Welcome to Prescient

Prescient is a media measurement, forecasting, and optimization platform powered by Marketing Mix Modeling, or MMM.

It helps marketing teams understand how media investment affects business performance and forecast what may happen when budgets change.

With Prescient, you can:

Measure the incremental impact of your media.
Understand how channels influence one another.
Identify campaigns with room to scale.
Detect saturation and diminishing returns.
Forecast revenue and customer acquisition at different spend levels.
Compare potential media plans before changing live budgets.
Use AI-powered insights to identify important changes and recommended actions.

Prescient combines rigorous statistical modeling with AI-assisted analysis so that teams can move from reporting historical results to making forward-looking media decisions.

What questions does Prescient answer?

Prescient is designed to help answer three types of questions.

What happened?

How much revenue did paid media generate?
How many new customers were acquired through media?
Which channels, tactics, and campaigns contributed most?
How did performance change compared with a previous period?
Why do Prescient’s results differ from platform-reported attribution?

Why did it happen?

Did performance change because of media, seasonality, promotions, or an underlying business trend?
Did one channel create demand that converted through another?
Is a campaign producing direct results or influencing performance elsewhere?
Is media efficiency declining because a campaign is becoming saturated?
Are results supported by enough historical evidence?

What is likely to happen next?

What could happen if campaign spend increases or decreases?
How much revenue could a proposed media plan generate?
How could a budget change affect ROAS or CAC?
Which campaigns have the greatest opportunity to scale?
How should a fixed budget be allocated across the media mix?
Which modeled opportunities should the team investigate first?

Measurement creates the foundation for these decisions. Forecasting turns that measurement into a plan.

How Prescient Works

Prescient learns from the historical relationship between your media activity and business results.

The platform brings together data such as:

Media spend
Impressions and clicks
Revenue
Orders
New customers
Promotions
Seasonal patterns
Channel and campaign activity

Prescient’s models analyze how these variables change together over time. They estimate the portion of business performance associated with media while accounting for factors that can affect results independently of advertising.

These modeled relationships are then used to forecast how media may perform under different future spend scenarios.

At a high level, the process follows five stages:

Connect and validate data
Build and evaluate models
Measure historical media contribution
Forecast performance at different spend levels
Use AI and optimization tools to support action

What Is Marketing Mix Modeling?

Marketing Mix Modeling is a statistical method used to estimate how marketing contributes to business outcomes over time.

Unlike attribution systems that attempt to follow an individual customer through clicks or tracked interactions, MMM evaluates aggregated business and marketing patterns.

This allows MMM to measure media that may be difficult to evaluate through user-level tracking, including:

Connected and linear television
Podcasts
Influencer marketing
Direct mail
Retail media
Organic and branded demand created by advertising
Cross-storefront effects
Upper-funnel campaigns with delayed conversions

MMM is also not limited to the attribution rules of an individual advertising platform. It evaluates channels together as parts of one connected marketing system. Prescient’s modeled metrics therefore provide an independent perspective alongside channel-reported results.

From Measurement to Forecasting

Traditional reporting describes past performance.

Prescient uses past performance to estimate future outcomes.

For example, reporting may show that a campaign spent $100,000 and generated a modeled ROAS of 3.0 during the previous month.

Forecasting asks a different question:

What may happen if that campaign spends $80,000, $120,000, or $150,000 next month?

The answer cannot be calculated by multiplying the historical ROAS by the new spend amount. Media performance is nonlinear. As investment increases, campaigns may reach less responsive audiences, enter more expensive auctions, increase frequency, or approach the limit of available demand.

Prescient models these changing spend-to-performance relationships so that forecasts reflect saturation and diminishing returns rather than assuming constant efficiency.

Multiple Models, Not One View of Performance

There is rarely only one statistically plausible explanation for complex business performance.

Prescient uses an ensemble modeling approach that combines multiple additive models. Different models can capture different aspects of the relationship between media and business outcomes.

For example, models may differ in how they represent:

Seasonal demand
Long-term business trends
Campaign response
Media carryover
Cross-channel effects
Promotional periods
Nonlinear performance
Revenue and customer acquisition

Combining models reduces dependence on the assumptions of a single model and creates a more complete representation of the marketing environment.

What is an additive model?

An additive model separates a total business outcome into contributing components.

At a simplified level:

Observed performance = Media contribution + Seasonality + Business trend + Promotions + Other modeled factors

The media component can then be broken down further by channel, tactic, and campaign.

This allows Prescient to estimate media contribution without automatically crediting advertising for every increase in revenue or customer acquisition.

How Prescient Supports Forecast Accuracy

Forecast accuracy does not come from a single metric or model feature. It comes from a system of data validation, model design, comparison, and ongoing evaluation.

