How can I use website analytics software to track conversions and connect data to our warehouse?

You can do this in two layers:

1. track conversions in the analytics tool
2. pipe the raw or modeled event data into your warehouse

Here’s a practical approach.

1) Define what a conversion is

Start by listing the actions that matter for your business, for example:

• purchase completed
• lead form submitted
• trial started
• demo booked
• signup confirmed

Then decide whether each conversion is:

• a simple event: e.g. signup_completed
• a funnel milestone: e.g. view_pricing -> start_checkout -> purchase
• a revenue event: e.g. order_completed with value, currency, items

2) Instrument the site/app with analytics events

Use your website analytics software to send events when key actions happen.

Typical event payload fields:

• event_name
• user_id or anonymous ID
• session_id
• timestamp
• page_url
• source / medium / campaign
• conversion-specific properties:
  • order_id
  • revenue
  • currency
  • plan
  • lead_type

Example event names:

• page_view
• signup_started
• signup_completed
• checkout_started
• purchase_completed

Important

Use a consistent event naming and property schema. That makes analysis much easier later in the warehouse.

3) Set conversion rules in the analytics platform

Most tools let you mark events as conversions or define funnel goals.

Examples:

• “conversion = event purchase_completed”
• “conversion = visiting /thank-you”
• “conversion = event demo_booked with property status=confirmed”

If the software supports it, create:

• conversion events
• funnel reports
• segments/cohorts
• attribution reports

4) Send analytics data to your warehouse

There are a few common ways to do this.

Option A: Native warehouse export

Some analytics platforms have direct integrations to:

• BigQuery
• Snowflake
• Redshift
• Databricks
• Postgres

This is usually the easiest and most reliable approach.

Option B: Reverse ETL / ELT connector

Use a data pipeline tool such as:

• Fivetran
• Airbyte
• Stitch
• RudderStack
• Segment Connections
• Hightouch / Census for activation workflows

These tools can:

• ingest analytics events
• load them into the warehouse
• keep schemas updated
• handle retries and backfills

Option C: Send events to your own event collector first

If you want full control, send web events to your own API or event pipeline, then forward them to:

• analytics software
• warehouse
• other tools

This is often the best option for larger teams or custom setups.

5) Match identities across systems

To make warehouse reporting useful, you need identity resolution.

Capture and consistently pass:

• anonymous browser ID
• logged-in user ID
• email or hashed email where appropriate
• account/org ID for B2B

Common pattern:

• anonymous user visits site
• later signs up
• you call an identify or alias step to connect the anonymous history to the known user

This lets you analyze the full conversion journey in the warehouse.

6) Model the data in the warehouse

Once the data lands in your warehouse, build tables/views such as:

• events
• users
• sessions
• conversions
• funnel_steps
• marketing_attribution
• revenue_by_campaign

Then use SQL or dbt to create metrics like:

• conversion rate
• assisted conversions
• CAC payback
• revenue by landing page
• funnel drop-off by device/channel

7) Validate and monitor

Make sure tracking works by testing:

• event fires correctly
• conversion is counted once
• revenue matches backend orders
• IDs are consistent
• warehouse rows match analytics platform counts within expected variance

Set up alerts for:

• sudden drops in event volume
• schema changes
• missing properties
• duplicate purchase events

8) Recommended architecture

A common setup looks like this:

Website/app → analytics SDK / tag manager → analytics platform → warehouse → BI / dashboards / dbt

Or, if you want more control:

Website/app → event collector / CDP → analytics platform → warehouse → BI / activation

9) Practical tips

• Track the backend-confirmed conversion, not only the front-end click
• Include unique transaction/order IDs to dedupe
• Capture UTM parameters and referrer early
• Keep event schemas stable
• Store raw events in the warehouse, then derive clean metrics from them
• Respect privacy laws and consent requirements

Example implementation pattern

For a purchase flow:

1. User lands on site with UTM params
2. Analytics records page_view
3. User adds to cart → add_to_cart
4. Checkout begins → checkout_started
5. Payment succeeds on backend → purchase_completed with order_id, revenue
6. Events stream into warehouse
7. dbt model calculates conversion rate by channel and page

If you want a simple tool stack

For many teams:

• Analytics: GA4, Amplitude, Mixpanel, or PostHog
• Warehouse: BigQuery or Snowflake
• Pipeline: Segment, RudderStack, or Airbyte
• Modeling: dbt
• BI: Looker, Power BI, Tableau, Metabase

If you tell me which analytics software and warehouse you use, I can give you a more exact setup and event schema.