Profit maximization through optimal initial pricing: based on a unique price database in which all articles are historicized on the basis of attributes.
Problem 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.
Flexible price adjustments in real-time: by constantly monitoring competitive prices, local demand and current events, we enable dynamic pricing for all channels.
Problem The tracks that customers leave behind when surfing the Internet are worth their weight in gold for retailers. Retailers can evaluate every visit history and not only determine which products customers are interested in, but also how much customers are willing to pay for them. In this way, prices can be dynamically adjusted to current demand behavior, also taking competitors prices into account. With the introduction of electronic price labelling, dynamic pricing can also be implemented efficiently in brick-and-mortar retail. This opens up entirely new possibilities, especially for chain stores. Even if it doesnt appear so at first glance, the customer is also the focus of dynamic pricing. After all, the ultimate aim is to determine a price for the consumer in line with the market, measured in terms of price acceptance.
aifora Dynamic Pricing Our algorithms are initialized through a combination of historical and real-time data and are permanently self-optimizing. We provide competitive prices which are weighted and taken into account when determining prices. The algorithms constantly analyze the interplay between price optimization and customer reaction. The user can immediately understand the effectiveness of the price settings and thus react quickly and flexibly to changes in competitors prices, demand and other market conditions.
Key Feature 1 – Exception Management Our dynamic pricing algorithms are self-learning and improve each time prices are set. They not only support the decision-making process, but also automate it as far as possible. Thanks to intelligent exception management, however, the user can identify and intervene at any time, taking into account the predicted impact.
Key Feature 2 – Competitor Prices The integrated price scraper automatically captures and records all prices of relevant competitors. In contrast to the widely used re-pricing approach, we consider competitors prices as weighted pricing factors. The danger of participating in downward price spirals is thus ruled out. In addition, the retailer ensures that competitors prices are always measured in the context of their own brand strength.
Key Feature 3 – Multiple Pricing Strategies Our service is able to depict different pricing strategies. The target functions can be flexibly configured to maximize sales and profit or to optimize inventories, all down to article level. Our customers are thus able to pursue differentiated strategies within the product ranges and hence exploit the entire pricing potential.
Increase demand and customer loyalty: use self-learning algorithms and external data to achieve better results in promotion planning and implementation.
Problem Promotions have been part of the everyday pricing policy in retail for years. Particularly the popularity of price promotions has recently increased significantly. Retailers use instruments such as special offers, sets or coupons to respond to temporary fluctuations in demand. However, the user often lacks knowledge about the efficiency of past promotions. Price promotions can lead to customers purchasing more in the short-term but then changing brands, categories or even shopping locations. In the long term, promotions even lead to a change in loyalty. Information about the different effects of promotions are essential success factors when it comes to planning and managing future promotions.
aifora Promotion Planning aiforas promotion planning relies on state-of-the-art data mining technologies and takes external market data into account. The software automatically identifies campaigns and determines optimal recommendations for future actions. Mathematical forecast models illustrate the calculated effect of promotions per country, store or channel to the user. Using defined KPIs, the user can also compare various promotions with one another. In addition, relevant evaluations with regard to performance and costs provide the necessary transparency in performance measurement.
Key Feature 1 – Campaign Calendar The calendar function provides the user with a comprehensive campaign and promotion planning tool. The user can quickly and easily create campaigns, specifying the type, the budget and the start and end dates. The concrete design of the events is based on a rolling, interdisciplinary process. The system automatically informs the users involved about the current progress and thus guarantees a target-oriented planning of all promotions.
Key Feature 2 – Shopping Basket Analyses With the help of intelligent shopping basket analyses, the user can understand the typical shopping behavior of customers. The software finds products that are often purchased together. It also identifies key products that bind customers to the company and useful article combinations for cross-selling campaigns. Taking into account purchase probabilities, interconnection and cannibalization effects, the compilation of promoted articles is improved and thus the overall pricing is optimized.
Key Feature 3 – Result Forecast New price campaigns often lead to great uncertainty due to a lack of forecasting ability. The promotion planning of aifora offers the possibility to simulate different scenarios and to compare the predicted results with regard to the financial objectives. The forecasting algorithms analyze the current performance and suggest ideal promotions at all times.
aifora enables retailers and brands to optimize their prices and inventories across all channels and to automate the underlying processes. Through the use of artificial intelligence, data is processed on the aifora platform in such a way that it achieves maximum benefit. This results in a significantly higher level of profitability. The aifora SaaS solutions are quick and easy to integrate, can be individually configured and thus deliver a very fast return on investment.
Clients include companies such as NKD, KiK, Reno and Peek & Cloppenburg.