Just caught the latest Forrester Wave™ for Attribution and Measurement Q2 2025, and wow—some of the placements really got me thinking. I was fully expecting to see the usual suspects in the leader quadrant, but one in particular being positioned as a *strong* leader surprised me a bit, given the chatter I hear in the trenches about implementation complexity.
It got me reflecting on our own cloud cost attribution journey. Finding the right tool to attribute spend (whether marketing or infrastructure) feels similar: it's all about the model's accuracy and how it handles messy, real-world data. In FinOps, we argue over tagging vs. hierarchy-based allocation; in marketing attribution, it's last-click vs. data-driven models.
So for those who've dug into these platforms:
* **What's your take on the methodology weightings in these reports?** Do they align with what actually matters day-to-day, like cross-device and cookieless measurement capabilities?
* **For the named leader, does the real-world savings (or ROI clarity) they deliver match the hype?** In our world, we'd quantify it: "Using platform X, we improved our RI coverage by 35%, saving $80k/month." Are you seeing that level of concrete outcome?
I'm especially curious about data connector breadth. A platform can have a fancy model, but if it can't ingest our event streams cleanly from all our cloud services and SaaS tools, the attribution gets fuzzy fast.
Let's compare notes. Which platforms have you tried, and what was the actual lift in decision-making clarity?
Right-size everything
You're right to question the methodology weightings. In my experience, Forrester's criteria often overweight feature checklists and vendor roadmaps, while underweighting the operational tax of data pipeline hygiene and model drift. A platform can score high on "advanced modeling" but require a dedicated data engineering team to keep its input data accurate, which the report barely factors into the cost analysis.
On real-world savings, I've never seen a vendor-provided ROI case study that survived a third-party audit. The "35% savings" claims usually ignore baseline complexity - they compare their model's output to a intentionally naive single-touch model, not to a reasonably maintained first-party data warehouse solution. The actual lift is often in the single-digit percentages once you account for the platform's own subscription and implementation costs.
I'm more interested in seeing a benchmark where these platforms process the same messy, multi-source dataset. The variance in attributed spend between them would tell us more about their "accuracy" than any vendor's marketing slide.
Show me the benchmarks
You've hit on a key disconnect between analyst evaluations and operational reality. The methodology weightings often prize a platform's theoretical maximum capability, not its mean time to value in a typical enterprise environment. A platform scoring high on "advanced modeling" might indeed handle cookieless measurement, but the cost of configuring and validating those models against your specific data pipelines is an afterthought in the scoring.
On ROI, I'm deeply skeptical of any percentage claim without a fully transparent baseline. In infrastructure, we'd demand the exact query used to establish the pre-tool spend. For attribution, the parallel would be the control model. If a vendor claims a 35% improvement, is that versus a properly maintained first-party model, or versus a deliberately broken last-click setup? The real metric should be delta versus a reasonably optimized status quo, and you're right to ask for hard numbers like improved RI coverage. I've yet to see a case study that provided the granularity needed to audit that claim.
infra nerd, cost hawk
> "the exact query used to establish the pre-tool spend"
This is the whole game in cloud cost tooling, too. I've watched vendors demo a "30% savings" claim and when you dig into the baseline, it's comparing their cost allocation to a raw untagged account where everything is just "no-owner/unknown." No shit you can save 30% when the starting point is anarchy. In FinOps we call that "the dogfood baseline" - it's deliberately messy so the vendor looks like a hero.
The real test is: give me the same raw CUR dump, run it through your model vs. a simple tag-based cost center allocation, and tell me what the actual delta is. Extra points if you can reproduce the vendor's number with a $5/mo Lambda script. Ever seen one that actually publishes the SQL they used? Me neither.