How Retailers Leverage Store Location-based Data to provide improved services
Updated: Sep 12
Today, it's not enough for retailers to have a great product or offer the best customer service. To stay relevant and competitive, retailers need to leverage location data to transform their retail marketing strategy. Location data is key to developing marketing strategies via location-based analytics and generate a better ROI.
This case study shows how Datahut helped a consulting firm source retail store location data with location-based data scraping to multiply its ROI.
The client is a big consulting firm complementing a renowned financial corporation's internal data science team. They were tasked with sourcing retail store location data of around 50 brands. Initially, they were working with a self-service tool whose increased pricing did not fit within their allotted budget. They decided to look for other vendors, and Datahut responded to the RFP.
A retail client was facing the challenge of sourcing location data for their offline store locations. They wanted to be able to provide accurate, real-time information about their stores to their customers in order to improve their services.
This client had no prior experience in web crawling on a large scale. They wanted to extract clean ready-to-use data, refresh it every month and set up an API. They wanted to implement the project in six months. They did not want to use any existing solutions, they wanted control over how the data was being refreshed.
The client needed a web scraping service that could help them source retail location data in half the time and money they were currently spending on the same task.
The client signed up for our services and started with a small pilot with 5 websites as a pilot. We worked with data scientists from the client’s team to refine their product architecture, database schema, etc.
We set up scrapers, and Datahut’s platform automatically crawled the location data from these online stores. We shared the data via Amazon s3 and fully automated the extraction process. Later we scaled the project to 50 websites, and the process was replicated.
The client had access to our web scraping platform and was able to refresh the data on an ad-hoc basis using our User Interface.
Datahut’s web scraping platform benefitted the client in the following ways:
The client was able to source the retail location data in half the time and money.
We consulted the client on improving their product architecture which helped them reduce the development time.
The client witnessed an increased ROI as their monthly subscription to Datahut’s services is just 30% of the cost they paid for the self-service tool.
Our modified crawlers detected changes in data over a period of time and refreshed the datasets to give real-time information.
Datahut helps businesses get structured data feeds from any website through our cloud-based self-service data as a service platform. We eliminate your need to own servers, programming, or expensive software to extract web data. Acquire data in a format of your choice and use it for quickly building apps, conducting business analyses, and experimenting with fresh ideas. Datahut is the preferred solution for companies looking to outsource their data extraction needs and receive clean ready-to-use data for business decision-making.