Marketing dashboards tell you what looks efficient. SpendSignal tells you what actually creates revenue by modeling incrementality, halo effects, and delayed lift—then turns that into allocation recommendations you can act on. Uncover unattributed ROAS, calculate true ROAS, and forecast returns using ROAD—so teams can reallocate budgets with confidence.
Multiple industries · multiple geographies · real money at risk
SpendSignal is informed by operating experience inside some of the largest consumer and B2B platforms — where small attribution errors compound into multi-million-dollar mistakes.
Aggregated across platforms and operating roles.
Real questions from industry leaders across D2C, SaaS, Marketplaces, Fintech, Retail, Media, and Agencies—answered with incrementality intelligence.
Scaling Isn't About Spending More. It's About Scaling What Actually Works.
Most marketing dashboards are confidently wrong.
Credit only what's clicked, not what's influenced
Ignore delayed impact and word-of-mouth effects
Reward channels that harvest demand instead of creating it
Push teams to optimize for reports, not revenue
The result?
Budgets get misallocated. Efficient channels get cut. Growth stalls for reasons no one can explain.
SpendSignal exists to fix this—by measuring incrementality, not attribution theater.
Incrementality modeling without experiments—extracting causal lift from your existing data.
How much revenue did ads really drive—beyond what attribution shows?
Unattributed ROAS
What is the true return on each channel after correcting for bias and lag?
True ROAS (tROAS)
How should we reallocate budget to maximize incremental revenue?
Source Budget Recommendations
What will happen if we actually follow that recommendation?
Predicted ROAD
Everything in the platform ladders to these four outcomes—metrics that speak CFO language.
The mathematical foundation behind decision-grade marketing metrics
Return on Ad Spend, corrected for incrementality
Standard ROAS = Attributed Revenue / Ad Spend
Credits only clicked conversions, ignoring view-through, halo effects, and delayed conversions
Over-credits retargeting and branded search (demand harvesting)
Under-credits prospecting and brand awareness (demand creation)
Baseline Revenue is what you would have earned without any advertising—estimated using historical trends, seasonality, and control variables.
Causal attribution: Models what revenue was actually caused by ads, not just correlated
Lag effects: Captures delayed conversions (e.g., ads today → revenue next week)
Halo effects: Includes unattributed lift from awareness and word-of-mouth
Retargeting harvests existing demand but creates little incremental revenue
Prospecting creates new demand but gets under-credited due to attribution lag
Measures the expected return from each marketing dollar after SpendSignal's recommended budget reallocation
ROAS and tROAS are backward-looking—they tell you what happened, not what will happen
Returns diminish at scale—spending 2x doesn't guarantee 2x returns
Channel interactions matter—cutting one channel affects others
Unlike ROAS, which looks backward, ROAD forecasts the real economic impact of future spend using incrementality-based modeling.
Diminishing returns: Accounts for saturation effects as spend increases
Channel interactions: Models how channels support or cannibalize each other
Confidence intervals: Provides upper/lower bounds, not false precision
Same total budget, reallocated based on predicted marginal returns
Attribution-based. Backward-looking. Click credit only.
Incrementality-based. Backward-looking. Causal impact.
Incrementality-based. Forward-looking. Predictive allocation.
Not just analytics—actionable intelligence that drives budget decisions.
Identify halo and word-of-mouth revenue driven by ads but never credited to them
How much revenue is influenced but unattributed
Which channels create downstream demand
How large your hidden ROI really is
Reported ROAS tells you what got clicked. tROAS tells you what actually worked
Recalculated returns using incrementality
Lag effects and causal impact analysis
Under-credited channels get their due
Once the signal is clear, SpendSignal moves from insight to action
Current vs recommended spend per channel
Reallocation based on marginal incremental returns
Guardrails to control risk and volatility
ROAD (Return on Adjusted Dollars) measures expected returns after budget reallocation
Expected incremental revenue per channel
Overall uplift from reallocation
Confidence ranges, not false precision
ROAS is backward-looking. ROAD is forward-looking.
"How did this channel perform last month?"
"If I invest this much next month, what will I get back?"
ROAD (Return on Adjusted Dollars) measures the expected return from each marketing dollar after SpendSignal's recommended budget reallocation. Unlike ROAS, which looks backward, ROAD forecasts the real economic impact of future spend using incrementality-based modeling.
