What Is Marketing Mix Modeling (MMM)?

Marketing mix modeling has come a long way since its introduction in the 1960s. Originally developed to help brands understand how different elements of their marketing mix impact revenue, MMMs use statistical methods to analyze historical data and measure marketing effectiveness. Early models were relatively simple, looking at broad relationships between marketing spend and business outcomes.

Today's MMMs are more sophisticated, leveraging advanced statistical techniques and machine learning to provide deeper insights. While the core goal remains the same—understanding how marketing drives revenue—modern MMMs can process more data and uncover more complex relationships than their predecessors could have imagined.

Channel Level vs Campaign Level

Traditionally, MMMs could only provide insights at the channel level, telling you how platforms like Google or Meta were performing overall. While useful, this level of analysis missed crucial details about how individual campaigns were driving results.

Campaign-level insights represent a significant advancement in marketing mix modeling. This increased granularity helps marketers make more precise decisions about where to invest their budget. Instead of knowing only that Google is performing well, you can see which specific campaigns are driving that performance and optimize accordingly.

This evolution didn't come easily. Analyzing at the campaign level requires more sophisticated models that can handle significantly more complexity. The models need to understand not just how channels interact with each other, but how individual campaigns within those channels affect both immediate revenue and longer-term business outcomes.

Prescient's MMM Advances the Technology

Prescient's approach represents the next evolution in marketing mix modeling. While other MMMs still rely on techniques developed decades ago, we've built something fundamentally different. Our platform layers multiple models together in what we call an ensemble approach—something no other MMM currently offers (more on that in the next section).

This ensemble approach allows us to capture marketing relationships with unprecedented accuracy. We can understand not just how your campaigns directly drive revenue, but how they influence other channels through halo effects, how they build up over time, and how they interact with factors like seasonality and market trends.

By processing your data daily and providing campaign-level insights, our platform helps you make faster, more precise decisions about your marketing spend. This means you can optimize your budget more effectively, understanding exactly which campaigns are driving the best results and how to allocate spend for maximum impact.

What Is an Additive Model?

Think of Prescient's platform, Omen, like a master chef who knows that the perfect dish comes from combining multiple ingredients in just the right way. While other MMMs use a single recipe, we layer multiple models together—what we call an ensemble—to capture the full complexity of your marketing environment.

Each model in our ensemble is what we call an "additive model." These models are particularly good at understanding how different pieces add up to create your total revenue. Just as your total revenue is the sum of revenue from different channels (Google, Meta, TikTok, etc.), our additive models break down these relationships to understand exactly how each piece contributes to your bottom line.

We chose additive models because they're flexible enough to capture the wide range of relationships in modern marketing. Every channel and campaign can impact revenue differently, and our models adapt to learn these unique patterns. While other MMMs might use additive models too, Omen is unique in how it layers them together to provide more accurate insights.

Ensembles vs Single Models

Marketing relationships are complex, and every model has its strengths and weaknesses. While one model might excel at understanding seasonal patterns in your data, another might be better at capturing how your campaigns interact with each other. By combining multiple models strategically, Omen leverages the strengths of each model while offsetting their limitations.

This ensemble approach is unique to Prescient—no open-source MMMs currently use it. We layer multiple additive models together to capture the nuanced ways your marketing impacts revenue. This helps us understand both straightforward relationships (like how increased spend affects immediate sales) and more complex ones (like how your prospecting campaigns influence organic search traffic over time).

The result? More accurate insights that reflect the true complexity of your marketing environment. This means you can make budget decisions with more confidence, knowing that our ensemble of models is capturing the full picture of how your marketing drives revenue.

Features of Prescient’s Models

Prescient's models were built to capture the true complexity of modern marketing. We've designed specific features to measure aspects of marketing performance that other platforms miss or oversimplify. Each of these features helps paint a complete picture of how your marketing drives revenue, both directly and indirectly.

Halo Effects

Marketing campaigns don't just drive direct conversions—they create ripple effects across your entire business. Halo effects represent the indirect impact of your marketing efforts, like how your Facebook ads might drive increased organic search traffic or how your YouTube campaigns influence Amazon sales. Prescient's models specifically measure these indirect effects, helping you understand the full value of your marketing spend beyond just last-click attribution.

