Sales engagement data in Snowflake: Bring Outreach activity next to the rest of your GTM data

TL;DR: Outreach Data Sharing gives you a live, read-only view of your Outreach sales engagement data inside your own Snowflake account. Query emails, meetings, and sequences with SQL, with no APIs or ETL to build.

Your revenue team's activity is some of the most useful data in your business. Every sequence step, email reply, and booked meeting tells you something about what's working. But for many RevOps and data teams, getting that activity into the warehouse where analysis happens is a project in itself.

Usually that means building an API pipeline, managing rate limits, and maintaining it as things change. One Outreach beta customer spent more than three months and roughly $25K a year in engineering time just to extract their data.

Outreach Data Sharing, in Snowflake

Outreach Data Sharing is available on the Snowflake Marketplace and runs on Snowflake Secure Data Sharing. It gives you a live, read-only view of your Outreach data inside your own Snowflake account. Nothing is copied or exported, and there are no API calls, rate limits, or ETL jobs to maintain.

The data is granular. You can query record-level email engagement (sent, opened, replied), meeting activity (booked, completed), and rep task activity, alongside sequence and pipeline data. Because it arrives as SQL-ready tables, you can join it with Salesforce, Marketo, and product data you already store there.

Beta customers had usable data in under a day, with no engineers required.

What can you do with Outreach engagement data alongside the rest of your GTM data? Here are two ways teams can use Outreach Data Sharing in Snowflake to improve lead scoring and connect sales activity to revenue outcomes.

Lead scoring that keeps up with buyers

Rules-based lead scoring goes stale quickly. When scores refresh once a day, a prospect who turns hot at 9 a.m. may not reach a rep until the next morning, when they may have already missed out on the potential opportunity to a competitor who reached out immediately.

Snowflake's own SDR team ran into this. With Outreach engagement data flowing into Snowflake, they feed it into their scoring models and push updated scores back to Salesforce and Outreach for reps to act on. They moved from once-daily score refreshes to updates every two hours. A lead that heats up in the morning can now reach a rep by lunch. That gives reps a better chance to engage buyers while their interest is high and turn those conversations into pipeline opportunities.

If your team is already scoring in Snowflake, this closes a common gap: your model can now see what reps and prospects are actually doing, not just firmographics.

Improve attribution from first touch to closed-won

Marketing hands leads to sales, and visibility often stops there. Without shared data, it's hard to say which campaigns lead to revenue.

With Outreach engagement data (sequences, replies, meetings booked) in the same place as Marketo, Salesforce, and web and product data, you can build one account-level view from first touch to closed-won. Customers who unified this data reported richer attribution models, along with higher prospect response rates and more pipeline generated.

It also gives revenue leaders a way to answer a question that's hard to answer in-app: how do rep and sequence activity connect to revenue? Armed with this data, teams can do more of what works (and spend less time on what doesn’t) to grow revenue faster. 

Turn sales engagement data into revenue insights

Your team already has valuable sales engagement data in Outreach. Bringing it together with the rest of your GTM data in Snowflake gives you a clearer picture of what's driving results — and where to focus next.

With Outreach Data Sharing, you can sharpen lead scoring so reps act on buying signals sooner, connect sales and marketing activity to closed-won revenue, and identify which sequences and interactions contribute to pipeline. And because the data is available directly in Snowflake, you can get those insights without building and maintaining custom API integrations or ETL pipelines..

The result is less time spent managing data and more time using it to make better decisions, prioritize opportunities, and drive revenue growth.