How Saucey Set Up A Data Warehouse In Minutes

The Company

Saucey is a convenient alcohol delivery app developed by three friends over lunch. The founders wondered why there was delivery available for everything—groceries, household goods and even breakfast—but there was no app for alcohol delivery.

The founders started to look into the alcohol industry and pretty quickly became obsessed with its incredible history that hasn’t changed much since the repeal of prohibition. Saucey set out to fill that gap and offer alcohol delivery on-demand through its app.

The Challenge

For Saucey, the concept is simple, but the execution is complex. There's a lot that goes into a customer placing an alcohol order and having it delivered in 30 minutes. Saucey wanted to enable their team members to spend more time analyzing data to make strategic decisions, instead of pulling data or waiting on data requests. Their goal was for employees to know their business inside and out, and therefore needed clear insights into all aspects of the business, immediately—not days or even hours later.

Their data needs are robust. Saucey has three primary elements to their business, each with their own set of KPIs and targets to track. First are the customer facing applications, where they track a lot of traditional e-commerce events and data like conversion, repeat and retention rates, purchase frequency, AOV, LTV, etc. Next is the retail partner side, where Saucey analyzes their menu to deliver consistency of offers and pricing to all of their customers. On the logistics side of the business they track delivery times, fulfillment rates, ratings and efficiency of their batching and route optimization.

Saucey was continually challenged with pulling all of their data together from disparate sources. They needed a solution that was easy-to-use, reliable and fast. From a pipeline standpoint, they wanted plug-and-play while maintaining the ability to customize to their business’s specific needs. They didn’t want to worry about performance and therefore wanted a managed warehouse. Cost-effectiveness was an obvious concern as well. From a visualization standpoint, they needed a solution that allowed for self-service reporting, while still allowing for the option to run more complex queries and ad-hoc analyses when needed.

The Solution

Ultimately they chose Panoply as the foundation of their data stack. Implementing Panoply enabled them to quickly get a cloud data warehouse setup with minimal data engineering support. They found that the addition of Panoply was the fastest way for them to update their data stack for a flexible future. They use Panoply to bring together Mongo for their internal data, Stripe for transactions, Twilio for messaging, Typeform for courier applicants and survey data, and Google Sheets for some one-off or offline data.

The Results

The results include efficiency across the organization. Employees have most of their everyday data readily-available and have reports automatically delivered to their inboxes daily or weekly as needed. 

With Panoply, Saucey can be hands-off with their data warehouse, which was the goal. They felt the Panoply setup was straightforward and because it works well, there isn’t much they need to do to maintain it.

Saucey had trouble in the past properly transforming Mongo data with other ETLs, and now, the way Panoply handles nested objects has solved what felt to them like a never ending issue. Daniel Leeb notes:

Ultimately, Saucey was able to solve their complex data challenges with Panoply. Daniel and the founders shared this about implementing Panoply: