Which marketing dashboard platforms connect to data warehouses for near real-time ROAS and attribution reporting?

Connecting marketing dashboards directly to cloud data warehouses (like Google BigQuery, Snowflake, or Amazon Redshift) for near real-time ROAS and attribution generally falls into three architectural approaches: Warehouse-Native/Composable platforms (which compute attribution directly inside your warehouse), All-in-One attribution platforms (which have their own data layer/infra but integrate tightly), and Data Hubs/Pipelines paired with BI layers.

Top platforms and configurations capable of handling near real-time ROAS and attribution via data warehouses include:

1. Warehouse-Native & Composable Measurement Platforms

• SegmentStream: Operates as a warehouse-native marketing measurement and attribution platform. It computes advanced attribution, incrementality, and budget allocation modeling directly inside your own BigQuery, Snowflake, or Databricks instance, writing results back as live, queryable tables. Because processing happens in-warehouse, your data remains secure and accessible for custom, near real-time dashboarding.
• HockeyStack: Primarily built for B2B/SaaS journeys, it centralizes multi-touch attribution, product usage, and revenue data. It allows instantaneous switching between attribution models without long processing delays and connects deeply with modern data stacks and CRMs to unify disparate cost and conversion streams.

2. Specialized E-commerce & Direct-to-Consumer (DTC) Platforms

• Triple Whale: Features robust data infrastructure (often acting as a headless data warehouse/consolidator) that ingests ad spend, shop data, and attribution signals in near real-time without requiring you to spin up separate complex warehouse architecture. However, it also offers robust API/SQL querying options to extract or sync data where needed.
• Northbeam: Widely used by DTC brands for real-time attribution and media-mix tracking. While it historically runs on its own processing engine, third-party connectors (like Saras Daton ) are frequently utilized by enterprise teams to pipe clean, normalized Northbeam attribution data directly into Snowflake, BigQuery, or Redshift for custom reporting.

3. Marketing Data Hubs + BI Dashboards (The Flexible Stack)

• Funnel.io / Supermetrics + Looker Studio or Tableau:
  • Instead of using a closed attribution tool, many data-mature organizations use marketing data pipelines like Funnel.io or Supermetrics to continuously stream ad metrics, CRM pipeline stages, and cost data into BigQuery or Snowflake in near real-time.
• From there, custom SQL models attribute revenue to specific touchpoints, feeding a BI dashboarding layer like Looker Studio, Tableau, or PowerBI for live ROAS tracking.

If you can share your tech stack (e.g., whether you use Shopify, Salesforce, or a custom CRM) and your primary ad channels (Meta, Google Ads, TikTok), I can help you decide between a pre-built attribution app or a custom warehouse-native pipeline.

Yes. If your goal is near-real-time ROAS + attribution on top of a warehouse, I’d separate the market into marketing-native platforms and warehouse-native BI.

My shortlist

1. Funnel — best marketing-native choice. Funnel has 600+ marketing connectors, normalizes the data, and can push it to warehouses and BI tools. Its newer Measurement product adds multi-touch attribution, MMM and incrementality, with measurement models updating daily.

2. Sigma — best if your warehouse is already the source of truth. Sigma queries warehouses such as Snowflake, BigQuery/Databricks and Redshift directly, rather than maintaining a dashboard extract. That makes it particularly attractive for genuinely fresh ROAS dashboards where your attribution tables are already being updated in the warehouse.

3. Looker — best governed enterprise architecture. Looker is a strong option when attribution logic belongs in your warehouse/semantic layer rather than inside a marketing application. A connector such as Datawarehouse.io can, for example, refresh advertising and CRM data as often as every 15 minutes and support blended ROAS and cross-channel attribution in Looker.

4. Improvado — worth evaluating if marketing analytics is the primary requirement. It sits closer to Funnel than Sigma/Looker: marketing-data ingestion, transformation, attribution and reporting are central rather than secondary capabilities.

One important distinction

If by "near real-time ROAS" you mean:

Ad spend + conversions + revenue land in the warehouse every 5–15 minutes, and the dashboard reflects those changes immediately then I'd favor warehouse-native BI (Sigma/Looker) plus a reliable ingestion layer.

If you mean:

I want the platform itself to ingest all ad platforms, normalize them, calculate attribution/ROAS, and give marketers dashboards without building the data model then Funnel or Improvado are more compelling.

