NetSpring is purpose-built for product and growth teams to self-serve funnel, path, cohort, retention, and exploratory analysis across any product or customer business data in the data warehouse.
Our pricing model is built on insights, not event volumes. You can get started at $49/month per seat – that simple.
Your customer data never leaves the warehouse. Maintain a single governed source of truth to ensure consistent results.
Avoid additional ETL or reverse ETL pipelines. Control the trade-offs between cost and performance based on your requirements.
With complete customer journey analytics, optimize your campaigns and product experience to drive business-impactful metrics like CSAT or ARR.
Create behavioral cohorts that segment users based on the actions taken in your product. See what brings users back to your product again, and make those features more visible to everyone. Reveal trends in conversion and retention that drive revenue.
Define business-impactful metrics to tie campaigns and usage to revenue. Deeply understand how patterns drive meaningful business outcomes.
Self-serve from a rich library of report and ad hoc exploration templates. Start with funnels to understand where users drop off and remove those hurdles. Investigate paths to highlight behaviors that lead to conversion. Or study first-use of a feature and its impact on overall journey.
Answer your next question at the speed of thought by pivoting seamlessly between any report or ad hoc exploration. Slice-and-dice across any dimension, anytime. Drill into the business outcomes that lead to the best user experience.
Flexibly model across all tables in your data warehouse and express analytic computations of arbitrary complexity, all from a UI-driven interface. Eliminate your dependence on data and BI teams. Innovate at the speed of thought and release new content and product updates faster.
Easily merge and enrich multiple event streams with rich customer business context from offline data, to answer any question. Directly querying your data warehouse allows you to maintain a single source of truth for consistent, trustworthy results.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