Essentially, halo effects (the term we use for spillover effects) answers the question: what happens when someone doesn't click on X campaign after they see it?

Base Revenue vs. Halo Effects

When measuring campaign performance, it's crucial to understand both direct and indirect impact. Base revenue represents the immediate, attributable revenue from people engaging directly with your ads. Halo effects capture the additional revenue generated when people see your ads but convert through other channels—like seeing a TikTok ad but later converting through organic search. By measuring both, our models give you a complete view of how your campaigns contribute to revenue, helping you make more informed decisions about budget allocation and avoiding undervaluing campaigns that drive significant indirect benefits.

Platform Defaults & Priors

Priors play a crucial role in Prescient's marketing mix modeling approach. They serve as initial probability distributions that inform our models about the likely relationships between marketing activities and outcomes before examining the actual data. The selection of appropriate priors is essential for creating robust and reliable marketing attribution models.

Our Approach to Priors

Prescient's priors approach was chosen after careful evaluation of various potential prior metrics, including those commonly used in other MMM implementations. We selected our current approach because it:

Provides a reliable proxy for marketing activity volume
Introduces minimal bias into the modeling system
Maintains consistency across different marketing channels
Offers high data quality and availability

Future Developments

While our current approach has proven effective, we recognize that marketing measurement needs can vary across organizations. We are developing capabilities that will allow for more customization in prior selection, enabling organizations to align their MMM implementation more closely with their specific measurement requirements and data availability.

Technical Considerations

When implementing marketing mix models with priors, it's important to consider:

The relative strength of the prior distributions
The interaction between priors and other model components
The impact of prior selection on model convergence and stability
The balance between prior influence and data-driven insights

Organizations using Prescient's MMM should understand that our prior selection is designed to provide reliable attribution insights while maintaining model flexibility and adaptability to different marketing scenarios.

Amazon

Measuring marketing impact on Amazon sales requires a different approach than measuring direct e-commerce performance. Prescient's models are specifically designed to understand how both Amazon and non-Amazon marketing efforts influence your Amazon revenue.

Our models analyze Amazon sales through a causal lens, separating revenue into two key components: sales driven by Amazon advertising (like Amazon PPC campaigns) and sales influenced by your marketing efforts on other platforms. This distinction is crucial because Amazon traffic behaves similarly to organic traffic—customers might discover your product through a Facebook ad but ultimately purchase through Amazon.

By decomposing your Amazon sales data this way, we can attribute revenue more accurately across your entire marketing mix. We measure both direct effects (like Amazon PPC campaigns driving immediate sales) and indirect effects (like how your YouTube ads influence Amazon purchases). This helps you understand the true ROI of both your Amazon advertising and your broader marketing efforts.

Over time, our models continue learning from your data, improving their understanding of these relationships and providing increasingly accurate insights about how your marketing drives Amazon sales.

Understanding Omnichannel Customer Behavior

For brands selling both on Amazon and their own e-commerce site, understanding customer behavior across platforms is crucial but complex. While we can't track individual customers moving between platforms, our models can measure the broader impact your marketing has across channels.

Through our analysis of halo effects, we can quantify how your non-Amazon marketing influences Amazon sales. For example, we can show how your Meta campaigns or influencer partnerships drive revenue on Amazon, even without direct click tracking. We can also measure how these marketing efforts generate new customers on Amazon.

This omnichannel view helps you understand the full impact of your marketing spend, even when customers discover your brand on one platform and purchase on another. While we can't tell you exactly how many customers abandon your site to buy on Amazon, we can show you how your marketing efforts contribute to revenue across all your sales channels, helping you make more informed decisions about budget allocation.