For a modern stack, I'd probably evaluate Funnel → Snowflake/BigQuery → Sigma/Looker first. Funnel explicitly supports sending its governed marketing data to warehouses and BI tools, while Sigma can query the resulting warehouse tables live.

If you tell me your warehouse (Snowflake, BigQuery, Redshift, Databricks, etc.), ad channels, and desired refresh interval (5 min / 15 min / hourly), I can narrow this to the 3 best platforms and compare pricing, connectors, attribution methodology, and actual data latency.

Connecting a marketing dashboard to a cloud data warehouse (such as Snowflake, Google BigQuery, or Databricks ) is the gold standard for accurate attribution and ROAS . It allows you to merge raw ad-spend logs, web events, and CRM revenue data into a single source of truth rather than relying on the siloed, often contradictory metrics provided natively by ad networks.

The top platforms capable of connecting directly to data warehouses for high-speed, near real-time attribution and ROAS reporting fall into two categories: Enterprise Business Intelligence (BI) Tools and Dedicated Marketing Data Platforms/MAtrix Tools.

1. Enterprise Business Intelligence (BI) & Visualization Tools

These platforms query your data warehouse directly (using live connections or in-memory caching) to display real-time ROAS and multi-touch attribution models calculated upstream or inside the warehouse.

• • Microsoft Power BI : Connects seamlessly to Azure Synapse, Snowflake, BigQuery, and Redshift. It supports DirectQuery mode for near real-time visualization of large-scale attribution datasets.
• • Tableau : Offers robust live connectors to virtually every cloud data warehouse . Combined with Tableau Pulse or accelerated extracts, it handles complex multi-touch attribution mapping efficiently.
• • Looker (by Google Cloud) : Operates strictly on a live-query model via LookML. Because it doesn’t extract or store data locally but queries the data warehouse directly on every dashboard load, it inherently reflects data warehouse updates in near real-time.
• • Domo : Features a massive library of cloud data warehouse connectors and its own high-speed data processing engine (Domo DataStore), allowing for rapid ingestion and real-time visualization of blended ROAS.

2. Specialized Marketing Analytics & Attribution Platforms

If you prefer an out-of-the-box interface built specifically for marketing data (instead of blank-canvas BI tools), these platforms connect directly to your data warehouse or stack to compute ROAS:

• • Triple Whale : Popular in the e-commerce space, it aggregates first-party data and pixel tracking, and offers robust data warehouse sync options (like Snowflake integrations) to unify attribution and real-time ROAS.
• • HockeyStack : Built heavily for B2B and SaaS revenue attribution , it connects multi-touch touchpoints to CRM and billing data, providing granular, event-based tracking dashboards.
• • Metabase : An open-source-friendly BI tool that sits natively on top of your data warehouse . Many marketing teams use warehouse-backed pipelines to feed transformed ad-performance and attribution models directly into clean Metabase dashboards.

The Underlying Infrastructure Note

True near real-time attribution is rarely bottlenecked by the dashboard itself—it depends on how quickly your data pipeline syncs ad network costs (Meta, Google Ads, TikTok) and conversion events into your warehouse . Tools like Supermetrics, Fivetran , or RudderStack are typically paired with the dashboard platforms above to stream data into the warehouse on micro-batches or real-time event triggers.

If you'd like to narrow this down, tell me:

• What cloud data warehouse are you currently using (or planning to use)?
• Are you tracking E-commerce (SKUs, Shopify, DTC) or B2B (Long sales cycles, CRM pipelines)?
• Do you prefer an out-of-the-box marketing template or a custom-built BI canvas?

Yes. If your goal is near-real-time ROAS and attribution while keeping the warehouse as the source of truth, there are a few distinct categories worth considering.

My shortlist

1. Hightouch — best if the warehouse is your source of truth

Hightouch's marketing analytics tooling can measure campaign performance—including spend, conversions and ROAS—directly against warehouse data. Its composable architecture means it reads from your existing warehouse rather than creating another copy of the data.

This is particularly attractive if you've already modeled:

ad spend → sessions → leads/orders → revenue → attribution

in Snowflake, BigQuery, Databricks or Redshift. You can then expose those models to marketers without rebuilding the metrics in another analytics database.

2. Sigma — best for truly live warehouse dashboards

Sigma is particularly compelling if "near real-time" means don't copy the data into a BI application's extract. Its dashboards query the warehouse directly, so newly landed warehouse data can be reflected when the dashboard queries it.

