Why Meta, Google, your CRM and Sissel show different numbers
Learn what each system counts, why totals disagree and how to reconcile a difference without forcing false parity.
The systems answer different questions
A difference is not automatically an error. Each system sees a different part of the journey and applies its own definitions. Before comparing totals, write down the exact business question you expect each number to answer.
| System | What it primarily knows | Question it can answer |
|---|---|---|
| Ad platform | Activity and conversions reported to that platform | What received credit under this platform’s rules? |
| Website analytics | Sessions and actions observed on the website | What happened during tracked website visits? |
| CRM | People, stages, deals and recorded values | What did sales accept, progress or close? |
| Sissel | Consented visits linked to CRM outcomes | What source received credit under this named model and evidence? |
Common reasons totals differ
- Different outcome definitions
One system may count form submissions while another counts unique qualified people or won deals.
- Different attribution rules
A platform can credit itself under its own click or view rules while Sissel applies your selected source-based model.
- Different windows and dates
Reports may use the click date, outcome date, upload date or a different timezone and lookback.
- Identity and consent gaps
A CRM record may exist even when no consented browser journey can be linked to it.
- Duplicates and revisions
A CRM can contain duplicate people, repeated webhooks or a deal whose value changed after its first creation.
- Processing delays
CRM syncs, ad-platform imports and delivery acknowledgements do not necessarily arrive at the same moment.
- Missing campaign information
An untagged link or stripped click identifier can leave a real visit without a usable campaign source.
A practical reconciliation sequence
- 1Name the outcome
Compare the same event, such as qualified lead—not “conversions” in one system and all leads in another.
- 2Match the reporting window
Use the same start, end and timezone. Confirm whether the report groups by visit date or outcome date.
- 3Match the attribution definition
Document model, lookback and whether views, direct visits or cross-device assumptions are included.
- 4Compare a sample of records
Open several individual journeys on both sides instead of reasoning only from aggregate totals.
- 5Classify each difference
Separate expected definition differences from missing collection, identity, mapping or delivery evidence.
- 6Fix the data path, then rerun
Correct the source issue and save a fresh named comparison. Do not rewrite the definition merely to make totals equal.
When a difference is useful
A stable, explained difference can be healthy. For example, the CRM may show more qualified leads than Sissel because some came from referrals, manual entry or journeys without usable website evidence. The correct result is to label those outcomes honestly, not assign them to a campaign without proof.