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.

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.

In the fast-evolving realm of digital marketing, the ability to predict and measure the impact of advertising campaigns holds paramount importance. Geo-lift analysis has emerged as a powerful tool in this context, enabling marketers to gauge the efficacy of their campaigns with precision. Through a deep dive into the specifics of a geo-lift analysis package, we can glean insights into the process of making data-driven, counterfactual predictions. The Underpinnings of Geo-Lift Analysis Geo-lift analysis is predicated on the concept of geo-experimentation, where different geographic regions are exposed to varied marketing interventions to observe potential variances in performance metrics such as conversions or sales. A year or two of conversion data, segmented on a daily or weekly basis, forms the backbone of such an analysis. This granular approach to data segmentation allows for a nuanced understanding of market behavior. The preparation phase in geo-lift analysis is critical. Input data must be formatted to suit the requirements of the analysis package, necessitating a clear definition of data sets, time periods, and geographical markers. Once the data is formatted, visualizing it and inspecting for abnormalities is vital before proceeding to predictive modeling. Anomalies, if present, can significantly skew the analysis, necessitating careful scrutiny. Designing a geo-test is a strategic exercise that requires deciding on the number of geos to include, the duration of the test, and the selection of test and control markets. Utilizing synthetic control models, the package assists in simulating various scenarios, helping to forecast the outcomes of different configurations on market performance. This foresight is instrumental in crafting robust test designs that yield reliable predictive results. Analyzing Results with Counterfactual Predictions Post-experiment analysis is equally crucial. Incorporating observed data from the test period into historical data allows for a comparative study of expected versus actual performance. A crucial step here is to confirm the significance of the results through p-value analysis. Only results with p-values below the threshold of 0.1 (assuming a 90% confidence level) are deemed significant enough to inform decision-making processes. Visualization tools can help shine a light on the dynamics between control and test geos over time. Incremental conversions and lifts are plotted, providing a visual representation of the campaign's impact. These visuals serve not only as confirmation of successful test design but also as a medium for communicating results to stakeholders in an approachable way. The real test of geo-lift analysis, however, lies in its translation to business decisions. The alignment (or lack thereof) between different methodologies—like difference-in-differences estimates, linear models, and time series predictions—can lead to varied interpretations. The disparity in estimates, which may seem trivial from a statistical standpoint, could translate into significant differences when applied to business strategies, thereby underscoring the need for calibrated, context-aware decision-making. An astute marketer must balance the precision of algorithmic predictions with the nuance of human judgment. When control markets exhibit atypical trends, the integrity of the analysis can be maintained by adjusting the selection or discarding outliers as needed. This interplay between algorithmic suggestion and human discretion is critical in obtaining an accurate representation of the market's response to advertising stimuli. Embracing Complexity and Nuance in Geo-Lift Analysis The journey from data ingestion to actionable insights is laden with complexity. One must navigate through the intricacies of data science with an unwavering commitment to integrity in analysis, especially when under pressure to deliver favorable outcomes. The combination of data science and marketing wisdom ultimately yields the most potent results, enabling marketers to execute campaigns that are not only data-informed but also strategically sound. As we move toward a more data-centric marketing era, the ability to harness such analytical tools will become increasingly crucial. The interdependence of data science and marketing expertise, coupled with the power of visualization, creates a robust framework for understanding and leveraging geographic trends in marketing. With ongoing advancements in analytics, the prospect of plug-and-play solutions becomes more tangible, albeit still reliant on the critical eye of the marketer to discern the narratives behind the numbers.

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. 