Prescient’s forecasting framework includes the following concepts.

Validated source data

Reliable forecasting begins with reliable data.

Before the first model run, customers validate synced ecommerce, advertising, and analytics metrics through the Metrics QA process. This helps identify connector, account-selection, historical coverage, and reporting discrepancies before the data is used for modeling.

Multiple model configurations

Prescient can evaluate alternative model configurations rather than assuming the first available model is the best representation of the business.

Model Comparison allows configurations to be reviewed side by side using model health, fit, modeled contribution, expected performance, and outlier criteria. The goal is to select the model that represents the business most realistically—not the model that produces the highest reported ROAS.

Model fit

Model fit measures how closely modeled total performance follows reported business performance.

A strong fit indicates that the model captures much of the variation observed in the underlying revenue or new customer data.

A strong fit alone does not prove that every campaign-level result is correct. It is one part of a broader model evaluation process.

Forecast-versus-actual validation

Prescient can compare modeled performance with actual reported outcomes.

Metrics such as symmetric Mean Absolute Percentage Error, or sMAPE, help quantify the difference between predicted and observed results over time. Forecast errors should be evaluated across a meaningful period rather than judged from one unusually strong or weak day.

Priors and business expectations

Prescient uses Bayesian priors to provide the models with reasonable starting expectations before the customer’s data is evaluated.

Priors can help stabilize models when data is limited or noisy while still allowing observed customer data to influence the final result. Prescient evaluates the interaction between priors, model components, convergence, and model flexibility.

Confidence and uncertainty

Forecasts are estimates, not guarantees.

Prescient communicates uncertainty through confidence scores and forecast ranges. Confidence considers factors such as historical data coverage, data density, and the spread between upper and lower prediction bounds.

Higher confidence means that more historical evidence supports the forecast. It does not mean that the outcome is certain.

Continuous learning

As new media and business data enters Prescient, the platform can update its understanding of the relationships between spend and performance. Prescient’s public product materials describe daily-updated modeling and scenario analysis rather than the slow, static delivery cycle associated with traditional consulting-led MMM engagements.

Prescient’s AI Capabilities

Prescient’s statistical models and AI capabilities perform different but complementary roles.

The models calculate

Prescient’s modeling system is responsible for:

Estimating incremental media contribution.
Learning campaign response curves.
Measuring direct and indirect effects.
Accounting for seasonality, trends, and carryover.
Forecasting future performance.
Quantifying confidence and uncertainty.
Evaluating possible budget allocations.

The AI interprets and prioritizes

Prescient’s AI capabilities help users work with the model outputs by:

Surfacing meaningful performance changes.
Identifying potential opportunities or risks.
Explaining modeled results in accessible language.
Highlighting where users should investigate.
Helping translate analytical findings into possible actions.
Reducing the time required to review large numbers of channels and campaigns.

AI does not replace the underlying MMM or independently invent media-performance forecasts. It helps users understand and act on outputs grounded in Prescient’s models and their organization’s data.

Human judgment remains important

Prescient may not know about business developments that are not yet reflected in the connected data, such as:

A future product launch
A major creative change
Inventory constraints
Contractual media commitments
Changes to pricing
Audience restrictions
A new promotional strategy
An upcoming website or checkout change

Users should evaluate AI-supported recommendations alongside current business context.

Incrementality

Incrementality is the additional business outcome caused by a media investment that would not otherwise have occurred.

Suppose Meta reports $100,000 in attributed revenue.

That does not necessarily mean Meta created all $100,000. Some customers may have:

Purchased without seeing an ad.
Interacted with multiple advertising platforms.
Already intended to purchase.
Been credited to more than one platform.
Converted because of another marketing activity.

Prescient estimates the incremental contribution associated with media instead of accepting every platform-claimed conversion as causal.

Modeled and Channel-Reported Metrics

Prescient displays both modeled and channel-reported information.

Channel-reported metrics

Channel-reported metrics come directly from advertising platforms such as Meta or Google.

They follow each platform’s:

Attribution window
Conversion definitions
Tracking methodology
Click-through rules
View-through rules
Identity and matching capabilities

These metrics are useful for managing activity within the platform.

Prescient-modeled metrics

Modeled metrics are calculated through Prescient’s MMM.

They can account for:

Incremental contribution
Baseline business performance
Cross-channel influence
Delayed conversions
Seasonality
Business trends
Saturation
Media carryover

These metrics are useful for comparing investments across the complete media mix and forecasting future performance.

Neither view must exactly match the other. They answer different questions.

Channel reporting answers	Prescient modeling answers
Which conversions did this platform claim?	What incremental outcome was associated with this media investment?
What happened inside this platform’s attribution window?	What happened across channels and over time?
How is the campaign performing under platform rules?	How may the campaign affect the broader business?
How should activity be managed inside the platform?	How should budget be evaluated across the media mix?

