---
title: Why SourceMedium | The Ecommerce Data Stack, Done
description: "SourceMedium replaces your stack of analytics vendors with one product built on BigQuery: warehouse, integrations, dashboards, attribution, and AI included. Data you can trust. Infrastructure you can keep."
url: "https://sourcemedium.com/why-sourcemedium"
type: static
generatedAt: "2026-04-01T20:09:20.289Z"
---

Why SourceMedium
# Why SourceMedium? Five reasons you should switch.

One product instead of five vendors. Built on BigQuery you own. Data you can actually trust. AI you can verify. And if you leave, you keep everything.
Trusted by LMNT, CPAP, Neuro, and hundreds of scaling ecommerce brands.

## Behind every growth decision is a data question. Most brands can't answer it.

- Your team debates which numbers are right instead of deciding what to do next.
- You're paying for multiple tools, and still can't get a straight answer on CAC or LTV.
- Every board deck starts with a scramble to reconcile sources.
- Your last data hire left. Now nobody knows how anything was built.

These aren't reporting problems. They're infrastructure problems. And you can't solve infrastructure problems by adding more tools.
01
## One product. Not a stack of vendors.

Warehouse, integrations, dashboards, attribution, and AI: one purchase, one vendor, live in two weeks.

01

Connect

### Unify all your first-party data

Prebuilt connectors for commerce, ads, subscriptions, and operations.

02

Standardize

### One set of definitions across every source

We reconcile source differences across systems and run daily checks on key metrics.

03

Activate

### Activate across dashboards, SQL, and AI

BigQuery stays the source of truth for dashboards, reporting, and AI.

What's included
- 1 Managed BigQuery warehouse You own it. Query directly or connect any BI tool.
- 2 50+ integrations Direct connections to commerce, ads, email, subscriptions, and ops.
- 3 Data cleaning, merging, and transformation Managed by SourceMedium with production-grade conventions.
- 4 20 pre-built dashboard modules Native Looker Studio modules for reporting across your team.
- 5 Multi-touch attribution Cross-channel reconciliation with contribution insights.
- 6 AI Analyst in Slack Plain-English answers with SQL receipts you can verify.
- 7 Dedicated Customer Solutions Analyst Strategic support and implementation guidance for your rollout.

Everything included. One predictable platform fee.
[See pricing](/pricing)
“SourceMedium's managed BigQuery instance has been a revelation for our team. It has democratized access to data across our entire organization, empowering everyone to create custom metrics and transformations without relying on a dedicated BI team.”

Nick Osborn

Head of Growth, Catalina Crunch
02
## Built on BigQuery. Industry standard from Google.

Your data lives in BigQuery, Google's industry-standard data warehouse, not a vendor's proprietary database you're renting. Plug and play with any BI tool. AI-ready out of the box.

Any tool that supports BigQuery connects natively. Tableau, dbt, Python, Hex, your own scripts. No one will complain about the stack.

No warehouse sizing, no credit management. Native GA4 and Google Ads transfers. Free dashboards through Looker Studio for your entire team.
[See integrations](/integrations)
Your warehouse

Query directly. Connect any BI tool. Export anything.

Your schema

Best-practice naming. Documented. Stable enough for dbt.

Your dashboards

Copyable templates. Fork, customize, build your own.
03
## Data you can actually trust.

When your team trusts the numbers, meetings shift from 'where did you pull that?' to 'what should we do?' 2,500+ automated checks run daily so the data is right before anyone sees it.

Present to the board and explain where every number came from. Onboard a new analyst: they can use the documentation from day one. Your data team builds on the schema instead of rebuilding it.

Your team stops debating dashboards and starts shipping decisions.
2,500+

automated quality checks daily

180+

metric catalog

Schema commitment

supported long-term; build on it confidently

Documentation

you can actually read (and onboard new hires with)

“Thanks to SourceMedium, our team no longer wastes time debating metric definitions in meetings. Instead, we can focus on what really matters: making informed, data-driven decisions that drive results.”

Kevin Chen

President, iRESTORE
04
## AI that shows its work.
AI Analyst
## An AI Analyst you can verify.

