Vijay Ganesan
CEO
NetSpring (Now a Part of Optimizely)
NetSpring provides warehouse-native analytics that enable businesses to analyze product usage and customer behavior across all data sources without the need for data movement or ETL processes. This approach allows teams to gain insights into user engagement and retention while ensuring compliance with security and governance policies.
- Data & Analytics
- Enterprise Software
- Software Only
Updated 5 months ago
Funding
Raised to date
$13MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Product
Problem
Traditional product and customer analytics tools often require data to be moved and transformed, creating silos and increasing complexity. This ETL process can be costly, time-consuming, and introduce security and governance risks. Furthermore, these tools may not easily integrate with all data sources, limiting the scope of analysis.
Solution
NetSpring provides warehouse-native analytics, enabling businesses to analyze product usage and customer behavior directly within their existing data warehouse. By eliminating the need for data movement or ETL, NetSpring ensures secure, governed, and trustworthy analytics on a single source of truth. The platform offers a rich library of behavioral analytics templates for event segmentation, retention, funnel, and cohort analysis, allowing teams to quantify and visualize user behavior without SQL or dependence on data teams. Users can perform deep-dive explorations to identify drivers of user behavior and analyze the impact of product feature usage on revenue and support.
Target Audience
NetSpring targets product management, growth, data science, and engineering teams seeking to understand product usage patterns, optimize user engagement and retention, and analyze the entire customer journey.
Features
- Warehouse-native architecture that operates directly on data within Snowflake, Redshift, BigQuery, Databricks, and Azure.
- Library of reporting templates for event segmentation, retention, funnel, path, and cohort analysis.
- Self-service ad hoc exploration with a visual interface and optional SQL support.
- Customer 360 view to visualize entire customer journeys end-to-end, both in-product and outside.
- Multi-stream analysis across all customer touchpoints and channels.
- Role-based access control and secure sharing and collaboration features.
- Semantic modeling with lightweight annotations and UI-driven templates for building analytic applications.