Understanding the landscape of customer acquisition costs (CAC) and incremental CAC (iCAC) in digital marketing isn't just about budget allocation—it's about strategic growth and market capture. Opportunity analysis can serve as the guiding light for informed decision-making in advertising spend. When a brand considers scaling its marketing efforts, opportunity analysis is the pivotal tool to navigate this expansion. By assessing the cost-effectiveness of various advertising platforms, opportunity analysis can reveal where your dollars can have a higher impact and which channels you can pull spend from. Profit or Growth: The Strategic Decision Informed by Opportunity Analysis Opportunity analysis isn’t just about finding the cheapest way to acquire customers—it is about aligning investment with business objectives. While some brands might prioritize demonstrating growth potential, others may choose profitability. Opportunity analysis serves as a critical component for this decision, offering a data-informed pathway to achieving the chosen end goal. Opportunity analysis is the strategic engine that drives smart investments and underpins successful scale-ups, underscoring the necessity of deep analysis, beyond the apparent data, to uncover the real story behind numbers. It is a story about where and when to place bets in the marketing game—a game that requires skill, strategy, and a bit of savvy speculation. In digital marketing, where every dollar counts, brands face the challenge of ensuring their budget allocation directly contributes to strategic growth and market penetration. Opportunity analysis serves as the vital tool to unlock this potential, guiding marketers to informed decisions that maximize return on investment. The Process of Conducting an Opportunity Analysis Opportunity analysis begins by taking your advanced attribution multiplier and segregating baseline sales—the portion of customer inflow that occurs without paid media influence—from media-influenced sales. Analyzing past advertising data will show that only a portion of your sales are influenced by paid efforts, leading to an adjusted perspective on the true cost of acquiring a new customer. A brand looking at their data through a base attribution lens the cost to acquire a new customer seemed straightforward: $57 per customer. However, with a deep dive into the data, including platform-specific nuances and multi-touch attribution (MTA) systems, the CPA swelled to $119. This inflation underlines how different attribution models can drastically alter CPA perception. CAC is the broader measure of total media spend over total new customers acquired, but iCAC fine-tunes this by considering only the new customers driven by media spend. Here, the baseline understanding is crucial, suggesting that a significant portion of business comes from non-paid media sources. In pursuit of growth, brands will face diminishing returns—a point where each additional dollar spent yields less than the previous one. Through opportunity analysis you can find the thresholds of efficient spend and the pitfalls of uncalibrated scaling. This predictive foresight allows brands to move forward with a refined strategy that would likely avoid the inefficiencies typically associated with ad spend saturation. Taking Action on Opportunity Analysis Opportunity analysis is not a static strategy; it's a vital, ongoing exercise that equips brands with the insight to make informed decisions and pivot as needed. Are you making the most of your marketing spend? Does your strategy consider the nuanced dance between growth and profitability? As you ponder these questions, remember: opportunity analysis is your guide. Now is the time to leverage this approach, transforming data into actionable growth plans.

Sensitivity Analysis in businesses today often encounter the challenge of measuring the efficacy of various advertising channels. The heart of this lies in attribution - the analytical science of assigning credit to the touchpoints a customer interacts with before converting. One of the most enlightening yet intricate parts of this puzzle is understanding how sensitive different channels are to various attribution methods. Today we delve into the world of Sensitivity Analysis in marketing attribution and offer insights into optimizing decision-making based on nuanced data interpretations. The Chameleon Nature of Channel Performance Metrics Take, for instance, a fashion brand analyzing its performance. The company sees a total of 74,000 orders, split almost evenly between new and returning customers, with a total media spend of roughly $2 million. Specifically, they funnel $1.3 million into Facebook for new customer acquisition. But here's where it gets tricky: you may see drastically different conversion and cost-per-acquisition (CPA) numbers depending on what attribution window you use! For example, at a one-day click window, Facebook may appear to have driven 17,000 new customers. However, with a 28-day click window, that number could inflate to 27,000. The CPA across these windows fluctuates significantly, from as low as $50 to over $230, indicating a dramatic variation in perceived efficiency. The fashion brand in question used to measure Facebook's performance with a one-day click and view, which showed Facebook as driving 17,000 customers at a CPA of $57 - closely aligned with their overall customer acquisition cost. However, an overlooked aspect was that not all reported conversions were from new customers, despite exclusion lists being used to separate out existing customers due to match issues and inaccuracies. Upon integrating data from Shopify, which employed UTM-based attribution, it was discovered that only 67% of Facebook's attributed orders were from new customers. This revelation recalibrated the new customer CPA from $57 to $85. Without even touching upon advanced attribution methodologies, this illustrated the sensitivity of the calculated performance metrics to the depth of data scrutiny. Advanced Attribution Multiplier: Reassessing Facebook’s Value The journey to refine the marketing attribution model further involves applying an advanced attribution multiplier, offering a more sophisticated perspective on a channel’s true incremental value. For example, when adjusting Facebook's numbers with a benchmark multiplier for mid-funnel initiatives such as lookalikes and broad audiences, the CPA climbed even higher to $120. This new figure stands in stark contrast to the initial $57 CPA, underscoring how significant the impact of attribution adjustments can be. Using this more rigorous model, Facebook's contribution to new customer acquisition drops to 29%, a more realistic portrayal of its true contribution to the business. This analytic progression showcases the variance in a channel’s performance based on the lens applied, emphasizing the need for marketers to adopt a multi-faceted approach to attribution. Cross-Channel Analysis: A Holistic View of Attribution While the example provided focused on Facebook, the principles of sensitivity analysis apply across all channels. By combining data sets from various platforms (such as Facebook, Shopify and other eCommerce platforms, or Google Analytics), and employing consistent methodology, businesses can assess the collective impact of their media efforts on both new and existing customer orders. In doing cross-channel analysis with this advanced approach, businesses can ascertain the unique and overall contribution of each channel to the company's success. This becomes especially crucial in environments where the attribution can often be skewed by customer behavior, platform biases, or simplistic analytic models that fail to capture the multifaceted nature of digital marketing. Sensitivity analysis is not just about number-crunching; it's a strategic beacon guiding marketers through the fog of complex digital interactions. It aids in deciphering the encrypted messages behind raw data, transforming numbers into narratives that inform smarter business decisions. As the attribution landscape continues to grow in complexity, the value of this analysis only deepens, becoming an essential compass for navigating the dynamic and often murky waters of marketing investment and performance. Businesses should regard sensitivity analysis as more than a retrospective audit of what has occurred; it's a forward-looking tool, a precursor to adaptive marketing intelligence. By continually questioning and calibrating the data at hand, companies can ensure their marketing strategies remain robust, responsive, and, most importantly, profitable in the long run.  Learn more about Sensitivity Analysis in our Advanced Attribution Foundations short course. 

