Dynamic Pricing Models For Electronic Business
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Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product
or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have
to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing.
This article attempts to survey different models that have been used in dynamic pricing. We first motivate dynamic pricing
and present underlying concepts, with several examples, and explain conditions under which dynamic pricing is likely to succeed.
We then bring out the role of models in computing dynamic prices. The models surveyed include inventory-based models, data-driven
models, auctions, and machine learning. We present a detailed example of an e-business market to show the use of reinforcement
learning in dynamic pricing. To read the full-text of this research, you can request a copy directly from the authors…. Determining the right price of a product or service for a particular customer is a necessary, yet complex endeavour; it requires knowledge of the customer’s willingness to pay, estimation of future demands, ability to adjust strategies to competition pricing [1], etc. Dynamic pricing [2], [3] represents a promising solution for this challenge due to its intrinsic adjustment to customer expectations. Indeed, with the advent and establishment of digital channels, unique opportunities for the application of dynamic pricing are arising, thus enhancing research in the field [4], [5]……. This clear design allows us to specify a well-defined metric and to include it in the overall model in order to ensure fairness. Different approaches have been proposed to address the problem of maximizing revenue [2]. Among these is a promising technique consisting of optimizing pricing policies with Reinforcement Learning (RL) applied to different market scenarios (uni or multi-agent) [15]- [18]……. With both the bid (p) and fairness information (f), the reward r is obtained. Finally, a gradient descent step is executed to reduce the loss produced between the current and the target Q-value, see equation (2). In the experiments, we use the ADAM gradient descend algorithm [45] with a learning rate equal to 0. 01. berto MaestreJuan DuqueAlberto Rubio Juan ArevaloUnfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid unfair price differences between groups of customers. This paper shows how to solve dynamic pricing by using Reinforcement Learning (RL) techniques so that prices are maximized while keeping a balance between revenue and fairness. We demonstrate that RL provides two main features to support fairness in dynamic pricing: on the one hand, RL is able to learn from recent experience, adapting the pricing policy to complex market environments; on the other hand, it provides a trade-off between short and long-term objectives, hence integrating fairness into the model’s core. Considering these two features, we propose the application of RL for revenue optimization, with the additional integration of fairness as part of the learning procedure by using Jain’s index as a metric. Results in a simulated environment show a significant improvement in fairness while at the same time maintaining optimisation of revenue…. These are obstacles for implementing dynamic pricing in cloud. Auctions are appropriate in this scenario [13, 14, 3]. In reverse auctions, in particular, the customer is an auctioneer and the sellers are bidders……. A more detailed description of various power models can be found in [9]. We use (14) to obtain the cloud instance power consumption. It is to be noted that thus far it is impossible to obtain VM (or cloud instance) power consumption……. Consider the clould instance j; let the resource utilization be u i for some resource i. From (14) we get the cloud instance power consumption model as follows… cloud computing has confounded its early skeptics and is now a
mainstream technology with mass acceptance, there are significant
problems related to its adoption: viz., the lack of good models for
automated resource procurement with dynamic pricing; the lack of good,
computationally tractable approaches for allocation of resources by
cloud providers; and the need for a metering and pricing mechanism
that considers the time-varying nature of electrical power (often the
single biggest cost in cloud services), especially as supplied by a
smart power grid. This chapter presents some recent work that
addresses these issues, and offers solutions to address them.
To automate procurement, we present three mechanisms: C-DSIC, C-BIC,
and C-OPT for a cloud broker. C-DSIC is a low-bid Vickrey
auction. C-BIC is a weaker strategy compared to C-DSIC and it is
Bayesian incentive compatible. In C-BIC, vendors reveal the truth only
if other vendors reveal the truth, unlike C-DSIC where vendors reveal
the truth irrespective of others’ choices. C-OPT achieves both
Bayesian incentive compatibility and individual rationality, which the
other two mechanisms cannot achieve.
In order to facilitate effective resource allocation, cloud providers
have to allocate resources ahead of service demands (as service
requests must be fulfilled quickly, while allocation processes
typically run slowly), in a way that does not waste resources. The
calculation of optimal allocations requires integer programming, which
is computationally difficult to accomplish. We suggest an approach
using the uncertainty principle of game theory which achieves close to
optimal results, and show that it works well.
