Every Meta advertiser has seen this happen:

• Shopify shows a normal sales day.
• Meta shows 0 purchases.
• ROAS looks dead.
• Panic sets in.

The next morning, suddenly Meta “catches up” and attributes conversions in bulk.

It feels inconsistent.
It feels unpredictable.
It feels broken.

But in 2025, under Meta’s Andromeda delivery and measurement stack, this behavior is actually normal and expected.

This guide explains exactly why attribution appears broken some days - and how to read your data with accuracy and confidence.

The Core Insight: Attribution Is Not Real-Time (And Never Has Been)

Attribution is not a live feed of truth.

It is a model, based on:

• delayed signals
• partial privacy data
• server events
• behavioural inference
• probability-based matching

Meta does not report conversions the moment they happen - it reports them when it’s confident enough to attribute them.

In 2025, this matching process has more friction than ever.

Why Meta Attribution Looks “Broken” Some Days - The Real Reasons

Let’s break down the actual causes.

1. Delayed Attribution Windows

Meta attribution is often 24–48 hours behind, and sometimes longer.

Reasons:

• privacy delays
• batching of events
• cross-device matching
• delayed browser signals
• offline conversion matchbacks
• probabilistic attribution windows

This is why:

• a “bad Monday” becomes a “normal Tuesday”
• sales suddenly appear in bulk
• ROAS recalculates overnight

Takeaway:

Daily ROAS is unreliable. 3–7 day windows tell the truth.

2. Cross-Device Behavior Makes Attribution Harder

A user might:

1. view your ad on mobile
2. browse later on tablet
3. purchase on desktop

This becomes a matching challenge.

Meta can attribute the conversion eventually - but often not immediately.

Symptoms:

• conversions appear the next day
• long gaps in reporting
• ROAS looks dead until catch-up events arrive

3. Signal Loss From Privacy Rules

Meta no longer sees the full journey.

Post-iOS changes created:

• missing tracking data
• partial event visibility
• lost parameters
• hashed identifiers
• incomplete browser signals

So Meta must:

• estimate
• infer
• model
• triangulate

Which takes time.

Result:

Attribution often lands late or in “batches.”

4. Event Deduplication and Filtering

Meta filters out:

• duplicate events
• spam events
• bot traffic
• invalid submissions
• broken pixel fires
• misfired server events
• inconsistent timestamps

Sometimes this creates a gap where Meta waits to verify which events to keep.

Result:

Conversions appear delayed - not lost.

5. Auction Window Mismatch

Meta attributes based on:

• click-through windows (1-day, 7-day)
• view-through windows (1-day)
• impression paths

But your store reports the moment of purchase.

These timelines don’t match.

Example:

• User clicks Sunday
• Buys Tuesday
• Shopify reports Tuesday
• Meta attributes to Sunday

So on Tuesday:
Meta shows nothing.
Shopify shows sales.

On Wednesday:
Meta “catches up.”

6. Blended Revenue vs Platform Revenue

Your platform (Shopify, Stripe, WooCommerce) shows:

• total sales
• blended revenue
• returning customers
• email flows
• organic traffic
• direct traffic
• brand search

Meta only shows:

• what Meta believes it influenced

Different scopes → different numbers.

Expect divergence.

7. Meta Reallocates Attribution Based on Confidence

This is a key concept few advertisers understand.

Meta often:

• re-evaluates attribution
• moves conversions between ad sets
• reassigns credit when new signals arrive
• withdraws credit when confidence shrinks

This creates:

• sudden spikes
• sudden drops
• random-looking shifts
• delayed credit

Example:

3 conversions disappear from one ad set
→ then reappear 2 days later in another.

This is normal behavior.

8. Untracked Sessions Are Now a Huge Portion of Traffic

Users frequently:

• browse in private mode
• block tracking
• use ad blockers
• disable cookies
• use new privacy browsers
• bounce across devices

When Meta cannot observe the journey, it must rely on:

• probabilistic modeling
• history-based inference
• behavioral prediction

This takes longer - and is often imperfect.

9. Multiple Creatives, Placements, and Ad Sets Compete for Attribution

If a user:

• sees multiple ads
• clicks multiple creatives
• engages with several surfaces

Meta must determine which impression or click “caused” the purchase.

Sometimes this requires:

• multi-touch analysis
• trend matching
• event comparison

So attribution flows in later than expected.

10. The Attribution Model Itself Has Evolved

In 2025, Meta attribution blends:

• deterministic matching
• probabilistic modeling
• modeled conversions
• offline matchbacks
• event deduplication
• confidence scoring

More layers → more potential delay.

So Why Does Meta Look Broken Some Days?

Because attribution is:

• delayed
• modeled
• incomplete
• reallocated
• cross-device
• privacy-limited
• confidence-based
• non-linear

This creates the illusion of inconsistency.

The underlying conversion reality is usually stable, but Meta’s reporting is delayed.

How To Read Attribution Correctly in 2025

Here’s the modern approach.

1. Stop judging performance daily

Use 3-day or 7-day windows.

2. Look at blended revenue + spend

Platform ROAS tells the truth, not Meta ROAS.

3. Accept that attribution “catches up”

Don't react to early-day reporting.

4. Avoid knee-jerk pausing

You will kill winners prematurely.

5. Evaluate creative + pocket behavior, not daily ROAS

Creative performance is more predictive than attribution.

6. Trust trendlines, not snapshots

Snapshots lie. Trendlines do not.

7. Maintain proper pixel + CAPI setup

Signal quality reduces reporting delays.

Final Thoughts

Meta attribution isn’t broken - it’s evolving.

In a privacy-restricted environment, attribution must rely on:

• incomplete signals
• browser noise
• cross-device behavior
• probabilistic matching

This leads to:

• delayed reporting
• sudden “catch-up days”
• inconsistent daily ROAS
• attribution shifts between ad sets
• data that looks chaotic but isn’t

When you understand these patterns, your campaigns become far easier to evaluate - and you avoid sabotaging good ads based on misleading daily numbers.