About the client:
A U.S.-based business with a significant nationwide presence, specializing in architectural metals, gate hardware, and metal finishes. They offer a diverse range of quality metal products from premier European manufacturers, covering wrought iron components, forged steel balusters, posts for railings & balconies, aluminum hinges, and more.
The client noticed that it required much time and attention to establish firm control over multiple products under different departments.
The key driving factor behind the poor resolution of issues was the lack of not enough having information on customer buying patterns and behavior which affected their estimation for production in the following year’s probable orders. Which means, having limited control over stocking.
The client was looking for a way out to efficiently handle its orders, manage stocks, and increase quick sales to achieve its monthly sales targets.
They also felt the need to make quick decisions with pricing and promotions of their various product categories. A long-term solution that can indicate the company’s growth through data and manage this situation without compromising on sales. Social DNA Labs was tasked with creating, developing, and executing a robust predictive strategy to help quantify future risk at a prospect level.
What did we observe?
We first evaluated the client’s database landscape, and the existing workflow involved sales, logging, categorizing, and warehouse stocking. We found that there is a repository of fixed data under the control of various departments which is isolated from the rest of the organization. A robust solution that can curate data across multiple channels could solve this Data Silo problem. Thus, freeing the enterprise from most of the critical complications.
How did we achieve it?
Using an intelligent Database Management System, we defined, manipulated, retrieved, and managed the data in the database system. The rule to validate, update, and manipulate this data was done using the autorun services promptly.
Using the fourth-gen technology, the data was uploaded to the data warehouse, that is the central repositories of integrated data from one or more disparate sources. This was done for reporting and data analysis. This process is considered the core component of any business intelligence procedure.
There is an overwhelming number of stock valuation techniques available today. For this industry, we required different valuation approaches. Hence, the profit per transaction calculation algorithm was implemented.
After collecting the Data from various sources, the process of inspecting, cleaning, transforming, and modeling the data to discover useful information, suggestive conclusions, and supporting decision-making using Data analysis tools was applied.
The inventory database was updated using the past sales data to reflect in the current report system.
The reports that were finally generated after elaborate processing and intelligent analyzing helped in decision making, relating to Inventory management, Store performances, Sales, and Purchase Patterns of the customers.
What was the outcome?
- The implementation of intelligent data analysis resulted in overcoming challenges related to tracking customer buying behavior.
- Time-to-time reports reflected buying patterns, aiding in forecasting orders and identifying the most frequent and profitable customers.
- Controlled pricing and profitability were achieved, leading to improved financial performance and streamlined operations.
- The system also detected and prevented fraud, increased supply chain effectiveness, and enhanced collaboration with stakeholders.
We can help your enterprise to do a lot more. Let’s talk and create a progressive technology roadmap for you.