Automate acquisition by targeting customers who look like your best ones.
Most acquisition campaigns start with a demographic guess and a big budget.
DAS42 builds a different starting point: a model of what your best customers actually have in common, then a system that finds more of them.
Automated Customer Acquisition with Lookalike
Audiences in Snowflake with Logan Guerry | DAS42
What it replaces
Broad campaigns built on assumptions instead of data.
Marketing teams manually stitching together lookalike segments inside individual ad platforms, working from first-party data that made it into that platform and nothing else. Media buyers spend against audiences that look promising on paper and convert at half the rate of your actual best customers.
DAS42 replaces that guesswork with a model:
analyze the customers who already generate the most value, identify the traits that make them valuable, then target new audiences who share those traits. And we can do all this before a campaign launches, instead of optimizing toward them after the budget is spent.
HOW IT WORKS
The model runs on your first-party data in Snowflake
(purchase history, engagement, lifetime value), enriched with third-party demographic and behavioral data from Snowflake Marketplace.
That combination surfaces high-probability prospects
your team can act on, instead of a demographic bucket that includes them by accident.
Audiences activate directly into your ad platforms
through Snowflake’s data sharing, so the targeting stays current as customer behavior changes.
HOW IT WORKS
The model runs on your first-party data in Snowflake
(purchase history, engagement, lifetime value), enriched with third-party demographic and behavioral data from Snowflake Marketplace.
That combination surfaces high-probability prospects your team can act on, instead of a demographic bucket that includes them by accident.
Audiences activate directly into your ad platforms through Snowflake’s data sharing, so the targeting stays current as customer behavior changes.