Case Study Snapshot: How We Helped a Legacy Retail System Unlock Predictive Analytics

A lot of retail companies are still using old legacy systems that have been in place for several years. The outdated coding, fragmented data, and sluggish processes in these systems often make it very difficult to access valuable information. However, in today's world, data is the lifeline of retail success - from demand forecasting, inventory management, to price setting. This is where data engineering services come in. They offer the support that businesses need to collect and clean their data for the ease of making the right decision.

Successive Digital case studies demonstrate how the company collaborated with clients to update their retail legacy systems and make predictive analytics available. It outlines the difficulties faced, the pathways we constructed, and the consequent business outcomes.

Case Study 1: Building a Scalable Payment System for a Retail Enterprise

Context

A leading retail enterprise was experiencing payment problems caused by its legacy payment processing system. The system, which was initially designed based on outdated technology, had its limitations showing after a while. Payment failures became more and more common, transaction times grew longer, and a good check on the data coming from the different retail channels (POS, eCommerce, and mobile) was almost an impossible task.

Challenges

• High transaction failure rates, especially during peak hours at shopping time.

• New digital payment methods were not compatible with the legacy system.

• The system could not track transaction data, which limited the possibilities of the advanced analytics field.

• Manual reconciliation processes caused delays in financial reporting.

Solution

Successive Digital took over the mission to bring the architecture up to date, and with the team’s great efforts, the system was redesigned from the ground up as a microservice-based, cloud-native payment platform. Thanks to the containerized services and real-time transaction monitoring, the system ensured that its capacity would be automatically extended at the time of peak hours.

Furthermore, predictive analytics models were integrated into the flow of transaction data. These models indicated suspicious activities, predicted the trends of payments through a given channel, and also increased the predictability of cash flow for the finance team.

Outcome

• 70% of payment failure rates were reduced.

• Digital wallets, UPI, and cards were easily linked to the system.

• Real-time fraud detection prevented losses and decreased intrusion costs.

• Finance teams are now able to make payment settlement timelines using the predictive insights they have gained.

The change not only guaranteed the stability of a retail system that was critical to the mission but also triggered the predictive analytics capabilities, which are a vital part of the business strategy.

Also read: Transforming Legacy Systems into AI-fueled Innovation Engines

Case Study 2: Retail & eCommerce Predictive Analytics Transformation

Context

A medium-sized retail brand that operated both through physical locations and online was facing difficulties with its inventory and demand forecasting system. Their point of sales (POS), Enterprise Resource Planning (ERP) system, and online shop were not compatible. The reports were focused on the past situations, while the supply chain teams often found themselves in the position of reacting to issues instead of avoiding them. Supply shortages, overabundance of stock, and unutilized sales opportunities were among the regular problems they were facing. 

Challenges

• Fragmented data across store systems, online platforms, and supplier databases.
  

• Reporting was done manually, which caused a delay in decision-making. 

• Inventory carrying costs increased due to overstock, leading to 20% higher than usual. 

• Frequent stockouts of high-demand SKUs result in loss of sales and trust by customers.
  

Solution

Successive Digital had deployed a multi-pronged solution:

1. Data Consolidation: Developed data pipelines that standardized the point of sale system, Enterprise Resource Planning, eCommerce, and supplier data into one centralized cloud-based data warehouse.

2. Architecture Modernization: The retail microservices-based infrastructure was implemented to cater to the scalability and agility demands of the retail system.

3. Predictive Analytics: Created machine learning models for demand forecasting, stock optimization, and customer purchase pattern analysis.

4. User Dashboards: Created Store Managers and Supply Chain Team Dashboards, allowing them to carry out predictions and even receive alerts.

Outcome

• For the top-selling categories, forecasting accuracy has been up to 85% better.

• The number of stockouts has been cut to 40%, and therefore, customer satisfaction has increased.

• Inventory carrying costs have been slashed by 25%.

• Management is almost real-time about the retail operations, just like before.

This initiative is a classic case of how predictive will become a conclusive feat of a future-ready growth engine when it is entwined with modern architecture.

Conclusion

Modernizing legacy retail systems to open the doors to predictive analytics is as much a business change as it is a technical one. The endeavor is about more than just product development; it’s about the infrastructure, data maturity, processes, and culture that are required to make those models deliver business value. 

Successive Digital’s case studies demonstrate the building blocks (migration to cloud-native architectures, refactoring legacy code, data pipeline construction, model development, and embedment) in action from start to finish. Retailers that make these investments are making a promise to expect benefits in inventory efficiency, customer experience, costs, and decision-making. 

If you are on such a journey, start by asking these questions: Where is my data stuck? What forecasts would help most? What business decisions do I want to improve?