it would imply the channel isn't profitable after considering costs of goods sold, shipping, etc. Moreover, it could be a sign that Facebook needs to be optimized and is cooling off despite its earlier success.  The brand decided to take up incrementality testing with M-Squared to uncover the ground truth behind Facebook performance.   The Approach:Incrementality & Platform Lift Testing To uncover the truth, M-Squared recommended two measurement approaches to test Facebook Prospecting:  Geo Incrementality Test Facebook Platform Lift Study Both tests were conducted over 30 days, with the geo test designed as a multi-cell experiment to assess incrementality across various channels, including Facebook Prospecting. Catch a quick primer on different types of incrementality tests from the M-Squared masterclass here:   The Results: The results were eye-opening: The geo incrementality test showed a total lift of about 10%, primarily driven by new orders. Calculating the multiplier revealed a channel multiplier of 41%. When applied, this showed that the ROAS on Facebook prospecting was actually around 0.6 - significantly lower than initially reported. The UTM attribution reported ROAS seemed to closely approximate the incremental reads coming out of the tests. Interestingly, the Facebook platform lift study corroborated these findings, showing a multiplier of 41%. Triangulating reads across multiple studies is a common best practice that marketers are adopting en masse. Catch a quick primer on this best practice from M-Squared masterclass here: Implications and Next Steps Armed with this new data outside of platform-reported results, these consistent incrementality results provided the marketing team with a much clearer and concise direction. The data strongly suggested that cutting the Facebook budget and re-allocating resources elsewhere could be a logical next step for optimizing overall marketing performance and driving increased profitability, without harming topline revenue. M-Squared recommended testing budget re-allocation before rolling it out nationally by cutting spend on FB and re-allocating to other channels.  The Conclusion: In the world of data-driven marketing, having multiple consistent data points is crucial for avoiding false positives and making informed strategic choices. For this e-commerce clothing brand, the journey of advanced attribution not only revealed the true performance of their Facebook advertising but also opened up the discussion for new growth opportunities. By reallocating budget from underperforming channels, they can now explore other channels to drive more efficient growth and profitability. This case study serves as a powerful reminder of the importance of rigorous, multi-faceted attribution in modern marketing. In an era where data drives decisions, looking beyond surface-level metrics can uncover hidden truths and unlock new paths to success.  

The Brand Study.com provides high stakes online learning solutions to more than 34 million learners and educators a month across professional test preparation, college credit and K-12 education. Recognized as one of the world’s most innovative companies by Fast Company and the GSV150, Study.com has helped save students more than $475 million in tuition costs through its College Saver program and donated some $29 million across social impact programs committed to increasing educational equity The Challenge The company's new customer acquisition efforts had been primarily centered around SEO and PPC investments, and managed like a classic performance marketing program, governed to tight CAC guardrails agreed upon between marketing and finance. The strategy served the organization well for many years helping the brand drive predictable and profitable growth, but eventually these channels reached maturation and could not continue the accelerated growth rate desired. So like many other brands in the same predicament, Study.com has been seeking diversification and growth opportunities in upper funnel channels.  As Study.com explored other channels it also aspired to build out a stronger brand, investing in video creative with a branding narrative. The brand campaign-focused on inspiring students and educators to reach their academic and career goals through Study.com's online college courses, exam preparation and classroom resources. But as always the key question is “Will branding drive new customer demand?”.  When the company rolled out smaller scale paid video campaigns on platforms like YouTube, it saw few last-click conversion although other positive indicators led to the desire to continue investment at scale and with more precise measurement. There were several back-and-forth discussions to determine if the organization should put more budget behind this and to launch in CTV, but would it be a risk worth taking? Would this bring in new students?  