Product and Customer Journey Analytics

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, 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.

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.

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.