Four new Apps for Snowflake that make customer analysis easier in a privacy-first world
By Jackie Davies
If you’re like most Snowflake users, you have to extract your data from the warehouse in order to analyze, segment, or enrich it. Not only does this make for extra work, but it exposes your private customer data and increases the risk of a privacy breach.
With our newest apps built natively inside of Snowflake, you can analyze customer data inside the data warehouse, without moving data, and without the need for data extraction.
Our apps enable you to turn your data warehouse into a real-time CDP and are built for data-driven customer acquisition and engagement in a privacy-first world.
Customer Profiling (Customer Analytics): conduct customer profiling across all customer data and attributes inside Snowflake. Identify common behaviors and over-indexing attributes of target segments. Use it across first-, second- and third-party data to generate rich personas for data-driven strategies.
Propensity Modeling: generate custom propensity scores for all IDs in the warehouse based on target criteria like churn, upsell, engagement, etc.
Segment Overlap: identify ID overlaps between your first-party data and that of other data partners and publishers on Snowflake - without parties exposing raw PII.
Look-alikes: automatically create scalable probabilistic audiences (lists of IDs) within Snowflake, for first party and/or external audiences created in a data partner’s or publisher’s environment, that are ready for activation.