How can you measure it since it serves more as an upper funnel tactic? To find out, Study.com embraced geo-based incrementality testing with M-Squared to learn the efficacy of branding campaigns on CTV and YouTube.   “Really just taking the test-and-learn approach and leaning into creativity”- Emily Johnson Watch as Emily Johnson unpacks her experience with us! Exploratory Data Analysis As with any brand, the devil is in the details. Real businesses have real complexity in their business model, and in their data. Marketing measurement practitioners have to process these complexities and assess which of those nuances are meaningful to incorporate into the measurement solution architecture and which ones are irrelevant to the business questions being considered. For Study.com, there were many such dimensions of complexity:  Like many businesses, that service multiple audiences with several products, there is a mix of hero and long tail products.  Different products drive unique LTV and hence different levels of contribution margin for the business over the long term. The tactics being tested are demand generation (vs demand harvesting) and the video medium, which intuitively means the measurement has to account for a longer time-to-conversion period, and low last click attribution. Should the measurement plan consider upper funnel outcomes like Engaged Sessions or Email/Phone collection which could serve as leading indicators of demand being generated. Would the data collected so far support that measurement plan? The Approach:Geo Match Market Test - Design At M-Squared we take a structured approach to designing a geo test.    Catch the 11 minute snippet on geo testing from the M-Squared masterclass:  Study.com already had evergreen YouTube campaigns in flight, and there had been a significant push over the summer months to launch a new branding video.  Post geo testing feasibility analysis, we determined that the current spend levels on YouTube might not clear minimum detectable lift (MDL) levels and hence may not yield statistically significant reads with a holdout test. As a result, we tested YouTube with a scale cell where spend was intentionally elevated to support readability. A holdout cell was also included to measure the lift at current spend levels, with the understanding that the reads may come back inconclusive, but provide valuable insight on incremental vs marginal contributions. CTV was a brand-new channel and had no prior spend or performance history. It was not meaningful to select a holdout treatment for incrementality testing. Instead, a scale cell was designed as part of the feasibility analysis. Since it was a new channel, warming up the channel was recommended before starting measurement. So the first 2 weeks were slotted for campaign warmup and the next 4 weeks for the read, running a total of 6 weeks in test flight. Market selection algorithms were run and DMA’s were identified for the three different testing cells. Test budgets and test flight period were determined as part of feasibility analysis.  YouTube Scale cell  YouTube Holdout cell CTV Scale cell Markets: 14 DMAs Flight: 6 weeks Budget: $120K Markets: 14 DMAs Flight: 6 weeks Budget: No spend in selected markets during test period Markets: 13 DMAs Flight: 6 weeks Budget: $120K The test was flighted in Q4 2024, and the flight was monitored for execution aligning to the test design that was put in place.  The Results:Lift Reads & Interpretations for Growth As with the design, at M-Squared, we take a structured process for estimating the lift reads from the test.  Catch the 7 minute mini course on estimating lift from the M-Squared masterclass:  After carefully estimating the lift with multiple algorithmic approaches, Study.com learned: CTV drove a 6%  incremental lift to new member acquisition at a 3.8 ROAS YouTube showed good potential as well 3% incremental lift on  new member acquisition at a 2.5 ROAS Both of these reads provided meaningful insights on employing upper funnel tactics for driving customer acquisition growth for Study.com.  With reasonable assumptions on scale and diminishing returns, and annualizing the estimates for seasonality, employing YouTube and CTV in their customer acquisition plan could drive an estimated 20% growth for the brand. The Conclusion: These insights now provided Study.com a clear path to diversify its media mix into upper funnel channels using a test-learn-grow approach for risk mitigated and fiscally responsible growth. Disclaimer: The data presented is blinded to protect brand’s P&L confidentiality but preserve insights for educational purposes.