An approach for time-varying tariffs for cloud services, considering
varying load levels on the cloud provider’s infrastructure, and the
time-varying pricing of electricity from a smart grid, is also
proposed. This involves the creation of a per-instance power
consumption model for VMs on a cloud, and a power-aware cloud metering
architecture…. In dynamic pricing, pricing of the resources depends on their usability and availability which not only increases a healthy competition but also results in good resource utilization. In turn, this becomes beneficial for both providers and customers [4, 7]……. An auction mechanism is said to be computationally tractable if the allocation and payments can be calculated in polynomial time. Economic efficiency of an auction mechanism is measured in terms of total social welfare (valuations of winning users) generated by the mechanism [7]. An efficient mechanism maximizes the total social welfare… offers spot instances to cloud customers using an auction-like mechanism. These instances are dynamically priced and offered at a lower price with less guarantee of availability. Observing the popularity of Amazon spot instances among the cloud users, research has intensified on defining the users’ and providers’ behavior in the spot market. This work presents an exhaustive survey of spot pricing in cloud ecosystem. An insight into the Amazon spot instances and its pricing mechanism has been presented for better understanding of the spot ecosystem. Spot pricing and resource provisioning problem, modeled as a market mechanism, is discussed from both computational and economics perspective. A significant amount of important research papers related to price prediction and modeling, spot resource provisioning, bidding strategy designing etc. are summarized and categorized to evaluate the state of the art in the context. All theoretical frameworks, developed for cloud spot market, are illustrated and compared in terms of the techniques and their findings. Finally, research gaps are identified and various economic and computational challenges in cloud spot ecosystem are discussed as a guide to the future research…. Bichler et al. [18] state that uncertainty about the prices of goods, and little knowledge about market participants, are obstacles to dynamic pricing. Auctions are in particular helpful in this kind of situation [18], [19]… hybrid cloud computing, cloud users have the ability to procure
resources from multiple cloud vendors, and furthermore also the option
of selecting different combinations of resources. The problem of
procuring a single resource from one of many cloud vendors can be
modeled as a standard winner determination problem, and there are
mechanisms for single item resource procurement given different QoS
and pricing parameters. There however is no compatible approach that
would allow cloud users to procure arbitrary bundles of resources from
cloud vendors. We design the CLOUD-CABOB algorithm to solve the
multiple resource procurement problem in hybrid clouds. Cloud users
submit their requirements, and in turn vendors submit bids containing
price, QoS and their offered sets of resources. The approach is scalable,
which is necessary given that there are a large number of cloud vendors,
with more continually appearing. We perform experiments for
procurement cost and scalability efficacy on the CLOUD-CABOB
algorithm using various standard distribution benchmarks like random,
uniform, decay and CATS. Simulations using our approach with prices
procured from several cloud vendors’ datasets show its effectiveness at
multiple resource procurement…. een pricing and manufacturing and Swann (2006) also considered pricing decisions influencing the operations of a firm. A similar research in the operations direction was conducted by Celik andMaglaras (2008) and they propose the combined use of pricing and lead time quotations to optimize long term revenue and profits of the rahari et al. (2005) surveyed different models used in dynamic pricing and discussed the situations under which each model is likely to succeed. Araman and Caldentey (2009) introduced the learning factor in the setting of dynamic and Caldentey (2003) studied the impact of consumer behavior on demand and pricing. venue management is the science of using past history and current levels of order activity to forecast demand as accurately as possible in order to set and update pricing and product availability decisions across various sales channels to maximize profitability. In much the same way that revenue management has transformed the airline industry in selling tickets for the same flight at markedly different rates based upon product restrictions, time to departure, and the number of unsold seats, many manufacturing companies have started exploring innovative revenue management strategies in an effort to improve their operations and profitability. These strategies employ sophisticated demand forecasting and optimization models that are based on research from many areas, including management science and economics, and that can take advantage of the vast amount of data available through customer relationship management systems in order to calibrate the models. In this paper, we present an overview of revenue management systems and provide an extensive survey of published research along a landscape delineated by three fundamental dimensions of capacity management, pricing, and market segmentation…. For any price customisation to succeed, buyers