Fivetran > Case Studies > Leveraging IoT for Data-Driven Decision Making: A Case Study of Sleeping Duck

Leveraging IoT for Data-Driven Decision Making: A Case Study of Sleeping Duck

Technology Category

Analytics & Modeling - Big Data Analytics
Platform as a Service (PaaS) - Application Development Platforms

Applicable Industries

Oil & Gas
Transportation

Applicable Functions

Procurement
Sales & Marketing

Use Cases

Last Mile Delivery
Picking, Sorting & Positioning

Services

Cloud Planning, Design & Implementation Services
Data Science Services

About The Customer

Sleeping Duck is an innovative mattress company based in Australia. Founded in 2014, the company has made a name for itself in the crowded online mattress space by delivering high-quality, customizable sleep experiences to customers. The company's business model and focus on customer service have made it one of Australia's most recognizable brands and a leader in a highly contested market. Data drives every decision that the company makes, from streamlining the customer journey and optimizing advertising spend to pinpointing new markets for growth and ensuring superior customer service.

The Challenge

Sleeping Duck, an Australian mattress company, was facing the challenge of managing and deriving actionable insights from data scattered across various sources. The data resided in Software as a Service (SaaS) platforms, web apps, marketing platforms such as Facebook and Google Ads, and in the company’s own product. The process of extracting relevant information from these disparate sources was complex and manual. The company's engineers would have had to write and maintain custom scripts to extract data, a practice that was neither scalable nor sustainable. The company needed a solution that could efficiently pull in data from these sources, manage it, and feed it into their business intelligence solutions for data-driven decision making.

The Solution

Sleeping Duck adopted a two-pronged solution involving Snowflake and Fivetran. The first step was to migrate to a reliable data warehouse in the cloud. They chose Snowflake due to its SQL-based platform that could scale compute and storage separately and quickly. This flexibility was crucial as the company's data analytics needs were not expected to scale in predictable ways. The second part of the solution was to implement a data pipeline solution that could pull in terabytes of data from various sources without much coding or configuration. Fivetran, a data integrator option in Snowflake, was chosen for this purpose. Fivetran pulls critical business data from dozens of sources in the cloud to the company’s Snowflake data warehouse. From there, the data can be manipulated as needed and sent to a business intelligence platform for analysis. The data can be synced monthly, weekly, daily or even every 15 minutes, providing near real-time business intelligence.

Operational Impact

The implementation of Snowflake and Fivetran has transformed Sleeping Duck's business operations. The company can now leverage data to make critical business decisions. Stakeholders from across the organization can present a question and the data analytics team can provide the analytics to answer it. Whether it's the advertising team wondering which TV spot would give them the most bang for their buck or the mattress delivery team wanting to narrow the delivery time ranges they give customers, data can provide the answer. The company can optimize marketing spend, streamline the customer journey, and put the customer first by having a 360-degree view of the customer when someone calls with an issue. The company is also constantly looking at ways to improve service and customer experience, and is about to embark on a project that will require a deeper dive into the company’s customer demographic, in order to design a loyalty program that will lead to referrals and repeat business.

Quantitative Benefit

Fivetran pulls data from 70 sources in the cloud into a data warehouse
Eliminates the need for engineering resources to write custom scripts
Data can be synced monthly, weekly, daily or even every 15 minutes, providing near real-time business intelligence