In the realm of digital marketing, the pursuit of optimizing marketing spends across various channels is a never-ending quest. Two pivotal tools in this journey are Facebook's Robyn and Google's Lightweight MMM. These open-source marketing mix modeling libraries offer unique features and methodologies to measure and predict the effectiveness of marketing campaigns. Setting Up the Models Methodological Distinctions A key difference between the two models lies in their methodologies. Google's Lightweight MMM adopts a Bayesian regression-based approach, necessitating prior information about media variables. In contrast, Facebook's Robyn operates on ridge regression with constraints. This methodological variance influences how each model handles data and predicts outcomes. The Google model emphasizes data scaling to ensure uniformity across various metrics. This is crucial when the model includes diverse data like impressions and clicks. In comparison, Robyn's approach may differ in handling such data transformations. Model Comparison: Advantages and Limitations The comparison reveals several distinct features: Environment and Granularity: Robyn operates in R, while Google's model uses Python. Furthermore, Google's model supports both national and geo-level data, providing more granular insights. Transformation Methods: Robyn offers more options in terms of transformations, including both geometric and variable transformations. Google's model, however, focuses on ad stock transformations. Handling of Saturation and Price: Both models approach saturation differently. Robyn applies saturation by default, whereas Google's model offers more flexibility. In terms of price, Robyn's approach can be more rigid, while Google's Bayesian approach incorporates probabilistic variance. Seasonality and Visualization: Robyn excels in decomposing seasonal and trend elements, whereas Google’s model requires a deeper understanding of hyperparameters for Fourier transformation. Robyn also stands out in terms of visual representation of outputs. Budget Allocation Support: Both tools offer robust support for budget allocation, a crucial aspect for marketers. Insights from Response Curves The response curves generated by these models offer valuable insights. For instance, Robyn's linear response curve against media channels and Google's C-shaped curve highlight the varying impacts of channels like Facebook, Google Ads, and TikTok. Understanding these curves is fundamental for marketers to optimize spending across different channels. Bayesian Regression: A Game Changer Bayesian regression, as used in Google's Lightweight MMM, presents significant advantages. It allows for the incorporation of varied information sources and acknowledges the fluidity of market dynamics over time. This approach is not just about estimating a single point but understanding the entire distribution of efficiencies, leading to more informed decision-making. The Challenge of Optimization With multiple channels and complex response curves, optimizing marketing spend becomes a sophisticated task. Models with S-shaped curves, for instance, demand careful consideration to avoid getting stuck in local optima. Marketers must consider various initial points in optimization to ensure the best allocation of resources. Both Facebook Robyn and Google Lightweight MMM offer profound insights into marketing mix modeling, each with its strengths and limitations. Understanding these tools' nuances helps marketers craft more effective, data-driven strategies. As the digital marketing landscape evolves, leveraging these models can be a cornerstone in optimizing marketing spends and achieving desired business outcomes.

In the ever-evolving world of marketing, the ability to predict and analyze consumer behavior is crucial for success. Data modeling in marketing analytics has become an indispensable tool for understanding and influencing customer decisions. This blog post delves into the intricacies of data modeling, focusing on the challenges and strategies involved in creating effective predictive models. Understanding the Holdout Window in Training Data At the core of predictive modeling is the concept of a "holdout window" in training data. This term refers to the portion of data intentionally excluded from the initial model training phase. For instance, in a dataset, one might only utilize 80% to 90% for model training, holding out the remaining portion for testing. This approach could involve omitting a final month or chunking out periodic intervals, such as one week every eight. The primary goal here is to prevent overfitting, ensuring that the model can generalize well to unseen data. When presenting models to clients, especially in marketing analytics, it's crucial to be prepared for their queries and concerns. Sophisticated clients, well-versed in marketing analytics, often express puzzlement over certain model outcomes, like higher training errors. It's essential to walk such clients through the concepts of training and testing phases, emphasizing that marketing models are more about following trends rather than predicting exact peaks and valleys. The Role of Attribution Modeling Attribution modeling is a significant aspect of marketing analytics. For example, understanding how much credit to assign to different marketing channels, like Facebook or Google, is vital. In cases where models attribute unusually high percentages to certain channels, it's crucial to be able to explain these results convincingly to clients. This aspect becomes even more complex when dealing with brand-heavy clients or e-commerce businesses, each having different benchmarks and expectations. The addition of external factors like seasonality, economic variables, and holidays can dramatically refine a model's accuracy. For instance, including variables like trend, seasonality, and holidays can shift attributions significantly, redistributing credit from over-attributed channels like Facebook to these external