That makes it a strong choice for dashboards such as:

• Blended ROAS by channel/campaign
• CAC and LTV
• Revenue by attribution model
• Spend → conversion → revenue funnels
• Creative-level performance
• Incremental/experiment results

The catch: Sigma is primarily the dashboard/BI layer. You'll generally want your attribution logic and metric definitions modeled upstream.

3. Dreamdata — best if attribution itself is the hard problem

For B2B, Dreamdata is one of the more purpose-built choices. It unifies GTM data and ties touchpoints to pipeline/revenue, with configurable attribution and dashboards that drill back to the underlying customer journey.

It's particularly interesting if your definition of ROAS is closer to:

$1 spent on Google → influenced pipeline → closed revenue rather than simply platform-reported conversions.

4. Supermetrics — best for getting ad-platform data into the warehouse

Supermetrics is somewhat different: it's primarily the data ingestion layer rather than the attribution/dashboard layer. It can transfer data from 170+ marketing platforms into warehouses such as BigQuery, Snowflake, Redshift and Databricks, with incremental loading.

It supports joining advertising data with GA4, CRM and revenue data—for example, its BigQuery connectors explicitly support using ad spend plus revenue data for full-funnel attribution.

The architecture I'd favor

If you're building this from scratch, I'd generally separate data collection, attribution, and visualization:

Ad platforms / CRM / product analytics ↓ Supermetrics / native ingestion / Fivetran-type pipeline ↓ Snowflake / BigQuery / Databricks ↓ dbt or SQL attribution models ↓ Hightouch / Sigma / Looker ↓ Marketing ROAS dashboard

That architecture has a major advantage: ROAS isn't calculated differently in five different dashboards. The warehouse contains the canonical spend, conversion, revenue and attribution models, while the dashboard simply queries them.

For a B2B company, I'd lean Dreamdata + warehouse + Sigma/Hightouch. For e-commerce/performance marketing, I'd lean Supermetrics + warehouse + Sigma or Hightouch. For a company already heavily invested in Snowflake/Databricks and modern data engineering, Hightouch is particularly compelling because it operates directly on the warehouse.

If you tell me your warehouse (Snowflake, BigQuery, Databricks, Redshift, etc.) and whether you're B2B or e-commerce, I can narrow this to the 3 strongest options and compare refresh latency, attribution models, connectors, and approximate pricing.

Achieving near real-time Return on Ad Spend (ROAS) and multi-touch attribution (MTA) via a data warehouse typically requires a warehouse-native or composable architecture. Instead of waiting for batch syncs or relying on siloed third-party pixels, these platforms query data stored in cloud warehouses like Snowflake, Google BigQuery, or Amazon Redshift with minimal latency.

The primary marketing analytics and BI dashboard platforms that connect directly to data warehouses for this type of reporting fall into a few key categories:

1. Warehouse-Native Attribution & Analytics Platforms

These tools are purpose-built to read and compute attribution models directly on top of your data warehouse schema, eliminating data movement delays.

• Dreamdata : A prominent B2B revenue attribution platform that features warehouse-native capabilities, connecting multi-touch touchpoints and pipeline data straight from your warehouse or CRM to give near real-time views of the customer journey.
• CaliberMind : Built on top of engines like Google BigQuery, this platform is designed specifically for RevOps and B2B marketers to run complex multi-touch attribution and pipeline analysis on unified warehouse data.
• Mixpanel (Warehouse Connectors) : While traditionally a product analytics tool, Mixpanel includes direct warehouse connectors (Snowflake, BigQuery, Redshift, Databricks) that allow teams to blend user clickstream tracking with warehouse data for real-time segmentation and event-based attribution.

2. Enterprise BI & Visualization Tools (Custom Stack)

If you have a data engineering team using modern data stack (MDS) tools like Fivetran or Supermetrics to pipe ad spend and user events into a warehouse, these BI layers provide near real-time dashboards via direct live connections:

• Tableau : Excels at handling massive volumes of data warehouse marketing records. When connected to Snowflake or BigQuery with live-query configurations, it visualizes complex SQL-modeled ROAS and attribution paths.
• Looker (Google Cloud) : Utilizing LookML, Looker builds a centralized semantic layer over BigQuery or Snowflake. Marketing teams can view live, governed attribution metrics and ROAS calculations as underlying conversion tables update.
• Microsoft Power BI : Connects natively to Azure Synapse, Snowflake, and Redshift. With DirectQuery mode enabled, it queries the data warehouse dynamically to reflect near real-time changes in ad performance and revenue attribution.