**Every BI tool has an AI agent now. The problem is nobody trusts any of them.**

Ask questions in Slack. Get answers with the SQL behind them. Runs on your data in BigQuery, not a chatbot, not a black box. Not a chatbot. Not a web app. A data analyst that lives where your team already works, with receipts.

Try asking:

- What changed in revenue and CAC last week?
- Which channels are actually driving LTV?
- What products drove margin erosion last month, and why?

Questions you can ask
# data-questions MK Marketing Lead 10:15 AM
What's our average CAC by channel in the last 30 days?
SourceMedium AI APP 10:15 AM
This 30-day result has two paid sales channels with measurable CAC: **marketplace = $77.81** and **online_store = $399.99**. `online_store` is about **5.1x** higher, so this is the priority channel to troubleshoot if your target CAC is sub-$100.

- [Table: Executive Summary Daily](/docs/data-activation/data-tables/sm_transformed_v2/rpt_executive_summary_daily)
- [Metrics Reference](/docs/onboarding/data-docs/metrics)
- [SQL Query Library: CAC templates](/docs/data-activation/template-resources/sql-query-library)
Data: Average CAC by Channel (Last 30 Days)
| Channel | Ad Spend | New Customers | CAC |
| --- | --- | --- | --- |
| **marketplace** | $393,270 | 5,054 | $78 |
| **online_store** | $1,919,529 | 4,799 | $400 |
Chart: CAC by Channel marketplace online_store
```
-- Analysis: CAC by channel using executive summary table

-- Assumptions: timeframe=last_30_days | metric=cac=ad_spend/new_customer_count | grain=sm_channel | scope=channels_with_spend
SELECT
  COALESCE(NULLIF(LOWER(TRIM(sm_channel)), ''), '(unknown)') AS sm_channel,
  SUM(ABS(ad_spend)) AS ad_spend,
  SUM(new_customer_count) AS new_customers,
  SAFE_DIVIDE(SUM(ABS(ad_spend)), NULLIF(SUM(new_customer_count), 0)) AS cac
FROM `your_project.sm_transformed_v2.rpt_executive_summary_daily`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
  AND ad_spend IS NOT NULL
  AND new_customer_count IS NOT NULL
GROUP BY 1
HAVING ad_spend > 0
ORDER BY cac ASC
LIMIT 10;
```
# data-questions MK Marketing Lead 10:15 AM
Which platform and campaign type has the highest ROAS in the last 30 days?
SourceMedium AI APP 10:15 AM
Top efficiency in this window is **microsoft_ads / search_&_content at 8.4x ROAS** on **$27.4k spend**, followed by **snapchat / sales at 7.3x**. The weakest row shown is **meta_ads / outcome_sales at 3.0x**, which is about **64% lower ROAS** than the top row despite much larger spend.

- [Table: Ad Performance Daily](/docs/data-activation/data-tables/sm_transformed_v2/rpt_ad_performance_daily)
- [Common Analysis: ROAS](/docs/help-center/common-analyses/roas)
- [SQL Query Library: ROAS templates](/docs/data-activation/template-resources/sql-query-library)
Data: ROAS by Platform + Campaign Type (Last 30 Days)
| Platform | Campaign Type | Spend | Revenue | ROAS |
| --- | --- | --- | --- | --- |
| **microsoft_ads** | search_&_content | $27,410 | $230,320 | 8.4 |
| **snapchat** | sales | $48,253 | $350,949 | 7.27 |
| **amazon_ads** | sponsored_products | $329,196 | $1,882,155 | 5.72 |
| **amazon_ads** | sponsored_brands_&_video | $49,182 | $259,280 | 5.27 |
| **meta_ads** | outcome_sales | $660,746 | $2,014,891 | 3.05 |
Chart: ROAS by Platform/Campaign Type microsoft_search_content snapchat_sales amazon_sponsored_products amazon_sponsored_brands_video meta_outcome_sales
```
-- Analysis: top platform/campaign-type ROAS combinations

-- Assumptions: timeframe=last_30_days | metric=roas=platform_reported_revenue/ad_spend | grain=platform+campaign_type | scope=rows_with_spend_and_revenue
SELECT
  COALESCE(NULLIF(LOWER(TRIM(source_system)), ''), '(unknown)') AS platform,
  COALESCE(NULLIF(LOWER(TRIM(ad_campaign_type)), ''), '(unknown)') AS campaign_type,
  SUM(ad_spend) AS ad_spend,
  SUM(ad_platform_reported_revenue) AS platform_reported_revenue,
  SAFE_DIVIDE(SUM(ad_platform_reported_revenue), NULLIF(SUM(ad_spend), 0)) AS roas
FROM `your_project.sm_transformed_v2.rpt_ad_performance_daily`
WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
  AND ad_spend > 0
GROUP BY 1, 2
HAVING ad_spend >= 5000
  AND platform_reported_revenue > 0
ORDER BY roas DESC
LIMIT 20;
```
# data-questions MK Marketing Lead 10:15 AM
What are our top products by net revenue in the last 30 days?
SourceMedium AI APP 10:15 AM
The top SKU is **SKU-A100 (Flagship Device) at $3.40M**, followed by **SKU-B220 at $1.46M** and **SKU-A090 at $1.40M**. The top two contribute about **67%** of the top-five revenue in this slice, so performance is concentrated in the flagship device family.