factors. This adjustment often leads to a more realistic representation of the impact of different marketing initiatives. A critical advancement in marketing modeling is the inclusion of auto-regressive terms. These terms use data from previous periods (like sales from past weeks) to predict current outcomes. This approach can unveil patterns and influences that traditional models might miss, offering a more nuanced understanding of customer behavior and marketing effectiveness. Model Comparison and Qualified Opinions The process of developing the most suitable model for a business scenario typically involves comparing multiple models. This comparison helps in identifying common patterns and understanding the variations caused by different inputs. The final model choice should balance technical accuracy with practical business application, forming a 'qualified opinion' based on comprehensive analysis. This approach ensures that the selected model aligns closely with the business's real-world dynamics and strategic objectives. The journey through data modeling in marketing analytics is a complex but rewarding process. It requires a deep understanding of statistical methods, a keen awareness of the business context, and the ability to communicate effectively with clients. By carefully considering factors like the holdout window, client expectations, attribution modeling, external influences, and the use of advanced techniques like auto-regressive terms, analysts can develop models that not only predict consumer behavior but also align with and drive business strategies. Ultimately, the power of data modeling lies in its ability to transform vast datasets into actionable insights, guiding marketing decisions in an increasingly data-driven world.

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 Difficulty of Accepting A New Reality In a world driven by data and performance metrics, understanding the incremental impact of media investments such as advertising on platforms like Facebook, is essential for businesses seeking growth and efficiency. The conversation about the real cost of customer acquisition (CPA) and the scalability of media spends is not just theoretical but rooted in the daily challenges faced by marketers. The cost to acquire a customer is not just a number; it's a dynamic metric that encapsulates the effectiveness of marketing strategies. Determining a CPA that reflects true incremental value is critical. For example, a business might identify a sub-$65 incremental CPA for customer acquisitions on Facebook, which may seem like a victory. However, the deeper question is how scalable this figure is. Can the business increase spending by 30% and still maintain a CPA under $100? This is where the conversation turns from simple number-crunching to strategic planning. Scaling Media Spend: A Delicate Dance Scaling media spend is akin to a delicate dance where one must balance the budget with potential diminishing returns. The concept is straightforward: if the CPA is under a certain threshold, it's time to scale. But how much? Can you scale by 50%? Or should it be 70%? The intricacies of these decisions are profound because they can fundamentally alter the outcome of your marketing activities. Marketers must consider if adding a new test cell to gauge the impact of increased Facebook spending could provide valuable insights. It's a strategic move to understand not just the current value of an investment but also its future potential. The "Oh, Sh*t" Moment in Marketing Every marketer knows the "oh, sh*t" moment—it's when the unexpected arises, and you must question the sustainability of your current growth trajectory. Is the performance level you believe you are at actually where you stand? This juncture is pivotal and having a trusted advisor who can present a clear representation of the numbers is invaluable. It's about peeling back the layers of data to reveal the true state of business performance. The role of a consultant in the marketing space is often to anticipate the unexpected. One might enter a room with the intention of discussing scaling strategies for a revenue target, only to find that the conversation quickly pivots to evaluating the fundamental worth of current spending. This is a common scenario, one that speaks to the dynamic nature of marketing consultancy. It's not just about having the answers but also about asking the right questions and being prepared to switch gears when necessary. The transition from making assumptions to creating robust test designs is where the consultancy skill set truly shines. Drawing on experiences from prior engagements, consultants learn to craft clear outlines of the objectives and testing matrices. This meticulous approach helps clients visualize the pathway from data to actionable insights. Crafting these detailed plans is not just about delivering a presentation; it's about building a muscle—a muscle that gets stronger with each challenge and each solution provided. Building a Consulting Muscle In essence, becoming proficient in this area of marketing is about developing a muscle that strengthens over time. It's about continuous learning, adapting, and preparing for the unforeseen. It requires a deep understanding of both the granular details of test design and the broader strokes of strategic planning. For businesses looking to navigate the complex landscape of media investment and for marketers aiming to hone their consulting skills, the conversation is ongoing. It's a rich blend of analytics, strategy, and adaptability—a trifecta that is essential for thriving in today's ever-evolving market. The world of marketing is fraught with challenges, but with the right tools, expertise, and mindset, it is possible to turn these challenges into opportunities for growth and learning. Whether it's determining CPA or scaling investments, the ultimate goal remains clear: to understand and harness the incremental impact of media for sustainable business success.

