Omnichannel retailer implements Stitch for ETL, gets better BI
Online fashion retailer AMARO kicked off their business intelligence strategy in 2016, when they hired team lead Murilo Nigris and tasked him with building the company's BI architecture and team from the ground up. Even with a ground-up strategy, Nigris knew the components would have to scale within weeks, to keep up with AMARO's fast-paced growth.
AMARO is a direct-to-consumer omnichannel fashion brand, selling at amaro.com and through its Guide Shops. The company sells originally designed women’s clothing, accessories, and footwear, and integrates technology everywhere, from product design to delivery at home. AMARO invests in data intelligence within its platforms to better understand the customer journey and offer a sophisticated omnichannel experience.
To build out its BI infrastructure, Nigris first had to understand and map the challenges and data sources of a direct-to-consumer fashion brand, which has many different areas; from product design to manufacturing and logistics to omnichannel sales, considering web, mobile app, and AMARO's Guide Shops. He then conducted a detailed research within the BI market, assessing tools of all types (data warehouse, ETL, data pipeline, data visualization) to find the best combination of tools and frameworks. "They had to be fast to build, light on resources consumed, and scalable, all under a limited budget."
AMARO undertook a thorough process of evaluating traditional ETL tools like Pentaho and Talend and seven cloud ETL platforms, in addition to considering writing their own data pipelines using programming languages. "Our need was straightforward: to integrate a list of data sources to our data warehouse. Stitch had the connectors we needed already available at a fair price. It was easy to use and needed no maintenance."
Stitch had the connectors we needed already available at a fair price. It was easy to use and needed no maintenance.
AMARO's main data sources today include:
Marketing sources such as:
Before structuring a team, Nigris worked solo for the implementation project. “I built most of the main data cubes and reports using Stitch, Amazon Redshift, and Looker, and the maintenance was practically zero. Based on previous experience, I estimate it would have taken twice as long if we had decided to use traditional ETL tools and processes."
Stitch's Support team was a valuable resource throughout the process. "During the first few months, we interacted at least once a week with the Support team," Nigris says. "They were always very helpful, even when the error was on our side (for example, an error with a column in our data source table). After a while, we got to understand better how Stitch works and what is within its scope, and then we needed less help from Support."
Now, Nigris says AMARO plans to scale up their BI infrastructure and stack. "We will most likely try out Stitch's new S3 destination and use Singer integrations, since we are building a data lake that will consolidate data from several different sources," he says.