In the dynamic world of digital marketing, the ability to assess and understand the impact of various media channels on consumer behavior is crucial for any successful campaign. This intricate process involves a series of meticulously planned phases, each playing a pivotal role in unraveling the complexities of market responses to different media strategies. From designing the test to analyzing its results, this journey is both an art and a science, requiring a blend of analytical rigor and creative thinking. Let's dive into these phases to gain a deeper understanding of how digital marketing tests are conducted and interpreted for maximum impact. Phase 1: Test Design The cornerstone of the testing process is selecting the right audience or market. This is especially crucial in split tests or geographical tests. The goal is to create statistical twins among the groups, allowing for a controlled comparison of campaign outcomes. Following market selection, a feasibility analysis is conducted. This step is less visible in less mature environments but is vital for understanding the potential impact of variables like media channels on specific markets. For instance, turning off a media channel in selected markets and observing the revenue impact provides insights into the channel's effectiveness. This involves comparing the test markets with anchor control markets like California or New York to differentiate the impact from seasonal variations. Phase 2: Test Flight In this phase, the designed test is implemented. Budgets are adjusted in selected markets, and campaigns are closely monitored to ensure they are not disrupted. This phase typically spans around four weeks, though it can vary depending on the nature of the test and the channels involved. Phase 3: Test Reads The key component here is analyzing the 'lift' - the difference between what happened in test markets versus what would have happened under normal conditions. This involves counterfactual predictions and can be approached through various data science methods or simpler estimation techniques. Post-lift analysis, the focus shifts to interpreting the results in terms of return on investment (ROI), cost per acquisition (CPA), and how they compare to other channels. This is where decision matrices come into play, helping to anticipate the implications of different outcomes. Decision matrices are crucial for pre-empting emotional biases in decision-making. By outlining potential scenarios and responses before the test, marketers can approach results more objectively, understanding that a negative outcome is not a failure of the test but rather a valuable insight. Practical Insights One insightful example is the testing of incrementality on platforms like Facebook in various markets. The analysis of Facebook's impact on revenue in specific markets, like Rhode Island or Maine, reveals the importance of understanding external factors like seasonality and market dynamics. Another case involved testing different types of TV advertising, where cable TV showed significant lift but at a high cost. This led to the realization that optimizing frequency could achieve similar results at a lower cost, demonstrating the nuanced nature of media testing. A common challenge is dealing with emotional attachment to campaigns. Marketers often find it difficult to accept negative test results on campaigns they've nurtured. This is where the importance of a decision matrix and objective analysis becomes evident. Media testing in digital marketing is a multifaceted process that requires careful planning, execution, and analysis. The key phases of test design, flight, and read, each have their unique challenges and opportunities. By understanding the nuances of each phase, marketers can make more informed decisions, leading to more effective and efficient campaigns. The use of decision matrices further enhances this process, allowing for a more objective and data-driven approach to media testing.

Incrementality testing is a cornerstone of data-driven marketing, allowing marketers to determine the true effectiveness of their campaigns beyond mere surface-level metrics. This form of testing is crucial in today's complex marketing landscape where multiple channels and strategies are employed simultaneously. The Role of the Marketing Funnel in Testing As mentioned in the transcript, the marketing funnel is a key framework in this context. It categorizes the customer journey into different stages - awareness, consideration, and decision. Each stage requires a different marketing approach and, consequently, a different testing strategy. For example, awareness campaigns might be measured differently compared to retargeting campaigns aimed at customers lower in the funnel. An Overview of the Most Common Incrementality Tests  Split Testing (Randomized Controlled Trials - RCTs) Example: A Facebook campaign targeting a broad audience. Process: The audience is split into two groups - one exposed to the campaign (treatment) and the other not exposed (control). The difference in outcomes, such as conversion rates, is attributed to the campaign's impact. Limitation: This method may not be feasible for all channels, especially where the audience is not directly accessible or owned by the brand. Geo Match Market Testing Example: Comparing marketing efforts in different states or DMAs. Process: Different geographic markets receive different marketing treatments, and their performances are compared. Advantages: Relies on first-party data, ensuring transparency and control. Applicable across various channels, enabling a holistic view of marketing effectiveness. Incrementality Testing Objective: To measure the immediate impact of current marketing investments. Example: Assessing the contribution of your investment on Facebook or Roku to overall business outcomes. Scale Testing Objective: To predict the outcomes of increased marketing investments. Example: Understanding the impact of doubling the investment on Facebook and predicting the returns on this additional spend. Addressing the Challenges While incrementality testing offers invaluable insights, it's not without challenges. One significant challenge is dealing with third-party datasets, which may lack transparency and control. For instance, platforms like Facebook use complex algorithms and methodologies (like the Ghost Ads approach) for their lift tests, which might not be entirely transparent to marketers. Marketers need to navigate a variety of tests, each with its own nuances. Understanding where each test fits - whether it's a third-party test, a first-party test, a designed experiment, or an observed experiment - is crucial for making informed decisions. Incrementality testing, through both split testing and geo match market testing, provides essential insights into the effectiveness of marketing efforts across different stages of the customer journey. By understanding and applying these insights, marketers can enhance the precision of their strategies, ensuring that each marketing dollar is spent where it has the greatest impact. The key is to balance the insights from these tests with the inherent challenges they present, especially regarding third-party data and platform-specific methodologies.

