Attribution and marketing mix modeling
Asked as “marketing attribution and marketing mix modeling platform”, and as “attribution and MMM tool”, on behalf of a mid-market B2B software company. 27 first choices recorded across the direct, paraphrase, budget and scale prompts, six models each.
Standing
Dreamdata30% of first choices with no negative labels at all across 22. The incumbents are the criticized half of this category: GA4 at 43% negative, Adobe Marketo Measure at 57%.
First-choice share
Recommended versus criticized
Every product with at least 10 labels here, on both axes. Leader at 30% or more of first choices; criticized at 25% or more negative labels. Markers are keyed to the table below.
Negative label rate →
25% negative
30% first choices
Criticized challenger
Criticized default
Accepted challenger
Endorsed leader
01
02
03
04
05
06
07
08
09
10
110%First-choice share →60%
Full standing
| # | Product | First choices | Negative rate | Labels | Quadrant |
|---|---|---|---|---|---|
| 01 | Dreamdata | 30% | 0% | 22 | accepted challenger |
| 02 | HockeyStack | 19% | 0% | 17 | accepted challenger |
| 03 | Google Meridian | 15% | 19% | 21 | accepted challenger |
| 04 | Google Analytics 4 | 15% | 43% | 14 | criticized challenger |
| 05 | Triple Whale | 4% | 27% | 15 | criticized challenger |
| 06 | Meta Robyn | 4% | 38% | 16 | criticized challenger |
| 07 | Measured | 0% | 7% | 14 | accepted challenger |
| 08 | Recast | 0% | 17% | 12 | accepted challenger |
| 09 | Rockerbox | 0% | 21% | 14 | accepted challenger |
| 10 | Northbeam | 0% | 36% | 14 | criticized challenger |
| 11 | Adobe Marketo Measure | 0% | 57% | 14 | criticized challenger |
Warned against
A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Google Analytics 443%
6 of 14 labels negative · 4 of 6 models · 1 hard negative
“**Avoid using attribution reports alone as the final authority for cross-channel investment.**” GPT-6 Astra, negative prompt
Adobe Marketo Measure57%
8 of 14 labels negative · 4 of 6 models
“Bizible became Marketo Measure under Adobe, and B2B practitioners have complained about slowed development post-acquisition.” Claude Opus 5, negative prompt
Google Meridian19%
4 of 21 labels negative · 4 of 6 models
“Meridian and Robyn are free and production-grade, but they require in-house econometrics and Python capability... At 500 people, buy, unless you already have econometricians.” Claude Opus 5, scale prompt
Meta Robyn38%
6 of 16 labels negative · 4 of 6 models
“Robyn’s documentation emphasizes analytical expertise, modeling assumptions, and experiment calibration. Neither framework removes the need for competent implementation.” GPT-6 Astra, negative prompt
Held under rewording
Whether each model's first choice on the direct prompt survived the paraphrase in this category, under the strict rule.
Claude Opus 5
Changed
Claude Opus 4.8
Held
GPT-6 Astra
Changed
GPT-5.6 Sol
Held
Gemini 3.1 Pro
Changed
Perplexity Sonar Pro
Held
Noise floor in this category
Flips between the edition run and its calibration repeat (simulated until 12 September 2026). Six prompts per model is a small sample; the index-wide floor is the number to trust.
Claude Opus 5
0 of 4 flipped
Claude Opus 4.8
1 of 3 flipped
GPT-6 Astra
1 of 3 flipped
GPT-5.6 Sol
0 of 4 flipped
Gemini 3.1 Pro
5 of 5 flipped
Perplexity Sonar Pro
1 of 4 flipped
What each model said, prompt by prompt
Six framings of the same buying question, each sent to every model in a fresh session with search on. Bold is the first choice, grey the alternatives, red what the answer argued against. Computed from the raw judge labels.