The Attribution Conundrum: In the fast-paced world of digital marketing, comparing the attribution of different advertising platforms can be a daunting task. Eli, a seasoned growth marketer, shared his unique and effective approach to solving this challenge. Even without advanced techniques like incrementality tests and marketing mix modeling, Eli found a way to allocate budgets efficiently and make data-driven decisions for his campaigns. Eli faced a significant hurdle when comparing attribution of Facebook and TikTok ads. These platforms, even while sharing the same attribution window, had vastly different attribution models. Facebook and TikTok use their data in distinct ways, resulting in different Return on Ad Spend (ROAS) and Cost Per Acquisition (CPA) numbers Eli's Solution: Post-Purchase Surveys Lacking the resources for complex tests, Eli turned to post-purchase surveys as a solution. Immediately after a customer purchased, they were presented with a survey asking where they had heard about the brand. Two crucial options were Facebook and TikTok, which Eli considered as comparable channels in terms of purchaser influence. The post-purchase survey provider supplied Eli with valuable data, including the number of orders, revenue, and the last click channel. He also emphasized the importance of response rates, recognizing that not all customers would fill out the survey. Eli's calculations started by extracting the revenue from survey responses. Given that only 42% of new customers filled out the survey, he needed to extrapolate the data to represent his full universe of prospects. To achieve this, he divided the revenue for Facebook and Instagram by the response rate. This provided him with an "implied ROI" for these channels. Eli repeated the same calculations for TikTok, giving him an apples to apples comparison and a method for making more informed budget allocation decisions. Though the method wasn’t perfect, this technique allowed him to validate data and identify anomalies. Triangulating Marketing Measurement: Eli's case study illustrates how growth marketers, particularly in smaller direct-to-consumer (DTC) brands, can gain an edge by being nimble and data-driven. An approach using post-purchase surveys alongside other measurement techniques allowed Eli to optimize his limited budget. The lesson for all marketers is that there is no one-size-fits-all answer in today's marketing measurement ecosystem, and the path to truth lies in navigating and normalizing data from various sources. This approach can help teams “gut check” results from platforms -  Eli and his team were cautious about accepting the results seen in the platforms at face value, as they seemed "too good to be true."  Using surveys to validate attribution allowed them to investigate further to ensure the data's accuracy in an ongoing manner. Lastly, Eli’s experience clarifies that collaboration is an essential aspect of any marketer’s role.  Though marketing, analytics, customer experience, and other teams may live in different departments, their alignment and collaboration are key to understanding attribution and optimizing their marketing efforts. Eli's innovative approach to marketing attribution, along with constant data-driven exploration of marketing practices, showcases the agility and resourcefulness needed to thrive in today's competitive marketing landscape. Have a look at the Perfect Jean Case Study exploring Post Purchase Survey taught by Eli Esagoff.

