--- title: Stop moving data. Start using it. | SourceMedium description: Put your commerce data to work in AI, reports, and applications. SourceMedium maintains the connections, models, and definitions underneath. url: "https://sourcemedium.com/" type: static generatedAt: "2026-10-05T20:26:24.000Z" --- The managed data foundation that puts your data to work # Stop moving data. Start using it. SourceMedium unifies your data into one trusted foundation behind your dashboards and AI agents. Live in two weeks. Fully managed. No data team required. [Book a walkthrough](#demo) [Explore workflows](https://sourcemedium.com/use-cases/) [ChatGPT](https://sourcemedium.com/mcp/#agents)[Claude](https://sourcemedium.com/mcp/#agents) [Connect your agent →](https://sourcemedium.com/mcp/) Trusted by operators at 100+ e-commerce brands Your tools. A maintained foundation. Connect → Model and maintain → Use everywhere Supported sources Commerce Marketing Lifecycle Operations Connections, models, definitions, and checks **Commerce data foundation** BigQuery Choose how you use it Direct warehouse access is available with Pro. Brands running on SourceMedium You know the feeling ## “Is that number actually right?” You're about to make a call on budget, promo, inventory, or forecast. The room stalls. ### When the numbers don't agree Meetings turn into debates about which platform is "truth." The dashboard says one ROAS. The bank account says another. AI answers feel risky, so you verify everything by hand anyway. ### With one trusted foundation Decide where to move budget without arguing about Meta vs. Shopify numbers Understand what changed this week in revenue, margin, and returns Get answers to new questions without waiting on an analyst *One KPI. Three interpretations.* ## Ask a business question. Get an answer you can verify. Work with SourceMedium Agent in Slack, or connect the agents you already use. Every answer traces back to the same maintained foundation. SourceMedium Agent ### Ask in Slack. Audit the SQL behind the answer. **Analytical answers can show the chart, the supporting references, and the underlying SQL, so your team checks the work instead of taking a number on faith.** No new analytics interface to learn and no query language needed to ask. This conversation shows illustrative sample data. Questions you can ask # data-questions JR Jordan R. 10:23 AM What's our average CAC by channel in the last 30 days? SourceMedium Agent APP 10:23 AM This 30-day result has two paid sales channels with measurable CAC: **Marketplace = $77.81** and **Online store = $399.99**. Online store CAC is about **5.1x** higher. If your target CAC is below $100, investigate that channel first. - [Table: Executive Summary Daily](https://sourcemedium.com/docs/data-activation/data-tables/sm_transformed_v2/rpt_executive_summary_daily) - [Metrics Reference](https://sourcemedium.com/docs/onboarding/data-docs/metrics) - [SQL Query Library: CAC templates](https://sourcemedium.com/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 JR Jordan R. 10:23 AM Which platform and campaign type has the highest ROAS in the last 30 days? SourceMedium Agent APP 10:23 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](https://sourcemedium.com/docs/data-activation/data-tables/sm_transformed_v2/rpt_ad_performance_daily) - [Common Analysis: ROAS](https://sourcemedium.com/docs/help-center/common-analyses/roas) - [SQL Query Library: ROAS templates](https://sourcemedium.com/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 JR Jordan R. 10:23 AM What are our top products by net revenue in the last 30 days? SourceMedium Agent APP 10:23 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](https://sourcemedium.com/docs/data-activation/data-tables/sm_transformed_v2/obt_order_lines) - [Common Analysis: Top-Selling Products](https://sourcemedium.com/docs/help-center/common-analyses/top-selling-products) - [SQL Query Library: product ranking](https://sourcemedium.com/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 JR Jordan R. 10:23 AM What's our return rate and net return cost by acquisition channel last 30 days? SourceMedium Agent APP 10:23 AM Return rates vary significantly by channel: **paid_social leads at 8.9%** ($52.2k net return cost), followed by **paid_search at 7.2%** ($38.6k). Email/SMS and organic are below 4.5%, less than half the paid_social rate. Paid_social customers are returning nearly 3x more than direct or organic traffic. Worth segmenting by campaign type to find whether specific creatives or audiences are driving the difference. - [Table: Returns](https://sourcemedium.com/docs/data-activation/data-tables/sm_transformed_v2/fct_returns) - [Table: Executive Summary Daily](https://sourcemedium.com/docs/data-activation/data-tables/sm_transformed_v2/rpt_executive_summary_daily) Data: Return Rate by Acquisition Channel (Last 30 Days) | Channel | Orders | Return Rate | Net Return Cost | | --- | --- | --- | --- | | **paid_social** | 8,432 | 8.9% | $52,180 | | **paid_search** | 6,891 | 7.2% | $38,640 | | **email_sms** | 4,203 | 4.1% | $15,421 | | **organic_search** | 2,918 | 3.3% | $8,841 | | **direct** | 1,847 | 2.8% | $6,210 | Chart: Return Rate by Channel paid_social paid_search email_sms organic_search