Data warehouse and reverse ETL for marketing
Asked as “marketing data warehouse and reverse ETL stack”, and as “reverse ETL and warehouse setup for a marketing team”, on behalf of a mid-market B2B software company. 91 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 eight 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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14Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative60%
Key
01Hightouch42%
Hightouch42%02BigQuery24%
BigQuery24%03Snowflake20%
Snowflake20%04Census5%
Census5%05RudderStack1%
RudderStack1%06Polytomic1%
Polytomic1%07Fivetran0%
Fivetran0%08Airbyte0%
Airbyte0%09dbt0%
dbt0%10Improvado0%
Improvado0%11Amazon Redshift0%
Amazon Redshift0%12Databricks0%
Databricks0%13Segment0%
Segment0%14Stitch0%
Stitch0%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.
Amazon Redshift30%
10 of 33 labels negative · 9 of 6 models · 2 hard negative
“**Avoid for tight budgets**: Snowflake/Redshift (higher baseline compute ~$300+/month)” Grok 4.1 Fast, budget prompt
Census19%
12 of 63 labels negative · 8 of 6 models · 1 hard negative
“❌ **Census**: Requires Enterprise for most features; quote-based pricing” GLM 4.7 FlashX, budget prompt
Hightouch14%
9 of 65 labels negative · 8 of 6 models
“Both Census and Hightouch excel at the "reverse" direction but require separate solutions... This fragmentation increases complexity and maintenance burden.” Claude Haiku 4.5, negative prompt
BigQuery11%
7 of 65 labels negative · 7 of 6 models
“BigQuery (Proceed with Query Guardrails) ... can cost the company hundreds of dollars in a single click” Gemini 3.5 Flash, 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
21 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. 5 of those lists are Google grounding redirects that name no site and are left out of the counts. 248 links across 98 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 · Improvado11 answers · 29 citations · 2 models
vendor site · Cometly8 answers · 17 citations · 2 models
vendor site · Hightouch8 answers · 16 citations · 3 models
vendor site · Domo7 answers · 8 citations · 2 models
vendor site · Supermetrics6 answers · 8 citations · 2 models
vendor site · LeadJourney6 answers · 6 citations · 2 models
vendor site · Funnel5 answers · 9 citations · 2 models
4 answers · 6 citations · 1 model
4 answers · 4 citations · 2 models
4 answers · 4 citations · 1 model
4 answers · 4 citations · 1 model
vendor site · Porter Metrics4 answers · 4 citations · 2 models
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.
6 answers · 2 models
6 answers · 2 models
6 answers · 2 models
6 answers · 2 models
6 answers · 2 models
5 answers · 2 models
5 answers · 2 models
4 answers · 2 models
4 answers · 1 model
4 answers · 1 model
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 data warehouse and reverse ETL stack for a mid-market B2B company?”Paraphrase“Which reverse ETL and warehouse setup for a marketing team would you recommend to a mid-sized B2B company?”Comparative“What are the top data warehouse and reverse ETL tools for marketing teams and how do they differ?”Budget-constrained“What is the best marketing data warehouse and reverse ETL stack for a company with a limited budget?”Scale-constrained“We are a 500 person company evaluating a marketing data warehouse and reverse ETL stack. What should we look at?”Negative“Which data warehouse and reverse ETL tools for marketing teams 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
Oracle read as Oracle Autonomous Data Warehouse
Unresolved, counted raw
Adapters
Databricks Lakehouse / SQL Warehouse
Databricks SQL / lakehouse platforms
Excel/Spreadsheets
Firebolt
Microsoft Azure Synapse
Multywoven
Neon/Supabase
Oracle ADW
Portable
PostgreSQL + TimescaleDB
Raygun
SAP
Segment's reverse ETL
Sheets/Looker Studio
Supabase Postgres
Trino
Twilio Segment Reverse ETL / Profiles Sync
Discontinued, still offered
Grok 4.1 Fast named Grouparoo as alternative on the budget prompt. Open source reverse ETL, shut down 2023.
MiniMax M2.5 named Grouparoo as alternative on the negative prompt. Open source reverse ETL, shut down 2023.