| Prompt | Claude Opus 5 | Claude Opus 4.8 | GPT-6 Astra | GPT-5.6 Sol | Gemini 3.1 Pro | Perplexity Sonar Pro |
|---|---|---|---|---|---|---|
Direct recommendation What is the best marketing attribution and marketing mix modeling platform for a mid-market B2B software company? | Dreamdata, HockeyStack Factors.ai, Google Meridian against: Native HubSpot/Salesforce attribution, Recast | Dreamdata, HockeyStack Factors.ai, Improvado, Measured, SegmentStream | CaliberMind Dreamdata, HockeyStack | CaliberMind Dreamdata, HockeyStack, Ruler Analytics | Dreamdata, HockeyStack InfiniGrow, RevSure against: Adobe Marketo Measure, Meta Robyn, Nielsen | Dreamdata Integrate, RevSure |
Paraphrase Which attribution and MMM tool would you recommend to a mid-sized B2B software company? | Dreamdata Factors.ai, Google Meridian, HockeyStack, HubSpot Marketing Hub +1 against: 6sense, Adobe Marketo Measure, Recast | Dreamdata, HockeyStack Google Meridian, HubSpot Marketing Hub, Meta Robyn, Recast against: Adobe Marketo Measure | Dreamdata HockeyStack against: Paramark | CaliberMind Google Meridian, HockeyStack against: Paramark | HockeyStack, InfiniGrow Cassandra, Dreamdata against: Adobe Marketo Measure | Dreamdata CaliberMind, HockeyStack, HubSpot Marketing Hub, SegmentStream against: Adobe Marketo Measure |
Comparative What are the top marketing attribution and marketing mix modeling platforms and how do they differ? | no first choice Dreamdata, Google Meridian, Haus, HockeyStack +7 against: Analytic Partners, Ebiquity, Funnel +4 | no first choice | no first choice Ekimetrics, Northbeam, Recast, Rockerbox | Analytic Partners Adjust, Adobe Marketing Campaign Analytics, Adobe Marketo Measure, AppsFlyer +11 | Northbeam, Recast, Triple Whale Adobe Marketo Measure, Cometly, Dreamdata, Lifesight +3 against: Google Analytics 4, Google Meridian, Meta Robyn | SegmentStream Dreamdata, Measured, Northbeam, Rockerbox against: Google Analytics 4 |
Budget constrained What is the best marketing attribution and marketing mix modeling platform for a company with a limited budget? | Google Meridian Dreamdata, Google Analytics 4, HubSpot Marketing Hub, Meta Robyn +2 against: Northbeam | Google Analytics 4, Google Meridian, Meta Robyn ActiveCampaign | Google Analytics 4 Google Meridian, Triple Whale against: Rockerbox | Google Analytics 4, Google Meridian Stella against: Funnel | Google Meridian, Triple Whale AnyTrack, Dreamdata, Fibbler, Meta Robyn +3 against: Measured, Rockerbox, SegmentStream | Google Analytics 4 Cometly, Dreamdata, Triple Whale |
Scale constrained We are a 500 person company evaluating a marketing attribution and marketing mix modeling platform. What should we look at? | no first choice against: Google Meridian, Meta Robyn | no first choice | no first choice Dreamdata, Google Meridian, HockeyStack, Measured +3 | no first choice Google Meridian, Meta Robyn | no first choice against: Kantar, Nielsen | no first choice |
Negative framing Which marketing attribution and marketing mix modeling platforms should I avoid or be cautious about? | Google Meridian, PyMC-Marketing against: Adobe Marketo Measure, Adometry, Attribution 360 +8 | no first choice | no first choice against: Google Analytics 4, Google Meridian, Meta Robyn +3 | no first choice against: Adobe Attribution AI, Google Analytics 4, Google LightweightMMM +8 | Measured, Recast Dreamdata, Google LightweightMMM, HockeyStack, LiftLab +2 against: Google Analytics 4, Kantar, Looker Studio +6 | no first choice against: Adobe Analytics, Adobe Marketo Measure, Google Analytics 4 |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
Category-scoped readings
HubSpot read as HubSpot Marketing Hub
Unresolved, counted raw
Adobe Marketing Campaign Analytics
Native HubSpot/Salesforce attribution
Salesforce
Discontinued, still offered
Gemini 3.1 Pro named Google LightweightMMM as alternative on the negative prompt. Deprecated by Google in favor of Meridian.