direct ``` -- Analysis: return rate and net return cost by sm_channel -- Assumptions: timeframe=last_30_days | metric=return_rate=returns/orders | grain=sm_channel | scope=matched_returns_only SELECT o.sm_channel, COUNT(DISTINCT o.sm_order_key) AS orders, ROUND( SAFE_DIVIDE(COUNT(DISTINCT r.sm_order_key), COUNT(DISTINCT o.sm_order_key)) * 100, 1 ) AS return_rate_pct, COALESCE(SUM(r.net_return_cost), 0) AS net_return_cost FROM `your_project.sm_transformed_v2.rpt_executive_summary_daily` o LEFT JOIN `your_project.sm_transformed_v2.fct_returns` r ON o.sm_order_key = r.sm_order_key AND r.is_order_matched = TRUE WHERE o.date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) AND o.is_customer_order = TRUE GROUP BY 1 ORDER BY return_rate_pct DESC LIMIT 10; ``` # data-questions JR Jordan R. 10:23 AM How has attribution health trended weekly in the last 6 months? SourceMedium Agent APP 10:23 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](https://sourcemedium.com/docs/ai-analyst/diagnostics/attribution-health) - [Attribution Health Guide](https://sourcemedium.com/docs/data-inputs/attribution-health/index) - [Attribution Source Hierarchy](https://sourcemedium.com/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 JR Jordan R. 10:23 AM How should we rebalance paid platform spend next month to improve efficiency? SourceMedium Agent APP 10:23 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](https://sourcemedium.com/docs/ai-analyst/workflows/standard) - [Table: Ad Performance Daily](https://sourcemedium.com/docs/data-activation/data-tables/sm_transformed_v2/rpt_ad_performance_daily) - [SQL Query Library: efficiency templates](https://sourcemedium.com/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 JR Jordan R. 10:23 AM What's the difference between first-touch and last-touch attribution? SourceMedium Agent APP 10:23 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](https://sourcemedium.com/docs/mta/mta-overview) - [MTA Models Reference](https://sourcemedium.com/docs/mta/mta-models) - [MTA FAQs](https://sourcemedium.com/docs/mta/mta-faqs) [Explore SourceMedium Agent →](https://sourcemedium.com/ai/) Silent looping video. - Claude - ChatGPT - Claude Code - Codex - Cursor - Hermes - Grok - Gemini - Antigravity - OpenClaw - Slackbot - GitHub Copilot - Muse - and your own agent ### Data on a silver platter for all your agents. [Explore use cases →](https://sourcemedium.com/mcp/#prompts) ## A foundation your team can inspect. You bring the questions. We deliver the trusted, modeled data to answer them. 01 Connect ### Connect the data your business runs on Managed integrations connect commerce, ads, subscriptions, lifecycle, analytics, and support. The integration library lists 64 connectors; custom sources are scoped separately. - - Amazon Seller Central - [See all integrations](https://sourcemedium.com/integrations) 02 Model and maintain ### Model and maintain one trusted foundation We map fragmented source data into a stable commerce schema, reconcile platform differences, document business definitions, run automated QA, and manage source changes. **Commerce data foundation** BigQuery - **4,000+** quality checks daily - **180+** defined metrics - **20+** core models What comes out - New vs repeat - CAC - LTV - Contribution margin - Cohorts - Attribution [Explore the data foundation](https://sourcemedium.com/bigquery/) 03 Use everywhere ### Use it everywhere your team works The same maintained data supports your AI, SourceMedium Agent, dashboards, and customer applications through documented access paths. ## Keep what you’ve built. Hand off the data work underneath. Wild Earth wanted reliable cohorting and drill-down without maintaining every layer of its data stack. - Your data spans Shopify, Amazon, retail, subscriptions, ads, lifecycle, support, or operations. - Different tools report different versions of revenue, CAC, LTV, margin, or attribution. - Your team needs more flexibility than a closed analytics product provides. - You have a lean team and a growing analytics backlog. ## What operators say “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/) “Best-in-class attribution, excellent team, and the customer experience is beyond anything I've experienced with a SaaS product. We've surfaced new, actionable insights about our business using the Source Medium platform.” Will Rossiter CRO [LMNT](https://drinklmnt.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/) “SourceMedium's intuitive design provides us with a clear view of our KPIs and is what our team relies on for daily decision making. Really impressed with the product's ability to adapt and grow with us!” Brian Tate Founder & CEO [Oats Overnight](http://www.oatsovernight.com) “We've spent months looking for an analytics solution that would present accurate data we 100% trust in a digestible format that's easily understandable by any team member. We found that in SourceMedium.” Kevin Lee Co-Founder [Immi](https://shop.immieats.com/) “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/) “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/) “We spent months evaluating attribution tools for our clients. What sold us on SourceMedium? They don't add another pixel or tracking layer. They