How To Use Fospha & Ad Platform Reporting

Ad platforms and Fospha are both valuable tools for understanding marketing performance, but they're designed to answer different questions. This guide explains how each approach works, why their numbers won't always match, and how to use them together to get the fullest possible picture of what's driving results.

The underlying difference between ad platforms and Fospha

1. Revenue baseline and cross-channel visibility

Ad platforms: measure conversions within their own ecosystem and don't have visibility into sales driven through other marketing or sales channels, such as Amazon or TikTok Shop. Because each platform reports independently, adding together the revenue reported across all ad platforms typically produces a total that's higher than the actual revenue recorded in a brand's ecommerce platform, as more than one platform can claim credit for the same purchase.

Fospha: starts from 100% of a brand's actual revenue, reconciled against Shopify, custom ecommerce, or other order sources, and distributes credit across every channel using our five-step model methodology. We isolate each channel's individual impact based on the strength of the relationship we find between that channel's impressions, views, and clicks, and that day's sales. This means every channel is measured on a consistent basis, and the total always reconciles to 100% of the revenue achieved on a given day.

2. Attribution windows

Ad platforms: attribute conversions within a set attribution window — a defined period of time after someone views or clicks an ad within which a resulting purchase will be counted (common defaults sit around a 7-day click and 1-day view window, though this varies by platform and campaign type). A purchase that happens outside the chosen window won't be attributed to that platform, even if the ad played a part in the customer's decision.

Fospha: ingests two years of historical data to understand a brand's seasonal trends, then applies a dynamic 90-day learning window. This means a sale is never missed simply because it fell outside a fixed reporting window.

3. Data collection method and privacy

Ad platforms: have traditionally relied on pixel tracking to observe user behaviour. Since the iOS 14 privacy updates, this has created gaps in the data, as purchases made by users who've opted out of tracking can go unrecorded. Server-side tracking (such as Conversions APIs) has recovered some of this signal, but only a portion of it. Because this tracking is tied to a brand's own website, it also has limited visibility into revenue generated on other sales channels such as Amazon or TikTok Shop, which means platforms can miss out on credit for demand they helped generate there.

Fospha: doesn't rely on pixels or user-level tracking. We model using aggregate data, which means we're privacy-safe by design and aren't exposed to the data loss that pixel-dependent tracking can experience. Because we're not limited to pixel-based tracking, we can measure the impact of marketing across a brand's website as well as on other sales channels, including Amazon, TikTok Shop, and app, giving each channel credit for the sales it influences beyond the website alone.

4. Attribution model

Ad platforms: generally use their own version of a last-touch or click-based attribution model to assign credit for a conversion. For example, Meta and TikTok both prioritise the most recent click within their attribution window, while Google Ads primarily uses a data-driven attribution model within the Google ecosystem. This works well for capturing the immediate effect of a click, but it can undercount the impact of awareness and consideration activity, particularly where an impression didn't lead to a click, or the resulting purchase fell outside the attribution window.

Fospha: uses five models, including a click-based model that fully captures the impact of demand-generation activity, and an MMM-style model that identifies the relationship between impressions, views, and clicks and the downstream sales they lead to. This gives channels that build demand, not just capture it, fair credit, and means Fospha is well-suited to measuring the full funnel, not just the bottom of it.

5. Reporting cadence

Ad platforms: data typically updates hourly, making it a strong source for intra-day insight, particularly for on-platform metrics like CTR and CPM, and for a directional read on how creative is performing.

Fospha: looks at the complete cross-channel picture and updates daily. It's designed to give a full, blended steer on marketing decisions, rather than to support hour-by-hour optimisation.

Why ad platform and Fospha outputs are different

Given the differences above, it's normal, and expected, for ad platforms and Fospha to report different numbers for the same activity. This isn't a sign that either source is wrong; it reflects genuine differences in what each is measuring and how.

Why this is more than 'just another attribution model'

Fospha starts from that same GA4 last-click baseline, then builds up from it in a series of deliberate steps, each one designed to capture something a last-click view structurally can't.