- [Table: Order Lines](/docs/data-activation/data-tables/sm_transformed_v2/obt_order_lines)
- [Common Analysis: Top-Selling Products](/docs/help-center/common-analyses/top-selling-products)
- [SQL Query Library: product ranking](/docs/data-activation/template-resources/sql-query-library)
Data: Top Products by Net Revenue (Last 30 Days)
| SKU | Product Title | Net Revenue | Units Sold | Orders |
| --- | --- | --- | --- | --- |
| **SKU-A100** | Flagship Device | $3,396,028 | 1,912 | 1,885 |
| **SKU-B220** | Companion Device | $1,463,088 | 3,648 | 3,636 |
| **SKU-A090** | Core Device | $1,396,569 | 1,777 | 1,771 |
| **BUNDLE-A100-B220** | Flagship + Companion Bundle | $668,722 | 308 | 308 |
| **SKU-A050** | Entry Device | $316,465 | 690 | 687 |
Chart: Net Revenue by SKU SKU-A100 SKU-B220 SKU-A090 BUNDLE-A100-B220 SKU-A050
```
-- Analysis: top product SKUs by net revenue from order lines

-- Assumptions: timeframe=last_30_days | metric=net_revenue=SUM(order_line_net_revenue) | grain=sku | scope=valid_orders_only
SELECT
  sku,
  ANY_VALUE(product_title) AS product_title,
  SUM(order_line_net_revenue) AS order_line_net_revenue,
  SUM(order_line_quantity) AS units_sold,
  COUNT(DISTINCT sm_order_key) AS orders
FROM `your_project.sm_transformed_v2.obt_order_lines`
WHERE is_order_sm_valid = TRUE
  AND DATE(order_processed_at_local_datetime) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
  AND sku IS NOT NULL
GROUP BY 1
ORDER BY order_line_net_revenue DESC
LIMIT 10;
```
# data-questions MK Marketing Lead 10:15 AM
How has attribution health trended weekly in the last 6 months?
SourceMedium AI APP 10:15 AM
In these latest full weeks, attribution is volatile and needs attention: **orders with UTM source/medium** dropped from **45.34%** (2026-01-19) to **39.90%** (2026-02-09), while **orders unattributed** peaked at **62.57%** in the week of 2026-02-02. Revenue unattributed is still high (**33.85%–37.39%**) across all shown weeks.