Making informed decisions in the world of digital marketing is a complex task, requiring teams to juggle various factors to ensure that the strategies are not only effective but also efficient. In this two-part blog post, we will delve into the critical aspects of multi-touch attribution and how it impacts decision-making. We will also explore some tactics to illustrate these concepts, focusing on Facebook prospecting and non-brand search. Choosing the Right Tactics Effective multi-touch attribution necessitates careful consideration of the tactics used. For this discussion, we have selected Facebook prospecting and non-brand search, two common strategies employed by digital marketers. Facebook prospecting is frequently employed by direct-to-consumer (DTC) businesses due to its success in identifying and reaching their target audience. However, the metrics you see in the platform might not always reflect its actual performance. On the other hand, non-brand search often suffers from under-investment, primarily because last-touch metrics tend to undervalue its contribution. Examining these two tactics can shed light on the complexities and opportunities in multi-touch attribution. Applying, Comparing, and Optimizing in Multi-Touch attribution In our next post, we will break down the process of multi-touch attribution into three fundamental steps: applying, comparing, and optimizing. These three steps are essential to making informed decisions in the complex landscape of digital marketing. To apply multi-touch attribution effectively, we need to perform some straightforward media math and use the multipliers we derive from this. This allows us to calculate the new advanced attribution cost per acquisition (CPA) and return on ad spend (ROAS). And no worries, we will walk you through the process of doing this math. The comparison phase is crucial in making sense of the data generated through multi-touch attribution. This stage involves comparing the newly calculated advanced attribution CPA and ROAS against predefined goals. The thought process here is similar to analyzing last-touch metrics, albeit with more comprehensive insights. Once comparisons are made and the results are understood, the optimization phase begins. This is where decisions about budget allocation and tactic-specific adjustments come into play. We will explore both macro and micro optimization strategies: macro optimization involves reallocating budgets across platforms, while micro optimization focuses on fine-tuning tactics to enhance performance. Learning how to create efficiencies at both levels is key to unlocking greater opportunities in digital marketing. You don’t have to set out on the journey of multi-touch attribution alone. Many organizations rely on external partners, such as measurement solution providers and agencies, to guide them through this intricate landscape. These experts can offer recommendations on macro optimization decisions. However, understanding the finer details of optimization and being able to communicate effectively with your partners is essential to ensure that every aspect of your marketing strategy is optimized for success. By mastering the art of multi-touch attribution and optimizing strategies with precision, digital marketers can unlock the full potential of their campaigns. In the ever-evolving world of marketing, informed decisions are the cornerstone of success.