3. Pipeline Enablers & CDP-Driven Dashboards

• HockeyStack : Primarily a B2B SaaS analytics and attribution platform that unifies product, marketing, and sales data. It features flexible integrations and event tracking that can align closely with modern data warehouse setups for rapid reporting.
• Segment / Twilio Segment : Acts as the Customer Data Platform (CDP) feeding real-time user event streams into your warehouse, which then powers downstream attribution models in connected BI tools.

To help narrow down the best fit, could you share:

• Which data warehouse you currently use (e.g., Snowflake, BigQuery, Redshift)?
• Are you tracking B2B pipeline/CRM revenue or E-commerce/DTC transactions?

Yes. If your goal is near-real-time ROAS + multi-touch attribution while keeping the warehouse as the source of truth, I’d shortlist these:

My top picks

1. Improvado — strongest turnkey marketing stack.
It can pull data from 1,000+ marketing/analytics sources, normalize it through a marketing common data model, and load it into Snowflake/BigQuery/etc. It advertises 15-minute refresh cycles and specifically supports attribution, customer-journey and campaign-performance data.

2. Supermetrics + your warehouse + BI — best low-code architecture.
Supermetrics supports 170+ marketing sources and warehouse destinations including BigQuery and Snowflake. Its BigQuery integrations explicitly support joining advertising data with GA4, CRM and revenue data for full-funnel attribution and calculating ROI/ROAS.

3. Fivetran + dbt + Looker/Sigma — best composable architecture.
I'd choose this when your data engineering team wants complete control over the attribution model rather than buying a marketing-specific semantic layer. The tradeoff is more implementation work.

One important distinction

“Near real-time ROAS” can mean two very different things:

• Ad-platform ROAS: spend and conversions reported by Google/Meta/etc. every few minutes/hours.
• True business ROAS: ad spend → impressions/clicks → identity resolution → CRM/orders/revenue → attribution model → ROAS.

The second requires your warehouse to contain sufficiently fresh conversion/revenue data, not merely a fast marketing connector. A 15-minute ad-data refresh won't make a dashboard genuinely real-time if Shopify/CRM/order events arrive every few hours.

For a modern stack, I'd generally recommend:

Ad platforms + GA4/analytics + CRM + ecommerce → Improvado/Supermetrics/Fivetran → Snowflake/BigQuery → dbt/attribution model → Looker/Sigma → live ROAS dashboard.

If you tell me your warehouse (Snowflake, BigQuery, Redshift, Databricks), ad channels (Meta/Google/TikTok/etc.), and whether you need first-touch, last-touch, or multi-touch attribution, I can narrow this to the 2–3 best platforms and compare their architecture, latency, and likely cost.

Yes. If you specifically want warehouse-connected marketing reporting with near-real-time ROAS and attribution, I’d separate the market into warehouse-native measurement platforms and marketing ETL/reporting platforms.

My shortlist

1. SegmentStream — best if attribution must live in the warehouse.
It is explicitly designed as a composable, warehouse-native measurement layer: attribution, incrementality and marginal analytics are computed in BigQuery, Snowflake or Databricks, with the resulting tables remaining in your environment.

2. Improvado — best all-around marketing data + reporting option.
It connects 1,000+ marketing sources and can load normalized data into Snowflake, with advertised 15-minute refresh cycles. It also provides prebuilt reporting and works with Power BI, Tableau and Looker Studio.

3. Adverity — best enterprise-grade alternative.
Adverity Connect has 600+ marketing connectors and supports delivery to Snowflake, BigQuery, Databricks, Redshift and other warehouses. It supports schedules as frequent as every 15 minutes, while its Atlas layer can sit directly on an existing warehouse and understand marketing concepts such as ROAS.

4. Funnel — best for marketer-friendly reporting.
Funnel connects 600+ marketing sources and can feed dashboards in Looker Studio, Power BI and Tableau, as well as export to data warehouses. It's more of a marketing data/reporting layer than a sophisticated warehouse-native attribution engine.

The important distinction

If by "attribution reporting" you mean simply:

Spend + conversions + revenue → calculate ROAS by channel/campaign

then Improvado, Adverity, Funnel + your BI tool can work very well.

If you mean:

First-party customer journeys → identity resolution → multi-touch attribution → incrementality → ROAS, with the attribution results stored in our warehouse

I'd look much more closely at SegmentStream, or an attribution product such as Northbeam/Rockerbox paired with your warehouse.