In the rapidly evolving world of digital advertising, marketers are constantly seeking more effective ways to reach and engage with their target audiences. Key to this pursuit is understanding the intricacies of modeled audiences, conversion optimization algorithms, geo-testing, and incrementality testing. These strategies, when applied judiciously, can significantly enhance the effectiveness of digital campaigns. The Rise of Modeled Audiences One of the most prominent trends in digital advertising is the use of modeled audiences. Platforms like Facebook have led this charge, with a significant portion of their ad spend being directed towards what they call 'broad audiences' - previously known as lookalike audiences. This approach involves creating a pyramid-like structure of potential customers, starting with a seed audience, such as a company's best customers from the past six months. The platform then identifies potential targets, ranking them based on their likelihood to convert. For instance, a fashion brand selling shoes can leverage signals from various shoe-related activities captured by pixels across websites. Facebook's algorithm can identify consumers actively looking for shoes, those who might be interested soon, and a broader audience who generally show interest in shoes. This segmentation ensures that ads are served to the most relevant audience first, enhancing the likelihood of conversions. The conversion optimization algorithm plays a crucial role in determining the effectiveness of a campaign. It operates on a top-to-bottom approach, serving impressions to the most likely buyers first. This strategy aims to achieve strong last-click attribution, improving campaign metrics like CPM (Cost Per Mille) and encouraging increased ad spend. However, it's crucial to note that as you move down the pyramid, the conversion rates tend to decline, leading to diminishing returns in broader audience segments. Geo-Testing: A Strategic Approach Geo-testing offers a practical solution for testing and scaling marketing strategies. By categorizing different states or regions into tiers based on factors like penetration rate and conversion propensity, marketers can execute controlled tests. For example, finding a similar but smaller markets with similar markets as a larger one like California (a tier three state) allows for low-risk testing with scalable insights. This approach enables marketers to extrapolate findings from smaller markets to larger ones, ensuring efficient allocation of marketing resources. Incrementality testing, or holdout testing, is vital in understanding the actual contribution of a specific marketing channel. By comparing control markets (where a particular media, like Facebook, is turned off) with active markets, marketers can measure the true impact of that media on revenue. For example, if a company observes a 26% drop in revenue in the absence of Facebook ads, it can infer that Facebook contributes 26% to its business. The next step involves comparing these findings with platform-reported metrics. If Facebook Ads Manager reports a higher number of conversions than what the incrementality test suggests, the marketer can apply a multiplier to align reported conversions with actual impact. This multiplier becomes a critical tool in ongoing operational reporting, ensuring that marketers account for the true incremental value provided by platforms like Facebook. Choosing the Right Attribution Model Deciding on the appropriate attribution model is another crucial consideration. Whether a marketer relies on platform reporting, Google Analytics, or a media mix model, the chosen method must accurately reflect the impact of different channels. A heterogeneous approach allows for the integration of diverse data sources, offering a comprehensive view of a campaign's performance across various platforms. Diminishing Returns in Marketing The concept of diminishing returns is pivotal in marketing, especially when managing ad campaigns. Imagine your marketing efforts as a pyramid. At the top, the conversion rates are high, but as you progress down, these rates start to decrease. This phenomenon is due to the diminishing impact of each additional dollar spent. The first dollar might bring significant returns, but the next dollar is less efficient, creating a typical curve of diminishing returns.  Consider a scenario where a brand is spending $100,000 a week on advertising. When they double this expenditure, the crucial question is how significantly the returns will diminish. For a new or smaller brand, it’s sometimes hard to see them hit diminishing returns. It could take 6 months to a year before they see it hit them. For larger brands, they can double their spend and barely see a spike in conversions. It’s akin to driving down a mountain; the slope's severity can vary greatly. This uncertainty necessitates rigorous testing to understand where your brand stands on this curve of diminishing returns. Incrementality testing is a powerful tool used to gauge where your campaign is on the diminishing returns curve. It helps to determine how much the returns diminish with increased spending. For example, small and emerging brands might double their ad spend repeatedly without seeing a notable change in returns. This could be due to their large potential audience and the universal appeal of their products, like shoes or t-shirts. In contrast, well-known brands might see a steeper curve, where increased spending leads to higher costs per thousand impressions (CPM) and diminished returns. Testing Strategies There are various testing strategies, like geo testing and split testing, which fall under two primary categories: incrementality tests and scale tests. Geo tests are based on first-party data and offer high control and transparency, making them a preferred choice for many brands. However, third-party platform lift tests also play a vital role as part of a comprehensive testing strategy. Beyond incrementality testing, marketers can employ advanced attribution techniques to refine their strategies further. These include: Marketing Mix Modeling: This technique evaluates the effectiveness of different marketing tactics and channels, helping allocate resources more efficiently. Multi-Touch Attribution: Although complex, this method provides insights into how various touchpoints contribute to conversions. Post-Purchase Surveys: These are increasingly used as a low-fidelity, cost-effective method for initial incrementality assessments. They offer directional insights and can be a stepping stone toward more sophisticated testing methods. As digital advertising continues to evolve, understanding and implementing these advanced strategies becomes increasingly important. The key is not just in gathering data but in interpreting it correctly to make informed, strategic decisions. By mastering the art of modeled audiences, conversion optimization, geo-testing, and incrementality testing, marketers can significantly enhance the effectiveness of their campaigns, ensuring they reach the right audience with the right message at the right time.

