This post was originally published when Workshop was named Memex.

AI-Powered Ad Campaign Measurement Across Your Data

Describe what you want in plain English—Workshop Cloud joins the data, runs it securely, and publishes a shareable app.

The Workshop Team
February 12, 2026

Measurement isn't hard because conversion rate is a complicated metric. It's hard because the workflow is fragmented.

In a real ad campaign measurement workflow, you're usually dealing with three problems at once:

1. The inputs are scattered across warehouses, files, and internal systems
2. Making them usable means figuring out how they connect (schemas, joins, definitions, mismatched labels)
3. Even after you get the answer, it's rarely shareable in a way other people can explore without recreating your setup

So a "quick" question turns into glue work: stitching sources together, getting the right environment running, and then redoing it all whenever someone wants a different slice.

A concrete example: ad campaign measurement across systems

In the walkthrough video below, we show one simple but common scenario.

The exposure and conversion logs live in Snowflake. The campaign context—labels, channels, and creative groups—lives in Google Drive. And the question we want to answer requires both:

Where are we seeing strong conversion performance, and when does frequency start hitting diminishing returns?

How Workshop Cloud approaches it

Workshop Cloud is built to solve this end-to-end: connect the sources, make sense of how they fit together, run the analysis in a real environment, and publish it as a shareable app.

Here's the flow:

1) Connect your sources (without over-scoping access)

In the demo, we connect Snowflake in a couple clicks. Then we connect Google Drive, but with an important detail: to follow security best practices, we scope access to only the file we need (Campaign Metadata), rather than granting broad Drive access.

The connection process is streamlined—you can securely connect to both structured data warehouses like Snowflake and unstructured sources like Google Drive files, with granular permission controls to ensure you're only accessing what you need for the specific analysis.

2) Describe what you want in natural language

Next, we use Genius mode to get a strong first pass, and we describe the app we want to build:

"Show conversion rate by frequency bucket, with filters for channel, publisher, and creative group."

From there, Workshop handles the heavy lifting: it writes the code, joins the datasets, and produces an interactive app you can review and tweak.

3) Run it in a managed environment

Instead of running locally or managing infrastructure, the app executes in Workshop's secure serverless compute. No provisioning. No dependency setup. No "it only works on my laptop."

4) Publish it so others can explore it

Once the preview looks good, you can publish the app with one click. If you need restricted access, publish as a Private App. If you want easy sharing, publish it publicly—either way, others can explore the exact same analysis without setting anything up.

The publishing feature makes your analysis immediately shareable and interactive, allowing stakeholders to explore the same campaign measurement insights through filters and visualizations without needing to recreate your technical setup.

Why this matters

The goal isn't just to answer one question faster. It's to make ad campaign measurement repeatable: connect sources once, build the app, and share it so the next question doesn't restart the whole process.

If you'd like, watch the video and let us know what measurement workflow we should cover next—incrementality, lift, creative fatigue, MMM inputs, or something else.

You can start building your next app for free today. And if you want to talk through what you're trying to create, join us on Discord.