
When diving into the data of any brand, there are so many factors up for consideration. As we already know - advanced attribution is not a one size fits all game and nor should it be - all data is different and all company’s needs and targets are too. Getting to the meat of any brand - we first need to get to know them better and jump in with two feet in order to deeply understand the value of the business. When we first began our engagement with a well known cosmetics brand, the team was trying to answer a few simple questions, like ‘What is the ROI from our media investments?’ or - more pointedly, ‘How much budget should be allocated to the Top of Funnel?’. The last question, and probably one of the most important ones - is, ‘What is the Contribution Margin and Revenue per customer?’ All valid angles to approach and all important for making and the next move. Understanding the true value Let’s begin - In order to take the first step, we need to understand the true value being driven by marketing. Our first step in the process was to understand the Contribution Margin from an observed media perspective and an advanced attribution standpoint. For us to calculate the contribution margin, the team worked closely with the brand’s marketing team to gather their underlying factors, such as Cost of Goods Sold (COGs), Promotional Spend, and Shipping Cost. Once we understand the client's contribution, we can begin the analysis of Revenue per customer. To calculate this, we divide the newly discovered contribution margin by the total number of customers as shown in the graphic below: Analysis of media spend Diving even further - once we understood the contribution margin, we wanted to look at the entirety of media spend to understand the impact seen across their media portfolio. Since the client had no custom attribution methods, we used platform-driven attribution as our anchor. In order to understand the advanced attribution of their media portfolio we applied M-Squared’s multipliers to estimate the true impact of their media. Through this process we were able to gather some impactful insights; such as their Meta campaigns are drastically underperforming as compared to the industry average. Another insight would be that their Google Shopping campaigns are the most impactful to their overall bottom line, and we should continue to fund that platform. The final insight that caught our attention was that affiliate marketing is one of the strongest driving factors within their overall media portfolio. Have a look at the graphic below highlighting the lowest and highest returns: Test and Growth Plan Now that we had some hard facts to play with, we could start testing different marketing routes and develop a sustainable growth plan. From our analysis of their media performance, we can instill what’s called a ‘test and grow plan’. This specialized report calls for shifts within the company to go bigger - such as budget reallocation to more robust performing media channels, conducting measurement experiments to better understand diminishing returns within specific platforms that are not performing the way we wanted them to and why. Some examples of the recommendations would be to run a Geo Scale test within Meta and some of their display partners in order to ascertain the scaling opportunities within the market. We also recommended a Pulse Test for their affiliate program to better correlate the impact of sales periods and the affiliate program itself. In the next graphic you can see that through our analysis, we estimated that we can grow revenue by 10%, all the while cutting the budget by 80k!

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.

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.

One of the buzzwords always echoing around the digital marketing industry is "multi-touch Attribution" or MTA. As marketing strategies and consumer behavior change, it's crucial to keep pace with changes in this area. In this post, we will explore the changing landscape of multi-touch Attribution and what you need to be aware of when utilizing it. MTA in the Marketing World MTA has become a staple in the marketing world, with many businesses exploring in-house versions of this model in addition to utilizing vendor offerings. The fundamental idea behind it is to assign credit for conversions to not one, but multiple touchpoints in the consumer journey. However, at further inspection, many teams discover that the effectiveness of multi-touch Attribution is no longer as robust as it once was. Data Scope Matters One of the critical aspects of multi-touch attribution is the scope of data it encompasses. In the past, you could collect comprehensive data from various platforms, including impressions, clicks, and conversions. This holistic view allowed for a clearer understanding of the entire consumer journey. However, the landscape has changed significantly. Now, the data scope is fragmented, and the ability to obtain a full consumer journey is becoming increasingly challenging. Each platform only provides insights into its own ecosystem. For example, Facebook's reporting focuses solely on Facebook impressions and clicks, and Google Analytics predominantly tracks on-site visits, often omitting critical information from other platforms like Snapchat or display advertising. The industry's journey towards a unified consumer experience has been disrupted. The Relative Approach In this new landscape, the best way to use multi-touch attribution is a more measured approach. Instead of seeking an absolute representation of the entire consumer journey, it's more effective to compare models and data sources. By analyzing relative comparisons, you can gain insights into how different marketing channels and touchpoints contribute to the customer journey. A practical example of the relative approach is comparing first-click attribution with last-click attribution. By examining the relative contributions of Facebook, Google Ads, and other channels in these different attribution models, you can uncover deeper insights consumer behavior. For instance, you might discover that many consumers initially interact with your brand through Facebook but only convert on a subsequent visit via search. In this way, you can gain a more nuanced understanding of the customer journey. Adapting to the Changing Landscape In today's landscape, the data you collect is often a sample of the overall consumer behavior. You're not observing the entire journey but rather a portion of it. Understanding this sampling approach will help you make more informed marketing decisions based on the data at your disposal. Conclusion Multi-touch attribution has shifted from an all-encompassing model to one that requires a more relative perspective. By comparing different attribution models and understanding the realities of data sampling, you can navigate the evolving marketing landscape more effectively. In a world where consumer behavior and marketing channels continue to change, adaptability and a willingness to learn and adjust are the keys to success. Learn more about advanced attribution techniques from our short courses.

