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 raised prices (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.