Confidence Scores & Bands

Prescient's confidence scores help you understand how reliable our forecasts are for specific campaigns or scenarios. These scores combine three key factors:

Data coverage: The amount of historical data available
Data density: How consistently you've spent at similar levels
Forecast range: The spread between upper and lower prediction bounds

Our models generate a score between 0 and 1, which we translate into easy-to-understand confidence levels:

Low: Below 0.50
Medium: 0.51 - 0.69
Medium-High: 0.70 - 0.79
High: 0.80 and above

These scores help inform budget decisions by showing which forecasts are built on more reliable historical patterns. Higher confidence scores typically come from campaigns with consistent spending patterns over longer periods, as this gives our models more data to understand seasonal patterns and typical performance ranges.
For example, a campaign with two years of steady spending history will generally have a higher confidence score than a new campaign or one with irregular spending patterns. This helps you align budget decisions with your risk tolerance, making it easier to identify which opportunities are most reliable for scaling spend.

Optimizer

The Optimizer leverages our ensemble of additive models to understand complex relationships between spend and performance across your marketing mix. Rather than looking at campaigns in isolation, our models analyze how changes in one campaign's budget affect performance across your entire marketing ecosystem.

Our models process multiple layers of relationships:

Direct revenue impact of spend changes
Cross-campaign effects
Temporal patterns in campaign effectiveness
Saturation curves at different spend levels
Halo effects across channels
Seasonality impacts on campaign efficiency

The models use Bayesian statistical methods to generate predictions about campaign performance at different spend levels, accounting for both historical patterns and current market conditions. This approach allows us to identify opportunities where reallocating budget could drive better overall performance, while providing confidence scores that reflect the reliability of these predictions.

Each optimization scenario considers the full complexity of your marketing mix, ensuring recommendations account for both direct impacts and indirect effects across channels.

Understanding Relationships

Spend and Revenue

Marketing spend doesn't have a simple, linear relationship with revenue. Spending twice as much rarely means twice the revenue, and these relationships can vary significantly across channels and campaigns. Prescient's models capture these complex patterns by analyzing how changes in spend affect revenue over time.
Our models examine multiple aspects of the spend-revenue relationship:

Immediate impact of spend changes
Delayed effects that emerge over time
Saturation points where additional spend becomes less effective
How different campaigns within the same channel can have distinct spend-revenue patterns
Seasonal variations in spend effectiveness

Rather than assuming all campaigns follow the same pattern, our models learn the unique spend-revenue relationship for each campaign. This helps you understand exactly how changes in spend will affect revenue, accounting for factors like diminishing returns and varying efficiency across different spend levels.

This campaign-level understanding directly informs our budget optimization recommendations, helping you identify opportunities to adjust spend for maximum impact while avoiding inefficient spending patterns.

Spend and ROAS

Return on ad spend (ROAS) changes dynamically based on how much you invest in a campaign. Prescient's models capture these changes by analyzing how ROAS shifts across different spend levels and time periods.

Our models recognize that ROAS doesn't follow a one-size-fits-all pattern. Some campaigns might maintain high ROAS as spend increases, while others see efficiency decline more quickly. We measure these relationships at the campaign level, helping you understand:

How ROAS changes at different spend levels
When increased spend leads to declining efficiency
Where opportunities exist to scale while maintaining ROAS targets
How seasonality affects ROAS performance
The impact of campaign maturity on ROAS

This detailed understanding helps you make more strategic decisions about budget allocation, balancing the desire for growth with efficiency targets. Instead of pursuing a single ROAS target across all campaigns, you can optimize each campaign based on its unique characteristics and performance patterns.

Spend and New Customers

Marketing spend affects new customer acquisition differently than overall revenue or ROAS. Prescient's models specifically track how your campaigns drive new customer growth, recognizing that some campaigns excel at acquisition while others are more effective for repeat purchases.

Our models measure several key relationships:

How spend levels impact new customer acquisition rates
Which campaigns are most efficient at driving new customers
How acquisition costs change at different spend levels
Seasonal patterns in new customer acquisition
The relationship between acquisition costs and customer lifetime value

Prospecting campaigns play a crucial role in driving growth, but their value can be underestimated by traditional measurement approaches. Our models capture both the direct and indirect impacts of these campaigns, including:

Immediate new customer acquisition
Contribution to organic and direct traffic growth
Impact on branded search volume
Influence on future conversion rates
Long-term effects on customer acquisition costs

By measuring these broader effects, we help you understand the true value of your prospecting efforts. While these campaigns might show lower direct ROAS, our models reveal their full contribution to your customer acquisition funnel and overall business growth.