Creating a successful digital marketing campaign is a complex process that involves more than just running tests and analyzing results. It starts at the very beginning, with a strategic approach to setting up your campaigns. This thoughtful preparation can significantly impact the effectiveness of your digital marketing efforts. The Importance of Pre-Planning Before launching any campaign, consider the foundational elements of your strategy. These include your tactics, audience, objectives, and geography. Each of these factors plays a critical role in shaping your campaign's structure and potential success. When it comes to tactics, it's crucial to segregate different approaches into separate campaigns. For instance, retargeting and prospecting should never be mixed in the same campaign. These tactics yield different results and have distinct multiplier effects. Blending them makes it challenging to benchmark and analyze outcomes accurately. A recommended best practice is to exclude site visitors and existing customers from your prospecting tactics. This separation allows your prospecting to focus on acquiring new customers, leaving room for targeted campaigns aimed at site visitors and existing customers. Within each tactic, segmenting different audiences into their own campaigns is essential. This approach applies even to tactics that might seem similar, such as lookalike modeling and interest targeting. Separating these audiences allows for a clearer understanding of each group's performance and a more accurate application of multipliers. Facebook & Geographical Considerations On platforms like Facebook, having distinct objectives for each campaign is vital. For example, a lookalike conversion tactic should be tested differently from a lookalike traffic tactic, as they yield different multiplier results. Clear labeling and separation of these tactics are crucial for effective analysis. Geographically, it's advisable to run campaigns for different countries separately. While it might seem convenient to combine countries like the US and Canada, this approach complicates future geo-testing. If running multiple countries in a single account is necessary, ensure they are clearly and consistently labeled for ease of analysis. Consistency and Clarity in Campaign Management Consistency in campaign setup and labeling is crucial, especially when managing multiple campaigns across various geographies. Inconsistent labeling can lead to confusion and difficulty in identifying and analyzing specific campaigns. Consider the example of a client running campaigns in Australia. The campaigns were labeled inconsistently (Au, au S, Australia, international), making it difficult to sift through hundreds of campaigns to find specific ones. This inconsistency highlights the importance of a well-organized and clearly labeled campaign structure. In digital marketing, success often hinges on the clarity and strategic planning of your campaign setup. By paying close attention to the segregation of tactics, audience differentiation, clear objectives, and geographical considerations, marketers can create more effective and analyzable campaigns. Remember, the key to unlocking the potential of your digital marketing efforts lies in the details of your campaign's foundation.

In the ever-evolving world of digital marketing, businesses continuously seek efficient strategies to outperform their competition. One effective approach to achieve this involves a three-step process: Apply, Compare, and Optimize. This method offers a comprehensive way to enhance marketing performance using advanced attribution metrics. Step 1: Apply - Embracing Advanced attribution Metrics The first step in this transformative journey is to apply advanced metrics for more precise marketing analysis. Traditional last-touch metrics, while useful, often do not provide the complete picture of customer interactions. By employing advanced attribution Cost Per Acquisition (CPA) or Return on Ad Spend (ROAS), marketers can gain a more nuanced understanding of their campaigns' effectiveness. These metrics consider multiple touchpoints in a customer's journey, offering a more holistic view of their interaction with marketing tactics.Applying these advanced metrics requires a bit of media math. The idea is to take various multipliers and apply them to understand the advanced attribution CPA or ROAS. This mathematical approach allows for a deeper comprehension of how each marketing channel contributes to the overall success of the campaign. Step 2: Compare and Assess Against Goals Once these advanced metrics are in place, the next step is to compare these new results against set goals. This comparative analysis is crucial in determining the success of different tactics. If a campaign is outperforming the goals, it might be time to scale up the funding. Conversely, if a tactic is underperforming, reducing the budget or reevaluating the approach may be necessary.Comparing advanced Attribution results to goals enables marketers to make informed decisions about budget allocation. This process is similar to assessing last-touch metrics but provides a more comprehensive insight due to the advanced nature of the attribution models used. Step 3: Optimize - Fine-Tuning for Maximum Efficiency The final step is perhaps the most critical - optimization. This phase involves two key aspects: macro and micro optimization.Macro optimization focuses on the broader aspect of budget allocation across various platforms. It’s about understanding where to increase investment and where to cut back to maximize returns. This might involve reallocating budgets from underperforming channels to those delivering better results.Even if a tactic is meeting its goals, there’s always room for improvement. Micro optimization delves into the finer details of each tactic, identifying areas that can be further enhanced to boost performance. This could mean tweaking ad copy, adjusting target demographics, or experimenting with different content formats. The Role of External Guidance Companies often rely on external agencies or consultants for macro optimization recommendations, such as where to allocate or remove budget. However, the micro optimization often remains an area less explored by these external entities. Here, in-house teams can play a significant role. By understanding the nuances of micro optimization, they can work closely with agencies to ensure that every aspect of a campaign is fine-tuned for peak performance.The journey doesn't end with optimization. The digital marketing landscape is dynamic, and continuous learning and adaptation are key. Marketers should always be on the lookout for new tactics, technologies, and strategies to stay ahead in the game.By adopting a structured approach of applying, comparing, and optimizing marketing strategies, businesses can significantly enhance their digital marketing efforts. This methodology not only improves the efficiency of current campaigns but also opens up doors for future investment and growth. Embracing both macro and micro optimization ensures that every dollar spent is working towards achieving the best possible results. Ultimately, this approach is about being proactive, informed, and adaptable in the fast-paced world of digital marketing.

In marketing, scaling up your efforts, especially within Facebook Prospecting, can be a pivotal move toward achieving your marketing growth goals.. Explore effective strategies and optimization insights to succeed in this evolving landscape.

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.
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