What it is

Beam is your incremental forecasting tool to uncover untapped revenue potential across marketing channels.

Using your last 90 days of performance data, it forecasts revenue, conversions, and ROAS at different spend levels, so you can scale confidently, cut waste, and justify your budget decisions.

Whether you're planning budgets for a sale period, or reallocating spend mid-quarter, it helps you:

See where growth is still on the table

See the growth potential in each of your channels—so you know exactly where to increase spend without overspending or driving ROAS into the ground.

Forecast with confidence

Whether you’re scaling up or shifting budget between channels, you’ll have a clear, data-backed view of what’s likely to happen next—making it easier to act quickly and defend every decision.

Get finance on board faster

With forecasted revenue, ROAS and conversions, you can speak the same language as your CFO - and make the case for investment with numbers they’ll trust.

How It Works

Incremental Forecasting uses saturation curves — visual models showing how revenue grows with spend, and where returns start to flatten.

It visualizes this curve and shows the expected revenue, ROAS, and conversions at every spend level — so you can find the sweet spot before overspending.

Growth Potential is Fospha’s way of showing how much more you could profitably spend in a channel — like Performance Max — before hitting diminishing returns.

Think of it as answering:

"How much headroom do I have to grow this channel without wasting budget?"

It doesn’t mean you can boost spend by 59% and keep the same ROAS — it means that, based on your past performance, you could increase spend by up to 59% and still drive profitable growth, even if efficiency gradually drops as you scale.

How Incremental Forecasting Uses Bayesian Inference

Incremental Forecasting doesn’t just observe past results — it infers the most likely causal relationship between spend and conversions.

Unlike traditional MMMs, which estimate conversions based only on spend, Incremental Forecasting already knows how many conversions happened at different spend levels. It then uses Bayesian inference to model the underlying cause-and-effect relationship.

What that means in practice:
Bayesian inference learns a performance response curve — showing how results change as spend increases — and continuously refines this curve using your historical data.

Example:
If £100 on Meta drove 10 conversions, and £200 drove 16, the model doesn’t just plot these points. Instead, Bayesian inference asks:
"What’s the most probable relationship between spend and conversions that explains this pattern — and how confident are we?"

This is more than curve-fitting. It estimates the most likely shape of your performance curve while accounting for diminishing returns, uncertainty, and variability in the data.

Why it matters:

Causal impact estimation: Goes beyond correlation to estimate how much spend actually drives incremental revenue.
Robust scenario planning: Provides confidence intervals — a statistically grounded range of predicted outcomes — so you can plan high-stakes campaigns with certainty.
Adaptive predictions: Learns from shifts in seasonality, creative performance, and platform changes to keep forecasts reliable.

How To Use Incremental Forecasting for Sale Period Budget Planning

Planning budgets for Black Friday, Cyber Week, or mid-season promos is a balancing act: push hard, but not blindly.

Incremental Forecasting shows how much headroom you have to profitably scale — even in high-stakes sales periods.

The catch: if your recent data includes a major sale, it can inflate your “normal” performance. That’s why Sale Period Flags matter.

What Sale Period Flags Do

Exclude sales spikes from your model to:

See true BAU headroom → Remove inflated sale data to understand everyday channel performance.
Plan both sale & BAU budgets → Toggle between views to compare what’s realistic outside of sales and what’s unlocked during them.
Defend budgets with credible forecasts → Strip out urgency/discount boosts to set realistic expectations.

How It Works

Automatic detection → Fospha flags likely sale periods in your recent data.
Manual control → Confirm or adjust flagged dates in Incremental Forecasting.
Dynamic switching → Instantly compare sale-inclusive vs. sale-exclusive curves.

Why It’s Essential for Sale Period Budget Planning

With sales included, you can:

See how spend scaled in past sales
Spot top-performing channels under pressure
Forecast potential revenue lift this time

How To Use Incremental Forecasting For Budget Optimization

Read full guide on how to use Fospha for cross-channel optimization here.

