Overview

Prescient’s Modeled Attribution is the foundation of our insights, offering you a full picture of how each marketing effort contributes to revenue and customer acquisition.

In this article, you’ll learn:

What modeled attribution is and why it matters.
Where modeled attribution appears across the platform.
Key differences between modeled metrics and channel-reported metrics.
How to use modeled attribution for strategic decision-making.

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If you’d like deeper insights into Halo Effects, Campaign Forecasting, or Spend Optimization, please refer to our dedicated articles on those topics. This overview focuses on the fundamentals and usage of Modeled Attribution itself.

What is Modeled Attribution?

Modeled Attribution in Prescient AI determines the true drivers of incremental revenue and customer acquisition across your marketing mix. It leverages advanced Marketing Mix Modeling (MMM) methodologies to capture:

Delayed Conversions: Accounts for revenue generated by marketing actions that convert days, weeks, or even months later, reflecting the true lifecycle of customer behavior.
Channel Interactions: Measures how different channels interact and influence one another, such as how Facebook prospecting lifts branded Google search or how YouTube campaigns create demand that converts later via email marketing.
Base vs. Halo Effects: Separates direct, immediate revenue (base) from cross-channel, indirect effects (halo) that materialize over time. This enables you to measure the long-term, ecosystem-wide impact of your campaigns.
Saturation Curves: Identifies the diminishing returns of spend on each channel, helping to pinpoint when additional investment no longer drives significant incremental value.
Ad Stock Effects: Captures the lingering impact of advertising efforts over time, showing how campaigns influence customer decisions beyond the immediate spend period.

Prescient's MMM is unique in its ability to combine these elements into actionable insights that go beyond traditional attribution models. Unlike single-touch or last-click attribution, which overemphasize individual touchpoints, Modeled Attribution evaluates the full marketing funnel to provide a long-term, holistic perspective. By doing so, it uncovers hidden value and equips you to make smarter, data-driven decisions that maximize both immediate and incremental returns..

Where You'll See Modeled Attribution in Prescient

Modeled Attribution is seamlessly integrated into Prescient AI to ensure data-driven insights are always accessible:

Homepage

Base vs. Halo Effect Rollups: Visualize how each channel contributes to direct (base) and indirect (halo) revenue—all derived from Prescient’s modeled data. Quickly see which channels are driving conversions versus top-of-funnel awareness.
MMM % of Total: Quickly see the share of your overall revenue (or new customers) that’s driven by paid media, according to Prescient’s models. This chart highlights the percentage of revenue or new customers attributed to media, versus non-media activity (word-of-mouth, unmeasured campaigns, etc.).
Trends: Monitor changes in your modeled metrics over a given period, comparing totals to the previous timeframe. This helps you spot increasing or decreasing performance based on MMM data.
KPI Report Select from Prescient’s modeled KPIs—such as Modeled Revenue—to drill deeper into how each channel contributes to your business. Use the chart’s filter options to compare performance periods, view absolute numbers or percentages, and tailor the data to your specific needs.

Performance Page

Hero Metrics: Modeled metrics (labeled “MMM”) can be selected as hero metrics at the top of the Performance page.
MMM Columns: In Channel, Tactic, and Campaign views, compare modeled results (e.g., MMM Revenue, MMM CAC) with channel-reported data to uncover discrepancies or optimization opportunities.
Insight Drawers
- Performance Tab: Compare spend to MMM Revenue or MMM ROAS over time, helping you identify when channels or campaigns reach diminishing returns.
- Attribution Tab: Dive deeper into the breakdown of base vs. halo effects for campaigns.
- Forecast Tab: Leverage modeled projections to plan future campaign strategies.

Optimization

Scenario Planning: Modeled Attribution fuels the optimizer’s recommendations, allowing you to simulate spend changes and visualize their incremental impacts on revenue or customer acquisition.

Modeled vs. Channel-Reported Metrics

Prescient offers two distinct types of metrics, each with its unique purpose:

Channel-Reported Metrics

Data comes directly from advertising platforms like Google Ads or Facebook.
Tracks conversions credited to the platform within its attribution window.
Often over-credits bottom-funnel actions while under-representing top-funnel efforts.

Modeled (MMM) Metrics

Combines cross-channel interactions, delayed conversions, and baseline trends.
Evaluates incremental revenue and new customer acquisition tied to each channel.
Provides a clearer understanding of which efforts are truly driving growth beyond what is captured in a single attribution window.

By comparing Channel-Reported and Modeled Metrics side-by-side, you gain a nuanced view of campaign performance and can make more strategic optimization decisions.