This comprehensive view enables you to make more strategic decisions about balancing acquisition and retention spend, ensuring you maintain a healthy pipeline of new customers while optimizing for efficiency.

Spend and Customer Acquisition Cost (CAC)

Prescient's models analyze how changes in marketing spend affect your customer acquisition costs across channels and campaigns. This relationship isn't always intuitive—sometimes increasing spend can actually lower CAC by improving campaign efficiency.

Our models track several key aspects:

How CAC changes at different spend levels
Which campaigns acquire customers most efficiently
Seasonal impacts on acquisition costs
The relationship between spend levels and acquisition efficiency
How different channels and campaigns interact to influence CAC

By tracking these patterns at the campaign level, we can identify opportunities where increased spend might lead to more efficient customer acquisition. Some campaigns may show improving CAC as they scale, while others might become less efficient. Understanding these relationships helps you:

Identify the most cost-effective channels for customer acquisition
Scale campaigns without unnecessarily inflating CAC
Balance acquisition efficiency with growth goals
Optimize budget allocation across prospecting and retargeting campaigns

This granular understanding enables more strategic decisions about where to invest for customer growth while maintaining acceptable acquisition costs.

What Can the Models Account For / Understand?

Modern marketing environments are complex, with numerous factors influencing campaign performance and revenue generation. Prescient's models are designed to capture and account for these various elements that impact marketing effectiveness. Understanding how our models handle each of these factors helps explain how we generate accurate insights and actionable recommendations for your marketing strategy.

Holidays & Seasonality

Holidays can dramatically impact your marketing performance, and no two holidays affect your brand the same way. Prescient's models learn the unique impact of each holiday on your business by studying your historical performance data. We also intelligently incorporate data about how similar businesses perform during these periods.

Our models specifically account for how holidays change both baseline revenue and marketing effectiveness. For instance, during Black Friday/Cyber Monday, not only might your overall sales increase, but your marketing campaigns may perform differently than they do during non-holiday periods. By accounting for these nuanced effects, we help you understand both how holidays impact your business and how to adjust your marketing strategy during these crucial periods.

This approach allows us to provide more accurate forecasts and recommendations during holiday periods. When our models make predictions about future holiday performance, they combine what they've learned about your specific business with broader patterns of holiday behavior. This gives you more reliable insights for planning your holiday marketing strategy and budget allocation.

Bayesian vs Frequentist Approaches

Marketing environments are complex and unique to each brand. When it comes to understanding events like holidays, there are two main statistical approaches: Frequentist and Bayesian. While both are valid, they think about problems differently.

A Frequentist approach would look only at your brand's historical holiday performance data to make predictions. Think of it as starting fresh each time, using just your past results. This can work well for brands with years of consistent holiday data, but it struggles when patterns change or when dealing with new situations.

Prescient uses a Bayesian approach because it systematically adapts to new evidence in a way that mimics real life. In particular, the model adapts to new data based on the strength of pre-existing evidence AND the informativeness of the new data. For instance, inferences about marketing effectiveness based on high quality, rich historical data are unlikely to shift much upon receiving small amounts of inconsistent recent data. However, your viewpoint would be more likely to shift if it was originally premised on spotty data and you then received a massive tranche of new, high quality data. The Bayesian framework inherently balances these considerations to determine how a continuous and ongoing stream of spend and revenue data should update the understanding of your marketing spend efficiency.

Year Over Year Differences

Marketing doesn't happen in a vacuum, and major events rarely impact your business the same way twice. A Black Friday campaign that worked perfectly last year might perform differently this year due to changes in consumer behavior, economic conditions, or your competitive landscape.

Prescient's models are designed to handle this reality. Rather than assuming each event will mirror its past performance, our models adapt to capture how these events evolve. We account for changing factors like promotional intensity, market conditions, and your brand's growth stage when analyzing how events impact your business.

This adaptive approach means you can plan more confidently for major events, even when conditions change. Whether you're preparing for established shopping holidays or emerging retail moments, our models help you understand not just what worked last time, but what's likely to work now.

