Advanced Analytics for End-Of-Season Discounts/Price Markdowns
The traditional price markdown process of calculating the discounts to apply in a given sales period is ready for a change. The process of setting end-of-season discounts is for many retailers, still, a labor-intensive process that requires much human attention to calculate these markdowns - one Excel-file in, out the other - and introduce them in the different sales channels. It is set up to bleed unnecessary value.
Why is there a need to shake things up?
The strategy of a markdown process is straightforward. It is about finding the optimal balance between selling at the highest possible margin while maintaining a solid rotation of items.
The goal is not to sell out at once. The strategy aims to clear out the inventory gradually, ensuring all stock is gone by the end of a given period.
The process contains a lot of manual and repetitive tasks and often leads to suboptimal results. Teams hardly have the time to focus on high-value-added tasks. Little time is left to evaluate, for instance, additional data elements such as weather impact, footfall data and product characteristics.
Such a way of working leaves money on the table. Typically, items are bought once -or maybe twice- a season in a given quantity, which the purchase teams project feasible to sell. If the items do not rotate as expected, they put the retailer at risk, locking up capital in inventory.
It results in some parts of the collection or assortment marked down too much. A more reserved markdown would not significantly decrease rotation and lead to a higher margin.
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.
By bringing data together and applying algorithms, AI-driven analytics can quantify what impact can be expected from a markdown decision. 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.
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.
How can an AI-driven tool make a difference?
By introducing an AI-driven markdown optimization tool, retailers can add assistance to the decision-making process. The tool gathers the appropriate data, applies business rules, and suggests the discount levels to use.
The markdown team can swiftly review what is proposed. It can look at the situation from a more strategic perspective and making manual adjustments where needed.
Here is how it makes a difference:
The tool resolves a lack of insights, leading to reduced quality and speed of decision-making. Most retailers set markdowns based on a rudimentary ERP extract post-processed in Excel, which shows stock-level, rotation, pricing and markdowns.
What they are frequently lacking are insights on the effects of markdowns, e.g., How much did the rotation change due to a markdown increase? Is the revenue increase offsetting the margin decrease?
The tool resolves a lack of accurate predictions, leading to reduced quality of decision-making. Even if a retailer has all the insights mentioned above, it is tough for a human to predict what the impact will be of the next 10% markdown.
The tool reduces the time spent on the process, leading to reduced speed and effectiveness of decision-making. We see the process of gathering and structuring the required data is often a hassle some process, taking up a lot of time. This time would be better spent analyzing and shaping decisions.
Working with multiple retailers on this topic, we saw a substantial increase in the overall gross margin. More specifically, we see that introducing artificial intelligent algorithms, suggesting optimal markdown levels, lead to revenue increases of up to more than 5%.
As an additional benefit; the process becomes substantially smoother, reducing the required time and energy of all parties involved. Leading to a higher frequency of taking action, which again results in more revenue and margin.