Core Modeled Metrics

MMM Revenue

MMM Revenue is Prescient’s estimate of the incremental revenue associated with media.

It includes Base Revenue and Halo Revenue.

MMM ROAS

MMM ROAS measures modeled revenue efficiency.

MMM ROAS = MMM Revenue ÷ Spend

It answers:

How much incremental revenue was generated for each dollar of media spend?

MMM New Customers

MMM New Customers estimates the number of incremental first-time customers associated with media.

MMM CAC

MMM CAC measures modeled customer-acquisition efficiency.

MMM CAC = Spend ÷ MMM New Customers

It answers:

How much media investment was required for each incremental new customer?

Revenue and customer-acquisition models may produce different recommendations because they optimize for different business outcomes.

Base and Halo Effects

Marketing channels do not operate independently.

A customer might:

See a television or Meta campaign.
Search for the brand several days later.
Click an organic or paid-search result.
Purchase on the brand’s ecommerce site or Amazon.

A click-based attribution system may give most of the credit to the customer’s final interaction.

Prescient can identify the wider relationship between the original media exposure and the resulting business outcome.

Base contribution

Base contribution represents the more direct relationship between a campaign and an outcome.

Halo contribution

Halo contribution represents the indirect influence that media has across other channels, storefronts, or future periods.

In simplified terms:

MMM Revenue = Base Revenue + Halo Revenue

Halo Effects are particularly important when evaluating upper-funnel, awareness, television, influencer, podcast, and prospecting investments that may create demand without receiving the final click.

Carryover, Lag, and Decay

Media can continue influencing business performance after the day it runs.

Carryover

Carryover, sometimes called ad stock, represents the continuing influence of media after the initial investment or exposure.

Lag

Lag is the delay between media activity and the resulting conversion or business outcome.

Decay

Decay describes how the remaining influence of media decreases over time.

Different media types can exhibit different patterns. Search may generate a relatively immediate response, while television, podcasts, direct mail, and awareness campaigns may influence customers over a longer period.

Prescient models these effects so that media is not evaluated only against same-day results.

Seasonality, Trend, and Promotions

Not every change in performance is caused by media.

Prescient separates media contribution from other business dynamics.

Seasonality

Seasonality represents recurring patterns such as:

Holidays
Weekday and weekend differences
Monthly demand cycles
Annual peaks
Category-specific purchasing periods

Business trend

Trend represents the longer-term direction of the business.

It may reflect:

Brand growth
Changes in organic demand
Distribution changes
Market conditions
Product maturity
Changes in customer behavior

Promotions

Promotions may temporarily affect conversion rates, order values, media efficiency, and baseline demand.

Accounting for these components helps prevent the model from automatically crediting paid media for performance changes caused by other factors.

Saturation and Diminishing Returns

Media performance is nonlinear.

Doubling spend does not usually produce exactly twice the revenue or twice the number of new customers.

As a campaign scales, it may:

Reach less responsive audiences.
Increase frequency among people already exposed.
Enter more expensive inventory.
Exhaust available demand.
Generate progressively less value from each additional dollar.

Saturation

Saturation is the stage at which additional spend produces increasingly limited incremental performance.

A saturated campaign may still generate additional revenue, but at a less efficient rate.

Diminishing returns

Diminishing returns occur when the next dollar of investment generates less incremental value than the previous dollar.

These relationships are represented through saturation curves and are fundamental to Prescient’s media forecasting capabilities.

Growth and Efficiency

Media planning often involves a trade-off between total growth and marginal efficiency.

A growth-oriented decision may prioritize:

More total revenue
More new customers
Greater market penetration
Scaling upper-funnel media
Accepting lower ROAS at higher spend

An efficiency-oriented decision may prioritize:

Higher ROAS
Lower CAC
Reduced investment in saturated campaigns
Stronger short-term profitability
Concentrating budget in the most efficient opportunities

The plan with the highest ROAS is not necessarily the plan that creates the most total value.

A campaign with extremely high ROAS may be underfunded. Increasing its budget could generate substantially more revenue, even if its forecasted ROAS declines.

Prescient helps users evaluate this trade-off rather than assuming that media efficiency remains constant at every spend level.

Channel, Tactic, and Campaign Levels

Prescient allows users to evaluate media at multiple levels.

Channel

A channel is a media platform or source, such as Meta, Google, Amazon Ads, TikTok, YouTube, connected television, or direct mail.

Channel analysis is useful for strategic media-mix decisions.

Tactic

A tactic groups campaigns based on their intended role.

