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Creating-a-Data-Analytics-Pipeline-for-Global-Streetwear-E-Commerce-Platform

Creating a Data Analytics Pipeline for Global Streetwear E-Commerce Platform

As one of the biggest global marketplaces of streetwear and fashion with a valuation of over US$3 billion, GOAT has become a data-rich company and looked to capitalize on all the data they have…

As one of the biggest global marketplaces of streetwear and fashion with a valuation of over US$3 billion, GOAT has become a data-rich company and looked to capitalize on all the data they have collected by way of more consistent and accurate data analytics.  

man in white crew neck t-shirt standing beside woman in white t-shirt

Overview

With the tremendous growth in the past few quarters and plans of expansion into other verticals, the e-commerce unicorn sought additional data to better plan and execute business strategies.  

As such, we delivered timely and granular competitor data to optimize:  

  • Assortment planning – by analyzing the number of SKUs listed by competitors, broken down by platform, brand and categories 
  • Pricing strategy – by tracking and identifying weekly price changes at the category and SKU level.
  • Trend analysis – by tracking and identifying best and worst sellers on multiple platforms. 

Since February 2021, we created a bespoke alternative data analytics pipeline for our customers, leveraging proprietary artificial intelligence that we’ve built up over the years. 

Results

We delivered and continue to deliver over 12 million data points on a weekly basis, and with our AI, we are able to do this in just 2 hours, versus the manual sifting of data which would take over 200 man-hours. 

The project continued to be expanded three times, with more platforms and brands being added on as we moved forward. By the end, we had created a comprehensive market tracking data pipeline spanning 6 e-commerce platforms and 75+ brands.  

Takeaway

With product and sales details cleaned and consistent, our customer was able to identify top-selling products, broken down by brand, color, and size, which ultimately enabled them to make better assortment choices, pricing decisions, and strategic plans to stay ahead of the competition. 

“Using Chain of Demand’s alternative data allowed us to save time, resources and understand how our competitor immediately. We’re able to make faster decisions and delegate more time focusing on strategy.   

— Julian Leung, Product Lead (Former Airbnb and WeWork) 

They did it with Jumpstart

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