Dynamic pricing is now standard practice amongst leading e-commerce and omnichannel retailers in the market. Find out what exactly dynamic pricing is, what types of dynamic pricing there are and what benefits this pricing strategy offers retailers here.
More and more customer data, ever-larger product ranges, price transparency through simple comparison options, and the competitive intensity of e-commerce retailing make optimal pricing strategy increasingly challenging. Retailers can choose between two types of pricing model – static and dynamic.
Static pricing works with a single price point, i.e. a fixed price. With dynamic pricing, there are several price points. The retailer continuously and (semi-)automatically adjusts its prices for products to market requirements. To determine the optimum sales price for an item, the pricing strategy used takes into account all relevant influencing factors such as inventory levels or supply and demand. Based on the retailer’s pricing strategy and KPIs (key performance indicators or corporate goals), the pricing solution’s algorithm can adjust item prices to increase or maximize sales and profits. This is not about particularly low prices, but about more intelligent pricing and a sustainable long term pricing strategy.
Dynamic pricing has become widespread, especially in e-commerce retailing. Three points, in particular, are responsible for the expansion of dynamic pricing in retail:
A retailer’s pricing strategy has a direct impact on profit and growth. Pricing decisions directly impact key business activities such as marketing and supply chain management, and the resulting impact on sales. Product prices should therefore reflect and support changes in these business areas.
Data collection in e-commerce retail has exploded in recent years, and it is likely that this growth rate will continue to increase. In retail, in particular, customers leave a data trail every time they make a purchase with their credit or loyalty card. Based on this data, retailers can quickly adjust their pricing model to account for changing customer behavior.
Selling through e-commerce channels has dramatically increased price and information transparency. Customers can compare product prices on major platforms with just a few clicks. Changes in external factors such as competitor prices have an immediate impact on their own business. To remain competitive, retailers must constantly adapt their pricing strategy to a changing market environment.
There are different approaches, methods and technologies to dynamic pricing. Most retailers and companies use a combination of different types of dynamic pricing. These are usually based on the following influencing factors:
Supply is especially important in industries with high fixed costs, such as the hotel industry or airlines. When supply decreases (free rooms, seats), prices rise. In retail, supply plays a key role for stock items, especially if they are short-lived or cannot be sold after a certain time, e.g. fashion items. In this case of this strategy, prices are often lowered during end of the season sales, to sell off inventory.
Increased interest in and sales of a product often leads to a price increase. With this pricing strategy, retailers try to exhaust their customers’ willingness to pay. A good example of this would be a popular sneaker. In an extreme form, surges in demand triggered by an emergency situation, for example, are used for dubious price increases. An example of this is the explosion in the price of face masks at the beginning of the coronavirus pandemic.
Competitor prices are one of the main factors of price changes in e-commerce retail. Especially when competitors sell the same product, it is easy for customers to compare prices. The difficulty for retailers is to determine which products are really price comparison products and at which price difference the customer switches suppliers.
This practice is often referred to as personalized pricing. Here, the price varies depending on the customer segment. The idea behind this is that customers have different levels of willingness to pay and therefore different prices can be demanded. A simple example is a discount for seniors visiting a museum. In e-commerce retail, a personalized pricing model is usually implemented through coupons. Companies using this form of dynamic pricing need to be mindful of a potential backlash from their customers, who may perceive this type of pricing strategy as unfair.
Amazon often changes its prices for high-demand items several times a day on special sales days like Black Friday. In doing so, the e-commerce retailer incorporates many influencing factors to set prices, not least the customer’s fear of being shortchanged. This example illustrates in an exaggerated form how a dynamic pricing strategy works.