Examples may include:

Prospecting
Retargeting
Awareness
Branded search
Non-branded search
Acquisition
Retention

Tactics make it easier to compare similar investments and understand performance across the marketing funnel.

Campaign

The campaign level provides the most granular view of media performance.

Campaigns within the same platform can have different:

Audiences
Objectives
Creative strategies
Historical efficiencies
Carryover patterns
Levels of saturation
Forecast opportunities

Prescient therefore does not assume that every campaign within a channel will respond identically to a budget change. The Attribution Page supports channel, tactic, and campaign views.

Measurability

Not every campaign contains enough historical evidence to support an equally reliable forecast.

Measurability can be affected by:

The number of active spend days
Average spend
Variation in spend
Campaign age
Data completeness
Campaign naming and identity changes
The size of the campaign relative to the business
The strength of the relationship between media and outcomes

For example, a campaign that spends almost exactly the same amount every day provides limited evidence about what may happen at a substantially different budget.

A campaign that is not individually measurable may need to:

Accumulate additional history.
Operate at a more meaningful spend level.
Experience controlled spend variation.
Be grouped with similar campaigns.
Be evaluated at the tactic or channel level.
Be validated through an experiment.

Prescient’s currently documented basic campaign-eligibility guidance requires at least seven nonzero-spend days during the previous 365 days and an average of at least $50 per nonzero-spend day. Eligibility does not guarantee high forecast confidence.

Forecasts, Scenarios, and Recommendations

A forecast estimates performance under a defined future scenario.

That scenario may involve:

Changing the spend of one campaign.
Reallocating a fixed budget across campaigns.
Changing the channel mix.
Comparing a current plan with a proposed plan.
Selecting revenue or customer acquisition as the objective.
Planning spend across future weeks.

Depending on the feature being used, Prescient may forecast:

Revenue
ROAS
New customers
CAC
Campaign saturation
Channel allocation
Weekly budget flighting

The Media Forecaster supports focused spend simulations and multi-campaign allocation decisions, and Budget Planner supports longer-term scenario and channel planning.

How to Interpret a Prescient Recommendation

A model-driven or AI-supported recommendation should be treated as decision support—not as an automatic instruction.

Before applying a recommendation, consider:

Objective
Is the priority revenue, ROAS, new customers, CAC, profitability, or strategic growth?
Confidence
How much historical evidence supports the forecast?
Size of the change
Is the proposed budget change close to historically observed spending, or does it require significant extrapolation?
Business context
Are there upcoming launches, promotions, inventory limitations, contractual commitments, or other factors not yet visible in the data?
Testing opportunity
Can the recommendation be implemented gradually or validated through a controlled exercise?
Monitoring plan
How will forecasted and actual performance be compared after implementation?

The strongest workflow combines model evidence, AI-assisted interpretation, business expertise, and experimentation.

What Prescient Is—and Is Not

Prescient is:

A forward-looking media measurement platform.
An independent view of incremental performance.
A system for forecasting media outcomes.
A way to compare potential budget scenarios.
A framework for understanding cross-channel influence.
An AI-assisted decision-support system.
A complement to experimentation and business judgment.

Prescient is not:

A reconstruction of every individual customer journey.
A replacement for advertising-platform execution tools.
A guarantee that a forecasted result will occur.
A system that assumes historical ROAS remains constant.
A reason to ignore current business context.
A substitute for accurate and complete source data.
A single model presented as unquestionable truth.

Key Takeaways

Prescient uses MMM to measure and forecast the incremental effect of media.
Measurement is the foundation; forecasting and decision support are the primary outcomes.
Prescient combines multiple models to represent complex media and business relationships.
Forecast accuracy is supported through data validation, model comparison, fit analysis, uncertainty measurement, and forecast-versus-actual evaluation.
Prescient’s AI helps interpret and prioritize model-driven information; it does not replace the underlying statistical models.
Modeled metrics and advertising-platform metrics answer different questions.
Media can generate direct Base contribution and indirect Halo contribution.
Media performance changes as spend changes because of saturation and diminishing returns.
Prescient accounts for carryover, delayed conversions, seasonality, trends, and promotions.
Forecast confidence reflects the strength of historical evidence—not certainty.
Recommendations should be evaluated alongside objectives, business context, and testing opportunities.

Continue Your Onboarding

Next, read:

Getting Started to connect and validate your data and prepare for your first model.
Using the API to access Prescient data programmatically.
Model Center to understand how models are managed and evaluated.
AI Action Center to review model-driven insights and opportunities.
Attribution to interpret modeled media contribution.
Media Forecaster to simulate future campaign performance.
Measurability to understand which media investments can support reliable forecasting.
Understanding Model Results & Methodologies for a deeper explanation of model design, validation, confidence, and interpretation.