1. Spot Scaling Opportunities with Incremental Forecasting

Once you’ve identified a channel segment to focus on (for example, Meta Awareness) in Channel Health Check, go to Incremental Forecasting and select the same segment to evaluate:

Where there’s headroom to scale
Where performance is saturated and spend is being wasted

2. Get Channel-Level Insights at a Glance

If you scroll down to the bottom of the dashboard, you'll see a summary table of all tracked channel segments. This gives a quick comparison across all channels and flags key opportunities and risks:

Metric	What It Shows
Average Daily Spend	Current spend over the past 7 or 30 days
Share of Wallet	% of your total biddable spend per channel
Conversions	New conversions driven by each channel
CAC	Actual CAC compared to your target
Target Status	Whether a channel is beating your CAC target
Saturation Point	The calculated max spend before CAC hits AOV
Headroom %	How much room there is to scale efficiently

Look for:

Channels under target CAC with high headroom = Safe to scale
Channels over CAC target or near saturation = Time to review or reduce spend
Channels past saturation point = Consider pulling back spend to optimize efficiency

Example:

Channel Segment	CAC	Target	Headroom	Action
Meta Advantage+	£32	£38	65%	Increase budget incrementally
Retargeting	£50	£38	-20%	Over-saturated – reduce spend
Google Shopping	£36	£38	10%	Monitor – small scaling possible

3. Dive into Individual Channel Saturation Curves

Saturation curves help you understand how revenue is expected to scale as you increase spend in a given channel segment. This visualisation is designed to support confident, data-backed budget decisions—especially when you're scaling or reallocating spend.

It gives you:

A spend vs. conversion forecast curve
A CAC vs. target comparison
A saturation threshold to avoid overspending
A confidence interval so you know how reliable the data is

Start with Context

Current Spend (7-day average): This shows how much you’re currently spending in the selected channel.
Historical Spend Line (90 days): Offers longer-term context to spot changes over time.
Target Line: This line represents your CAC target. Any predicted point above it = efficient.
Predicted Conversions Line (Blue Curve): Shows estimated conversions at different spend levels.
Confidence Interval (Grey Area): The narrower the band, the higher the confidence in the model's predictions.

Best Practice

Use 7-day view for recent spend, 30-day for broader trends
Look at Share of Wallet to check if you're over-committing to low-performing channels
Pair with Channel Health Check to align performance signals with budget strategy
Review regularly – the model updates with new data every few days

Tip: Hover over any data point to see expected conversions and CAC at that spend level.

How to Read the Curve:

X-axis: Daily spend (£)
Y-axis: Predicted new conversions
Blue Line: Expected conversions at each spend level
Dashed Red Line: CAC target threshold
Orange Line: Saturation point (where CAC = AOV)
Grey Shaded Area: Confidence interval around predictions

Maximum Observed Spend Indicator: Each channel segment now shows the highest daily spend observed over the last 90 days—this is the point up to which Fospha’s predictions are most reliable.

How to Interpret It:

Curve Shape	What It Means	Recommended Action
Steep & Upward	Higher spend → strong growth in conversions at good CAC	Scale incrementally (e.g., +10–15%)
Flat	Additional spend yields few new conversions	Hold spend or optimize elsewhere
Dips below target line	CAC goes above target as spend increases	Avoid scaling past this point
Stops at saturation line	Spend beyond this = CAC exceeds AOV	Reduce spend or reallocate

Use Confidence Intervals to Assess Risk

Narrow confidence band = high model confidence
- Safe to act on these predictions
Wide band = lower confidence (e.g., new campaigns, inconsistent data)
- Consider waiting before major changes or testing with a small budget shift

Understanding the RAG Status System in Spend Ranges

When you select a spend level, Fospha shows a range of predicted outcomes — low, most likely, and high — for key metrics like conversions, new customers, and revenue.

Each outcome is color-coded using a dynamic RAG (Red, Amber, Green) system:

Red: Below target. High risk of underperformance.
Amber: Near target. Moderate risk, room for improvement.
Green: On or above target. Strong performance.

RAG thresholds adjust based on your last 90 days of blended performance, giving you instant context and helping you decide where to scale or cut with confidence.

Filter Sales Periods (or Not) Based on Your Goal

In the Business Context settings, you can:

Include sales dates: Use when planning for another sale period
Exclude sales dates: Use to model typical performance (BAU)

If planning for peak (e.g. BFCM), use “Include Sales Dates” and/or compare the same period YoY.

Spot The Saturation Point

This marks the maximum recommended spend before your CAC exceeds your average order value—i.e., you stop making profitable customer acquisitions.

If you’re already over this line:

Reduce spend immediately on that channel
Reinvest into channels with headroom and target-beating CAC

4. Make Your Next Budget Decision Based on the Data

Scenario	What to Do
Channel is hitting CAC target and has 30%+ headroom	Increase spend gradually (e.g. 10–15%/week), monitor curve
Channel is close to CAC target but flat curve	Hold steady or reallocate test budget to higher-growth channel
Channel is above CAC target or nearing saturation	Reduce spend, use insights to test new creatives/audiences
Retargeting is saturated	Pull back budget and focus on TOF/MOF to feed funnel again