How to turn Snowflake into a self-service marketing analytics engine
Marketing self-service on Snowflake needs three things in order: ad platform data loaded into the warehouse, one analyst-owned definition that unifies the channels, and a way for marketers to read that definition without SQL. The first two are the analyst's work. The third is what makes it self-service.
Most companies that run on Snowflake have the marketing data in there somewhere. What marketers have is a queue. A question about last week’s spend by channel becomes a ticket, the ticket becomes an export, and the export is out of date before the meeting.
Self-service does not mean giving marketers a SQL editor. It means the marketing team can get a number they trust, on their own, in the tool they already use. This guide is for the analyst who has been asked for exactly that. It shows how to get there on Snowflake in three steps without giving up control of the definitions, with the SQL for the middle one.
Why self-service fails on raw Snowflake tables
Why self-service fails on raw Snowflake tables
Handing a marketing team access to raw tables rarely works, and the reasons are structural.
- Every platform has its own shape. Facebook reports
spend, LinkedIn reportscostInUsd, Google Ads counts cost in micros and Reddit counts spend in microcurrency. Nothing lines up until somebody makes it. - Definitions are not written down. Whether “spend” includes tax, which date a conversion belongs to, how campaigns are named. Each analyst decides again.
- The tool is the wrong one. A marketer works in a spreadsheet or a dashboard, not in a warehouse console.
So the work splits in two. The analyst builds one definition. The marketer reads it.
Step 1: collect ad platform data into Snowflake
Step 1: collect ad platform data into Snowflake
The first job is getting each platform’s data into the warehouse on a schedule, in tables you own. OWOX Data Marts does this with connectors: each one calls a platform’s API and writes the result into a table in your Snowflake database.
There is a step-by-step guide for each source: Facebook Ads, Google Ads, Microsoft Ads, LinkedIn Ads, TikTok Ads, Reddit Ads and Shopify.
Each connector is itself a Data Mart. You choose the fields, it creates the table, and its setup page shows exactly where the data goes.
Two things matter here for trust. The data is in your Snowflake account, so history does not disappear when a platform changes its API or you change tools. And the load runs on a trigger, so the tables are current without anyone remembering to refresh them.
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Step 2: unify the channels in one query
Step 2: unify the channels in one query
Now the analyst’s part. Each platform table has its own column names and grain, so the marketing team needs one table where a row means the same thing whatever the channel: a date, a channel, impressions, clicks, spend.
This query does that for three platforms. It was validated on Snowflake against tables loaded by the connectors:
SELECT
"date_start" AS date,
'facebook' AS channel,
SUM("impressions") AS impressions,
SUM("clicks") AS clicks,
SUM("spend") AS spend
FROM ads_owox."PUBLIC"."facebook_ads_ad_account_insights"
GROUP BY "date_start"
UNION ALL
SELECT
"stat_time_day" AS date,
'tiktok' AS channel,
SUM("impressions") AS impressions,
SUM("clicks") AS clicks,
SUM("spend") AS spend
FROM ads_owox."PUBLIC"."tiktok_ads_ad_insights"
GROUP BY "stat_time_day"
UNION ALL
SELECT
TO_DATE("dateRangeStart") AS date,
'linkedin' AS channel,
SUM("impressions") AS impressions,
SUM("clicks") AS clicks,
SUM("costInUsd") AS spend
FROM ads_owox."PUBLIC"."linkedin_ads_ad_analytics"
GROUP BY TO_DATE("dateRangeStart");Three details in it are the kind of thing only an analyst should decide.
The names are quoted. The connectors create schema, table and column names in Snowflake exactly as written, so they are case-sensitive. Without the double quotes, Snowflake reports that the table does not exist.
The spend column differs. LinkedIn’s cost is costInUsd; the others call it spend. If your accounts bill in different currencies, this is also where the conversion belongs.
The grain is chosen. One row per day and channel. Campaign-level detail would be a second definition, not a wider version of this one.
OWOX Data Marts does not write this SQL and does not transform the data. The analyst writes it, and publishes it as a Data Mart: a named definition with a description for each field, so “spend” says what it includes.
Add revenue when you are ready
Spend alone answers “what did we pay”. The question marketing actually gets asked is what it brought back. That needs the same treatment for orders: a definition of revenue by day and channel, joined to spend on date and channel. Build it as a second Data Mart and declare the join between the two, so the reader sees one set of columns. Joinable Data Marts cover how that works.
Start with spend. A unified spend table that everybody trusts is worth more than an ambitious ROAS model nobody can verify.
Step 3: give marketers a way to read it
Step 3: give marketers a way to read it
A published Data Mart can reach the marketing team in three ways, and they are not exclusive.
Pull it into Google Sheets themselves
The OWOX Data Marts extension for Google Sheets is the self-service part. A marketer opens it in a , picks a Data Mart, chooses columns, adds filters and sorting, and runs it. No SQL, no Snowflake login.
