The price is right: How Bonprix uses dynamic costing to improve margins

When it rains umbrella prices increase. At least that’s the truism for retailers adjusting the cost of products to match consumer demand. But now dynamic pricing algorithms have taken that principle to a whole new level.

German retailer Bonprix, which has a presence in 30 countries - including the UK, has been using price optimisation software from data since company Blue Yonder for the last three years. “Since introducing it, it as had a significant impact on margins,” says Florian Rueffer, project manager at the company.

The software crunches information on brands, seasonality, buying patterns, and national holidays, to calculate the appropriate price of each of its 20,000 items every day. Although, Rueffer points out that the company doesn’t actually change its prices that often. Generally for 60% of its products, there will only be one price change per week.

The company is keen to prevent annoying customers by changing prices too regularly. Equally, it needs to avoid introducing big price disparities for its catalogue customers, keeping prices that have appeared in print the same for up to three weeks. Although he acknowledges many of its catalogue customers use it for inspiration, and buy the clothes online.

Complex calculations 

After initially trialling it in The Netherlands, it now uses the technology in eight countries. That does not including the UK, as its presence is too small there. However, Bonprix is considering grouping similar geographies together - for example, calculating prices in Germany and using the same costing model in Austria and Switzerland. 

The algorithm makes countless different calculations based on the product line and the geography. It’s not just a case of rising the price if things are selling well, as it has to take into account whether there is a high number of consumers returning the product. Conversely, if something has a high buy price and isn’t selling well, it may not make sense to cut the cost, as it may make a loss.

What works well in some markets, may not in other. “Price changing is far less popular in Russia because customers are very price sensitive. So we limit price changes to two times a week.” However, if a product is selling well in Russia, for some items the company can significantly boost profits by lowering prices. “We can make more decreasing the price there.”

Since using the technology, one surprising result was the golden rule of not going above rigid price bands, such as '£9.99' no longer applies. “We broke off this classical pricing, and realised its perfectly fine to offer something for £10.99, for example. So the typical psychological price limits don’t exist.” 

Next, the company hopes to use the technology for predictive pricing, rather than having to send its best customers winter catalogues in summer to get a read on which lines will do well. “That is always expensive,” says Rueffer. With the aid of big data, it hopes to predict what will be successful or not. 

If such a feat were possible, it would no doubt be the answer to many buying departments dreams. 

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