The Warehouse-Native Way for Product Analytics

What does warehouse-native product analytics mean, and why is it important.

Product analytics is a crucial aspect of modern business, providing invaluable insights into user behavior, feature usage, and overall engagement. However, not all analytics solutions are created equal. Enter warehouse-native product analytics solutions, a new breed of analytics tools designed to offer more direct, flexible, and scalable insights. But what exactly does "warehouse-native" mean, and how does it differ from traditional analytics? Let's dive in!

What is a warehouse-native product analytics solution?

Warehouse-native product analytics solutions are analytics platforms built to work directly with your existing data warehouse. Unlike traditional analytics platforms that come with their own databases, and require ETL (Extract, Transform, Load) processes to move data into that database, these solutions tap directly into the data where it resides—in your warehouse. This means you don’t need to move or copy data from your warehouse to another database, saving time and computational resources.

Houseware is a warehouse-native, composable product analytics solution aiming to flip the value of analytics from a cost-center, back-office function to a pillar for success, putting the product forward. We see

Why choose warehouse-native?

Warehouse-native product analytics solutions offer a direct, flexible, and often more cost-effective way to analyze your product data. They are particularly useful for companies that already use a data warehouse and want to streamline their analytics processes. By eliminating the need for additional data movement and offering near real-time insights, these solutions are shaping the future of product analytics.

Consumer applications have reached a new height in the last decade. With the internet-native population's rise, the time applications take to grow beyond leaps and bounds has only been propelled to an all-time low. We call this new generation of applications compound applications, inspired by Parker Conrad’s talk on “Compound Startups.”

At Houseware, our approach to empowering these "compound applications" involves utilizing a data warehouse-first architecture for product analytics.

Three unique attributes define the compound applications:

  1. 💰 Multi-millions of active users.
  2. 🗓️ Usage frequency inching towards a daily-weekly use case.
  3. 🗂️ Large application footprint; wide variety of possible use cases.

Understanding users' behavior and usage patterns is the starting point and the pinnacle of success for these products. These three unique attributes, as highlighted above, put tremendous pressure on the challenge of understanding user behavior and usage patterns.

The traditional systems that were built to help collect, compute, and visualize customer usage data were essentially built as an all-in-one solution. Companies like Amplitude and Mixpanel, founding the categories “Product Analytics” and “Digital Analytics,” led the wave for companies to understand their users better.

However, for compound applications, product analytics solutions have failed to support them for several reasons:

  1. Unsustainable Pricing: Due to the heavy emphasis on SaaS and the high data volumes - most pricing in this market is consumption-based.
  2. Lack of support for evolving data sources: The solutions lack the support for evolving the source data in tandem with the advances in the modern data stack across their customers.
  3. Unable to scale with events volume: These products were designed when event volumes across applications were low. They have hence failed to adopt a solution for product teams supporting significantly higher user-base event volumes.

A warehouse-native solution helps to solve the above three to a certain extent. As the warehouse becomes one singular truth for your data, it can support evolving data sources and is fully capable of scaling with events volume.

Warehouse-Native Houseware

At Houseware, our approach to empowering these organizations involves utilizing a data warehouse-first architecture for product analytics while effectively breaking down the analytics workflow into seven crucial steps. We are rethinking product analytics and how it evolves in an AI-first approach with each step:

  1. Capture: Events data capture with sessionization, filtering, & consent management with Rudderstack, leading CDP on the market across web, mobile, and server SDKs.
  2. Connect: Connect to transactional data sources on the data lake and warehouse. Use Houseware’s schema transformations to utilize historical events data.
  3. Curate: Curate a set of events, user activities, and cohorts that form the foundation for product analytics.
  4. Create: Understand user events and clickstream data with the help of funnels, trends, flows, retention, and more rich documentation around your data.
  5. Collaborate: Work with your team across the analytics journey with comments, notifications, and annotations, as well as integrations with tools like Slack and MS Teams.
  6. Consume: Help marketing, sales, and operations teams utilize analysis, cohorts, and more in engagement tools like WebEngage.
  7. Control: Admin controls are first-class citizens, understand usage patterns, define guardrails and alerts, and have a state-of-the-art access control system.

Houseware has a unique opportunity to empower compound applications and help shape product analytics as a category built for them. A warehouse-first architecture is the key to taking this vision forward.

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