Attribution and marketing mix modeling · October 2026 Edition
HockeyStack vs Recast
Three of fourteen models named HockeyStack first on the direct prompt; zero named Recast. HockeyStack was named by fourteen of the fourteen models and Recast by nine and HockeyStack carries 29 labels and Recast 15, so the shares are not directly comparable.
Named in five categories this edition.
Named in one category this edition.
First-choice share5%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate7%13%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#7A position in a field of 17; printed, not drawn.
Labels2915A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, HockeyStack reading right to left. Rank and label count are printed, not drawn.Dreamdata was named alongside these two in fourteen of the fourteen direct answers. Dreamdata vs HockeyStack · Dreamdata vs Recast · Google Analytics 4 vs HockeyStack
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the attribution and marketing mix modeling page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
HockeyStackFirst choices, of fourteen modelsRecast
Direct301 against HockeyStack
Paraphrase001 against HockeyStack
Comparative00
Budget-constrained001 against Recast
Scale-constrained02
Negative001 against Recast
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.
Every model, every framing
The eighty-four answers behind the chart above, one cell each: where HockeyStack and Recast stood in it.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | | | | | | |
| Perplexity Sonar | | | ||||
| Grok 4.1 Fast | | | | |||
| Mistral Small | | |||||
| DeepSeek V4 Flash | | | | |||
| Llama 4 Maverick | | |||||
| Qwen 3.7 Flash | | |||||
| Kimi K2 | | | | | ||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | | |||||
| GPT-6 Luna | | | | |||
| Muse Glimmer 30B | |
HockeyStack Recast first choice named as an alternative argued againstblank: not namedEach cell is one answer, HockeyStack on the left and Recast on the right.The direct prompt
The plain question, one answer per model, grouped by where HockeyStack and Recast stood in it.
HockeyStack first, Recast not the choice
3 of 14 modelsRecast was named in the answer but not as the choice, or not at all.Claude Haiku 4.5Dreamdata, HockeyStack alternatives: CaliberMind, HubSpot Marketing Hub, Rockerbox, SegmentStream
MiniMax M2.5HockeyStack, Marketing Evolution alternatives: Adobe Marketo Measure, Dreamdata
GPT-6 LunaHockeyStack alternatives: Dreamdata, Paramark
Neither was the first choice, one was named
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.GPT-5.4 miniDreamdata alternatives: HockeyStack, Measured
Gemini 3.5 FlashCaliberMind alternatives: Dreamdata, HockeyStack, RevSure, SegmentStream
Perplexity SonarDreamdata alternatives: HockeyStack, Improvado, SegmentStream
Grok 4.1 FastDreamdata alternatives: Factors.ai, HockeyStack, Rockerbox
DeepSeek V4 FlashDreamdata, Rockerbox alternatives: HockeyStack, Marketing Evolution, Measured
Kimi K2Dreamdata alternatives: Factors.ai, Recast, Rockerbox
Muse Glimmer 30BDreamdata alternatives: Admetrics, Adobe Marketo Measure, HockeyStack, Rockerbox
Neither was named
4 of 14 modelsThe answer made no first choice from these two in this category.Mistral SmallDreamdata, MixModeler alternatives: Northbeam
Llama 4 MaverickRockerbox alternatives: Attribution, Dreamdata, Northbeam
Qwen 3.7 FlashRockerbox alternatives: Dreamdata
GLM 4.7 FlashXDreamdata alternatives: Cometly, HubSpot Marketing Hub, Keen Decision Systems, Rockerbox, Ruler Analytics
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
HockeyStack leads by three points.
HockeyStack3%#11 of 17Recast0%#13 of 17Mid-marketThe figures above
Enterprise
HockeyStack leads by three points.
HockeyStack5%#8 of 17Recast2%#11 of 17What the models said about HockeyStack
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.
“exited the attribution market in April 2026 ... so it's no longer recommended for attribution-only needs.” Kimi K2 · direct prompt · hard negative
“Pivoting away from pure attribution; now positioned as a broader GTM AI platform (~$2,200+/month)” DeepSeek V4 Flash · paraphrase prompt · soft negative
“HockeyStack connects marketing activity directly to pipeline and revenue, making it well-suited for B2B SaaS teams with long sales cycles.” Claude Haiku 4.5 · direct prompt · first choice
“HockeyStack offers better value and faster implementation for mid-market companies.” MiniMax M2.5 · direct prompt · first choice
“I'd start with HockeyStack—but only if its Enterprise pricing works for you” GPT-6 Luna · direct prompt · first choice
What the models said about Recast
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Enterprise-grade, managed MMM and multi-touch attribution (MTA) platforms (like Recast, Measured, or Rockerbox) routinely cost upwards of $50,000 to $150,000+ per year.” Gemini 3.5 Flash · budget prompt · soft negative
“Recast has limitations if you need custom transformations—like modeling promo mechanics with different lag structures per SKU.” Claude Haiku 4.5 · negative prompt · soft negative
“Recast | Self-serve Bayesian MMM, fast refresh, no data science team needed | Companies wanting modern MMM without heavy lift” Kimi K2 · scale prompt · first choice
“If you have 1-2 analysts with intermediate statistics experience, look closely at Recast or Haus.” Gemini 3.5 Flash · scale prompt · first choice
“Add MMM with: Recast ... offers the best balance of modern methodology and mid-market accessibility” Kimi K2 · paraphrase prompt · alternative
Also compared
Dreamdata vs HockeyStackDreamdata vs RecastGoogle Analytics 4 vs HockeyStackGoogle Analytics 4 vs RecastRockerbox vs HockeyStackRockerbox vs RecastGoogle Meridian vs HockeyStackGoogle Meridian vs RecastHockeyStack vs MeasuredHockeyStack vs CometlyMeasured vs RecastRecast vs CometlyAll twenty-eight head-to-head pages and the full Attribution & MMM standing
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.