must be willing to pay different prices for the same good, and it should be possible to categorise buyers based on the purchasing behaviour (Narahari et. al., 2005). Surveys of online customers (Brown and Goolsbee, 2000) establish that buyers are willing to pay an enhanced price for a good with high value attributes like seller’s reputation and after sale support. Further, buyer’s preference for different product attributes may vary over time. To succeed in e-market, sellers must offer special deal.. attract buyers in the uncertain and distrusted environment of e-market, seller agents must use flexible and adaptive strategies. Being able to compute the right price of a good is vital for a seller agent to succeed in e-market that allows for prices to fluctuate due to uncertainty, different conditions, context and buyers’ requirements. This paper addresses the problem of dynamically computing the appropriate selling price of a good for a prospective buyer, in response to the buyers’ specifications for the goods’ attributes in linguistic terms using artificial neural network in a competitive e-market. The proposed model helps in improving buyer-seller satisfaction by offering customised products to buyers where at the same time realising the expected revenue of sellers by enticing buyers to return in future transactions. It encourages trustworthy sharing of information among sellers by associating the concept of reputation among selling peers…. [9] state that uncertainty about the prices of goods and lack of knowledge about market participants are obstacles to dynamic pricing. Auctions are in particular helpful in this kind of situation [9], [10], [11]. If the buyer is an auctioneer and the suppliers are bidders, then the auction is called a reverse auction… present a cloud resource procurement approach which not only automates the selection of an appropriate cloud vendor but also implements dynamic pricing. Three possible mechanisms are suggested for cloud resource procurement: C-DSIC, C-BIC and C-OPT. C-DSIC is dominant strategy incentive compatible, based on the VCG mechanism, and is a low-bid Vickrey auction. C-BIC is Bayesian incentive compatible, which achieves budget balance. C-BIC does not satisfy individual rationality. In C-DSIC and C-BIC, the cloud vendor who charges the lowest cost per unit QoS is declared the winner. In C-OPT, the cloud vendor with the least virtual cost is declared the winner. C-OPT overcomes the limitations of both C-DSIC and C-BIC. C-OPT is not only Bayesian incentive compatible, but also individually rational. Our experiments indicate that the resource procurement cost decreases with increase in number of cloud vendors irrespective of the mechanisms. We also propose a procurement module for a cloud broker which can implement C-DSIC, C-BIC or C-OPT to perform resource procurement in a cloud computing context. A cloud broker with such a procurement module enables users to automate the choice of a cloud vendor among many with diverse offerings, and is also an essential first step towards implementing dynamic pricing in the cloud…. Providers of cloud services can benefit as well through establishment of an ecosystem of partners, such as brokerages, who enhance the provider’s service and draw customers to it. In [10] authors Dynamic pricing is the dynamic adjustment of prices to consumers. It depends on the value these customers attribute to a product or service…. K. Ravikumar ngeethaThe Cloud resource procurement of cloud resources is an interesting and yet unexplored area in cloud computing. cloud vendors choose a fixed pricing strategy for pricing their resources and do not provide any incentive to their users. That’s why to choose only automates the selection of an appropriate cloud vendor and also to implement the dynamic pricing. A cloud broker which can implement the procurement module enables users to automate the choice of cloud vendor among with different offerings and also to implementing the dynamic pricing in the cloud…. Generally, we further refer to [28] and [29] for an overview of pricing methods in the literature… Bauer Dietmar JannachIn today’s transparent markets, e-commerce providers often have to adjust their prices within short time intervals, e. g., to take frequently changing prices of competitors into account. Automating this task of determining an “optimal” price (e. g., in terms of profit or revenue) with a learning-based approach can however be challenging. Often, only few data points are available, making it difficult to reliably detect the relationships between a given price and the resulting revenue or profit. In this paper, we propose a novel machine-learning based framework for estimating optimal prices under such constraints. The framework is generic in terms of the optimality criterion and can be customized in different ways. At its core, it implements a novel algorithm based on Bayesian inference combined with bootstrap-based confidence estimation and kernel regression. Simulation experiments show that our method is favorable over existing dynamic pricing strategies. Furthermore, the method led to a significant increase in profit and revenue in a real-world evaluation…. First, price dispersion can be spatial price dispersion in which several sellers offer a given item at different prices or temporal price dispersion; a given seller varies his/her price for a given product based on the time of sale and supply-demand situation. Second, price discrimination relates to offer up different