Profit maximization through optimal initial pricing: based on a unique price database in which all articles are historicized on the basis of attributes.
For many retailers, price is by far the most important marketing instrument. Faced with low margins and cost pressure, the price is the key profit driver. In practice, however, the retail sector is sticking predominantly to conventional methods of pricing. Initial pricing is characterized by rules of thumb and cost-plus thinking. Generally, the acquisition costs serve as the basis for the markup calculation. Customer-centric pricing, which takes into account customers’ needs and willingness to pay, only takes place in very few cases.
aifora Initial Pricing
For initial pricing, aifora uses a unique price database. Here, the article data of all brands is historicized with the help of attributes. During the product development process, it is already possible to take into consideration how certain product attributes have influenced past sales. Combined with current market and competitor data and an innovative value-based pricing approach, this ensures that the focus is on the customrs, even during initial pricing.
Key Feature 1 – Reference Articles and Attributes
The comprehensive price database provides the decision-relevant information. Historical articles are classified and saved based on their attributes. When listing new articles, suitable reference articles are found via clustering and a price suggestion is generated. In addition, information such as order quantities, sales quantities, sales periods and locations are taken into account in pricing.
Key Feature 2 – Market and Competitor Information
In addition to historical article data, we provide pricing policy information on relevant competitors. A special matching procedure allows the user to identify articles with similar attributes and features and to take them into account when setting prices. This ensures the strategic pricing and positioning strategy. In addition, it is possible to integrate further external databases (e.g. Trend Forecasting).
Key Feature 3 – Interconnection Effects
The measurement of cross-price elasticities is of key importance for overall product price optimization. Changes in demand resulting from price adjustments are analyzed and the specific product range relationships are forecast on the basis of defined attributes. The algorithm processes this information automatically and determines the optimum initial price for each product. This approach makes targeted use of substitutive and complementary product relationships in order to achieve an optimized result across all articles.