
Short answer
Quick answer: buy attribution for budget allocation across channels; run holdouts for whether a specific programme works. Teams that conflate the two argue about model settings for months. Pricing shapes below as of September 2026 — most vendors here quote; verify live.
Paid-media allocation: which channels deserve the next pound, at a granularity ad platforms cannot be trusted to self-report. Journey visibility: what a long B2B buying process actually touched before the deal, across anonymous and known stages. Reporting to someone else: a defensible number for a board or a client. Most tools do one well and the others adequately, and most disappointment comes from buying for the first and expecting the second.
| Tool | Strongest at | Data it needs | Pricing shape |
|---|---|---|---|
| Dreamdata | B2B journey stitching | CRM + ads + site | Tiered then quoted |
| HockeyStack | B2B revenue analytics | CRM + ads + site | Quoted |
| Northbeam | Ecommerce paid media | Ad platforms + store | Quoted |
| Triple Whale | Ecommerce dashboards | Shopify-centric | Tiered |
| GA4 | Free baseline | Site + tagging discipline | Free |
| Warehouse-native | Owning the model | A warehouse and SQL | Engineering time |
| Questera | Did the message move behaviour | Lifecycle events + holdouts | Tiered by usage |
Dreamdata and HockeyStack earn their place in B2B, where a deal involves a dozen touches over months and the CRM holds the outcome. Both do real work joining anonymous activity to known accounts. Neither escapes the fundamental limit: they model credit across what they can see, and a great deal of B2B influence is invisible — the Slack recommendation, the conference conversation, the colleague who forwarded a link.
Northbeam and Triple Whale live in ecommerce, where attribution pays for itself at media scale and the feedback loop is days rather than quarters. GA4 deserves more credit than it gets: for most teams under a certain spend, a properly configured GA4 answers the allocation question well enough to make a paid tool a luxury. Warehouse-native attribution — models written in SQL over your own data — is the honest endpoint for teams who want to argue about the model rather than about the vendor.
Questera is in this list for the distinction rather than the category, and we would rather be clear about it than claim a category we do not compete in: we do not do paid-media attribution. What lifecycle messaging needs is incrementality — the same cohort, with and without the message — which our holdout design worksheet covers, and which no attribution model can substitute for. If you came here to allocate ad budget, buy one of the six above.
Ask: "Show me a conversion where your model and the ad platform disagree, and explain which touches each of you counted." Every vendor has these — platforms self-report generously and models differ — and the quality of the answer tells you whether you are buying a measurement philosophy you understand or a number you will have to defend without being able to explain. Pair it with our attribution window worksheet, because window length quietly decides more of the reported result than the model does.
What is the best marketing attribution software?
Dreamdata or HockeyStack for B2B, Northbeam or Triple Whale for ecommerce, GA4 as the free baseline, warehouse-native when you want to own the model. For lifecycle messaging, run holdouts instead.
Is multi-touch attribution accurate?
It is precise, not accurate: a model distributing credit by rules you chose. Use it to compare periods under one stable model rather than to settle causation.
Attribution or experiment?
Attribution for allocating budget across channels; an experiment for whether a specific programme worked. They answer different questions and substituting one for the other is the root of most attribution arguments.
See it in action