In the vibrant and competitive realm of digital marketing, the ability to make informed, data-driven decisions can be the key to success. This is where the concept of split testing, often referred to as A/B testing, plays a pivotal role. What is Split Testing? Split testing, or A/B testing, is a scientific approach in digital marketing where different versions of a marketing element - such as ads, web pages, or emails - are presented to distinct segments of an audience at the same time. The objective is to identify which version drives superior outcomes in terms of engagement, click-through rates, or conversions. This method involves creating variations of a marketing element, randomly assigning these variations to audience segments to ensure statistical similarity, and then measuring performance based on relevant Key Performance Indicators (KPIs). The results are analyzed to determine the most effective version, allowing marketers to base their strategies on solid, empirical evidence rather than assumptions. Why Split Testing? The rationale for employing split testing in digital marketing is multi-dimensional. It enables a transition from guesswork to data-driven decision-making, a critical shift in a field as dynamic as digital marketing. By understanding what truly resonates with the audience, split testing not only improves the user experience but often leads to higher conversion rates, thereby maximizing the return on investment for marketing efforts. This method also serves as a risk mitigation tool, allowing marketers to identify and address potential issues before fully committing resources to a campaign. Furthermore, it fosters a culture of continuous improvement and learning, as marketers consistently test new ideas and refine their strategies based on real-world audience feedback. Core Principles of Split Testing In the intricate world of digital marketing, split testing is anchored on several core principles that guide its successful implementation. At its foundation lies the model audience pyramid, a conceptual framework that categorizes audiences from the broadest at the top to the most targeted at the bottom. As marketers navigate this pyramid, they encounter varying layers of audience specificity. Typically, the conversion rates tend to diminish as one moves deeper into the pyramid, where the audience becomes more defined and potentially more valuable. Another vital principle in split testing is the adoption of Randomized Controlled Testing (RCT). This approach mirrors the rigors of clinical trials in medicine, where different marketing treatments are randomly assigned to segments of the audience. This random assignment is crucial as it ensures an unbiased evaluation of each treatment's effectiveness, providing a clear picture of their impact. Hierarchical sampling is also a cornerstone principle in split testing. Unlike simple random sampling, this technique involves categorizing the audience based on distinct characteristics or behaviors. It is especially useful in handling large and diverse audience sets, allowing for more targeted and relevant testing scenarios. This method enables marketers to focus their efforts on specific segments of the audience, ensuring that their testing is as efficient and effective as possible. Together, these principles form the bedrock of split testing, providing a structured approach to understanding and engaging with various audience segments. By adhering to these principles, marketers can ensure that their split testing efforts are not only methodical but also yield valuable insights that drive campaign optimization and success. Practical Applications in Marketing In the realm of digital marketing, the practical applications of split testing are varied and impactful. This approach is especially crucial in determining the most effective strategies for campaign management and optimization. One significant application is scale testing. This involves methodically increasing the budget of a campaign to discern the point at which the returns begin to diminish. It's a strategic process of balancing investment against returns, aiming to discover the optimal spending level where the investment yields the highest returns without wastage. Another crucial application is in the realm of creative testing. Marketers test various elements of their ad creatives - ranging from images and copy to calls to action. The goal is to identify which combination of these elements resonates most effectively with the target audience. This approach is instrumental in enhancing the appeal and effectiveness of marketing messages. Optimization strategy testing is yet another important application. Marketers experiment with different campaign strategies, such as varied bidding methods or targeting criteria, to ascertain the most effective approach. This experimentation helps in maximizing conversions and optimizing the Return on Ad Spend (ROAS), ensuring that each campaign delivers the best possible results. Attribution testing also plays a vital role. In this approach, marketers use split testing to find the most effective attribution model for their campaigns. This might involve determining the best look-back