From Data to Decisions in Minutes
Upload a CSV or Excel file with your spend and revenue data. SpendSignal automatically detects date and revenue columns, channel-wise spend, and optional controls like promotions or events.
SpendSignal applies incrementality modeling using lagged spend features, seasonality and trend correction, and counterfactual 'what-if spend didn't exist' analysis.
You instantly see unattributed ROAS, true ROAS per channel, recommended budget allocation, and predicted revenue impact.
SpendSignal is built for how people actually think.
"Which channel is under-credited?"
"What happens if I move 15% from Meta to Search?"
"Recalculate excluding festive weeks."
"Give me a conservative forecast."
SpendSignal responds with answers, charts, and revised metrics—not just numbers on a screen. Each conversation becomes a decision trail you can revisit or share.
SpendSignal is API-first by design. Developer APIs + Chat Interface = actionable insight pipes.
The chat UI is just one client. The engine is built to plug into your stack.
We focus on actionable insight pipes, not just data plumbing.
SpendSignal doesn't just output numbers—it guides decisions with confidence and explainability.
Run what-if scenarios interactively:
Decision intelligence that executives trust:
This level of decision intelligence is what sets SpendSignal apart from vanity dashboards.
SpendSignal is not a theoretical product.
Its models, assumptions, and decision logic are shaped by:
Platforms valued at over $15 billion
$6.5+ billion in cumulative advertising budgets
Environments where attribution errors impact profit, valuation, and board confidence
These learnings are now encoded into a product that makes the same class of decisions accessible without enterprise complexity.
At $6.5B+ in annual advertising spend:
A 1% misallocation isn't noise — it's a material error
Over-crediting one channel distorts capital planning
Under-crediting another quietly suppresses growth
SpendSignal exists because at this scale, incrementality is not a marketing problem — it's a capital allocation problem.
Thousands of dollars
Short feedback loops
Disposable experimentation
Budgets are locked months in advance
Decisions survive audit and scrutiny
Outcomes show up in earnings, not dashboards
SpendSignal was built after seeing the same pattern repeat at scale:
Reported ROAS stayed flat
Budgets kept increasing
Profit growth lagged expectations
The issue wasn't execution.
It wasn't creative.
It wasn't channel mix.
It was measurement bias — and it compounded faster as spend grew.
SpendSignal is the product that emerged from fixing this problem where the cost of being wrong was measured in millions.
Marketing teams optimize dashboards.
Finance teams optimize outcomes.
No black-box scores. No unexplained magic.
Enterprise teams don't trust answers they can't explain.
All modeling assumptions documented and visible
Lag and incrementality calculations explained
Every scenario can be replayed and audited
Confidence ranges, not false precision.
If you can't defend it in a boardroom, it doesn't belong in the product.
SpendSignal does not rely on cookies, user-level tracking, PII, or device graphs.
It works on aggregated spend and outcome data, making it resilient to regulatory changes, platform deprecations, and regional privacy laws.
Enterprise-safe by design.
No Cookies
No User Tracking
No PII Required
No Device Graphs
SpendSignal is built with enterprise expectations in mind.
Per-customer data isolation
Granular access controls
At rest and in transit
Scales with spend and data volume
Security is treated as a foundation, not a feature.
(SOC2 / ISO badges can be added once formally certified.)
No Experiments
No geo-tests. No budget freezes.
SpendSignal delivers incrementality insights without geo-experiments, budget freezes, complex test setups, or weeks of waiting.
This matters when spend is large and mistakes are expensive.
"This finally explained why cutting 'inefficient' channels kept hurting growth."
Enterprise Marketing Leader
"For the first time, marketing and finance agreed on what was working."
Enterprise Marketing Leader
"ROAD made future spend discussions concrete instead of emotional."
Enterprise Marketing Leader
So decisions are made before money moves — not after damage is done.
Test budget changes safely
Predict revenue impact
Know worst-case scenarios
Side-by-side analysis
Market dynamics vary by region—SpendSignal adapts to local CAC pressures, attribution constraints, and regulation
Choose the plan that fits your needs
For founders and small teams getting started
Decision-makers in fast-growth companies use SpendSignal to move from guesses to clarity
Brands optimizing Meta + Google spend across acquisition funnels
Long sales cycles where attribution breaks down
Regulated markets with high CAC and complex buyer journeys
Two-sided markets balancing supply and demand spend
Omnichannel attribution across online and offline
High LTV users with long engagement windows