Netsmartz’s engagement with an eCommerce firm was to develop a data transformation engine to increase its profitability through deep data analytics.
This eCommerce firm is based out of Austin, Texas. The client collaborated with several vendors to craft a portfolio of products to sell on Amazon.
The engagement with Netsmartz was rolling resources based – on-need resources allocated per the project’s needs, requirements, and stage of progress. A project coordinator was the constant SPOC for this engagement.
The client wanted to analyze how they were faring – against its competitors in the market. That meant they had to research and study their central seller data compared to similar industry sellers using the same eCommerce portal.
They looked at multiple data sources and needed help identifying the right data provider to further the data analysis.
Furthermore, they required DevOps support to host data analytics and transformation services.
Essentially, the client was importing products and selling them on Amazon. But to assess the viability of those vendor partnerships, shipments, and products, the client wanted a data-backed solution.
One that could assess & report on the efficacy of purchases that the client made. That is, address the question of total profitability on every range and SKU of product that they ordered.
That meant analysis of the campaign, products, sales, offers, purchases, etc. The client wanted us to make a data engine replete with functionality.
Netsmartz’s flexible engagement model allowed the client to add and churn developers on demand. The rolling basis engagement was meant to reduce TCO, faster time-to-market, and add skills to the roster when needed.
The project coordinator helped assess and craft the architecture to enable a smoother data transformation process.
The team analyzed 2 data source providers and their constituent datasets and endpoint APIs to propose the final provider based on factors like the sanctity of data, refresh interval, performance, etc.
A MySQL central database was created in AWS, and an ETL library was developed to populate and refresh the MySQL data warehouse.
Further notifications were set up to alert about the non-availability of data.
Netsmartz followed the following process to fulfill the requirements of the client:
The preliminary task was to identify skilled resources to work on the project. Since the project was based on a rolling resource engagement, the resources were allocated as and when needed, significantly reducing the investment required by the client.
After the resource selection, the developer candidates were introduced to the client for further vetting and interviews. This process also included getting the developers acquainted with the client, their requirements, procedures, and methodologies.
The project coordinator assessed the client’s requirements and understood their tech stack and engineering requirements.
As per the rolling resources engagement, the team was scaled and churned on-demand as and when the progression required.
The primary achievements of this project were:
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