customer prices for the same product (Varian, 1996). Dynamic RM depends on optimal pricing policies that are typically computed on the basis of an underlying deterministic demand price…. Dr-Emad Mohamed AbdelaalThis study presents a new model of intelligent pricing that concentrates on using data-driven decision-making (DdDM) as a new approach to hotel revenue management (RM). The study aims to assess the role of DdDM in leveraging hotel RM. The results were divided into three main parts. First, the findings indicated that (traditional data) observations, focus groups and structured interviews achieved the highest manager perceptions degrees, respectively. It is noted that (Digital data) electronic surveys, search engine queries and click stream approximately represent high degrees of perception, respectively. Eye-tracking data represents higher degrees of perception than facial electromyography one. Second, transactional, historical and marketing data approximately represent high degrees of manager perceptions, respectively. Also, hotel distribution sources revealed of the approximately perception to all sources. It was noted that hotel Res centers, integration with Res system, global distribution system and hotel website represent the highest degrees, respectively. Third, there was no significant relation between sources of data and using data-driven decision-making. On the other hand, the types of data, hotel distribution sources and managers’ concerns about DdDM have positive and moderate relations with using DdDM. The results showed also, in line with expectations, a significant and positive correlation between using DdDM as an intermediate variable and pricing decisions…. Dynamic pricing, which is defined as the dynamic adjustment of market price, has been widely adopted by marketing researchers and practitioners [1][2][3][4][5][6]; this is because price is one of the most controllable variables. In the past, dynamic pricing strategies were widely applied to traditional marketing, with a significant cost associated with changing prices [7][8][9]… paper addresses the discount pricing in word-of-mouth (WOM) marketing. First, a dynamic model capturing WOM spreading processes is suggested. Second, the problem of finding an optimal discount strategy boils down to an optimal control problem. Third, the existence of an optimal control for the control problem is proved, and an optimality system for finding an optimal control is presented. Thereby, the dynamic discount strategy associated with the optimal control is recommended. Some examples of the optimal control are given. Finally, the influence of different factors on the optimal expected net profit is examined…. Individual application of dynamic pricing in the airline industry is studied in another name called yield management or revenue management [17]. It involves the process of segmentation of the passengers/travelers under three categories, which are business travelers, casual travelers and hybrid travelers…. Rajan GuptaChaitanya PathakPricing in the online world is highly transparent & can be a primary driver for online purchase. While dynamic pricing is not new & used by many to increase sales and margins, its benefit to online retailers is immense. The proposed study is a result of ongoing project that aims to develop a generic framework and applicable techniques by applying sound machine learning algorithms to enhance right price purchase (not cheapest price) by customers on e-commerce platform. This study focuses more on inventory led e-commerce companies, however the model can be extended to online marketplaces without inventories. Facilitated by statistical and machine learning models the study seeks to predict the purchase decisions based on adaptive or dynamic pricing of a product. Different data sources which capture visit attributes, visitor attributes, purchase history, web data, and context understanding, lays a strong foundation to this framework. The study focuses on customer segments for predicting purchase rather than on individual buyers. Personalization of adaptive pricing and purchase prediction will be the next logical extension of the study once the results for this are presented. Web mining and use of big data technologies along with machine learning algorithms make up the solution landscape for the study…. Dynamic pricing itself is an independent area of inquiry and has been reviewed by Narahari et al. (2005) in the context of e-business. Gallego and Van Ryzin (1994) has provided a concise summary of different types of price differentiation and studied many examples of dynamic pricing and its connections with operational issues like inventory control…. Xiaofeng Liu Ou TangPei HuangPurpose – The purpose of this paper is to study how supermarkets can maximize profits of selling perishable food through price adjustment based on real-time product quality and values. Design/methodology/approach – The value of the perishable food can be traced based on an automatic product identification technology radio frequency identification (RFID). With the support of the RFID, an optimization model can be developed to enable product tracking. Findings – The analysis of the model shows promising benefits of applying a dynamic pricing policy and obtains the optimal ordering decision in respects of deterministic and stochastic demand function with RFID. Research limitations/implications – Although technological approaches for tracking products have attracted increasing attentions in