window for attributing conversions or comparing the efficacy of different types of conversions, such as click-through versus view-through. This nuanced analysis aids marketers in understanding and crediting the right interactions that lead to conversions. These diverse applications underscore split testing's role as a versatile and indispensable tool in a marketer's arsenal, helping to fine-tune campaigns for maximum impact and efficiency. The Split Testing Process Audience and Campaign Selection - The first step is choosing the right audience segments and campaigns, guided by factors like the rate of audience penetration and ad exposure frequency. Budgeting and Experiment Design - Post-selection, it’s crucial to estimate the budget for each test segment and design the experiment considering factors like duration and scale factors (e.g., 2x, 3x budget). Implementation and Analysis - The test is rolled out, often via an ad platform’s API for enhanced flexibility. Data is collected and scrutinized throughout the testing phase to assess each variant’s performance. Interpreting Results - The final and most crucial step is deciphering the results. Key metrics like conversion rate, ROAS, and CPA (Cost Per Acquisition) are analyzed to determine which campaign variant outperformed and why. Split testing stands out as a pivotal tool in the arsenal of a digital marketer. By systematically examining different facets of a campaign, marketers can unlock valuable insights into audience behavior, optimize spending, and drive superior results. The essence of successful split testing lies in a strategic approach, a solid grasp of statistical principles, and the agility to adapt based on empirical evidence. As the digital marketing landscape continues to evolve, split testing remains an indispensable technique for staying ahead in the game.

The fundamental rule of marketing is that approaches and strategies employed can make or break the effectiveness of a campaign. One of the pivotal elements that often goes unnoticed, yet plays a critical role, is the art of testing – a domain that combines analytical rigor with creative problem-solving. Setting Objectives and Crafting Requirements At the outset, it's crucial to understand that testing is not just about following a set of predefined steps; it’s about setting clear objectives and transforming these into actionable requirements. This task, though seemingly straightforward, involves navigating through a maze of stated and unstated needs. Often, the journey begins with engaging leaders to outline their explicit objectives. However, as the conversation with these stakeholders unfolds, it becomes apparent that what's on the surface may only be the tip of the iceberg. The real challenge lies in discerning the actual goals that might be entirely different from those initially presented. Delving into testing specifics, particularly in geo match market testing, uncovers layers of complexities. It’s like peeling an onion, where each layer may trigger a different response, revealing hidden angles and unforeseen challenges. The articulated objectives of leadership may lead down one path, but the discovery process could take a sharp turn, revealing a need to test something completely unexpected. This is where the rubber meets the road for practitioners, consultants, agencies, and vendors alike. The nuanced understanding required to extract the real testing objectives from a discussion is similar to a detective unraveling a mystery. Skits as Learning Tools To navigate this complexity, role-playing exercises serve as a creative way to distill requirements and explore different perspectives, from a nascent direct-to-consumer (DTC) brand to a well-established fashion giant. In these skits, participants step into the shoes of key stakeholders – such as a CEO of a burgeoning DTC brand or a CMO of a mature fashion brand. By dramatizing these roles, the participants get a taste of the challenges and decisions these executives face. By bringing these characters to life, learners can experience firsthand the complexities of defining testing requirements in a dynamic and often ambiguous market environment. Reflection and Applicability Reflecting on these exercises provides rich fodder for discussion – how does this feel in a corporate setting? How can these insights be applied to different situations that practitioners face? By reviewing recorded sessions and diving into discussions, learners can gain a multi-dimensional view of the objectives-to-requirements process, applicable across a broad spectrum of real-world situations. Testing in marketing is fraught with hidden challenges and requires a blend of sharp analytical skills and creative thinking. The process of setting objectives and defining requirements is an iterative, exploratory, and sometimes circuitous journey. Learning to uncover the true needs behind a set of stated objectives is an invaluable skill for anyone in the marketing space, one that demands not just expertise but also empathy and insight. Through innovative teaching methods and real-life simulations, marketing professionals can arm themselves with the acumen needed to navigate these waters successfully.