just make sense of the quality data you're already collecting. Clean, auditable, actionable.” Jacob Rokeach [FluencyFirm](https://fluencyfirm.com/) “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 Former Head of Growth [Catalina Crunch](https://us.catalinacrunch.com/) “We have grown rapidly the last 18 months, and that level of growth wouldn't have been possible without FERMÀT and Source Medium.” Image: Christina Blaisdell Oyler Christina Blaisdell Oyler VP of E-commerce [Hero Foods](https://shop.hero.co/) “The open-source dashboard templates provided by SourceMedium are simply the best in the business. The ability to fully customize and edit these dashboards internally has given our teams the flexibility and agility they need to make informed decisions quickly on behalf of our clients.” Image: Jonathan Snow, DMD Jonathan Snow, DMD Chief Innovation Officer [Avenue Z](https://avenuez.com/) “Amazing team-we really enjoy having direct access to a great data bench via Slack. It's service beyond anything I've experienced with a SaaS product.” Drew Sanocki Executive Chairman [oVertone](https://overtone.co/) “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 [XCVI](https://xcvi.com/) “Thanks to SourceMedium and Loop, we have a pulse on data in real-time and can make decisions much faster.” Alicia Zeng Founder & CEO [Lilac St.](https://lilacst.com/) “My portfolio companies use SourceMedium because it answers the hard questions - which creatives actually drive LTV, what's the real incrementality of each channel. As an investor, seeing SourceMedium in the tech stack tells me they're making decisions on truth, not modeled guesses.” Scott McLeod Chief of Staff & Investor [Resident Home](https://www.residenthome.com/) “We almost paused three landing pages last quarter. Good thing we checked SourceMedium first - they were driving 30% of our email signups that convert later. Also found out our welcome series is an absolute beast. Game changer for our CRO roadmap.” Junior Reyes VP of Marketing [iRESTORE](https://www.irestorelaser.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/) “We consider SourceMedium data as our source of truth and will continue to make strategic decisions based on the metrics presented in the dashboard!” Matt Holtwick Manager of Growth & Retention [Chomps](https://chomps.com/) “Source Medium gives us the edge we need to stay ahead of the data curve, without needing our own team of analysts.” Jack Benzaquen [Duradry](https://duradry.com/) “At my last company I had ten analytics people and three data scientists. Here I have none of that, and I don't need it. SourceMedium gives me the warehouse, the dashboards, the attribution, and an AI analyst in Slack. That's a full data stack without the headcount.” Nick Zhou VP of Growth [Elix](https://www.elixhealing.com/) “Source Medium is an incredibly powerful dashboard that we use across our organization for daily reporting and decision making.” John Sherwin Co-Founder [Hydrant](https://www.drinkhydrant.com/) “Source Medium is the first tool I open every morning. As a CEO it has become an integral tool to assess the daily health of the business and make long term strategic decisions.” Mike Abadi What SourceMedium replaces ## Choose how much of the data work you want to own. You didn't build your storefront. You bought Shopify. You didn't build email. You bought Klaviyo. There's a reason. Data should work the same way. One maintained foundation and one accountable partner. We help your team adopt the dashboards and AI workflows, not just ship tables and disappear. ### Analytics platform Compare maintained scope, inspectable definitions, extension options, and the complete price. These vary by vendor. ### SourceMedium Maintained connections and commerce models, used through your AI and reporting tools. Pro adds direct warehouse access. ### DIY data stack Maximum flexibility, plus the engineering work to assemble every layer and repair it each time a source API changes. ## Know what your first scope includes. Before signing, agree the plan, monthly fee, brands and stores, sources, access rights, compute and AI usage, support hours, and any additional charges. Start with one useful workflow. Agree the sources, access, responsibilities, and validation before implementation begins. - Choose Foundation or Pro based on how your team will use the data. - Month-to-month commitment. - Usage overages may apply and are explained before signing. ## Ready to stop debating the numbers? In 30 minutes, we will 1. 1 Map your current systems and reporting gaps. 1. 2 Show one workflow relevant to your team. 1. 3 Outline likely fit, implementation scope, and next steps. Book a walkthrough Share a little context, then choose a time immediately after submitting. ## Choose a time for your walkthrough Your details are saved. Choose a time to book your walkthrough. Loading available times... We couldn't load available times here. Open the scheduling calendar in a new tab. If you cannot schedule now, we will still follow up. [Open scheduling calendar](https://meetings.hubspot.com/feifanw/mc?utm_source=sourcemedium-site&utm_medium=website&utm_campaign=platform-walkthrough) > 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/)