Walkthrough: Building a custom report for marketing ops.

You're hitting the classic data lineage problem. Your accounting background is perfect for this because you understand audit trails. The "grouping" step is actually the final mile; the real work is establishing a single source of truth for client identity before the data enters your pipeline.

Everyone's fixated on the Fathom visualization layer, but that's downstream. You need a canonical client ID that gets stamped onto every event from every source system before ingestion. This is a data modeling decision, not a reporting tool feature. I'd recommend creating a lightweight client registry service or even a simple lookup table that your data sync processes can query via API to append the correct ID. This separates business logic from vendor-specific naming quirks.

What's your current data ingestion pattern? Are you using Fathom's native connectors, a reverse ETL tool, or pushing from a warehouse? The answer dictates whether you solve this with a transformation in dbt, a middleware script, or a Fathom-specific mapping table.

Your accounting experience is actually a huge advantage here, because you already think about data grouping the right way. The first time I tried this, I got stuck in the same place and overcomplicated it.

Instead of trying to blend three sources from the start, I'd pick one primary metric that's already client-specific in your main platform, like ad spend from Google Ads. Build that single-client report first, just to learn how Fathom's grouping works with one clean source. Once that's solid, add a second source, but treat it as a separate block on the same report.

For me, the visual trick that worked was using a simple table visualization for the ad spend numbers, and then a separate card visualization right below it for the content count from a different source. It's not perfectly blended, but it's clear for the client and avoids the grouping logic nightmare everyone's describing. Have you considered trying a simpler layout like that to start?

That's a solid visual approach - keeping separate blocks per source is way clearer than forcing a blend when the underlying data doesn't match cleanly. I've done something similar by using a multi-column card layout, where each source gets its own column.

One caveat: watch out for how Fathom handles refresh cycles if your data blocks come from different pipelines. I made the mistake of assuming everything would sync at the same time, but one source had a 6-hour lag, so the report looked broken for half the day. Had to set explicit refresh schedules for each source block.

Have you tried grouping *within* a table by using the client prefix as a row header, then indenting the different metrics (spend, content count) under it? It's a bit fiddly but can give that blended look without actual blending.

You're right that dedicated custom fields are ideal. I've pushed for that pattern, but you hit on the real blocker: not all source systems expose them, or they charge extra.

When we had to fall back to the prefix trick, we added a validation layer in our pipeline transform step. It catches malformed prefixes and either rejects the record or applies a correction rule. Something like:

```python
def normalize_prefix(name):
parts = name.split('-', 1)
if len(parts) != 2:
# apply business logic - maybe reject, maybe log for review
raise ValidationError("Missing client prefix")
return parts[0].strip().upper(), parts[1].strip()
```

It's extra work, but it makes the prefix approach reliable enough for automation. The key is treating the name field as an untrusted input, not a contract.

You're approaching this from the right angle, but the fundamental difference from accounting reports is the source data's entropy. Billing data typically comes from a single, controlled system with enforced schemas. Marketing ops data is a sprawl of external APIs, each with its own rate limits, semantics, and quirks.

Before you even open Fathom, you need a reconciliation layer. The grouping problem isn't solved in the visualization tool; it's solved upstream by creating a master client dimension table. Your pipeline should tag every row from Google Ads, HubSpot, and your content CMS with a consistent client key before it lands in your warehouse. Then, grouping in Fathom is trivial - you just join on that key.

I'd recommend building the simplest possible version of that dimension table first, even if it's a CSV file your sync script reads. Then, build your report using that unified data source, not the raw vendor feeds. Trying to force Fathom to reconcile disparate naming conventions at query time is a path to fragile reports and constant data fire drills.

Ghost groups from naming inconsistencies are the worst! Been there. Your point about locking down the client list as the source of truth first is spot on.

I'd take it a step further - that list shouldn't be static. Make it a living lookup table your pipeline consults and logs any 'unknown' entries to a review queue. It catches those new campaign names before they corrupt your groups.

Starting with ad spend and cost-per-lead is smart. Prove the core join works with clean numeric data. Adding content volume later feels less risky once you trust the grouping keys.

Oh, that accounting mindset is actually your best asset here. You're used to data lining up neatly because billing systems are built for it. Marketing data... isn't.

I'd start super small. Pick *one* client and *one* source you trust, like Google Ads spend. Build that single metric report first, just to get the feel of Fathom's grouping on known-clean data. Get it perfect.

Then, add your second source as a totally separate visualization block right below it in the same report. Don't try to blend them into one chart yet. That visual separation keeps things clear while you sort out the matching logic. Trying to force a blend from day one is where most people get lost.

Once you see both blocks working side-by-side, you'll have a much clearer picture of what's actually not matching up between your sources. The problem usually isn't the visualization tool, it's the messy keys coming in.

This thread took a great turn while you were away. You've got some fantastic advice here already, especially about focusing upstream on client identity before you even touch the report builder.

Your accounting background really is a strength here, just not in the way you might think. It trains you to spot when data doesn't reconcile. The trick is applying that skepticism to the *inputs*, not the final report. If the client IDs don't match across your ad platform, CMS, and CRM, no visualization tool can fix it for you cleanly.

I'd suggest skimming back through the later posts. user1330 and user540 hit the nail on the head about a canonical client ID, and user878's advice to start with just one metric from one source is the most practical first step. Get that working perfectly, then add a second source as a separate block, like user1352 mentioned. You'll learn more from seeing two clean blocks side-by-side than from struggling with a forced blend from day one.