They can only reach what the analyst made available, which is why it is safe to let them explore.
Get it delivered
For the numbers everybody looks at, a scheduled report writes the Data Mart into a shared sheet, or sends it to Data Studio, Slack or email. Snowflake reports in Google Sheets is the walk-through.
Ask for the story
AI Insights runs on the same published Data Marts and delivers a written summary to the channels the team already reads. It works from the analyst’s definition and not from raw tables, so it reads the same numbers the sheet does. For questions asked in an AI chat instead, see governed AI analytics on Snowflake.
What the marketing team can answer on its own
What the marketing team can answer on its own
With a unified spend Data Mart and the extension, these stop being tickets:
- What did each channel spend yesterday, last week, this month?
- Which channel’s cost per click moved most since last month?
- How does spend this quarter compare with the same quarter a year ago?
- Which days did a channel stop spending?
And with revenue joined in: what each channel brought back for what it cost.
What stays with the analyst
Self-service works because some things are not self-service.
- The definitions. What counts as spend, which date, which currency.
- What is published. A Data Mart is visible to business users only when the analyst makes it available for reporting.
- The cost. Every run uses a Snowflake warehouse. A dedicated one for reporting, with auto-suspend on, keeps that predictable. Snowflake pricing explained shows the numbers.
A first month
You do not need a programme to start. A realistic first month looks like this.
- Week 1. Connect Snowflake as a storage and load the two or three ad platforms that carry most of the budget.
- Week 2. Write the unified spend query, check it against each platform’s own interface, and publish it with descriptions.
- Week 3. Put one scheduled report in front of the team, and have two people install the extension.
- Week 4. Add the remaining platforms and, if orders are in Snowflake, the revenue definition.
For the analyst’s side of building this layer, see Snowflake as a reusable reporting layer. If you are still getting to know the platform, start with what Snowflake is.
Turn your data into decisions.
Governed data marts give you the clean foundation ML needs to actually work.
- No AI hallucinations
- Analyst-governed definitions
- Every number traces to SQL
Frequently Asked Questions
What does a first month of self-service marketing analytics on Snowflake look like?
Week 1: connect Snowflake as a storage and load the two or three ad platforms that carry most of the budget. Week 2: write the unified spend query, check it against each platform's own interface and publish it with descriptions. Week 3: put one scheduled report in front of the team and have two people install the Sheets extension. Week 4: add the remaining platforms and, if orders are in Snowflake, a revenue definition.
How does AI fit into self-service marketing analytics on Snowflake?
AI Insights in OWOX Data Marts runs on a published Data Mart and delivers a written summary to the channels the team already reads, such as email or Slack, on a schedule. It works from the analyst's definition and not from raw tables, so it reads the same numbers as the sheet.
Where should metrics like spend, ROAS and CAC be defined?
Once, in SQL the analyst owns, and published as a Data Mart with a description for each field. Start with unified spend by day and channel. Add revenue as a second definition and declare how the two join, so ratios such as ROAS are computed from the same spend and revenue everywhere. Compute ratios from sums, never by averaging a ratio column.
How can marketing data from platforms like Google Ads, Meta, TikTok, and LinkedIn be connected to Snowflake?
With connector Data Marts in OWOX Data Marts. Each connector calls one platform's API and writes the result into a table in your own Snowflake account, on a trigger you set. You choose the endpoint and the fields. Each platform keeps its own column names, which is why the next step is one query that maps them to a shared set.
How does OWOX Data Marts help marketing teams leverage Snowflake effectively?
OWOX Data Marts covers the two ends around the analyst's work. Connectors load ad platform and shop data into tables in your Snowflake account on a schedule. The analyst then publishes a unified query as a Data Mart, and marketers read it through the Google Sheets extension, scheduled reports or AI Insights, without SQL or a Snowflake login. It does not transform the data; the unifying SQL is the analyst's.
What is a marketing Data Mart on Snowflake?
A Data Mart is a SQL query, table or view that an analyst publishes under a business name, with a description for each field. For marketing it is typically one row per day and channel with impressions, clicks and spend. Because every report and every self-service pull reads that one definition, the marketing team works from the same numbers.
What challenges do marketing teams face when using raw Snowflake data for self-service analytics?
Raw tables have the shape of their source: each platform names its date and its cost differently, and nothing says what a metric includes. Definitions are made again by each person who writes a query, so reports disagree. And the tool is wrong for the reader: a marketer works in a spreadsheet, not in a warehouse console, so every question becomes a ticket.
How can marketing teams turn Snowflake into a self-service marketing analytics engine?
In three steps. Load each ad platform into Snowflake on a schedule. Have an analyst write one query that unifies the channels into a single table of date, channel, impressions, clicks and spend, and publish it with documented fields. Then let marketers read that definition themselves from Google Sheets, or receive it as a scheduled report.