Large fast fashion retailers such as Zara or Mango offer an average of 6,000 items for sale in a season. The challenge is to sell these products within the corresponding season, as the life cycles of fast fashion items are much shorter than, for example, classics such as Rolex watches. Fashion wholesalers therefore take into account inventory levels, among other factors, and set the prices of their merchandise in relation to stock levels in order to achieve seasonal targets. To do this, they work with discounts, sales and rebate promotions to create a pricing strategy that encourages customers to buy. For example, a blouse at Zara may cost 39.90 euros at the beginning of the season, 29.90 euros at the mid-season sale or a special discount promotion, 39.90 euros again thereafter, and 19.90 euros at the end-of-season sale. We have summarized here how markdowns can be optimized in fashion retail:
Approximately one third of all food is thrown away by retailers every year – among other reasons, because the best-before date has been exceeded or because it was planned incorrectly. This is not only an ecological and ethical problem, but also a financial one. After all, the loss due to discarded food amounts to 3 to 10% of sales. With advanced dynamic pricing software, markdowns can be optimized so that sales are intelligently managed. This dynamic and flexible pricing model takes into account all the key factors for each item and store, from current demand to buying patterns to seasonality. The software helps to set a strategy which minimizes the amount of unsold inventory while maximizing sales and profits.
As the ecommerce market becomes increasingly competitive, retailers need to be able to respond to market conditions and demand fluctuations with agility and ease. Intelligent pricing software and dynamic pricing models support this, by providing e-commerce retailers with a way to easily enable dynamic pricing strategies, including:
Dynamic pricing distinguishes between rule-based pricing models and machine learning-based pricing, which is based on AI (artificial intelligence) and self-learning algorithms. In rule-based pricing models, the focus is on specific pricing rules that are linked to movements in competitive prices (for example, product X should always be 5% cheaper than competitor Y).
In rule-based pricing models, it is precisely the definition of optimal rule sets that requires enormous effort and requires constant management and monitoring. While the parameters of these pricing rules are easy to measure, they contain limited information about customer behavior. More information on rule-based pricing is provided in this blog post.
An advanced machine learning-based solution (dynamic pricing software) can be used to target prices. The first step is to measure price elasticity. This tells us how demand for an item changes as soon as the sales price changes. Competitive prices play an important role in this context, but the software also takes into account many other influencing factors, from inventory levels to the retailer’s customer confidence to the weather, in order to provide a forecast of how certain pricing decisions will affect sales, revenue and profit. The user is not concerned with setting rules, just defining a pricing strategy and specific business goals they want to achieve. With the full potential of the data, the software implements exactly these goals.
Profit increases of 10% on average compared to rule-based pricing models are possible with machine learning-based pricing software for retailers. 7Learnings has proven this in A/B tests with customers from different retail segments.
Machine learning-based dynamic pricing software also provides access to real-time price trends for thousands of products. This gives retailers a clear idea of supply and demand for individual items and allows them to set a pricing strategy and to react flexibly to competitors’ price changes. Machine algorithms learn across clusters and categories. They analyze products and position them in relation to other items from different groupings, providing a 360° view of each item. E-commerce and omnichannel retailers can therefore use AI to obtain timely pricing information and make optimal decisions in real time that respond accurately to a dynamic market environment and ultimately lead to a competitive advantage.
At the same time, users benefit from improved inventory management. The machine learning-based software takes current inventory levels into account when making pricing decisions. For products with excessive stock levels, sales promotions and discounts are automatically suggested so that target stock levels are reached. Items where demand has increased and the sell-through rate is too high are also detected and prices are increased accordingly. Premature sell-outs can thus be avoided, while at the same time increasing sales and profits.
In addition, price floors can be defined, for example to protect brand value or specific margin targets. At the same time, the software prevents users from making human errors, reduces costs due to manual work and helps to save time, money and resources.
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With our dynamic pricing software, we have helped some of the leading e-commerce retailers in Europe to improve their pricing processes through machine learning. Pricing in retail is complex, and our software solution helps you set an intelligent pricing strategy and react quickly to any relevant changes. To learn more about the potential of next-generation dynamic pricing, sign up for a free demo.
At 7Learnings, we have more than 10 years of experience with machine learning technology. Leading European e-commerce retailers use our technology to optimize their prices. On average, we have been able to increase our clients' profits by more than 10% and prove it in A/B tests. If you want to learn more about the potential of next generation dynamic pricing, feel free to contact us to arrange a product demo.
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