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NetSpring Achieves Google Cloud Ready - BigQuery Designation

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The Next Generation of Product Analytics

Next-generation product analytics is warehouse-native, with the exploratory power of BI. Watch the video to learn more.

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Benefits

 

Modernize your product analytics stack for a fraction of the cost

Full Business Context

Access any data in the warehouse. Deeply understand the drivers of retention, referrals, and revenue.

Privacy & Security

Your customer data never leaves the warehouse. Maintain a single governed source of truth.

Cost & Maintenance

Simply connect to the warehouse and self-serve. No data movement. Cost not tied to event volume.

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, churn, at how to create more value, or how to make customers aware of new products and offerings. 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
  • 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
  • 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

Unlock more business value from product analytics

Get a complete view of account-level journeys, understand attribution, and unlock cross-functional business insights. Extend your templated funnel, cohort, or retention analysis with the ad hoc exploratory power of BI.

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Getting set up is easy.

Connect to Snowflake, BigQuery, Redshift or Databricks. Be up and running in hours.

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