From Data to Dollars: A Case Study on Scaling a Reselling Business with Facebook Scraping

About client

This case study is about a multinational retail conglomerate that provides services in China, Japan, the Middle East, India, Siberia, and Uzbekistan. This organization has its own numerous brands in antiques, electronics, fashion, health, and beauty. It runs a hybrid model that sells brands directly to customers or through external independent sellers. The company has an extensive workforce and a logistics network spanning over more than 20 countries. This case study shows how this client has adopted automated sourcing and reduced manual work using the standardized data scraping methods. The project requires an experienced team that can complete the project on time. By leveraging it, the client has gained significant growth in their business.

Clients Requirements

The main requirement of the client was that their sourcing team relies on manual browsing of Facebook listings, one proper workflow, and scattered data formats. The client was looking for an automated and ideal discovery of inventory for multiple geographies. They demand a scalable infrastructure that enables them to extract real-time data from Facebook and convert it into actionable sourcing intelligence. The client wants a solution that is compliant with regional privacy laws, makes decisions faster, streamlines business operations, and gains more profit.

Client’s Challenge

The client was facing a persistent bottleneck in scaling inventory sourcing across an informal digital marketplace. Here are the key pain points that the client was facing:

The Client visits Facebook listings in more than 40 cities, resulting in varied outcomes
The client’s regional team missed the opportunity to sell desirable products because they couldn’t act quickly.
There was a lack of centralized data to source to measure the performance of the sourcing activity.
The client wasn’t able to expand new market opportunities, because it requires a third-party company to solve this issue. Moreover, it was also very slow and expensive.

Solution

Business Impact

By using our Facebook data scraping tool, the client has sourced high-value items in just 2 hours. They have also increased monthly release revenue from $1.2 million to $3.8 million. With this solution, the client was able to analyze data without hiring a special sourcing agent.

The client has successfully transformed sourcing into strategic intelligence. They find inventory gaps in real-time that lead to a reduction in overstock. By leveraging our data scraping solution, the client has efficiently managed the stock.

Conclusion

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