Adstock / Saturation

Marketing campaigns don't have a simple linear relationship with revenue. Spending twice as much doesn't automatically mean twice the results, and the effects of your campaigns can build up or fade over time. This is where concepts like saturation and ad stock become critical.

Prescient's models capture these complex relationships by learning the unique saturation patterns of each of your campaigns. While some marketing platforms assume all campaigns saturate the same way, we've found this isn't true in practice. Some campaigns might reach peak efficiency quickly, while others could have multiple points of increased efficiency—meaning you might be pulling back spend just before hitting another sweet spot.

Ad stock represents how your marketing efforts accumulate and persist over time. Our models track how impressions and engagement from your campaigns build up, measuring both immediate impact and lingering effects. This helps you understand the true timeline of your campaign performance instead of just looking at same-day results.

By modeling these relationships at the campaign level, we provide more accurate recommendations for budget allocation. You'll know when to scale spend on campaigns that haven't hit their saturation point, and when to optimize or redistribute budget from campaigns that have reached peak efficiency.

Diminishing Returns

Most marketers are familiar with the concept that at some point, spending more won't drive proportionally more revenue. But understanding exactly where this happens—and how it varies across different campaigns—is crucial for optimizing your marketing budget.

Prescient's models identify the specific points where your campaigns start showing diminishing returns. This isn't as simple as finding a single "ceiling" for each campaign. We've found that different campaigns can have very different patterns: some might show steady returns up to a certain point before dropping off sharply, while others might have multiple phases of efficiency.

What makes this particularly valuable is that we measure diminishing returns at the campaign level rather than the channel level. This means you can identify which specific campaigns within a channel are approaching diminishing returns, allowing you to redistribute budget more strategically. Instead of pulling back spend on an entire channel, you can optimize at a more granular level, maintaining efficiency while maximizing your marketing impact.

This campaign-level understanding of diminishing returns directly informs our budget optimization recommendations, helping you find the right balance between scaling spend and maintaining efficiency across your marketing mix.

Trend

Prescient's models capture long-term trends in your marketing performance that go beyond seasonal patterns. These trends might reflect changes in brand strength, market conditions, or evolving consumer behavior.
Our models separate trend analysis into distinct components:

Overall business growth trajectory
Changes in channel effectiveness over time
Shifts in consumer response to different campaign types
Evolution of campaign performance as they mature
Long-term changes in acquisition costs and efficiency

By isolating trend from other factors like seasonality and campaign-specific performance, our models help you understand the underlying direction of your business and marketing effectiveness. This enables more accurate forecasting and helps identify when changes in performance reflect temporary fluctuations versus lasting shifts in effectiveness.

This trend analysis directly informs our predictions and recommendations, ensuring they account for the evolving nature of your business and market conditions.

Promotional Periods

Prescient's models recognize that marketing performance during promotional periods differs significantly from baseline performance. Our models account for:

Changes in conversion rates during promotions
Shifts in campaign efficiency and ROAS
Impact of promotion intensity on performance
Cross-channel effects during promotional periods
Post-promotion effects on revenue and customer behavior

By modeling promotional periods separately, we provide more accurate forecasts both during and outside these events. This helps you understand true campaign performance independent of promotional uplift and better plan promotional strategy across channels.

Our models also account for varying promotional impact across different campaigns and channels, recognizing that some marketing efforts may become more or less efficient during promotional periods.

Modeling FAQs

Can Prescient's Models Handle Frequent Campaign and Creative Changes?

Marketing is dynamic—you're constantly updating campaigns, testing new creatives, and adjusting your strategy. Prescient's models are designed to adapt to these frequent changes without losing accuracy or requiring manual updates.

Our models focus on measuring media impact rather than creative performance. By updating daily, they continuously learn from new relationships between spend and revenue. When you change a creative and it affects performance, the model automatically incorporates this learning into its understanding of the campaign's effectiveness.

Since we measure at the campaign level, you maintain clear insights even as you test different creatives within your ad sets. If performance metrics like ROAS change after a creative update, our models quickly reflect this shift, helping you understand the impact of your changes faster than traditional measurement methods. This dynamic approach means you can continue optimizing your campaigns and creatives while maintaining reliable performance measurement.