- [AI Analyst Diagnostic: Attribution Health](/docs/ai-analyst/diagnostics/attribution-health)
- [Attribution Health Guide](/docs/data-inputs/attribution-health/index)
- [Attribution Source Hierarchy](/docs/data-transformations/attribution-source-hierarchy)
Data: Weekly Attribution Health
| Week Start | Orders | Orders w/ UTM % | Orders Unattributed % | Revenue Unattributed % |
| --- | --- | --- | --- | --- |
| **2026-01-19** | 3,447 | 45.3% | 54.7% | 35.4% |
| **2026-01-26** | 3,660 | 43.3% | 56.7% | 33.9% |
| **2026-02-02** | 3,858 | 37.4% | 62.6% | 37.4% |
| **2026-02-09** | 3,677 | 39.9% | 60.1% | 34.9% |
Chart: Attribution Coverage Trend Orders w/ UTM % Orders Unattributed % 01-19 01-26 02-02 02-09
```
-- Analysis: weekly attribution trend using order-level UTM coverage

-- Assumptions: timeframe=last_26_weeks_full | metric=utm_coverage+unattributed_share | grain=week | scope=valid_orders_only
WITH base AS (
  SELECT
    DATE_TRUNC(DATE(order_processed_at_local_datetime), WEEK(MONDAY)) AS week_start,
    COALESCE(NULLIF(LOWER(TRIM(sm_utm_source_medium)), ''), '(none) / (none)') AS source_medium,
    sm_order_key,
    order_net_revenue
  FROM `your_project.sm_transformed_v2.obt_orders`
  WHERE is_order_sm_valid = TRUE
    AND order_cancelled_at IS NULL
    AND DATE(order_processed_at_local_datetime) >= DATE_SUB(CURRENT_DATE(), INTERVAL 182 DAY)
    AND DATE(order_processed_at_local_datetime) < DATE_TRUNC(CURRENT_DATE(), WEEK(MONDAY))
),
weekly AS (
  SELECT
    week_start,
    COUNT(DISTINCT sm_order_key) AS orders,
    SUM(order_net_revenue) AS order_net_revenue,
    COUNTIF(source_medium = '(none) / (none)') AS unattributed_orders,
    SUM(CASE WHEN source_medium = '(none) / (none)' THEN order_net_revenue ELSE 0 END) AS unattributed_revenue,
    COUNTIF(source_medium != '(none) / (none)') AS orders_with_utm_source_medium
  FROM base
  GROUP BY 1
)
SELECT
  week_start,
  orders,
  ROUND(SAFE_MULTIPLY(SAFE_DIVIDE(orders_with_utm_source_medium, NULLIF(orders, 0)), 100), 2) AS pct_orders_with_utm_source_medium,
  ROUND(SAFE_MULTIPLY(SAFE_DIVIDE(unattributed_orders, NULLIF(orders, 0)), 100), 2) AS pct_orders_unattributed,
  ROUND(SAFE_MULTIPLY(SAFE_DIVIDE(unattributed_revenue, NULLIF(order_net_revenue, 0)), 100), 2) AS pct_revenue_unattributed
FROM weekly
ORDER BY week_start;
```
# data-questions MK Marketing Lead 10:15 AM
How should we rebalance paid platform spend next month to improve efficiency?
SourceMedium AI APP 10:15 AM
This scorecard puts **snapchat (0.813)** and **microsoft_ads (0.738)** at the top with the strongest ROAS (**9.07x**, **8.21x**) and momentum. **meta_ads** carries the biggest spend (**$1.94M**) but lower ROAS (**3.73x**) than peers, so the first optimization pass should focus on improving Meta efficiency before adding more budget.

