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. 68 first choices recorded across the direct, paraphrase, budget and scale prompts, six models each.
01The standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all six models, for a mid-market B2B software company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrant
Show the five products at 0%, ordered by negative rate
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its vendor page.
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
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22Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative40%
Key
01Dreamdata29%
Dreamdata29%02Google Analytics 412%
Google Analytics 412%03Recast9%
Recast9%04Measured4%
Measured4%05Triple Whale4%
Triple Whale4%06HockeyStack3%
HockeyStack3%07Google Meridian3%
Google Meridian3%08CaliberMind3%
CaliberMind3%09Improvado3%
Improvado3%10SegmentStream3%
SegmentStream3%11Rockerbox3%
Rockerbox3%12Mutinex3%
Mutinex3%13HubSpot Marketing Hub1%
HubSpot Marketing Hub1%14Ruler Analytics1%
Ruler Analytics1%15Factors.ai1%
Factors.ai1%16Meta Robyn1%
Meta Robyn1%17Lifesight1%
Lifesight1%18Cometly0%
Cometly0%19Northbeam0%
Northbeam0%20Adobe Mix Modeler0%
Adobe Mix Modeler0%21Adobe Marketo Measure0%
Adobe Marketo Measure0%22Analytic Partners0%
Analytic Partners0%02What they warned about
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.
Triple Whale28%
8 of 29 labels negative · 6 of 6 models · 5 hard negative
“❌ Great for smaller e-commerce stores (Shopify-centric) but lack the deep econometrics and offline/macro modeling required” Gemini 3.5 Flash, scale prompt
Northbeam31%
9 of 29 labels negative · 6 of 6 models · 4 hard negative
“none of the premium all-in-one platforms (Rockerbox, Analytic Partners, SegmentStream, Northbeam) make sense on a limited budget” DeepSeek V4 Flash, budget prompt
Google Meridian25%
9 of 36 labels negative · 6 of 6 models · 1 hard negative
“free/open-source (GA4, Google Meridian: lacks B2B depth)” Grok 4.1 Fast, direct prompt
Meta Robyn26%
8 of 31 labels negative · 6 of 6 models · 1 hard negative
“Meta-authored bias risk: Built by Meta; inherently calibrated to favor Meta channels” Kimi K2, negative prompt
03What they cite
Citations exist only for the models that return a source list: four of the six in this edition.
Sites the answers cite
22 of 72 answers in this category came back with a source list, from 4 of 6 models: citations where the model returns them, or the search results it consulted. 6 of those lists are Google grounding redirects that name no site and are left out of the counts. 212 links across 83 sites, every framing counted. Ranked by the number of answers carrying the site or page. The other 2 models searched through a gateway that returned no source list on this edition.
vendor site · Cometly11 answers · 32 citations · 2 models
vendor site · Improvado10 answers · 15 citations · 2 models
vendor site · SegmentStream7 answers · 9 citations · 2 models
vendor site · Factors.ai7 answers · 7 citations · 2 models
6 answers · 6 citations · 2 models
vendor site · Gartner5 answers · 6 citations · 2 models
vendor site · Funnel5 answers · 5 citations · 2 models
vendor site · HockeyStack5 answers · 5 citations · 2 models
vendor site · Guideflow4 answers · 4 citations · 2 models
4 answers · 4 citations · 1 model
vendor site · MCP Analytics4 answers · 4 citations · 1 model
4 answers · 4 citations · 1 model
Pages the answers cite
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
7 answers · 2 models
6 answers · 2 models
5 answers · 1 model
4 answers · 2 models
4 answers · 2 models
4 answers · 2 models
4 answers · 1 model
4 answers · 1 model
4 answers · 2 models
4 answers · 2 models
04How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ModelDirect“What is the best marketing attribution and marketing mix modeling platform for a mid-market B2B company?”Paraphrase“Which attribution and MMM tool would you recommend to a mid-sized B2B company?”Comparative“What are the top marketing attribution and marketing mix modeling platforms and how do they differ?”Budget-constrained“What is the best marketing attribution and marketing mix modeling platform for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a marketing attribution and marketing mix modeling platform. What should we look at?”Negative“Which marketing attribution and marketing mix modeling platforms should I avoid or be cautious about?”
Claude Opus 5no first choiceno first choiceno first choiceno first choiceno first choicenothing named
Claude Opus 4.8no first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-6 Astrano first choiceno first choiceno first choiceno first choiceno first choicenothing named
GPT-5.6 Solno first choiceno first choiceno first choiceno first choiceno first choicenothing named
Gemini 3.1 Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Perplexity Sonar Prono first choiceno first choiceno first choiceno first choiceno first choicenothing named
Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
05The record
One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Zero rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|
Noise floor in this category
Flips between the edition run and its calibration repeat. Six prompts per model is a small sample; the index-wide floor is the number to trust.
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
HubSpot (Starter Tier) read as HubSpot Marketing Hub
Salesforce read as Salesforce Sales Cloud
Unresolved, counted raw
Adobe Analytics / Customer Journey tools
Alviss AI
Analytic Edge
Atmosi
Attributer
Attribution Insights
Business Layers
C5i
Custom Python Scripts
Datamatrix
GA360
GA4 / platform-native attribution
Google
Google Ads reports
Google Meridian / Meta Robyn / PyMC
Heeet
Ipsos
Lightsource.ai / Hivestack
Littledata
Measued
Measurled / Measured
Mediaocean
Meta
Nielsen/NIQ
PIMMS
Pretty Insights
Rybbit
Scalefu
TripleWhale
Turntide
Wavestone
Zignal Labs
Discontinued, still offered
Qwen 3.7 Flash named Google LightweightMMM as alternative on the budget prompt. Deprecated by Google in favor of Meridian.
Kimi K2 named Google LightweightMMM as mention on the budget prompt. Deprecated by Google in favor of Meridian.