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Several distribution companies in the grocery, durable goods, and cosmetic sectors want to understand which of their products are the most profitable, or which yield the highest returns versus their cost-of-goods and promotional investments. This granular view of dead net profit is an important data point that their pricing teams want to use to negotiate better deal-levels with their manufacturers and vendors.
If companies sell hundreds or thousands of SKUs a month, and spend millions of dollars on procurement, promotions, discounts, marketing campaigns, and operational costs, it becomes difficult to pinpoint which products yield the highest net profits at the most granular levels in their data: Item, Location, and Point-in-Time. While sales data is transactional and typically available at these most granular levels, the cost-of-goods sold, lump sum funding, coupons, and other item promotional deals are typically struck at different levels in the product, geography, and time hierarchies. Costs often have convoluted freight, pallet, and distribution components. Lump sum funding can be given as a bulk amount, perhaps on a quarterly cycle, and be applied to promoting entire product categories from a given manufacturer. Item level discounts, coupons, and other promotions can be for specific SKUs only and last only a handful of days at a time. How can an organization allocate a specific amount of investment or promotion to a given SKU, for specific regions, in a given time period? Typically, the allocation of promotional funding is done as a percent of business share at the item or vendor level over regions and periods of time. Freight and pallet costs can be similarly allocated by fill percentage. When data volumes reach billions of rows, efficient pipelines and processing are crucial to providing allocated results to the pricing and procurement teams for the slices and selections in the data that they’re interested in. Once costs and promotions are aligned with granular sales data, net profit analysis can help procurement teams better understand the source of their margins.
Implemented a fully automated data extraction and processing system to collate all sales, costs, and promotional funding data from different tables and systems at a large grocery distributor. The pipelines processed large data volumes and calculated the dead net profit of thousands of SKUs across hundreds of retail stores. The results were displayed using custom-built frontline applications. Dead net profit could be analyzed by any relevant dimension such as SKU, category, and vendor.
With new analytic applications integrated into their daily work streams, pricing and procurement analysts could make data-driven decisions for promotion strategies using the dead net profit reports. For example, category managers saw that the top five vendors in a specific category were yielding negative profits. They pressed for more promotional funding and more favorable splits on discount levels in the next purchasing cycle. Procurement can now use the quantifiable impact of activities on profit margins as leverage in price and promotion negotiations.
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