Modern data stack
Cost-effective, trustworthy, secure analytics in a single tool on the single source of truth – your data warehouse. No data copies. No ETL or reverse ETL. Business-impactful analytics across all data in the warehouse.
Connect your cloud data warehouse, and NetSpring directly queries against all your event and reference state data.
Have fine-grained control over cost-performance ratios.
Ensure high trust and security/privacy compliance by ensuring no data leaves your secure warehouse ever.
Directly reflect the relational model in the warehouse that is unique to your business.
Take advantage of the ecosystem of warehouse-centric data transformation tools
|
Traditional, non warehouse-native tools like Ampltitude, Mixpanel, Heap, Adobe |
Warehouse-native tools like NetSpring |
|---|---|
|
Limited feature usage analytics working off just product data (user interactions in product). |
Deeper analytics across product data and all customer business data (campaign, account, support, etc.) to understand business impact of features, and drivers for adoption, engagement, and retention. |
|
Data silos with some data in the warehouse and some in black-box SaaS services of non warehouse-native vendors. |
Single repository for all data – the enterprise data warehouse or data lake. |
|
Fragmented, inconsistent analytics across data silos – the “number don’t add up” problem. Lack of trust in metrics. |
Fully auditable and trustworthy analytics on the single source of truth. Confidence in publishing metrics C-suite. |
|
Significant cost in managing product analytics and BI tools, with recurring need to rationalize inconsistencies across them. |
Simplicity of managing a single analytics tool for product analytics and BI, that works directly off the data warehouse. No inconsistencies in data. |
|
High TCO with error-prone ETL and reverse ETL to/from data warehouse |
No ETL or reverse ETL needed. |
|
Low ROI with data volume-based pricing that is disproportionately high compared to value |
Significantly lower usage-based pricing leverage inexpensive storage and elastic compute of modern cloud data warehouse. |
|
Heavy dependence on data teams for business users to get deeper insights that involves data outside of the product silo. |
More self-service by business users to get deeper insights across all data in the warehouse. |
|
Security, privacy and governance risks with critical user data outside of the secure enterprise environment. |
Leverage existing security, privacy, governance, and quality checks of data in the central enterprise data warehouse. |
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.