Understanding the Marketing Funnel Today more than ever, it’s crucial to understand the intricate relationship between marketing measurement and the marketing funnel. The funnel concept guides marketers from broad-reaching methods to targeted approaches like retargeting, moving potential customers from awareness to consideration and finally to purchase. Measuring marketing effectiveness is a complex task, requiring a mix of methodologies tailored to different audience segments and stages within the funnel. Levels of the Marketing Funnel The marketing funnel serves as a foundation for understanding the effectiveness of various advertising channels, from linear TV's broad reach at the top of the funnel to the narrow, focused efforts of retargeting campaigns aimed at users with demonstrated interest. Each level of the funnel serves a distinct purpose, with corresponding metrics and measurement strategies that align with the audience's stage in their journey. Types of Measurement Tactics Broadly, measurement tactics are categorized into two types: base and advanced attribution. Base attribution covers the direct data obtained from platforms, web analytics, mobile marketing platforms (MMPs), app attribution vendors, and direct mail reporting. Advanced attribution, on the other hand, delves deeper, using marketing mix modeling and various testing methodologies to parse out the impact of specific marketing efforts. Advanced Measurement Methods Geo-testing and split-testing are examples of advanced methods that gauge the performance of marketing actions by comparing results across different geographic regions or among varied audience samples. These methods provide a clearer picture of a campaign's effectiveness beyond the immediate data points. The Power of Post-Purchase Surveys One often overlooked tool that straddles the line between base and advanced attribution is the post-purchase survey. This method asks customers directly where they heard about a product or service, offering a straightforward and often insightful look into customer awareness. The simplicity of this technique can yield robust insights, allowing brands to attribute sales to marketing efforts effectively and pressure-test assumptions about acquisition sources. Understanding Attribution Multipliers Attribution multipliers are essentially coefficients used to give weight to different marketing channels based on their expected impact on consumer behavior. When calculating these multipliers, marketers analyze data from testing against baseline conversions to evaluate the additional lift that marketing efforts contribute. Strategic Imperative for Modern Marketers Understanding marketing measurement in the context of the funnel is a strategic imperative for modern marketers. By harnessing the power of both base and advanced attribution methods, and considering the nuances of attribution multipliers, businesses can better navigate the complexities of the digital landscape and drive meaningful growth. Learn more in our Self Paced Advanced Attribution course.

The transition from modeling and measurement to tactical action within digital marketing is often riddled with challenges. The final numbers—those showing effectiveness, reach, and return—may prompt a daunting realization that past strategies need a revamp. Today we’ll navigate through the complexities of applying advanced attribution methods to overhaul marketing strategies, using detailed examples to illustrate these points. The Realities of Marketing Attribution Marketing attribution is an essential, though sometimes convoluted, aspect of any marketer's playbook. It's the process that connects the dots between the spend on marketing channels and the resulting business outcomes. In digital marketing, where the terrain shifts as rapidly as the online audience's attention, the right attribution model can mean the difference between a profitable campaign and a costly misstep. From Insights to Action For many direct-to-consumer (DTC) brands, Facebook prospecting is a cornerstone of their digital strategy. The process usually starts with a solid understanding of the attributed metrics provided by the platform—these are the self-attributed ROAS figures that Facebook provides based on its tracking. However, these numbers often tell an inflated story. By applying a multiplier that corrects for various biases and external factors, we arrive at a more accurate measure of Facebook's actual contribution to revenue. For instance, suppose a brand has been attributing $800,000 in revenue to Facebook prospecting. Upon applying an advanced attribution model that accounts for overlaps and cross-channel influence, the brand might find that Facebook's true contribution is only 70% of that amount. The 'haircut' this revenue takes would mean adjusting the figure down to $560,000. This new number—when divided by the same media spend—yields a more modest, yet more realistic ROAS. These adjusted figures force a strategic shift, prompting marketers to reassess the scale and efficiency of their Facebook spend. Non-brand search is another critical area, but one where brands often under-invest due to misleading data from traditional last-click attribution models. A test showing the true value of non-brand search can justify shifting more budget into this underestimated channel. For example, a Google platform might report $200,000 in revenue from non-brand search. By applying a larger multiplier based on incremental value—say, 80%—the adjusted revenue figure would be $160,000. When this new figure is set against the actual spend, the resulting ROAS