- [AI Analyst Workflow: Specific Questions](/docs/ai-analyst/workflows/standard)
- [Table: Ad Performance Daily](/docs/data-activation/data-tables/sm_transformed_v2/rpt_ad_performance_daily)
- [SQL Query Library: efficiency templates](/docs/data-activation/template-resources/sql-query-library)
Data: Paid Platform Prioritization Scorecard (12 Weeks)
| Platform | Spend (12w) | Revenue (12w) | ROAS (12w) | Recent Momentum | Priority Score |
| --- | --- | --- | --- | --- | --- |
| **snapchat** | $124,409 | $1,128,365 | 9.07 | 0.33x | 0.813 |
| **microsoft_ads** | $92,641 | $760,675 | 8.21 | 0.30x | 0.738 |
| **amazon_ads** | $1,365,763 | $8,317,940 | 6.09 | 0.25x | 0.704 |
| **meta_ads** | $1,938,953 | $7,240,925 | 3.73 | 0.28x | 0.665 |
Chart: Priority Score by Platform snapchat microsoft_ads amazon_ads meta_ads
```
-- Analysis synthesis: combine ROAS, spend scale, and recent momentum into a platform priority score

-- Assumptions: timeframe=last_12_weeks | metric=priority_score_from_roas+scale+momentum | grain=source_system | scope=platforms_with_spend_and_revenue
WITH weekly AS (
  SELECT
    DATE_TRUNC(date, WEEK(MONDAY)) AS week_start,
    COALESCE(NULLIF(LOWER(TRIM(source_system)), ''), '(unknown)') AS platform,
    SUM(ad_spend) AS ad_spend,
    SUM(ad_platform_reported_revenue) AS platform_revenue
  FROM `your_project.sm_transformed_v2.rpt_ad_performance_daily`
  WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 84 DAY)
  GROUP BY 1, 2
),
platform_rollup AS (
  SELECT
    platform,
    SUM(ad_spend) AS spend_12w,
    SUM(platform_revenue) AS revenue_12w,
    SAFE_DIVIDE(SUM(platform_revenue), NULLIF(SUM(ad_spend), 0)) AS roas_12w,
    SAFE_DIVIDE(
      SUM(CASE WHEN week_start >= DATE_SUB(CURRENT_DATE(), INTERVAL 28 DAY) THEN platform_revenue ELSE 0 END),
      NULLIF(SUM(CASE WHEN week_start < DATE_SUB(CURRENT_DATE(), INTERVAL 28 DAY) THEN platform_revenue ELSE 0 END), 0)
    ) AS recent_momentum
  FROM weekly
  WHERE ad_spend > 0
  GROUP BY 1
  HAVING spend_12w >= 50000
    AND revenue_12w > 0
),
scored AS (
  SELECT
    platform,
    spend_12w,
    revenue_12w,
    roas_12w,
    recent_momentum,
    ROUND(
      SAFE_DIVIDE(roas_12w, NULLIF(MAX(roas_12w) OVER (), 0)) * 0.50 +
      SAFE_DIVIDE(spend_12w, NULLIF(MAX(spend_12w) OVER (), 0)) * 0.20 +
      SAFE_DIVIDE(recent_momentum, NULLIF(MAX(recent_momentum) OVER (), 0)) * 0.30,
      3
    ) AS priority_score
  FROM platform_rollup
)
SELECT
  platform,
  spend_12w,
  revenue_12w,
  roas_12w,
  recent_momentum,
  priority_score
FROM scored
ORDER BY priority_score DESC
LIMIT 10;
```
# data-questions MK Marketing Lead 10:15 AM
What's the difference between first-touch and last-touch attribution?
SourceMedium AI APP 10:15 AM
SourceMedium defines **first touch** as assigning all credit to the first valid touchpoint in the journey, and **last touch** as assigning all credit to the final valid touchpoint before purchase. It also provides **linear attribution**, which distributes credit across unique valid touches using session-based deduplication. In practice: first touch is your acquisition lens, last touch is your conversion lens, and linear gives a balanced contribution view.

- [MTA Overview](/docs/mta/mta-overview)
- [MTA Models Reference](/docs/mta/mta-models)
- [MTA FAQs](/docs/mta/mta-faqs)
## You own everything. We don't hold it hostage.

If you leave, you keep the warehouse tables, dashboards, and SQL. No rebuild. Move it to a BigQuery project you control.

### Your data stays yours

Keep the warehouse foundation and move it to your own Google account when needed.

- Your BigQuery data moves to your own Google account

### Your workflows keep running

Existing reporting assets and SQL still work without rebuilding your stack from zero.

- Your Looker Studio dashboards continue to work
- Your SQL queries still run
- Your schema knowledge transfers. It's all documented

### Your team keeps momentum

The operational context and model logic remain documented and transferable.

- You're not starting from scratch

“If we leave, what happens to our data, dashboards, and team workflows?”