Every prompt and answer
One row per call: the version string exactly as returned, whether the model searched, sources cited, latency and cost. Full answer text is in the responses download on the Data page.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency | Cost |
|---|---|---|---|---|---|---|---|
| Direct recommendation | Claude Opus 5 | claude-opus-5 | 2026-09-08 13:15 | yes | 31 | 74 s | $0.38 |
| Direct recommendation | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 13:16 | yes | 29 | 71 s | $0.32 |
| Direct recommendation | GPT-6 Astra | gpt-6-astra | 2026-09-08 13:18 | yes | 6 | 52 s | $0.51 |
| Direct recommendation | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 13:19 | yes | 5 | 91 s | $0.35 |
| Direct recommendation | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 13:17 | yes | 11 | 49 s | $0.06 |
| Direct recommendation | Perplexity Sonar Pro | sonar-pro | 2026-09-08 13:16 | yes | 19 | 8 s | $0.01 |
| Paraphrase | Claude Opus 5 | claude-opus-5 | 2026-09-08 13:21 | yes | 33 | 66 s | $0.28 |
| Paraphrase | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 13:21 | no | 0 | 24 s | $0.06 |
| Paraphrase | GPT-6 Astra | gpt-6-astra | 2026-09-08 13:23 | yes | 6 | 41 s | $0.38 |
| Paraphrase | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 13:24 | yes | 5 | 42 s | $0.18 |
| Paraphrase | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 13:22 | yes | 13 | 45 s | $0.06 |
| Paraphrase | Perplexity Sonar Pro | sonar-pro | 2026-09-08 13:21 | yes | 20 | 9 s | $0.01 |
| Comparative | Claude Opus 5 | claude-opus-5 | 2026-09-08 13:25 | yes | 41 | 90 s | $0.33 |
| Comparative | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 13:26 | yes | 40 | 62 s | $0.26 |
| Comparative | GPT-6 Astra | gpt-6-astra | 2026-09-08 13:29 | yes | 18 | 107 s | $0.77 |
| Comparative | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 13:31 | yes | 16 | 105 s | $0.40 |
| Comparative | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 13:27 | yes | 16 | 37 s | $0.05 |
| Comparative | Perplexity Sonar Pro | sonar-pro | 2026-09-08 13:27 | yes | 19 | 18 s | $0.02 |
| Budget constrained | Claude Opus 5 | claude-opus-5 | 2026-09-08 13:32 | yes | 38 | 66 s | $0.27 |
| Budget constrained | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 13:33 | yes | 25 | 59 s | $0.26 |
| Budget constrained | GPT-6 Astra | gpt-6-astra | 2026-09-08 13:35 | yes | 5 | 40 s | $0.38 |
| Budget constrained | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 13:36 | yes | 5 | 53 s | $0.24 |
| Budget constrained | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 13:34 | yes | 15 | 45 s | $0.05 |
| Budget constrained | Perplexity Sonar Pro | sonar-pro | 2026-09-08 13:33 | yes | 20 | 8 s | $0.01 |
| Scale constrained | Claude Opus 5 | claude-opus-5 | 2026-09-08 13:37 | yes | 37 | 91 s | $0.30 |
| Scale constrained | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 13:38 | no | 0 | 30 s | $0.07 |
| Scale constrained | GPT-6 Astra | gpt-6-astra | 2026-09-08 13:40 | yes | 8 | 63 s | $0.49 |
| Scale constrained | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 13:41 | yes | 4 | 77 s | $0.22 |
| Scale constrained | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 13:39 | no | 0 | 30 s | $0.03 |
| Scale constrained | Perplexity Sonar Pro | sonar-pro | 2026-09-08 13:38 | yes | 20 | 17 s | $0.03 |
| Negative framing | Claude Opus 5 | claude-opus-5 | 2026-09-08 13:43 | yes | 40 | 102 s | $0.40 |
| Negative framing | Claude Opus 4.8 | claude-opus-4-8 | 2026-09-08 13:44 | yes | 37 | 56 s | $0.24 |
| Negative framing | GPT-6 Astra | gpt-6-astra | 2026-09-08 13:46 | yes | 7 | 59 s | $0.40 |
| Negative framing | GPT-5.6 Sol | gpt-5.6-sol | 2026-09-08 13:47 | yes | 10 | 82 s | $0.32 |
| Negative framing | Gemini 3.1 Pro | gemini-3.1-pro-preview | 2026-09-08 13:45 | yes | 12 | 47 s | $0.06 |
| Negative framing | Perplexity Sonar Pro | sonar-pro | 2026-09-08 13:44 | yes | 20 | 12 s | $0.02 |