could inform a more strategic deployment of search ad budgets. Emotions and Economics: Setting Marketing Goals The emotional attachment to certain channels can cloud judgment. When confronted with data that challenges long-held beliefs, marketers must navigate the emotional response to strategic change. In setting marketing goals, many teams rely on a variety of rationale—from intricate spreadsheets to goals inherited from past campaigns or even finance department mandates. The key is to establish goals that can adapt as new attribution data comes to light, ensuring strategies stay aligned with the most current and accurate picture of performance. Step by Step: The Road to Optimization Optimizing a digital marketing campaign can be distilled into three primary steps: Applying Results to Attribution: After adjusting platform reporting with multipliers, you obtain advanced attributed ROAS, which serves as a truer performance indicator. Comparison Against Goals: Align the new data with organizational goals to make informed decisions about where to scale back or invest more heavily. Budget Optimization: Allocate resources across the campaign portfolio, considering both large-scale and micro-optimizations within each platform. The granular details of optimizing within platforms like Facebook and Google Ads are where strategic decisions come to life. On Facebook, optimization may involve adjusting campaign settings to better align with advanced attribution findings. This could include setting caps on daily ad spend, tweaking CPM bids, or expanding target audience definitions to scale up prospecting efforts. For non-brand search, the optimization may start with refining keyword strategies within Google Ads but extend to enhancing the landing page experience to improve conversion rates. From adjusting CPC bids to optimizing ad formats and even iterating on website design, every tweak is aimed at squeezing out incremental performance gains. Organizing campaigns with granularity ensures that future optimizations are possible. By setting up separate campaigns for different audience segments, marketers can more accurately measure and compare performance, making it easier to allocate budgets and tailor strategies to each segment's unique behaviors and responses. The Advanced Attribution Advantage Advanced attribution isn't just about better measurement—it's a holistic approach to marketing strategy. It empowers marketers to make decisions grounded in data and devoid of the biases that often skew perception. Whether it's scaling a Facebook campaign or refining a keyword strategy for non-brand search, the aim is to employ a methodical, data-driven approach to every marketing decision. As we continue our evolving journey in the digital marketing domain, let's leverage advanced attribution not just as a tool for measurement, but as a strategic ally in our quest for marketing efficiency and effectiveness. With this approach, digital marketing stops being a gamble and becomes a calculated, continuously improving enterprise. Learn more through our Advanced Attribution Foundations short course. 

Classroom MMM Exercise 1: Media Only Model Decoding the Impact of Media on Sales: A Classroom Exercise In a recent classroom exercise, we stripped down the complex world of marketing mix modeling to its basics. The task was to build a model using only media spending and intercept—no frills like seasonality, trend, or holidays. Just the raw influence of media channels on sales. The Bare-Bones Model Imagine you’re in a kitchen trying to figure out which ingredients affect your soup's flavor the most, but you can only taste the broth and the spices, not the salt or the garlic. That’s what we did with this model. We discovered that one mysterious lead-based variable—let’s call it the 'secret spice'—drove a whopping 53% of the company's sales. Facebook was a strong contender too, bringing in 33.4%, while Google Search and Snapchat added 8% and 4.9% to the flavor, respectively. Predictions vs. Reality The students then put their model to the test, comparing its predictions against actual sales. It's like predicting how many guests will enjoy the soup at a party. The model was pretty good, but not perfect, showing a 35% error rate with the training data (the initial taste test) and a more impressive 6% with the test data (the actual party). Learning from the Numbers This exercise wasn't just about crunching numbers; it was a real-life lesson in understanding the weight of different marketing channels on sales. By simplifying the model, students could clearly see the direct impact of each media component. It’s like understanding which ingredients stand out the most when you first learn to cook. The Classroom Takeaway The takeaway from this exercise is clear: even without the bells and whistles of external factors, media spending plays a significant role in driving sales. This classroom experiment sheds light on the fundamental elements of marketing influence, providing a baseline understanding before diving into more complex models. It’s a first step in the journey from marketing mix theory to practical, actionable insights. Classroom MMM Exercise 2: Addition Of Exogenous Variables Enhancing Marketing Models with Exogenous Variables In the dynamic field of marketing analytics, the addition of exogenous variables into models can significantly improve the accuracy of our predictions and insights. A recent analysis demonstrates how incorporating these variables reshapes our understanding of various marketing components, like social media platforms, and their impact on sales. The