## How SourceMedium compares

See how a unified platform stacks up against building in-house, assembling point tools, or using competitor products.
| Feature | | In-House |
| --- | --- | --- |
| Integrations & Data Sources | Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks | Build and maintain every connector yourself — each source API change is your team's problem |
| Data Freshness | Reconciled daily across all sources with full historical backfill — every metric traceable to its source | Whatever your team builds — freshness depends entirely on your engineering resources |
| Attribution Models | Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging | Must be designed, built, and maintained by your data team — typically months of development |
| Cohort / CLTV | 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one | Must be modeled from scratch — 'the longest and most complicated element' of ecommerce data modeling |
| Dashboards & Visualization | Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL | Build from scratch using a BI tool your team selects, configures, and maintains |
| Custom Metrics | Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer | Full flexibility — but every metric must be defined, documented, and maintained by your team |
| SQL / Export / API Access | Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively | Full control — but you provision, manage, and pay for your own warehouse infrastructure |
| Support & Success | Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping | You are the support team |
[Compare SourceMedium vs In-House](/compare/sourcemedium-vs-building-in-house) Integrations & Data Sources SM Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks Them SaaS-locked — data lives in Triple Whale's system, exports are one-way table dumps Data Freshness SM Reconciled daily across all sources with full historical backfill — every metric traceable to its source Them Claims real-time; constrained by each channel's API refresh limits Attribution Models SM Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging Them Seven models including proprietary Total Impact; requires Triple Pixel installation Cohort / CLTV SM 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one Them Cohort analysis included in plan features Dashboards & Visualization SM Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL Them Pre-built dashboards including Summary Page; SaaS-hosted with data in Triple Whale's system Custom Metrics SM Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer Them Custom Metric Builder available on Starter plan and above SQL / Export / API Access SM Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively Them API access plus warehouse exports to BigQuery, Snowflake, S3, and GCS — data originates in Triple Whale's system Support & Success SM Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping Them Chat/Ticket support; dedicated success manager tiered or offshore
| Feature | | Triple Whale |
| --- | --- | --- |
| Integrations & Data Sources | Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks | SaaS-locked — data lives in Triple Whale's system, exports are one-way table dumps |
| Data Freshness | Reconciled daily across all sources with full historical backfill — every metric traceable to its source | Claims real-time; constrained by each channel's API refresh limits |
| Attribution Models | Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging | Seven models including proprietary Total Impact; requires Triple Pixel installation |
| Cohort / CLTV | 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one | Cohort analysis included in plan features |
| Dashboards & Visualization | Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL | Pre-built dashboards including Summary Page; SaaS-hosted with data in Triple Whale's system |
| Custom Metrics | Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer | Custom Metric Builder available on Starter plan and above |
| SQL / Export / API Access | Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively | API access plus warehouse exports to BigQuery, Snowflake, S3, and GCS — data originates in Triple Whale's system |
| Support & Success | Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping | Chat/Ticket support; dedicated success manager tiered or offshore |
[Compare SourceMedium vs Triple Whale](/compare/sourcemedium-vs-triple-whale) Integrations & Data Sources SM Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks Them BYO warehouse required — connects to Snowflake, BigQuery, or Redshift that you manage Data Freshness SM Reconciled daily across all sources with full historical backfill — every metric traceable to its source Them No published data freshness SLA; varies from near-real-time to daily by source Attribution Models SM Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging Them Multiple attribution methods including GA-based, survey-based, and discount code tracking Cohort / CLTV SM 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one Them Customer and LTV use cases referenced in case studies; not a core product positioning Dashboards & Visualization SM Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL Them Dashboards and custom reports; some deployments use Looker instances Custom Metrics SM Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer Them Custom dashboards and reports available SQL / Export / API Access SM Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively Them Warehouse connections to Snowflake, BigQuery, Redshift — you manage the infrastructure Support & Success SM Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping Them Implementation fees required; ongoing support often billable
| Feature | | |
| --- | --- | --- |
| Integrations & Data Sources | Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks | BYO warehouse required — connects to Snowflake, BigQuery, or Redshift that you manage |
| Data Freshness | Reconciled daily across all sources with full historical backfill — every metric traceable to its source | No published data freshness SLA; varies from near-real-time to daily by source |
| Attribution Models | Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging | Multiple attribution methods including GA-based, survey-based, and discount code tracking |
| Cohort / CLTV | 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one | Customer and LTV use cases referenced in case studies; not a core product positioning |
| Dashboards & Visualization | Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL | Dashboards and custom reports; some deployments use Looker instances |
| Custom Metrics | Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer | Custom dashboards and reports available |
| SQL / Export / API Access | Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively | Warehouse connections to Snowflake, BigQuery, Redshift — you manage the infrastructure |
| Support & Success | Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping | Implementation fees required; ongoing support often billable |
[Compare SourceMedium vs Daasity](/compare/sourcemedium-vs-daasity) Integrations & Data Sources SM Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks Them Shopify-first; SQL access and intraday refresh are paid add-ons Data Freshness SM Reconciled daily across all sources with full historical backfill — every metric traceable to its source Them Pixel data real-time; Shopify 15-min on new pipeline; lower tiers may be daily Attribution Models SM Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging Them First-click, linear, U-shaped via Shopify App Pixel deployment Cohort / CLTV SM 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one Them Ecommerce KPIs via semantic layer; not explicitly positioned as CLTV product Dashboards & Visualization SM Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL Them Custom reporting limited to 1,000 rows in-app; no visualization exports or Slack/email chart delivery Custom Metrics SM Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer Them Custom reporting available in BI product SQL / Export / API Access SM Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively Them SQL on dedicated Snowflake; add-on pricing on some tiers Support & Success SM Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping Them Chat/Ticket support; dedicated success manager tiered
