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How Mattermost uses RudderStack to enable data-driven product decisions

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How Mattermost uses RudderStack to enable data-driven product decisions

Mattermost is an open-source collaboration tool for developers and IT teams

Challenges

  • With their previous vendor’s pricing model, there was a limit on the amount of telemetry and event data they could capture and sync
  • Their data engineering team wanted to build a non-proprietary stack to avoid vendor lock-in

Results

  • Track and capture all their event data to make data-driven decisions about their products
  • Run the product in their own VPC, solving any security concerns related to their customers’ high-trust environment
  • Get a cohesive view of their entire customer journey and optimize their platform

Mattermost’s Data Stack

  • Data Collection and Synchronization:
    RudderStack SDKs
  • Data Validation:
    RudderStack Schema API
  • Data Transformation and Enrichment:
    RudderStack Transformations
  • Data Warehouse:
    Snowflake
  • BI & Cloud Toolset:
    Looker
  • Data Orchestration:
    Kinesis

Mattermost's data challenges

Mattermost is an open-source, high-trust collaboration platform for developers and IT teams. It is self-hosted and allows businesses to accelerate their DevOps workflows through secure, cross-platform messaging. As a data-driven company, Mattermost makes extensive use of their customer event data to understand feature usage at scale and optimize their product.

Mattermost had initially invested in a popular customer data platform (CDP) for building their customer data pipelines but faced several challenges in getting the most out of it. Firstly, they could not capture all of their customer events. Because of their vendor’s pricing model, to stay within budget, they could only sync 2% of their event data to their warehouse. Also, all of Mattermost’s customer data was stored in their CDP vendor’s infrastructure. As a company built on open standards, Mattermost preferred an open source solution to avoid the problem of vendor lock-in. They also wanted their data in their own playing field - their data warehouse.

Scalable Data Collection

With RudderStack’s warehouse-first approach and sensible pricing model, Mattermost was able to collect and use all their customer events. They run RudderStack in their secure VPC, mitigating any issues related to their customers’ high-trust environments.

  • Mattermost implemented RudderStack’s Event Stream functionality through their web(JavaScript), mobile (iOS), and server-side(Go) SDKs for real-time event tracking.
  • They join these user events with data collected from cloud tools like Zendesk, Salesforce, and Marketo.
  • The result is a comprehensive dataset that is seamlessly synced to their Snowflake data warehouse.

When we learned about RudderStack offering an open-source alternative to our original vendor, we thought, ‘Yes, this is exactly what we need. Now we can remove restrictions to syncing events, and send all of the data we want to one central data warehouse. We can analyze and act on all of that important customer data, and ultimately become a more data-driven business.

-Alex Dovenmuehle, Head of Data Engineering at Mattermost

Complete Visibility Into the Customer Journey

Thanks to RudderStack’s real-time, cross-platform event tracking, Mattermost gets a 360-degree view of their entire customer journey. They have complete visibility on how the customers are using the product and can map their journeys across various product offerings and build custom cohorts. These cohorts are then used for more efficient and more personalized marketing and sales.

Data-driven Feature Insights for Product Optimization

Previously, Mattermost’s product managers had to rely on anecdotal customer feedback on the product. That approach can be effective, but Mattermost wanted objective, data-driven feedback. By implementing RudderStack, they could finally collect all of their product-specific usage details, enabling them to derive fine-grained insights into the features that impact their customers. As a result, the product managers have all the data they need to efficiently plan and prioritize feature development and optimization.

Mattermost Data Stack

Sources: Go, Javascript, iOS

Warehouse: Snowflake

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