12% Increase in Bottom Line by Streamlining Data to Enable Optimum Discounts

12% Increase in Bottom Line by Streamlining Data to Enable Optimum Discounts


This retail company, selling high-end apparel and footwear, operates several hundred stores across many cities. All products are classified into business segments. One of the company’s key performance indicators (KPI) is to analyze demand for products within these segments and identify those with lower or no demand. The company’s strategy for low performing segments is to ascertain and implement discounts on items with slow sales within these segments, thereby supporting cash flow. At present, this is a time-consuming, manual process that requires analyzing stock inventory, seasonal styles, and current consumer demand. 



In the current scenario, the analysis is slow and time consuming as several factors need to be considered to arrive at the optimum discounts for various products within the slow-performing segments. Given the manual work involved, the discounting methodology could contain errors, and may not offer the best cost benefits either given the limited data range used to determine the optimal discount offers. 

To streamline this process, the company required an insights-driven system that combined analytics and artificial intelligence to automatically analyze existing data and implement discounts in a more structured way. 



At the initial stage, Trabeya designed and used a business intelligence tool to visualize KPIs and also created segmentation models that could be easily interpreted to determine key influencing factors of volumes sold. Next, a forecasting engine was added to generate demand predictions and understand their variances. Trabeya also proposed multiple means of estimating price elasticity for items through price sensitivity surveys and external datasets.



Post implementation, the retail company has an efficient, streamlined data gathering and analyzing process that provides ready insights into demand patterns and sales of products, particularly those of the low-performing segments. This information is then used alongside external datasets to determine the optimum discounts, adding approximately 12% to its bottom line.

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