Frequently asked questions about Prescient AI

Most brands see their first actionable insights within 1–2 weeks of connecting data sources. Our models begin training immediately and update daily, so accuracy improves continuously from day one. No waiting 6–12 weeks for a consultancy deck.

No. Most data flows through native integrations that connect directly to your ad platforms and analytics tools — no engineering tickets, no data warehouse required. For some data sources, we also allow manual CSV uploads. If you can log into your ad accounts, you can use Prescient.

Prescient AI integrates natively with Meta, Google, TikTok, Snapchat, Pinterest, Amazon, CTV/OTT platforms, Shopify, and more. We also connect to analytics tools like Google Analytics and revenue sources. See the full list at prescientai.com/integrations. If you use it, we probably connect to it — and if we don't yet, our team will work with you.

The model updates daily with your latest spend and performance data. This means your saturation curves, forecasts, and optimization recommendations always reflect current market conditions — not stale quarterly analyses.

Prescient works alongside your agency. Many brands use Prescient to validate agency recommendations, set shared KPIs grounded in MMM data, and give their agency teams the forecasting tools they need to scale more effectively. It becomes the neutral reference point for budget conversations.

Traditional MMMs update quarterly, require a consulting team, and deliver results as static PowerPoint decks. Prescient AI updates daily, includes cross-channel halo effects, and gives you a live dashboard with scenario modeling. You get the rigor of econometric modeling with the speed your team needs.

Our Bayesian hierarchical model uses priors informed by benchmarks across 200+ brands. This means even channels with limited spend history get reliable estimates — not just "insufficient data" warnings. As you accumulate more data, the model continuously refines its estimates.

Yes. Prescient AI supports calibration with geo-lift tests, holdout tests, and platform-reported incrementality data. Our validation layer lets you compare model outputs against real experimental results — so you can trust the numbers before making budget decisions.

Every model makes assumptions about your business — how channels interact, how fast effects decay, how seasonality works. Those assumptions may not capture your unique dynamics. Without testing them against your real outcomes, you're trusting a model that might not understand your business well enough to guide million-dollar decisions. Validation Layer lets you test and customize those assumptions based on evidence.

You set the total budget, choose a forecasting timeframe (7, 14, 28 days or custom), and select which campaigns to include. Prescient's model — powered by your daily-updating MMM — simulates how different allocations would affect revenue and ROAS. You see the optimal allocation alongside your current spend and any custom scenarios you create.

Forecast accuracy depends on the underlying MMM, which Prescient validates against incrementality tests and out-of-sample data. Brands using Prescient typically see 90%+ revenue prediction accuracy. The optimization tool provides confidence intervals so you know the range of likely outcomes.

Yes. Create unlimited scenarios with different budget levels, timeframes, and campaign selections. Each scenario is named and saved so your team can review them together. You can compare any scenario against the "no change" baseline to see the projected impact.

Both. You can optimize at the channel level (e.g., shift budget from Meta to TikTok) or drill down to individual campaigns within a channel. You can also lock specific campaigns at their current spend level and optimize the remaining budget around them.

Since the underlying model updates daily, your scenarios always reflect the latest data. Most teams run new scenarios weekly during active budget planning or whenever they're considering a significant spend change. There's no limit on how many scenarios you can create.

Yes — the optimization tool is powered by Prescient's media mix model. The MMM measures incremental impact and the optimization tool uses those measurements to forecast outcomes of different budget scenarios. They work together as part of the Prescient platform.

Prescient AI uses a daily-updated media mix model with saturation curves and scenario forecasting to show you exactly where additional spend will drive profitable growth — and where it will hit diminishing returns. Instead of guessing, you can simulate budget increases channel by channel and see the projected revenue impact before committing a dollar.

Saturation curves show the relationship between spend and return for each channel. As you increase spend, every channel eventually hits a point of diminishing returns — where each additional dollar generates less revenue. Prescient maps these curves for every channel so you know exactly how much room you have to scale before efficiency drops.

Yes. Prescient's scenario planning lets you model different budget allocations and see the projected revenue outcome for each. You can test increasing Meta by 20%, shifting budget from Google to TikTok, or adding a new channel entirely — all before spending a dollar.

