Dynamic pricing: what is it exactly?

Car drivers know it all too well. Fuel prices change multiple times a day at the gas station, sometimes even while filling up! This is just one clear example of dynamic pricing, a tool as old as commerce itself. Dynamic prices – that can be flexibly adjusted to any market situation – make it possible to control the revenue generated by products and services.

Dynamic price management is primarily gaining ground in online retail at the moment, but has also been common practice in the prices of flights, travel and accommodation for a long time. Factors like capacity utilization, season, time and the behavior of competitors influence the price here, for example. Even in classic retail, digital displays on shelves are increasingly replacing conventional price tags and likewise enabling pricing to become flexible and largely automated.

There are many other everyday examples of dynamic pricing: If fruit has been left on the supermarket shelf for a while, it is usually sold at a discount. When fuel prices rise in time for the summer vacation and beach loungers become more affordable in rainy weather, we can also thank flexible prices for this. Whenever (regular) customers receive a discount, this is also an example of a dynamic price adjustment. Many ski resorts entice visitors in poor weather using discounts – and in the USA, ticket prices for sports events, for example, often vary depending on the weather, day, chances of winning or the appeal of a game.

These examples largely concern established dynamic pricing models that almost everyone comes into contact with daily. They all share one thing in common: the price changes over time, depending on the competition or as a result of strategic considerations and factors which the retailer considers suitable for maximizing profit or improving customer retention – ideally both at the same time.

The strategies are varied, but the goals tend to be the same: apart from maximizing profit, providers use dynamic prices particularly to increase customer retention – such as with discounts. After all, if the customer believes they’re getting a good deal, they are more likely to come back.

Thanks to digitalization, bigger opportunities are cropping up for dynamic pricing. The magic key is big data – and thanks to data-driven marketing fully automatic analyses in real time are no problem at all.

In e-commerce, dynamic price management is often based on algorithms that analyze customer data. Major online retailers in fact have access to the data of millions of customers – a highly valuable resource that analysis programs can utilize. Combined with current market events, this data provides a basis for adjusting prices to supply and demand, either with a just a few clicks or via an automated process – on a broad front, specifically for target groups or even for individual customers. A wide range of different strategies can also be applied here. The algorithms themselves are usually a well-kept secret, especially as they are critical to business success.

A look at the sales figures reveals which products are currently popular and bought over others, which could lead the price to increase to maximize profit depending on the strategy. The following question is always key: How high is the customer’s willingness to pay at the current time? From the clues provided by big data, it is possible to find answers to this question.

Let’s consider another approach. A popular product is reduced in price in order to beat the competition so that the customers purchase it from the cheaper provider. Quite often, accessories are offered to the buyer at the same time with dynamically raisedprices (sometimes sharply). If the customer is already in the purchase process and has found a bargain, they are also likely to buy an accessory – even if the price is substantially higher. Their incentive to look back at competitors, who already offered the product they were primarily interested in at a higher price, is then likely to be low.

Ideally, this allows the provider to sell more products thanks to targeted dynamic pricing models, and also increase profit further with the sale of accessories at a dynamically higher price, while the customer feels like they’ve found a bargain. Generally this is actually the case, and improves customer retention.

Sometimes prices even vary from customer to customer. That’s because valuable conclusions can also be drawn from habits, interests, demographic data and the behavioral patterns of every online customer. A personalized price means that different customers who look at the same product at the same time receive their own tailored price. The aim of this is to optimally make use of their maximum willingness to pay at any given time. This dynamic pricing is often based on the mechanisms of data-driven marketing.

Consider this scenario: Whoever surfs on the go with an expensive smartphone may also receive a higher price for products when shopping online simply for this reason. A corresponding analysis tool could classify users of expensive devices as generally being able to pay higher prices. If these users previously purchased expensive products, this could further support this tendency and lead to higher personalized prices.

Dynamic pricing models occur in almost every area of commerce. It’s often not possible to get around the flexible prices – for example, due to seasonal factors, such as during the run up to Christmas, and prices rise before coming back down. In many cases, we also benefit from flexible prices – for example, with discounts for loyal customers or in the case of the beach lounger mentioned earlier that becomes cheaper in poorer weather. So, dynamic prices can certainly be consumer-friendly, even though it may seem that they are business-friendly only.

If we shop with large online retailers like Amazon , there’s no getting around dynamic pricing. However, there are a few tricks that can potentially help to avoid peak prices in e-commerce. The problem is that the way the algorithms work is largely kept secret. The tips offered here are therefore based on observations and indications. For this reason, success may vary depending on how the algorithms are designed and adapted.

Consider the time of day when shopping online. Prices can rise sharply on weekends and evenings when lots of customers are shopping. If fewer customers browse during the day on weekdays, that is also when prices fall significantly. They may also often vary from weekday to weekday.

Compare the prices of different providers. Price comparison websites are a convenient resource to this end, but they sometimes struggle to keep pace if the providers change their prices too dynamically – i.e. too often or too quickly. If you go to a merchant via a price comparison website, you might receive a cheaper personalized price – after all, the merchant would like to rank as close to the top of the comparison portal as possible and thus may have to lower the listed price there.

Electronic vouchers or voucher codes attract customers often by reducing the purchase price for products and services quite significantly. On occasion, these vouchers can be found by entering the product and manufacturer’s name as well as the word “voucher” into a search engine. If you redeem a valid voucher with the respective online merchant, the generated price will also decrease accordingly. However, anyone who obtains these vouchers from third-party providers usually pays with their data. As always, you have to weigh the pros against the cons.

Observe the price of a product over the course of several hours, days or even weeks – the differences can sometimes be huge. Ask a friend or acquaintance to look at the same product to find out if there any price differences due to personalized prices. If this is the case, the person who has the cheaper price could then submit the order.

But beware: If a third party buys something for you online or you buy something for someone else, this will affect the data basis. If you buy expensive products for someone else, an algorithm could rate you as having a higher willingness to pay than you actually have. This could result in higher personalized prices for products and services in future for you when you shop online.