both research and practice, little research have proved the profit using RFID by mathematics, the result of this paper can prove the benefit by using RFID. Practical implication – The result of this paper can tell the supermarket how to make the price and the ordering decision by using the RFID. Originality/value – This study proves the benefit of using the RFID by mathematical model based on the conceptual model before, and tell the method how to use RFID for pricing and making ordering decision…. Dynamic pricing is the dynamic adjustment of the selling price to the buying agents depending upon various factors such as perceived value of the customers, market conditions, macroeconomic factors and the financial health of the seller [9]. Price dispersion and price discrimination are two critical aspects of dynamic pricing [14]. Different selling agents offer a resource at different price in spatial price dispersion…. Sumit ChakrabortyAbstract: This work presents an adaptive profitable discriminatory pricing mechanism for cloud computing based on secure function decomposition, cryptographic commitments and zero knowledge proof. Cloud computing is an emerging trend of enterprise resource planning where a selling agent or service provider (S) wants to allocate a set of computational resources and related IT services optimally and fairly among many buying agents or service consumers (B) within its capacity constraint. Each service consumer discloses its demand plan for an IT portfolio within its budget constraint and rank of preference. An IT portfolio may include SaaS, PaaS, IaaS, CaaS, DaaS and dSaaS. The basic objective of the service provider is to optimize its expected revenue within target profit margin. It is basically a problem of secure function evaluation where the concept of decomposition of a function is considered. It is a constrained nonlinear optimization problem; the search is governed by a set of intelligent moves. The communication complexity of the pricing mechanism depends on the time constraint of the negotiating agents, their information state and the number of negotiation issues; it also depends on number of negotiation rounds and the complexity of IT portfolio. The computational cost depends on the complexity of function decomposition. The security and privacy of strategic data of the trading agents provides business intelligence to the pricing mechanism. The ultimate objective of the mechanism is to predict a profitable discriminatory pricing plan for each consumer.
[Categories and Subject Descriptors] Pricing algorithm
[General Terms] Algorithmic mechanism
Keywords: Cloud computing, Nonlinear discriminatory pricing mechanism, Computational intelligence…. Based on the rationale that price and demand are dependent qualities, numerous variations of the problem have been tackled, for instance businesses that sell products to retailers [10], seasonal products [40], stochastic demand [9]. Electronic businesses, and therefore cloud businesses can benefit from dynamic pricing policies [30]. Cache services are distinguished from consumable products in two major ways: 1) they are not exhausted while they are consumed, and 2) the demand for a specific service pauses while this is not available… applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource-economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate pricedemand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution…. The weight changes are ascertained to decrease the error motion for the case being referred to. The entire procedure is rehashed for every one of the model cases, at that point back to the principal case once more, et cetera [27]. The cycle is rehashed until the point that the general error esteem dips under some pre-decided threshold……. If done carefully, it can be a valuable tool for the firm to achieve a number of different business goals, such as profit maximisation, demand management, value creation, etc. Conversely, a poor pricing policy could lead to a loss, and consequently extinction of the firm. Dynamic pricing [27] [15] [3] is a pricing strategy where a firm adjust the price for their products and services as a function of its perceived demand at different times. Traditionally, it has been Siddhartha Shakya Intelligent Systems Research Centre, BT Group Chief Technology Office, Adastral Park, Ipswich, IP5 3RE, UK, e-mail:… Dynamic pricing is a pricing strategy where a firm adjust the price for their products and services as a function of its perceived
demand at different times. In this paper, we show how Evolutionary algorithms (EA) can be used to analyse the effect of demand
uncertainty in dynamic pricing. The experiments are conducted in a range of dynamic pricing problems considering a number
of different stochastic scenarios with a number of different EAs. The results are analysed, which suggest that higher demand
fluctuation may not have adverse effect to the profit in comparison to the lower demand fluctuation, and that the reliability