| Feature | | Polar Analytics |
| --- | --- | --- |
| Integrations & Data Sources | Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks | Shopify-first; SQL access and intraday refresh are paid add-ons |
| Data Freshness | Reconciled daily across all sources with full historical backfill — every metric traceable to its source | Pixel data real-time; Shopify 15-min on new pipeline; lower tiers may be daily |
| Attribution Models | Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging | First-click, linear, U-shaped via Shopify App Pixel deployment |
| Cohort / CLTV | 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one | Ecommerce KPIs via semantic layer; not explicitly positioned as CLTV product |
| Dashboards & Visualization | Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL | Custom reporting limited to 1,000 rows in-app; no visualization exports or Slack/email chart delivery |
| Custom Metrics | Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer | Custom reporting available in BI product |
| SQL / Export / API Access | Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively | SQL on dedicated Snowflake; add-on pricing on some tiers |
| Support & Success | Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping | Chat/Ticket support; dedicated success manager tiered |
[Compare SourceMedium vs Polar](/compare/sourcemedium-vs-polar-analytics) Integrations & Data Sources SM Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks Them Marketing and ad data only — no warehouse included, data must be exported for SQL access Data Freshness SM Reconciled daily across all sources with full historical backfill — every metric traceable to its source Them Data sits in Funnel's Data Hub temporarily — must be exported to a warehouse for long-term access Attribution Models SM Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging Them Measurement add-on via Adtriba acquisition — MMM, MTA, and incrementality on Business/Enterprise at custom pricing Cohort / CLTV SM 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one Them Not included — no cohort analysis, no LTV calculations, no customer segmentation Dashboards & Visualization SM Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL Them Built-in data exploration and visualization — but Flexpoint-based pricing limits report refreshes Custom Metrics SM Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer Them Custom calculated metrics within Funnel's data model SQL / Export / API Access SM Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively Them No included warehouse — data must be exported to BigQuery/Snowflake for SQL access Support & Success SM Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping Them Ticket-based support; self-serve focus
| Feature | | |
| --- | --- | --- |
| Integrations & Data Sources | Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks | Marketing and ad data only — no warehouse included, data must be exported for SQL access |
| Data Freshness | Reconciled daily across all sources with full historical backfill — every metric traceable to its source | Data sits in Funnel's Data Hub temporarily — must be exported to a warehouse for long-term access |
| Attribution Models | Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging | Measurement add-on via Adtriba acquisition — MMM, MTA, and incrementality on Business/Enterprise at custom pricing |
| Cohort / CLTV | 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one | Not included — no cohort analysis, no LTV calculations, no customer segmentation |
| Dashboards & Visualization | Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL | Built-in data exploration and visualization — but Flexpoint-based pricing limits report refreshes |
| Custom Metrics | Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer | Custom calculated metrics within Funnel's data model |
| SQL / Export / API Access | Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively | No included warehouse — data must be exported to BigQuery/Snowflake for SQL access |
| Support & Success | Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping | Ticket-based support; self-serve focus |
[Compare SourceMedium vs Funnel](/compare/sourcemedium-vs-funnel-io) Integrations & Data Sources SM Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks Them No data loading included — requires Fivetran, Airbyte, or another ELT tool Data Freshness SM Reconciled daily across all sources with full historical backfill — every metric traceable to its source Them Depends entirely on your ELT tool and orchestration setup Attribution Models SM Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging Them Not included — must be built from scratch or purchased separately Cohort / CLTV SM 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one Them Not included — must be modeled from scratch using SQL/dbt Dashboards & Visualization SM Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL Them Not included — requires Looker ($3K–$10K/mo), Tableau, or another BI tool Custom Metrics SM Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer Them Must be defined and maintained by your data team in dbt or SQL views SQL / Export / API Access SM Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively Them Full SQL warehouse — but credit-based pricing means cost scales with every query Support & Success SM Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping Them Ticket-based support; dedicated engineering resources required
| Feature | | |
| --- | --- | --- |
| Integrations & Data Sources | Commerce, ads, email, subscriptions, and ops — reconciled daily with 2,500+ automated quality checks | No data loading included — requires Fivetran, Airbyte, or another ELT tool |
| Data Freshness | Reconciled daily across all sources with full historical backfill — every metric traceable to its source | Depends entirely on your ELT tool and orchestration setup |
| Attribution Models | Server-side multi-touch attribution using your existing tracking (GA4 + platform APIs) — no new pixels or site re-tagging | Not included — must be built from scratch or purchased separately |
| Cohort / CLTV | 20 pre-built analytics modules including LTV, repurchase, retention, and new customer analysis — ready to use on day one | Not included — must be modeled from scratch using SQL/dbt |
| Dashboards & Visualization | Pre-built dashboards, forkable Looker Studio templates, and an AI analyst that answers questions with auditable SQL | Not included — requires Looker ($3K–$10K/mo), Tableau, or another BI tool |
| Custom Metrics | Define a metric once, use it everywhere — dashboards, SQL, and AI always return the same answer | Must be defined and maintained by your data team in dbt or SQL views |
| SQL / Export / API Access | Managed BigQuery warehouse with included compute and unlimited storage — any tool that supports BigQuery connects natively | Full SQL warehouse — but credit-based pricing means cost scales with every query |
| Support & Success | Dedicated US-based CSA, included quarterly solution hours, and structured roadmapping | Ticket-based support; dedicated engineering resources required |
[Compare SourceMedium vs Snowflake](/compare/sourcemedium-vs-snowflake)
Customers

