In the Modern Data Stack, all data — including Snowplow’s first-party customer behavioral data — goes to a centralized enterprise data store in the cloud. This could either be a data lake, such as Databricks, or a data warehouse, such as Snowflake. Whether using a lakehouse or warehouse architecture, enterprises are standardizing on having all data reside in a central store. This offers advantages in terms of consistency, security, governance, and manageability.

For analytics, tools and capabilities such as Business Intelligence (BI) and AI/ML already work directly off data in these same stores. However, BI tools like Tableau and Looker do not effectively support behavioral event data (i.e. time series data). As a result they are often used in combination with product analytics tools like Amplitude and Mixpanel, which emerged a decade ago with purpose-built analytics for user event data. These vertically-integrated solutions preceded the modern data stack, and consequently do not work natively off the data warehouse or data lake. They are limited to product-only channels, have very limited out-of-the-box reporting, and do not offer best-in-class instrumentation.

A centralized data warehouse or data lake breaks down these silos, and paves the way to compose the data stack with best-in-class capabilities at every layer. 

Best-in-Class Instrumentation

Snowplow is a first-party, privacy-compliant customer data collection and processing platform that empowers organizations to generate and model first-party data from across their digital interfaces to capture descriptive customer journeys and build actionable behavioral profiles. Purposely designed for the data platforms as a single source of truth; Marketers, Data teams and CDPs can activate Snowplow data across Customer 360, Personalization and ML use cases.

By leveraging Snowplow, businesses gain a complete and unified descriptive view of their customer interactions, as well as interactions with partners, applications, and systems. Sourced from various digital (website, mobile apps, IoT, etc…), the first-party customer data is presented as rows of events, each containing contextual entities and properties, such as page and event location — linked together to form a journey for each customer interaction.

Snowplow's first-party customer data is predictive by nature and rich in context, making it the perfect fuel for advanced analytics.

Best-in-Class Analytics

When first-party behavioral data is further enriched with other business data in the data warehouse (e.g. account-level details from Salesforce, support logs in Zendesk, or payment history in NetSuite), you can construct a factually comprehensive view of the customer and their journey, for a deep understanding of the behavioral patterns over time that are driving retention, referrals, and revenue.

NetSpring is a next-generation, warehouse-native product analytics solution that offers self-service product analytics with the analytical power of BI. Product, Growth, and Success teams can get a complete view of account-level journeys, understand attribution, and uncover cross-functional business insights. They can enhance any event segmentation, funnel, path, cohort, or retention analysis with ad hoc analysis — to visually explore any data in the warehouse.

Warehouse-Native Architecture

A common warehouse-native approach allows both Snowplow’s Behavioral Data Platform (BDP) and NetSpring’s Product & Behavioral Analytics to meet at a common point —  a data warehouse —  such as Snowflake. 

With NetSpring’s next-generation product analytics capabilities, companies can begin to unlock the full value of Snowplow’s first-party customer data. Leveraging best-in-class instrumentation and analytics, joint customers are able to operationalize rich, first-party customer behavioral data and then dissect and understand the behavioral patterns across the entire customer journey – from adoption through engagement and account-level retention and revenue. 

Next Steps

An on demand recording of Unlock the Value of Snowplow 1P Customer Data with NetSpring is now available. Watch the joint demo to see how:

• Snowplow can instrument the entire user and customer journey across all channels and touchpoints
• A data warehouse can bring together complete and predictive customer profiles audiences from Snowplow, alongside all relevant customer and business context for a complete view of the entire customer journey
• Leveraging the data warehouse as the single source of truth, NetSpring brings the rich modeling and analytical power of BI for ad hoc visual exploration to funnel, path, and cohort analysis

Recommended Reading

• How and Why NetSpring is Building the Next Generation of Product Analytics on Snowflake(Snowflake blog)

• Behavioral Data Platforms for Product Analytics(Snowplow blog)

• Product Analytics in the Modern Data Stack(NetSpring blog)

• Stop Using 3+ Tools for Product Analytics(NetSpring blog)

• The Composable CDP And What It Means For Product Analytics(NetSpring blog)