of EA for finding accurate policy could be higher when there is higher fluctuation then when there is lower fluctuation…. It is therefore appropriate to think about strategies capable of learning from the past, with the potential to improve profits by adapting the model dynamically during the selling period, when one already has relevant information about actual demand as in Aviv and Pazgal [8], Araman and Caldentey [9], Lin [10], Narahari et al. [11]……. Different studies focused the attention on dynamic pricing in marketing and business [1]- [6]. The strategy to adopt changes with the application and with the typology of market… this paper the authors propose a web application oriented on business to business (B2B) and internet automotive marketing. The study case is related to an industrial project concerning tires recognition application and real time comparison of prices of competitors. By means of a smartphone camera able to identify and classify car plates, the system provides different associations of compatible tires for a specific car model and the best prices. The frontend system is based on Optical Character Recognition (OCR) method and the backend works on automatic extraction of competitor prices from different databases and from internet network. The dynamic pricing is due to the best price choice related to period of car plate recognition. The price choice is supported by data mining processing performed by a k-Means workflow. The paper summarizes the results of a research industrial project. The used approaches could be used for different automotive applications involving market of different car parts…. Based on recent contributions where we proposed a quantitative model for ERP diffusion [3], in this paper, we intend to investigate the benefit of adopting a dynamic pricing strategy. Dynamic pricing refers to the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service [4]… a highly uncertain and dynamic industrial business network, Enterprise Resource Planning (ERP) systems vendors face great challenges to enhance their market position and maximize their profit. Being able to simultaneously predict the diffusion of an ERP in an industrial network and to determine the right price to charge to a customer is a complex task. In this paper, we investigate the benefit of a dynamic pricing strategy for ERP systems vendors in a business network governed by a quantitative diffusion model. Based on a real scenario in the automotive industry, those quantitative models are integrated into a simulation-based optimization approach to tackle the problem. Our findings are promising and establish the foundation of a powerful decision support tool for ERP systems vendors. Dynamic pricing is a pricing strategy where price for the product changes according to the expected demand for it. Some work on using neural network for dynamic pricing have been previously reported, such as for forecasting the demand and modelling consumer choices. However, little work has been done in using them for optimising pricing policies. In this paper, we describe how neural networks and evolutionary algorithms can be combined together to optimise pricing policies. Particularly, we build a neural network based demand model and use evolutionary algorithms to optimise policy over build model. There are two key benefits of this approach. Use of neural network makes it flexible enough to model a range of different demand scenarios occurring within different products and services, and the use of evolutionary algorithm makes it versatile enough to solve very complex models. We also evaluate the pricing policies found by neural network based model to that found by other widely used demand models. Our results show that proposed model is more consistent, adapts well in a range of different scenarios, and in general, finds more accurate pricing policy than other three compared models. Malgorzata OgonowskaCe texte part de l’observation de nombreux faits stylisés dans le secteur du Tourisme au
cours des quinze derni
The Ultimate Guide to Dynamic Pricing | Omnia Retail
Dynamic pricing is when a company or store continuously adjusts its prices throughout the day. The goal of these price changes is two fold: on one hand, companies want to optimize for margins, and on the other they want to increase their chances of sales.
Dynamic pricing is a pricing strategy that applies variable prices instead of fixed prices. Instead of deciding on a set price for a season, retailers can update their prices multiple times per day to capitalize on the ever-changing market.
Dynamic pricing often gets confused with personalized pricing. But these two different types of pricing are extremely different from one another.
To put it simply, dynamic pricing looks at your products and and their relative value in relation to the rest of the market. Personalized pricing, on the other hand, looks at individual consumer behaviors and gauges (and changes) a product’s value based on past shopping experience.
Personalized pricing is controversial because it uses individual data and shopping formation that many consumers consider private and personal. It’s also somewhat risky in an age where consumers can interact with and talk to each other like never before. If Consumer A finds out they paid more for the exact same product than their best friend, their trust in a company will erode.
Dynamic pricing, on the other hand, allows you to capture extra sales and take advantage of a changing market without invading consumer privacy or trust.
Dynamic pricing in e-commerce
Dynamic pricing and e-commerce co-evolved together. As the internet became more sophisticated and online shopping grew, so has the need for dynamic pricing.
Consumer electronics was one of the forerunners in the retail landscape in terms of the trend towards online. As a category of elastic products that are sensitive to price changes, it makes sense. Retailers need dynamic pricing to stay on top of the market and continue to offer competitive prices.