Role of Exogenous Variables Exogenous variables are external factors that can influence the outcome of a model but are not influenced by the model itself. In this specific case, variables such as trends, seasonality, and holidays were introduced into the marketing mix model. Their inclusion provides a more comprehensive view of the factors affecting sales, beyond the immediate scope of marketing channels. Shifting Credit Where Credit is Due Initially, platforms like Facebook were credited with a substantial impact on sales. However, with the introduction of exogenous variables, the analysis painted a different picture. The trend emerged as a more significant factor, accounting for 5.8% of sales impact, overshadowing the previously assumed influence of Facebook. Seasonality, contrary to expectations, was not a major factor, and the influence of holidays was minimal, at about 0.3%. Refining Predictions and Understanding The integration of these variables led to a notable improvement in the model's predictive accuracy. The disparity between actual sales and model predictions reduced, indicating a tighter, more accurate model. This was quantified by an increase in the R-squared value, signifying that a higher percentage of variation in actual sales was now being explained by the model. The Importance of Exogenous Variables This exercise highlights the importance of considering external factors in marketing analytics. By accounting for elements like market trends and seasonal variations, marketers can develop a more nuanced understanding of what truly drives sales. This approach allows for a more strategic allocation of marketing resources, ensuring that credit is given to the most impactful factors. As this case study shows, such enhancements can lead to more precise predictions and better-informed marketing strategies, ultimately driving more effective and efficient marketing campaigns. Classroom MMM Exercise 3: Payman's View On The Trend / Seasonality Model The Impact of Seasonality in Marketing Models In the intricate world of marketing analytics, understanding the influence of seasonality on sales is crucial. A recent discussion highlighted the pitfalls of excluding seasonality from marketing models and how it can skew our perception of marketing effectiveness. Misattributing Sales Increases Often, marketers observe a rise in sales and attribute it directly to their marketing efforts. However, this can be misleading, especially if the model overlooks seasonality. Historical data frequently shows that during peak demand periods, marketing spend increases. Without accounting for seasonality, this increased spend might incorrectly be seen as the primary driver of sales growth. The Seasonality Proxy In many cases, the surge in marketing investment during high-demand seasons acts as a proxy for seasonality. Without explicitly modeling this factor, the rise in sales could be mistakenly credited to marketing strategies, when in fact, it's more closely tied to seasonal trends. Balancing Underestimation and Overestimation The challenge lies in accurately measuring the effect of marketing without underestimating or overestimating its impact. Ignoring seasonality can lead to an overvaluation of marketing efforts, while factoring it in might reveal a more nuanced view of how marketing contributes to sales. The Risk of Biases Neglecting crucial inputs like seasonality in a marketing model can introduce significant biases. These biases can distort the true effectiveness of marketing strategies, leading to misguided decisions and strategies based on incomplete data. Classroom MMM Exercise 4: Payman's View On The Basic Media Only Model In the complex world of marketing and brand building, capturing the long-term effects of media efforts poses a unique challenge, especially when relying solely on a traditional media mix model. This model, typically designed to measure the direct response from media stimulation to consumer action, often struggles to track the extended impact of marketing efforts on brand building and customer engagement. The Limitation of Traditional Models Traditional media mix models are adept at capturing immediate responses, such as a direct purchase following a marketing campaign. However, they fall short in measuring the prolonged effects, like enhanced brand awareness or sustained customer engagement, which are crucial in long-term brand building strategies. Breaking Down the Funnel One effective approach to address this challenge is to deconstruct the marketing funnel into smaller, more manageable sections. This method involves identifying specific stages in the customer journey, such as brand awareness or website engagement, and modeling them individually. By focusing on these distinct phases, it becomes easier to measure and understand the incremental impacts of media efforts. Implementing Cascaded Modeling The concept of a cascaded model funnel is particularly useful in this context. By breaking down the funnel and modeling each section separately, delays and long-term effects can be isolated and analyzed more effectively. This approach allows for a more granular understanding of how each stage of the customer journey contributes to the overall marketing objectives. Utilizing Key Performance Indicators (KPIs) Employing KPIs or other measures as leading indicators is crucial in this segmented approach. These indicators can provide early insights into the effectiveness of various marketing initiatives in raising brand awareness or improving customer engagement, well before they translate into direct sales or conversions.