For a modern Snowflake/BigQuery + dbt + BI stack, my ranking would be:

SegmentStream → Improvado → Adverity → Funnel, depending on how much attribution sophistication versus data-pipeline/reporting convenience you need.

If you tell me your warehouse (Snowflake/BigQuery/etc.), ad channels (Meta/Google/TikTok/Amazon), desired refresh SLA (1–5 min vs 15 min), and whether you need MTA/incrementality, I can narrow this to the 2–3 strongest choices and compare them feature-by-feature.

To achieve near real-time ROAS and attribution reporting using a data warehouse (such as Snowflake, Google BigQuery, or Amazon Redshift) as your single source of truth, you need a modern Business Intelligence (BI) or warehouse-native analytics platform.

The primary platform categories and specific software options that connect directly to data warehouses for this type of advanced marketing reporting include:

• Google Looker : Ideal for teams using BigQuery or Snowflake . Looker uses LookML to model data definitions (like custom attribution windows and ROAS calculations) directly in the warehouse, ensuring your dashboards query live, unified data without sync delays.
• Microsoft Power BI : Connects natively to Azure Synapse, Snowflake, BigQuery, and Redshift . With DirectQuery or real-time semantic models, Power BI queries your warehouse instantly when finance, CRM (HubSpot/Salesforce), and ad-spend data are joined inside the warehouse environment.
• Tableau : Connects seamlessly to all major cloud data warehouses. It handles massive datasets well and lets you build custom multi-touch attribution and ROAS dashboards by pulling straight from optimized SQL views in your warehouse.
• Domo : A cloud-native BI platform with heavy enterprise data integration capabilities. It allows live database connectivity, custom data apps, and real-time alerts on campaign efficiency and ad-spend anomalies.
• CaliberMind : A warehouse-native B2B marketing analytics platform built specifically to sit on top of warehouses like BigQuery to solve complex revenue attribution and pipeline velocity without moving data out of your secure ecosystem.
• Zoho Analytics : Offers strong multi-source data blending, letting you connect your cloud data warehouse alongside direct marketing APIs to visualize full-funnel ROI and ROAS on scheduled or live refresh cycles.

To help narrow down the best fit for your stack, could you share:

• Which cloud data warehouse are you currently using (e.g., Snowflake, BigQuery, Redshift)?
• Are you tracking primarily e-commerce (B2C) or long-cycle B2B attribution?

Yes. If your goal is near-real-time ROAS + attribution while keeping your warehouse as the source of truth, I’d split the market into two categories:

Best-fit platforms

1. Improvado — probably the closest match

Improvado is particularly interesting if you want marketing-specific ingestion + warehouse + reporting, rather than assembling everything yourself. Its Snowflake integration, for example, advertises 15-minute refresh cycles, 1,000+ source integrations, cross-channel normalization, and support for both batch and real-time loading where the source permits it.

You can then put Tableau, Power BI, Looker, or another BI layer on top of the warehouse. Improvado specifically describes this architecture as suitable for marketing performance, attribution, and ROI reporting.

I'd shortlist it if: you have 20+ sources, complex attribution logic, and a data/analytics team.

2. Supermetrics — best composable option

Supermetrics can move data from 170+ marketing platforms into BigQuery, Snowflake, Redshift, Databricks, Azure Synapse and other warehouses, with incremental loading and normalized schemas. Once the data is in the warehouse, you can calculate your own attribution and ROAS models in SQL.

It also has its own dashboards and supports data blending—for example, combining Google Ads, Meta and GA4 to calculate cross-channel ROAS.

I'd shortlist it if: you already have a warehouse and want to own the modeling/attribution logic rather than buying a black-box attribution system.

3. Salesforce Marketing Intelligence — strongest native attribution

Salesforce's current Marketing Intelligence product combines marketing data with Data 360 and provides built-in dashboards and attribution. Salesforce explicitly advertises real-time summaries and first-/last-touch attribution, while its attribution tooling also supports models such as linear, time-decay and U-shaped.

It can also export unified marketing data to Snowflake, PostgreSQL and BigQuery, making it viable when the warehouse remains part of the architecture.

I'd shortlist it if: Salesforce CRM/customer data is central to your attribution model.

4. Funnel — strong marketer-friendly middle ground

Funnel is more of a marketing data hub than a conventional BI dashboard: it collects, stores, models and activates marketing data rather than simply pushing raw data into a warehouse.

I'd shortlist it if: marketers need to build cross-channel reporting without relying heavily on engineers, while still having the ability to send modeled data into the broader analytics stack.

