Lifecycle Pricing
Use the right price markdown at the right time
Improve your performance with automated markdown pricing
Markdowns are an important instrument to react to falling demand and to increase the attractiveness of a product. Every day, retailers are faced with the task of setting the optimum price for each product at the right time.
However, the huge amount of data, a multitude of influencing factors and a volatile market environment make price management increasingly complex. Manually processing all information is no longer feasible. With automated retail markdown management you can bring your performance back on track.
However, the huge amount of data, a multitude of influencing factors and a volatile market environment make price management increasingly complex. Manually processing all information is no longer feasible. With automated retail markdown management you can bring your performance back on track.
How we drive demand with intelligent markdown optimization
Our Markdown Optimization tool predicts ideal pricing strategies for each product, based on current and historical transaction and inventory data.
Based on demand forecasts and price elasticities, we determine the correct markdown amount at the right time, for each location. By combining inventory- and profit-oriented pricing, the software is able to generate the optimum yield throughout the season. For this purpose, internal company data is extracted and combined with external data.
The machine learning algorithms react to changes in real time and learn continuously. When setting prices, we take into account business rules, budget targets and planned promotions in addition to the machine forecast. The integrated workflow engine supports the internal decision-making process and the implementation of price labeling in brick-and-mortar retail.
Based on demand forecasts and price elasticities, we determine the correct markdown amount at the right time, for each location. By combining inventory- and profit-oriented pricing, the software is able to generate the optimum yield throughout the season. For this purpose, internal company data is extracted and combined with external data.
The machine learning algorithms react to changes in real time and learn continuously. When setting prices, we take into account business rules, budget targets and planned promotions in addition to the machine forecast. The integrated workflow engine supports the internal decision-making process and the implementation of price labeling in brick-and-mortar retail.
Benefit from intelligent markdown prices
Intelligent markdown management enables you to actively drive demand more effectively and efficiently. The algorithms make 100% objective, data-driven decisions. As the pricing decisions are based on accurate demand forecasts that take into account your customers’ price acceptance, your overall markdowns will be lower.
Additionally, you save costs as your inventory level will be decreased efficiently by matching demand with your supply.
Additionally, you save costs as your inventory level will be decreased efficiently by matching demand with your supply.
Key Features
Mathematical Models
In contrast to many other solutions, our markdown algorithms are based on statistical models. This approach makes it possible to map and control the company-specific influencing factors in a mathematical function. This allows the concrete probability of occurrence to be calculated and displayed for each forecast. Blackbox decisions are thus excluded and the necessary transparency and user acceptance is ensured.
What-if Scenarios
To enable the simulation of different price strategies, we offer the possibility to perform what-if analyses. In this way, the interdependencies become clear and you can incorporate your specific knowledge into the forecast. The system predicts and evaluates the impact on the defined targets, which makes these scenarios an ideal decision support.
Clustering
Product, store and country clusters are formed on the basis of a behavior-based process. It is not the existing attributes in the master data that are decisive for cluster formation, but the similarity of the entities in sales behavior. Sophisticated algorithms first determine the optimal number of clusters in the historical data and then assign the current products to the respective clusters in a dynamic process.