Good ROAS today doesn't mean good ROAS at higher spend. Every channel has a saturation point where returns decline. Prescient shows you exactly where that inflection point is for each channel, so you can scale the channels that still have room and avoid overspending on channels that have already peaked.

Halo effects are the spillover revenue that one marketing channel generates on another. For example, a TikTok awareness campaign may drive branded search on Google, or a Streaming TV ad may lift Amazon sales. These cross-channel effects are invisible to last-click attribution but represent real, measurable revenue that should inform your budget decisions.

Prescient AI uses a mechanistic media mix model that decomposes each channel's revenue into base (direct) and halo (spillover) components. The model traces how spend on one channel lifts conversions across other channels and revenue destinations — including Shopify, Amazon, and TikTok Shop. It updates daily with fresh data, so your halo measurements always reflect current performance.

Upper-funnel channels like Streaming TV, Meta awareness campaigns, and TikTok typically generate the highest halo effects. They drive branded search, direct traffic, and cross-retailer conversions that attribution tools credit to other channels. But the exact mix varies by brand — Prescient shows you your specific halo breakdown so you can invest accordingly.

Yes. Prescient AI maps halo effects across revenue destinations including Shopify, Amazon Selling Partner, and TikTok Shop. You can see exactly how much spillover revenue each channel generates on each retailer — for example, how Streaming TV drives 46% of its halo revenue to Amazon and 39% to TikTok Shop.

When you account for halo effects, channels that look mediocre on direct ROAS often become your highest-impact investments. Brands that optimize for total impact (direct + halo) typically uncover 40%+ in hidden revenue and make fundamentally different budget allocation decisions — shifting spend toward channels that amplify the entire funnel.

Halo effect data is available within 1–2 weeks of connecting your data sources. The model updates daily, so your halo measurements always reflect your most recent spend and performance — not outdated quarterly snapshots. No data team required to get started.

Two types of metrics in a single table: in-platform numbers (impressions, clicks, CPM, channel ROAS) and MMM-derived numbers (paid revenue, new customers, CAC, paid ROAS). You see both what platforms report and what actually drove incremental results — so you can compare the two side by side.

Daily. In-platform metrics refresh automatically from your connected ad accounts. MMM metrics recalculate with each daily model run. By the time you open Performance in the morning, yesterday's numbers are already there.

Yes for online models — you can drill all the way down to individual campaigns. For offline models, you can drill to the tactic level. Click tabs at the top of the table to switch views. Every level shows the same set of metrics, so you're never switching context.

In-platform dashboards show one channel at a time with self-attributed metrics. That means every platform takes credit for the same conversion — and you're comparing apples to oranges. Prescient puts every channel in one table with both platform-reported and MMM-calibrated numbers — with halo effects included — so you can make real comparisons.

Yes. Hit "Export Performance" to download your data for presentations, board decks, or further analysis. You can also pull data directly from our API. Export the full table or just the date range and columns you need.

Model validation tests whether your MMM's predictions actually match real business outcomes. It includes backtesting against historical data, cross-validation across time periods, holdout testing on unseen data, and scenario testing to confirm recommendations make business sense. The goal is to prove the model works before you trust it with real decisions.

Calibration adjusts your model's parameters using external data like incrementality tests or surveys to improve alignment with known performance. Validation tests whether those adjustments actually improved accuracy by comparing predictions to real outcomes. Think of calibration as tuning the instrument and validation as checking if it plays the right notes. You need both — calibration without validation is tuning based on hope.

Validation Layer supports calibration with incrementality test results (geo-lift, holdout experiments), post-purchase survey data (KnoCommerce, Fairing, etc.), MTA platform outputs, and custom business assumptions about seasonality, promotions, and channel dynamics. You can incorporate any trusted data source and compare the calibrated model's accuracy against your baseline.

Yes, and this is exactly why Validation Layer exists. Prescient's research shows that incorporating external data can sometimes degrade model accuracy — especially if test data was compromised by external factors or doesn't reflect your actual marketing environment. Validation Layer lets you test whether calibration helped or hurt before you act on the results, preventing you from making budget decisions based on a worse model.

No. Validation Layer is built for marketing leaders, not data scientists. You configure calibration sources, run comparisons, and see accuracy scores — all through the Prescient platform. Guardrails prevent configurations that would break model health, so you get the freedom to customize without the risk. Your Customer Success Manager can also help you think through what to test first.