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.