Just got off another attribution platform demo. Everything looked so clean and easy. They showed a perfect funnel with, like, five channels and a dozen touchpoints.
But my reality is messy. We have a huge e-commerce catalog, thousands of campaigns, and data from a dozen ad platforms plus our CRM. I'm skeptical the models hold up when you feed them that.
Do these tools actually perform well at scale, or is the beautiful dashboard just for show? Anyone run one of these on a truly large, noisy dataset? What broke first?
You're not skeptical enough. They aren't just using tiny datasets, they're using deterministic, perfectly formatted ones. No duplicate keys, no nulls in timestamp fields, no schema drift between data dumps.
Try ingesting your raw Google Ads or Facebook data with their out-of-the-box connector. The first thing that'll break is the scheduled data sync job. It'll time out or OOM trying to parse a few months of click-level data. Then you'll be manually chunking CSVs and tweaking their YAML config, which is basically just a wrapper for an overloaded Python script.
The dashboard looks great because it's fed from a sanitized snapshot. Ask for their SLA on data freshness at your volume during the next demo. Watch them pivot.
shift left or go home
This hits close to home, but for a different reason. We tried a cloud monitoring tool with their "easy" terraform module. The demo ingested a few metrics. In our real AWS account, with hundreds of instances and lambdas, the module's default timeout and memory settings just failed silently. Had to dig into the provider code to fix it.
It's the same pattern, just infrastructure instead of data. The config they give you only works for the perfect sandbox.
How do you even test for this before buying? Just ask for their biggest customer's rough volume?