Zip unifies audience segmentation with Snowflake, Census, and dbt Cloud
This is the story of how Zip modernized their data platform and built a fit-for-purpose modern data stack
in production after 18 months
Challenge: Unifying customer data
As a fast-growing company, unifying customer data was one of Zip’s biggest challenges. For example, Zip’s growth team was already segmenting audiences in Braze, their CRM tool, but they weren’t able to serve the same segmented offers in their own Zip app. Their product & marketing teams needed more reliable and self-service access to data to power the business.
As a two-sided marketplace, Zip needed more data to not only personalize offers for their ‘Buy Now, Pay Later’ customers, but also to serve their merchants, who wanted to deliver cash-back offers to granular segments of customers.
Solution: Building a Best-of-Breed Modern Data Stack
Zip’s Sr. Product Manager Moss Pauly worked with the Data Engineering team to modernize their data platform and build a fit-for-purpose modern data stack. As Zip’s team was evaluating data solutions, their top priorities were seamless integration, cost scalability, and real business use cases.
“Cost scalability is a key consideration for us. We’ve been burnt before with event volumes so we went into cost scalability with eyes wide open,” said Moss Pauly, Sr. Product Manager at Zip.
Zip built out a best-of-breed modern data stack with Snowflake, dbt, Snowplow, Fivetran, and Census. One of the biggest benefits for Zip was that each of these tools was the best-of-breed in its domain, yet they had tight integrations with the other components.
With their new stack, the Zip team focused on:
- Self-service data access: By implementing Census, Zip was able to supercharge the marketing team with full access to all their 360° customer data in Snowflake.
- Cost-scalability & performance: As a company with an extremely high event volume, Zip needed scalable and performant, yet cost-effective tools for their stack.
- Single source of truth: Ingesting first-party and third-party data into Snowflake provides a centralized repository that powers business operations.
Snowflake Data Cloud
As compute and storage are the core of the modern data stack, choosing a data warehouse was Zip’s most critical decision. They evaluated multiple solutions extensively and ultimately decided on the Snowflake Data Cloud. Over the past 18 months of using Snowflake, Zip’s data team has been very satisfied with its ease of use, performance, and seamless integration with dbt.
Some of the questions Zip’s data team considered during their evaluation include:
- If you wanted to run a quick query, what’s the time to result? (Opening the tooling, navigating it if required, waiting for a cluster spin up etc…)
- What granularity of cost visibility can we have easily?
- How well written is the documentation and how easy is it to find answers to questions?
- What would the management impost be on the small team responsible for operating and maintaining the platform?
- How easily can we manage PII redaction in this stack to protect our customers’ privacy?
Data Transformation: dbt Cloud
Zip needed to store business logic and transforms to build data models in a scalable, future-proof way. Dependency management and documentation were both significant pain points of their previous transformation stack. They chose dbt Cloud and haven’t looked back, with 1000+ models in production after 18 months. The cloud-based IDE has been a game changer, and they’re also diving deep into the power of macros and incremental models.
Event Collection: Snowplow
With millions of customers, Zip’s previous stack was unable to deal with its sheer volume of raw events. Snowplow appealed to the data team because it was open source, flexible, and didn’t have a SaaS cost tied to Events/Month. Zip’s data team was explicit that they did not want a solution where cost concerns would limit what they could track, and they wanted to retain first-party ownership of their events.
With their first-party event collection solution solved, Zip knew they needed a solution for third-party data ingestion. They didn’t want their engineers spending time wrangling third-party data APIs and wanted to capitalize on standard models in dbt for third-party data sets where possible. They evaluated a few options in this space, but Fivetran clearly came out ahead. They had coverage for all their third-party integrations, thorough documentation of data structures, and pre-packaged dbt transform availability.
“Recently, our CIO wanted to query Twilio data and pinged me about the Twilio table structure while I was getting coffee. I was able to send back a link to the Twilio ERD in Fivetran about 5 seconds later that fully explained everything. Well-documented third-party integrations are really valuable,” explained Moss.
Data Activation: Census
Once they had the core elements of their stack built out, the data team realized they had several business use cases they couldn’t solve without connecting their data platform to their business tools. In particular, their product and marketing teams needed data in Braze, their customer engagement tool, to enable granular segmented offers. They evaluated a variety of options with Census coming out on top due to its performance, cost scalability of syncing high volume of data to Braze, and easy-to-use UI.
After implementing Census, Zip was able to supercharge the marketing team with access to all their 360° customer data in Snowflake. Zip’s growth team uses Census Segments, a visual audience builder, to segment customers then sync those audiences to all their marketing tools.
“Census’ Entity models have been a game changer to enable non-technical users to create segments that would normally need complex joins across a large number of data sources,” said Moss. Census Entities combined with Census’ seamless integration with dbt and their visual audience builder has been a force multiplier to make unified, trusted data available to their business teams to take action.
“This is the first time we’ve been able to have one unified audience that we sync from Census. This means that we’re more confident in the messages and the offers we have live. We can actually get more granular with who we’re targeting and what we’re saying to them,” said Bianca El-Jalkh, Growth Product Manager of Shop & Rewards at Zip.
Zip’s modern, future-proof data stack helps empower marketing and data teams alike to do their best work. Moss views building this modern data platform as really “the entry ticket into being a modern data-led company”. They’re currently underway with their next phase of activities: to further enable their data science teams, visualize model performance using Streamlit applications, and implement more monitoring and alerting for data SLA breaches.
For more, read Moss Pauly’s blog on Building a fit-for-purpose modern data stack.