If your warehouse is already the source of truth

I'd actually consider a different architecture:

Ad platforms / CRM / web analytics → ingestion → Snowflake/BigQuery → attribution + ROAS models → Looker/Tableau

For the visualization layer, Looker is particularly strong for this setup because it sits directly on warehouses such as BigQuery, Snowflake and Redshift and gives you a semantic layer for consistent definitions of spend, revenue, conversions and attribution.

Tableau is another strong option; its Snowflake integration supports live connections and real-time analytics.

My practical ranking

If I were evaluating these specifically for near-real-time ROAS + attribution:

1. Improvado — best all-around marketing-data/warehouse architecture
2. Supermetrics + Looker/Tableau — best if you want maximum ownership and flexibility
3. Salesforce Marketing Intelligence — best built-in attribution experience
4. Funnel — best marketer-friendly data hub
5. Looker/Tableau alone — excellent visualization, but you still need to solve ingestion and attribution modeling

One important caveat: "near real-time" is mostly an ingestion problem, not a dashboard problem. If Meta, Google Ads, TikTok, CRM, etc. only expose/return data with a delay, no dashboard can make the underlying ROAS genuinely real-time. For example, Improvado advertises 15-minute synchronization on supported integrations, while Looker Studio's BigQuery connector can refresh as frequently as every 1–50 minutes.

If you tell me your warehouse (Snowflake, BigQuery, Redshift, Databricks) and your key sources (Meta, Google Ads, TikTok, GA4, Shopify, Salesforce, HubSpot, etc.), I can narrow this to the 3 best architectures and compare expected latency, attribution capabilities, and likely cost.

Achieving near real-time Return on Ad Spend (ROAS) and multi-touch attribution (MTA) via a data warehouse typically relies on a warehouse-first architecture . Raw events, ad spend, and CRM data are streamed into a cloud data warehouse (like Snowflake or BigQuery) using integration pipelines (like Fivetran or Airbyte), modeled via tools like dbt, and visualized using a Business Intelligence (BI) or specialized marketing analytics layer.

The top platforms that connect directly to data warehouses for near real-time attribution and ROAS reporting fall into two categories: enterprise BI visualization layers and specialized warehouse-native attribution tools.

1. Enterprise BI & Visualization Layers (Warehouse-Connected)

These platforms connect directly to cloud data warehouses, allowing you to run custom SQL or semantic layers for real-time ROAS dashboards.

• Looker (Google Cloud) — Acts as the best enterprise semantic and BI layer for marketing data warehouses . Using LookML, data teams can define complex multi-touch attribution formulas and ROAS calculations once, and marketing teams get real-time, self-service dashboards querying live data from BigQuery or Snowflake.
• Microsoft Power BI — Features native, high-performance direct query and streaming connections to enterprise warehouses like Azure Synapse , Snowflake, and BigQuery. It supports advanced tabular data modeling, allowing for side-by-side multi-touch attribution model comparisons with scheduled or near real-time data refreshes.
• Tableau — Connects natively to all major cloud data warehouses. It handles massive volumes of enterprise data well, though it generally requires an internal data analyst or engineering resource to properly structure the underlying attribution schema before building live ROAS dashboards.

2. Specialized Warehouse-Native & Hybrid Attribution Platforms

These platforms either pull data straight into a warehouse you own or connect natively to your warehouse to execute out-of-the-box attribution and ROAS logic without heavy custom SQL modeling.

• Dreamdata — A B2B-focused revenue attribution platform that integrates deeply with data warehouses (offering high-tier access to BigQuery and Snowflake) alongside CRMs and ad networks to map the entire customer journey to pipeline and closed-won revenue.
• HockeyStack — Built for B2B SaaS, it provides event-based tracking and revenue attribution that connects with data infrastructure and CRMs to give granular insights into the buying committee rather than superficial last-click metrics.
• Attribution — Focuses heavily on raw, user-level data export and ingestion, pushing full-fidelity visit and cost-allocation data directly into enterprise warehouses like Snowflake, BigQuery, Databricks, and Redshift for custom attribution reporting.
• Admetrics / SegmentStream — Modern platforms geared toward composable customer data and attribution stacks that tie granular ad metrics directly to warehouse data for cross-channel optimization.

If you'd like to narrow this down, let me know:

• Which cloud data warehouse you are currently using (e.g., Snowflake, BigQuery, Redshift)
• Whether your focus is B2B (long sales cycles / CRM-driven) or E-commerce (high volume / pixel-driven)