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Use case and domain-specific

Analytic Applications

Build use case and domain-specific analytic applications. An application is a collaborative workspace for teams, with datasets, metrics, reports, dashboards, rules, and workflows.

Application

Collaborative analytic workspace for product and customer-centric teams.

  • Use case and domain-specific applications with isolation and access control
  • Separate connections to data warehouses per application, with appropriate data security
  • Template-driven building with no SQL or code

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Datasets

Tabular entities that accurately represent the state of your business entities and events.

  • 1-1 mirroring of the structure and relationships of warehouse tables
  • Derived columns and cohorts representing calculations that can even traverse other datasets
  • Derived datasets that chain together other datasets for modeling logical business entities

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Metrics

Governed, consistent business metrics shared across all teams.

  • Template, Block, and SQL based modeling of metrics of any complexity
  • Intuitive use of metrics in any exploration with context-aware query generation under the covers
  • Composability of metrics, ownership, and tagging for consistency and manageability

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Explorations (Reports) and Dashboards

Visualizations, explorations (reports), and dashboards.

  • Self-service authoring of explorations and dashboards by business teams using templates
  • Secure sharing and collaboration among team members
  • Advanced visualization configuration and parameterization

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Exploration Templates

Rich library of templates for easy authoring of explorations

  • UI-driven templates for point-and-click building of reports and ad hoc exploration
  • Block-based templates for composing metrics of any complexity
  • Full transparency of SQL underlying templates, and optional SQL-based authoring

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Alerts and Notifications

Continuous KPI monitoring and alerting on experience degradations and revenue opportunities.

  • Template-driven alert definitions based on any metric and dimension
  • Rich configuration of alert message formats and payloads
  • Notifications in application, mobile, or Slack

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Collaboration

Collaborative building and analysis across business and data teams.

  • Secure sharing of any object or folder with users and groups
  • Comments with tagging of team members, for all objects in the system
  • Alert notifications to groups within the application or Slack channels

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Activation

Act on insights to impact business outcomes.

  • Continuously publish audiences to the warehouse for consumption by marketing automation tools
  • Exports of datasets and exploration results for invoking downstream business processes
  • Alert-based triggering of APIs to external systems

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Security & Governance

Enterprise-grade governance and secure access control

  • Secure authentication with enterprise SSO integration
  • Robust role-based access control in every metadata layer, at user and group level
  • Manageability tools for schema resiliency, event taxonomy, usage reports, and dependency management

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Testimonials

  • NetSpring is the Holy Grail of product analytics. You don’t have to move your data anywhere.

    It sits directly on your data warehouse, looks across all data sets, and supports both traditional BI analysis and modern event-centric product analytics. It is also self-service, so you can expand the reach and impact to everyone in the organization, not just technical teams.

    And when it comes to cost, NetSpring is cost-efficient and scales with our business.

    Chang Yu, VP of Product
  • Relative to our peer Web3 companies, NetSpring gives us an important competitive advantage. With NetSpring working directly on our data warehouse, we now have a view into retention and activation others don’t have.

    We can track cohort-specific KPIs, then easily build and test hypotheses that are leading to improvements to our platform, especially around the first user experience. The ability to segment our creators by specific behaviors has helped us identify which segments matter most. This was a level of granularity previously hidden from us.

    Matt Alston, Co-Founder & CEO
  • In subscription businesses, you have to closely follow the customer lifecycle. You may be looking at feature adoption or churn, at how to create more value, or how to make customers aware of new products. And internally, for sales, marketing, or customer support departments – providing insights.

    Basically with this cloud architecture we have an ability to look at product telemetry data as well as business transaction data. The magic is when we intersect these and do cohort analysis. We can slice and dice from many different perspectives, and that’s where the insights come.

    Awinash Sinha, CIO
  • You would think that if I handed you a product analytics platform, I would be excited PMs are looking at retention rate. But only half of the cancellations happened inside the product. The other half happened because somebody picked up a phone to cancel.

    No events were ever created, and as such, our retention curves were materially misstated. That immediately starts to undermine the credibility of any first-generation tool.

    John Humphrey, Former Head of Data Platform Product
  • How can we make the experience of buying groceries on Instacart not just more convenient, but also more efficient and delightful than shopping in the store?

    To inspire product strategy, we spend a lot of time trying to understand patterns of shopping so we can build personalized experiences.

    Anahita Tafvizi, VP and Head of Data Science & Business Operations
  • Our product managers and growth managers rely heavily on data to see how customers are using the platform. How frequently are they using? What capabilities are they using? Which capabilities are resonating more or less them them.

    That informs our product roadmap.

    Saket Srivastava, CIO
  • Cloud data warehouses like Snowflake, Redshift, BigQuery, Databricks, and Azure have become the de facto place where businesses pull data out and use it for a business purpose.

    So the more compute you push on the cloud data warehouse, the closer it stays to the ecosystem, and the easier it is for anyone to even consume such a system.

    Sanjay Agrawal, Co-Founder & CEO
  • Amplitude and Mixpanel are basically a time series database underneath, with a UI. Time series data tends to be write once.

    You need to take advantage of those techniques data warehouses are born with. It makes sense to put this into a data warehouse, rather than a custom database like Datadog, Mixpanel, or Amplitude. Plus you have additional benefits from it because you can cross reference that data with the rest of the business data.

    Nikita Shamgunov, Founder & CEO
  • We want to look at product funnels and customer journeys, but then combine that with Salesforce data. But it was surprisingly hard to do with a lot of these cloud product analytics tools. They’re only designed to ingest a specific kind of data. And if you want to combine other data sources, it becomes really fragile and complicated to set up those data pipelines.

    Warehouse-native enables that and unlocks that set of use cases. Why do you need to ship everything to another vendor to do specific parts of your analytics? It just does not make good sense.

    Soumyadeb Mitra, Founder & CEO
  • True centralization aggregates data from all channels, not just what someone clicked on the website: offline, IoT, support, etc.

    Let’s have shipping and returns data, and everything else that is required to properly instrument a real business – put in a single place, which is governed, secure, complete, and accurate.

    Jason Davis, Founder & CEO
  • A core focus of DCP Midstream is our commitment to operational excellence.

    Leveraging NetSpring to analyze real-time data gives our team members key information to prioritize critical work, support quick response, and more effectively serve our customers.

    Rob Sadler, Group Vice President of Energy, Transition & Transformation

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