## “We finally stopped arguing about the data.”

“Thanks to SourceMedium, our team no longer wastes time debating metric definitions in meetings. Instead, we can focus on what really matters: making informed, data-driven decisions that drive results.”
Kevin Chen

President
[iRESTORE](https://www.irestorelaser.com/)
“SourceMedium has future-proofed our data infrastructure. Their comprehensive solution not only supports our current needs but is also scalable as we grow. With their managed BigQuery instance, we have full control over our data, creating custom metrics and insights rapidly. This has empowered our team to answer complex business questions and focus on strategic growth initiatives.”
Johnny Goodman

Co-Founder
[CPAP.com](https://www.cpap.com/)
“SourceMedium is our command center; it helps us identify problems before they get bigger. It's easy enough for everyone on the team to use, and it empowers all people.”
Ryan Chen

Co-Founder
[Neuro](https://neurogum.com/)
“Acquiring confidence in your data is literally money. We've nearly doubled revenue in the short time we've used SourceMedium. They are the layer through which all our data flows and gets transformed into a unified, coherent and reliable language that my entire team leverages regardless of analytical capability.”
Simon Cheng

[Pique Life](https://www.piquelife.com/)
“SourceMedium is a game-changer for our business. We have a single, reliable source of truth with their data infrastructure that allows us to make data driven decisions against the short and long term impact of our marketing and customer experience efforts.”
Ovadia Labaton

Co-Founder
[The Perfect Jean](https://theperfectjean.nyc/)
“The real win wasn't the cost savings (though those were nice). It's that my team stopped arguing about which dashboard was right. One source of truth for reporting AND attribution means we actually build things now instead of reconciling data.”
Eric Lowe

Director of E-commerce
## Ready to stop debating the numbers?

> Acquiring confidence in your data is literally money. We've nearly doubled revenue in the short time we've used SourceMedium. They are the layer through which all our data flows and gets transformed into a unified, coherent and reliable language that my entire team leverages regardless of analytical capability.
Simon Cheng

[Pique Life](https://www.piquelife.com/)
### See it for yourself.

15 minutes. We'll walk through the platform, show you what your data foundation would look like, and answer any questions.