Better sales with dynamic pricing



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Marketing Better sales with dynamic pricing

Published on 11.02.2020 by Stephan Lamprecht, journalist

Thanks to dynamic price adjustment systems, retailers can sell products at optimum margins and avoid write-offs. Here we explain the basic principles of dynamic pricing.

Prices that can change several times a day for the same product are part of everyday life for customers, even if they are not all aware of this.

The price at the pump of one litre of petrol changes throughout the day, while the same hotel room and the same flight are also offered at markedly different prices over time. The phenomenon of dynamic pricing has also been adopted in numerous online shops. On Amazon, up to 70 different prices can be displayed over the course of a single week for the same product. To remain attractive to customers, it can be worth using a special software package.

Repricing and dynamic pricing

The “place in the sun” on a marketplace such as Amazon is the so-called “buy box” – the button you press when you want to put a product in your shopping cart. To survive in the face of competition, many retailers use repricers, which are offered separately or as part of a marketplace software.

In technical terms, these are often very simple and essentially take the position of direct rivals into account. If a competitor adapts their price, the software follows suit within the limits set by the retailer. When it’s simply a matter of price leadership, using this type of system can be sufficient.

Sophisticated dynamic pricing solutions, however, can do much more and often use artificial intelligence (AI) and machine learning functions. The possibilities offered by such systems are correspondingly greater.

Instead of paying attention to just one factor and a small number of rules, as is the case with a repricer, the more complex solutions incorporate a range of data to determine the optimum price at the current point in time.

These may include:

  • time-related factors (days on which salaries are paid, public holidays, etc.)
  • regional factors
  • the weather
  • current stock levels
  • competitors’ prices
  • the time of year
  • company goals
  • current shop data, e.g. clicks, purchases, shopping carts
  • purchase prices

Using all these parameters, the optimum price – from a trader’s perspective – is constantly determined over time, with the values geared towards the company’s strategy. 

Advantages for the retailer

Prices can be adjusted in both directions. Machine learning ensures that the price displayed also leads to price acceptability on the part of the customer. If a retailer decides to use such a system, the prices will be adjusted in accordance with their strategic goals. These may and can change naturally over the course of a business year.

For example, when selling fashion or seasonal goods, writing off unsold goods is expensive. In the fast-moving world of fashion, it is part of a retailer’s everyday activity to adjust prices in good time to avoid being left with unsold stock at the end of a season. Reducing prices too early and by too much will mean they are surrendering their margin. If retailers act too late, they are faced with possible write-offs as customers no longer want the goods, even at a reduced price.

In this case, AI is superior to every experienced salesperson. Based on stock levels as well as current and historical data, the system forecasts the demand for the product and, in turn, a realistic sale date. If this differs from the desired date, the prices will be adjusted.

By using dynamic pricing, retailers benefit from a number of advantages. The manual effort required for price adjustments is reduced, sales, revenue and income can be optimized in accordance with margin constraints and stock can be sold by a desired point in time.

The market now offers a broad range of such solutions to suit every requirement and for use in almost every shop system.

Interesting

Stephan Lamprecht, journalist

Stephan Lamprecht has been following e-commerce developments in Germany, Austria and Switzerland for two decades as a journalist and consultant.

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