How Zip uses Snowflake & Census to unify audience segmentation
With Moss Pauly, Sr. Product Manager
Bianca El-Jalkh, Growth Product Manager
Nick Heron, Senior Analytics Engineer
Zip recently modernized their data stack with best-of-breed components, selecting Census, Snowflake, Fivetran, dbt, and Snowplow. Census’s data activation platform empowers Zip’s Growth Marketing team to segment audiences with all their data in Snowflake, and deliver unified experiences through Braze and in-app messaging.
Zip is a global ‘Buy Now, Pay Later’ company that helps millions of consumers access fair, flexible and transparent payment options. Zip integrates with thousands of merchant partners including Amazon, Best Buy, eBay, and Uber to deliver a better way for customers to pay.
Results
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More consistent customer experiences: Unified audiences between Braze and the Zip app ensure the same customers receive the same targeted offers across all channels.
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More powerful segmentation: With access to all their Customer 360 data, Zip teams can build granular segments and target specific user subsets that they weren’t able to before.
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Self-service data access: Census’s user-friendly UI for segmentation and syncs enables non-technical users to build and sync trusted customer lists themselves, and reduces manual work for analytics engineers.
"The real power of Census is that it has full access to all the data that we're collecting through Snowflake. Because we have access to such a wide range of data, we can create segments that we were never able to build before."
The challenge
Unifying customer data was one of Zip’s biggest challenges. 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 the Zip app.
Additionally, Zip’s merchant customers requested more granular targeting that they weren’t able to provide. For example, merchants wanted to run cashback offers but only to granular segments of customers.
“Previously, we would send comms to customers based off a Braze segment and have something different in the Zip app, and we were never sure they would match up.”
The Solution
Unified audiences with Census
After implementing Census, Zip was able to supercharge the marketing team with access to all their 360° customer data in Snowflake. Now, marketers only needed to build audiences in one place, and Census would automatically keep every audience fresh with scheduled syncs.
“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.”
Census Audience Hub enables non-technical users to build audiences without code
The growth team uses Census Audience Hub, a visual audience builder, to segment customers then sync those audiences to all their marketing tools. The simple point-and-click UI makes it easy for non-technical users to activate customer data without a single line of code.
“My background is marketing and I'm finding Census very, very easy to use compared to other tools. You don't have to be an engineer to actually understand how to use it.”
Better access to data also made Zip’s segmentation more granular and powerful. Zip can now offer detailed user audiences to their merchant customers, making their payment platform more robust and driving higher user engagement with targeted offers.
Census meets all of Zip’s Braze integration needs
“During the evaluation phase, we also looked at Hightouch and found Census to be so much more user friendly. It’s one of the best tools that I've ever used in terms of how easy it is to integrate with other tools, connect to Snowflake, and even build segments. It's literally very intuitive and it feels like the people who've created the product have really tried to put themselves in the user's shoes.”
From a technical perspective, the team had these key requirements for a Data Activation tool:
✅ Sub-3-hour updating of 4 million records in Braze
✅ Robust updating of only records that have changed (Braze charges per data point updated so this is a must for cost efficiency)
✅ Friendly UI to empower product and marketing teams to control what data ships where
Entity modeling makes Zip’s data team more efficient
To model data for activation, Zip’s data team leverages Census Entities. Entities are a simple way to define trusted models in the warehouse, ensure data governance, and expose important data for business action.
“Census’s Entity models are game-changing when it comes to enabling non-technical people to create segments that would normally need complex joins across a large number of data sources.”
Zip’s Modern Data Stack: Snowflake, dbt, Snowplow, Fivetran, and Census
Highlights
- 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 chose Snowplow as a scalable and performant, yet cost-effective tool for their stack.
- Single source of truth: Ingesting first-party and third-party data with Fivetran and transforming it with dbt provides a centralized repository to power business operations.
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.”
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 were the best-of-breed in their domain, yet they had tight integrations with the other components.
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.
Data Transformation: dbt
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 their 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.
ELT: Fivetran
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.”
For more, read Moss Pauly’s blog on Building a fit-for-purpose modern data stack.
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