![]() ![]() In the current, omnichannel environment, it no longer makes sense to set prices just once per season. Moreover, it can have a tremendous impact on any subsequent decisions related to the procurement of items and managing of inventory.Ĭlient Case: How we enable PHARMAPETS to maximize margins & revenue by predicting the optimal price for each item it sells. AI-driven analytics can significantly improve the accuracy of such a pricing decision with straightforward predictions. To do so, they are often confronted with the tedious task of bringing all useful data together and interpreting the data correctly, trying to figure out what drives the perception of the value of a given item.Įspecially for retailers with thousands of different items, such seems a daunting task. The challenge category managers face is weighing each factor that contributes to setting a given price correctly. Such creates increased importance of price perception making the setting of the initial or base price of an item ever more crucial. ![]() Customers have more information on pricing or item alternatives available at their fingertips. + Watch our webinar on the most important data & AI use cases in fashion retail!Į-Commerce has changed the playing field for every retailer. + Read more about our markdown optimization solution here Moreover, it allows a retailer to determine what the exact markdown should be for a given product at a given point in time to achieve the most optimal result Using these insights, a team can identify actions to be taken, quickly evaluate if these actions are adding or destroying value, and swiftly take action. While on the other hand, some parts of the collection or assortment are marked down too little - a higher markdown would increase rotation, decrease overstock and lead to higher overall revenue.īy bringing data together and applying algorithms, AI-driven analytics is able to quantify what impact can be expected from a price markdown decision. A more reserved markdown would not significantly decrease rotation and lead to a higher margin. Such results in some parts of the collection or assortment marked down too much. Little time is left to evaluate, for instance, additional data elements such as weather impact, footfall data, and product characteristics. Teams hardly have the time to focus on high-value-added tasks. The process contains a lot of manual and repetitive tasks and leads to suboptimal results. Yet, the current process applied by many retailers leaves money on the table. The strategy aims to clear out the inventory gradually, ensuring all stock is gone by the end of a given period. It is about finding the optimal balance between selling at the highest possible margin while maintaining a solid rotation of items. The strategy of setting end-of-season discounts or price markdown is straightforward. Learn more about the shift towards data-driven decision-making in retailĪ markdown optimization solution that doesn't leave money on the table Or whether a strategic shift in pricing is required to lure more customers to a given online or offline sales channel. Decisions such as whether one should even have a given item on sale from a category management point of view. Or deciding when to replenish items or redistribute these items across the store network, to avoid out-of-stock situations.Īnd we are talking about high-impact, strategic decisions. We are talking about smaller, tactical decisions such as how to adjust the price of a single item in a catalogue of 5000 items, every single day. This concerns both ‘big’ & ‘small’ decisions, meaning decisions of a strategic nature or a lesser-impactful tactical nature. Those recipes entail a seismic shift in how decisions within a retail organization are made. In each sector new companies are thriving because they have understood that we're entering a radically different era where new recipes decide over profit or loss. Emerging players have been demonstrating that a decade of digital transformation and drastic shift in customer behaviour provides a broad array of opportunities.
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