It's no secret that retailers are always looking for ways to improve their margins. One way to do this is through markdown optimization. A markdown strategy involves deciding what items to mark down and when. Several factors determine when and how much to mark something down.

Retailers have traditionally made these decisions manually, which can be time-consuming and often lead to poor results. This blog post will discuss how AI can help retailers make better decisions faster and improve their bottom line!

Why does markdown optimization matter for retailers?

According to a recent estimate, shared by RetailDive, retailers globally lose $300 billion every year due to inefficient markdown processes. That's why it's so important for retailers to have a good markdown strategy.

There are two main types of markdowns in retail: planned and unplanned.

Planned markdowns are done in advance, usually in response to seasonal changes or special promotions. Unplanned markdowns are done on the fly in response to changes in customer demand or other unforeseen circumstances. Both types of markdowns can be a major headache for retailers if not properly managed.

The goal of markdown optimization is to minimize the impact of markdowns on profits. To do this, retailers must understand how customers react to price changes and adjust prices accordingly. They also need a well-defined markdown strategy for planned and unplanned markdowns.

By taking these steps, retailers can ensure that their markdown strategies are working as efficiently as possible and that they're not leaving money on the table.

Common issues in retail markdown

Markdowns can be a significant source of lost profits if not managed properly. There are a few common issues that arise concerning markdowns:

• Many stores wait too long to mark down items. This results in lost sales and profitability, as customers are often more reluctant to buy items that have been sitting on the shelves for a while. Instead, stores should aim to mark items down as soon as they are no longer selling at full price.
• Some planners mark items down too deeply. The markdown may move merchandise more quickly, eroding margins and reducing profits. Retailers should balance remaining competitive and generating a decent profit on each sale.

Finally, some retailers fail to track their markdown activity properly. Not correctly tracking makes it difficult to measure the effectiveness of markdown strategies and optimize them over time. Tracking data helps retailers make informed decisions about their markdown strategy, such as:

• How often items are marked down
• How deeply they are discounted
• How quickly they sell 

How can AI help retailers with markdown optimization?

Markdown optimization is a key method for maximizing profits and minimizing losses in the retail industry. Retailers have relied on manual spreadsheet-based methods to determine when and how much to mark down items, considering only the “Week Cover” KPI.

However, this approach is often inaccurate. It also doesn't consider important factors such as future customer demand or price elasticity.

As a result, many retailers are turning to artificial intelligence (AI) to help with markdown optimization. AI-powered systems can analyze large amounts of data quickly and accurately.

They can also consider various factors when making recommendations, giving retailers a more complete market picture. As AI evolves, it will become an increasingly valuable tool for retail markdown optimization.

Key benefits of an AI-based markdown optimization solution for markdown in retail

For a retailer, maximizing margins and optimizing markdown strategies are essential to stay ahead of the competition; retailers must maximize margins and op​​timize markdown strategies. An AI-based markdown optimization solution can help them do just that.

Using product attributes and estimating price elasticity, you can make more informed decisions about when and how much to mark down items.

You can also evaluate various promotional strategies with optimally timed markdowns that differentiate between:

• Regions
• Climates
• Stores

Retailers can set different prices for each based on the location-level price elasticity.

Best of all, an AI-based tool can offer visibility into sales and inventory levels throughout the season so you can make adjustments as needed. With all these benefits, it's no wonder that retailers who use an AI-driven markdown optimization tool see a higher return on investment.

Maximize margins with markdown optimization.

In the retail industry, margins are everything. Selling items at the right price maximizes profits while remaining competitive.

However, pricing is a complex issue, and it can be challenging to determine when and by how much to mark down items. That's where AI-based markdown optimization comes in.

By using data and artificial intelligence, these tools can help retailers make the best possible pricing decisions, leading to increased margins and overall profitability.

In addition, AI-based markdown optimization can help to improve customer satisfaction by ensuring that shoppers always have access to the best deals. As the retail landscape becomes increasingly competitive, tools like these will become essential for success.

Lower markdown loss

Using an AI tool has many key benefits for retailers. Perhaps most importantly, it can lower markdown loss and increase sell-through for every item.

In other words, it can help retailers make the most of their inventory while reducing waste.

An AI tool can also provide real-time insights into pricing trends and consumer behavior. This allows retailers to make more informed decisions about pricing, product selection and promotional strategies. Ultimately, an AI tool can help retailers improve their bottom line and better compete in today's dynamic marketplace.

Increase sell-through

An AI-powered markdown optimization solution can help retailers increase sell-through by achieving maximum margin and reducing leftover inventory at the end of the season. The solution can optimize category sellthrough targets on each option. It also considers multi-step sell-through targets for different periods of the season.

Estimate price elasticity

AI uses product attributes to estimate price elasticity uniquely for each product, which allows retailers to see how sensitive customers are to price changes. With this information, retailers can make more informed decisions about when and how much to mark down their products. As a result, they can minimize losses while still achieving their desired sales goals.

Machine Learning Models estimate the impact of product attributes on sales with machine learning-based product clustering.