Interpreting Modeled Attribution

Prescient AI’s modeled metrics are designed to provide a clear, actionable understanding of your marketing performance. Below is a breakdown of the key modeled metrics you’ll encounter in the platform and how to interpret them effectively:

MMM Revenue & MMM ROAS
- MMM Revenue reflects the total incremental revenue driven by a channel or campaign. This includes both:
  - Base Revenue: The direct, immediate impact of a campaign or channel.
  - Halo Revenue: The indirect influence of a campaign on other channels over time.
- MMM ROAS (Return on Ad Spend) is calculated by dividing MMM Revenue by your total spend. It answers the critical question: "How much incremental revenue am I generating for every dollar spent?"
- Together, these metrics help you evaluate both the effectiveness (MMM Revenue) and the efficiency (MMM ROAS) of your marketing investments.
  - Example Interpretation: A high MMM Revenue combined with a high MMM ROAS indicates both strong campaign impact and efficient use of spend. Conversely, a low MMM ROAS paired with moderate MMM Revenue may suggest diminishing returns or inefficiencies in allocation.
MMM New Customers & MMM CAC
- MMM New Customers estimates the number of first-time buyers acquired through a specific campaign or channel.
- MMM CAC (Customer Acquisition Cost) represents the modeled cost of acquiring each new customer, derived by dividing spend by MMM New Customers.
- These metrics allow you to assess the balance between growth and efficiency:
  - Example Interpretation: If MMM CAC is high but MMM New Customers are low, it may indicate that the channel is over-invested relative to its incremental contribution to acquisition. Conversely, a low MMM CAC with strong MMM New Customers suggests an efficient acquisition strategy.
Halo Effects
- Definition: Halo Effects capture how one channel’s efforts indirectly influence conversions in another. For instance, a Facebook campaign may drive branded search volume on Google, resulting in additional conversions.
- Why It Matters: Channels with strong halo effects often show incremental value that is not reflected in channel-reported metrics.
- Example Interpretation: If a channel’s halo revenue is consistently high, it may warrant maintaining or even increasing spend, even if its direct returns (base revenue) appear modest.

Using Modeled Attribution to Drive Decisions

Budget Allocation & Optimization

Modeled Attribution reveals channels that may appear unprofitable on the surface but actually yield incremental revenue or future conversions. Use these insights to:

Increase spend where halo effects are strong: Channels with high halo contributions often drive conversions indirectly by boosting the performance of other channels.
Diversify away from diminishing returns: MMM Revenue trends over time can indicate when a campaign or channel is reaching saturation. Reallocate spend from these areas to higher-performing opportunities.

Compare Reported vs. Modeled Metrics

Review Channel Revenue alongside MMM Revenue to uncover hidden insights:

Over-Reported Channels: These are channels where platform data may overstate their contribution (e.g., retargeting campaigns that take credit for conversions largely driven by prospecting efforts).
Under-Reported Channels: These are channels like brand awareness efforts that often drive more conversions than last-click metrics reflect, as they influence top-of-funnel interactions that convert later.

Evaluate Incremental Value

Use MMM Revenue to identify hidden opportunities where a channel’s incremental impact might not be immediately visible in channel-reported data. For example, channels with low reported revenue but high MMM Revenue are likely driving significant indirect or delayed conversions, which are not captured by traditional attribution models.

Measure Efficiency vs. Growth

Balance MMM ROAS and MMM CAC to assess campaign profitability and acquisition potential:

High MMM ROAS + Low MMM CAC: Indicates both efficient spending and strong growth, making these campaigns ideal for scaling.
High MMM ROAS + High MMM CAC: Suggests efficient spend, but additional focus is needed on customer acquisition strategies.
Low MMM ROAS + High MMM CAC: Highlights inefficiency and limited growth, pointing to campaigns that may need optimization or reallocation.

Account for Halo Effects

Look at channels with significant halo contributions to understand their broader, long-term impact. These channels might appear less profitable in isolation but can drive considerable value across the marketing ecosystem. For instance, Facebook prospecting campaigns often generate indirect conversions on branded Google search or email marketing.

Validate Key Growth Metrics

Examine MMM New Customers vs. Channel-Reported Conversions to determine whether acquisition campaigns are truly expanding your customer base or simply remarketing to existing audiences. This clarity helps prioritize channels that drive sustainable growth.

Best Practices & Key Takeaways

Check Modeled Metrics Regularly: Monitor changes in MMM Revenue, MMM ROAS, and MMM CAC weekly or monthly to spot shifts in performance.
Watch for Halo Effects: Remember that a campaign can have low direct returns but high halo returns. Always consider the broader context.
Combine with Other Tools: Modeled Attribution is most powerful when used alongside Performance Measurement, Halo Effects, and Campaign Forecasting. Explore the recommended spend adjustments in the Optimizer to refine your budget allocation further.
Align with Business Goals: Whether your aim is short-term profitability (ROAS) or sustained new customer growth (CAC), let Modeled Attribution guide how to balance spend between lower- vs. upper-funnel tactics.