InVision designs predictive models using Segment warehouses.

To keep up with a growing customer base, InVision used Segment to unify their data across the diverse toolsets each team uses, giving the analytics team more time to run powerful new models and analyses.

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Their Segment-hosted data warehouse on Amazon Redshift enables advanced analysis. Their data also flows to a variety of tools used by product and marketing teams.

The Challenge

With a massive customer base highly sensitive to marketing and UX, InVision’s small analytics team needed to integrate event data into the diverse toolsets of a number of fast-moving teams, all while making time for their own robust analysis projects.

  • Massive and highly engaged customers that are sensitive to marketing and the overall user experience.
  • Small analytics team must support a rapidly growing company.
  • Constantly evolving product results in increasing demand for event-level tracking.
  • Must integrate and support a diverse toolset used by many departments.

On any given week we have hundreds of thousands of active users and log millions of events inside our application. Because Segment Warehouses uses Redshift, which is massively scalable, we are able to track all types of events across the entire customer lifecycle. That history is amazingly useful for analysis.

The Solution

InVision’s Segment-hosted data warehouse on Amazon Redshift stores millions of daily events, and because Segment seamlessly flows that same data to all the teams other destinations, the analytics team has more time for advanced analysis.

  • Improved onboarding experience with a Segment destination tool that messages users dynamically based on their feature usage.
  • Created sophisticated, side-by-side, analyses that allow teams to view data in ways that are most relevant to them.
  • Enabled modeling that identifies which content and promotions provide the most value to users.

The unified tracking ID makes it simple to track a user over time across our blog, website, and throughout InVision. Leveraging this data, I can do predictive modeling so we can be proactive with customers who may not be getting the full value of the product.

Industry: B2B SaaS/Design
Location: New York, NY
Employees: 185

If we did all the data engineering and integrations in-house we'd be spending a lot of time building and maintaining instead of analyzing.

Kai Rikhye

Analytics Lead

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