Foundation — data quality assurance: Before any modelling happens, Fospha validates the underlying GA4 data: deduplicating orders and removing zero-value or excluded transactions, all checked against a brand's ecommerce source of truth. This gives every step that follows a clean, reliable foundation, something a raw last-click export doesn't have.
Step 1 — Click measurement: Rather than relying on a single last-click rule, Fospha combines four separate click models, each with its own strengths, into one ensemble click model. This gives a more robust read on which paid and organic clicks are actually driving sales each day, rather than defaulting all the credit to whichever click happened last.
Step 2 — Post-purchase attribution: Fospha enriches click-based measurement with zero-party data, such as discount codes and post-purchase survey responses. Layering in what customers self-report ensures high-impact touchpoints are fairly credited, even in cases where click data alone wouldn't have picked them up.
Step 3 — Reconciliation: Fospha validates GA4's conversion and revenue data against a brand's ecommerce source of truth. It's common for this to reveal that a meaningful share of conversions, often around 11%, aren't captured in GA4 at all. Those missing conversions are then allocated proportionally across channels, based on the outputs of Fospha's click measurement, so the total always reconciles to 100% of actual revenue.
Step 4 — Impressions measurement: Finally, Fospha's impressions measurement captures the impact of upper-funnel, awareness-driving activity, crediting it appropriately even when the resulting demand shows up later through channels like Direct, branded search, or organic. This is the activity a click- or last-touch-only view is structurally unable to see.

Each of these steps is solving for something specific that last-click measurement misses: data quality, the limits of click-only tracking, self-reported influence, the revenue gap against a brand's own ecommerce data, and upper-funnel demand generation. That's why Fospha's output is a genuinely different, and more complete, number, not just a different flavour of the same model.

What this means for comparing outputs

An ad platform's numbers describe what happened inside that platform's own attribution window, based on the interactions it can track. Fospha's numbers describe the complete impact of every channel, including that platform, on a brand's total revenue, built from a broader and more complete view of channel performance rather than a single attribution window. Widening the comparison (for example, longer attribution windows, or channels with a strong awareness role) will generally widen the gap between the two, and that's expected too.

The two shouldn't be seen as competing for the 'right' number. They answer different questions, and used well, they reinforce each other: ad platforms are strong on immediate, in-platform signal, while Fospha is strong on the complete, cross-channel and incremental picture. Trends across both should generally point in the same direction, even where the absolute numbers differ.

How to use ad platforms and Fospha together

Ad platforms and Fospha are most powerful when used together, each for what it does best.

When to use ad platform data

Immediate budget decisions: reacting quickly to spikes or dips in performance, such as adjusting bids or spend within hours.
Time-sensitive sales moments: for events like BFCM or flash sales, in-platform data gives a real-time read on how things are tracking on the day.
Short-term, on-platform indicators: engagement metrics like clicks, CTR, and CPC are best tracked in-platform for up-to-the-minute insight.
Creative testing: seeing which creative variants are performing best hour-by-hour, before scaling the winner.

When to use Fospha data

Budget planning and optimisation: understanding which channels are truly driving incremental sales over a broader period, and optimising a fixed budget across channels accordingly.
Stable, cross-channel measurement: Fospha's outlier detection avoids over-attributing credit to channels during major spikes, such as Black Friday.
Full-funnel visibility: connecting upper-funnel, awareness activity to sales that happen days or weeks later.
Post-period and year-on-year analysis: using stable, revenue-based metrics to understand true channel effectiveness and make accurate comparisons against previous periods.

Bringing the two together

Step	What to do
Cross-verify performance	Use Fospha as the cross-channel view of blended performance, including new customer metrics, and ad platforms for immediate, in-platform signals on bidding, creative, and audience.
Reconcile complementary data	Where an ad platform reports a strong result but Fospha shows a different return once each channel's full impact on revenue is considered, use Fospha's cross-channel view to understand the channel's overall contribution, then look at refining budget allocation within that platform accordingly.
Spot optimisation opportunities	Use Fospha to identify diminishing returns or saturation across channels, then make bidding, targeting, or creative adjustments within the relevant ad platform in response.
Test and iterate	Use Fospha's incremental forecasting to plan changes in spend by channel, execute those changes in the relevant ad platform, and measure both the short-term in-platform result and the blended, cross-channel result in Fospha.
Prevent data silos	Use Fospha's ecommerce reconciliation to keep total revenue accurately attributed across the whole marketing mix, and feed granular, channel-level insight from ad platforms back into that cross-channel view.

Key takeaways

Fospha delivers a comprehensive, cross-channel perspective, useful for understanding true ROI across channels and the incremental impact and new customer value each one drives.
Ad platforms excel at maximising performance within their own ecosystem, providing fast, granular data to refine bidding, targeting, and creative.
Used together, they give a fuller picture: Fospha for the holistic, cross-channel view, and ad platforms for granular, real-time optimisation.

Understanding why the two are expected to differ, and using each for what it does best, helps ensure decisions are grounded in both the complete, cross-channel picture and the fast-moving, in-platform detail.