But as consumer spending rises in this category (and with it the online market share), two developments that affect dynamic pricing have emerged:
1. Increased price transparency
As more people shop for consumer electronics online, the amount of comparison shopping also increased. Consumers are now far more likely to evaluate a retailer’s prices against the company’s competition.
This shines a spotlight on your product price and makes it the most important part of each sale. Since consumer electronics are typically highly elastic, a 5%-10% difference between your price and your competitor’s could be the deciding factor for a consumer.
2. More frequent price changes
Because of this increased demand for price transparency and matching, the number of price changes every day has increased dramatically since the dawn of e-commerce. Traditionally, the supplier or the manufacturer would determine the price of a product with a consumer advised price (CAP). However, this CAP quickly became irrelevant with the growth of comparison shopping online.
Today, prices are determined by the retailer instead of a supplier, and are based on a variety of variables, including general market trends, competition prices, and stock levels.
A variety of other categories, such as Toys and Games, for example, follow the same pattern: when online spending rises, so does the demand for price transparency. This, in turn, leads to an increased frequency of price changes and the use of dynamic prices.
This trend often also attracts new players on the market without physical stores, which makes it difficult for traditional retailers.
Although the traditional retailers have the first mover advantage, they are generally less flexible in adapting their (pricing) strategy. However, the retailers that do capitalize on their omnichannel advantage can move ahead of the pack.
Dynamic pricing software
Most retailers practice a most basic form of dynamic pricing by discounting items at the end of a season or using a clearance sale to get rid of extra stock.
However, dynamic pricing can go much further than a discount at the end of a season. When you use a dynamic pricing software, you can wield the power of data to capture more sales and take control of your assortment.
Today, almost all major retailers will use some sort of dynamic pricing software.
Dynamic pricing online
Dynamic pricing has obvious benefits online: you can follow the competition, adjust prices instantly, and easily capture quantitative metrics about your store to improve your performance.
Dynamic pricing offline
Dynamic Pricing is also useful offline. Through the use of electronic shelf labels (ESLs), you can easily apply dynamic pricing practices to your physical store. This helps you keep your prices up-to-date with what you present online, and makes pricing management easier.
What are some dynamic pricing strategies?
Traditionally, there are three basic ways retailers set their prices: the cost-plus method, the competitor-based method, and the value based method.
The cost-plus method is the most simple out of all three. All you need to do is take the cost of your product and add the desired margin on top of that cost.
The competitor-based method follows your competition. If your competitor changes their price, you’ll change your price as a result, whether that’s to be lower or higher than your competition.
The value-based pricing method follows the price elasticity of a product. Different consumers value items differently, so everyone has a certain threshold that they are willing to pay for a product. A value-based pricing method capitalizes on the public’s perception of the value of a product and charge accordingly.
Dynamic pricing software allows you to combine different pricing methods at the same time. Some softwares also allow you to incorporate other useful information, such as your stock levels, popularity score, and even the weather forecast.
How to implement dynamic pricing
Implementing dynamic pricing is a journey, one that has a lot of twists and turns. And it does create a big change in your organization. That’s why you should view the adoption of dynamic pricing as an opportunity to improve your overall pricing strategy and internal systems, as well as your overall margin.
Is it hard to get started with dynamic pricing?
After hundreds of implementation projects, we’ve come up with a five-step process to successfully implement dynamic pricing:
Define your commercial objective: Your commercial objective is like your company’s compass: it’ll help you navigate any institutional changes and keep you heading in the right direction. The commercial objective applies to more than just pricing and marketing, but it’s the first step for a successful dynamic pricing strategy. Learn more about how to define your commercial objective here.
Build a pricing strategy: Your pricing strategy takes your commercial objective, then translates it into strategy that your team will use to sell products. An example? Say your overall commercial objective is to be known as the cheapest retailer on the market. Your pricing strategy would then be to make sure every product in your store is cheaper than the competition’s offering. Learn how to build a pricing strategy here.
Choose your pricing method(s): Your pricing strategy tells you what you want to do. Your methods are how you’ll achieve those pricing goals. Your pricing methods are more specific than your pricing strategy.
Establish pricing rules: Pricing rules tell your dynamic pricing software what to do. You should set a rule for every product that the software needs to track and change.
Test and monitor: The final step for getting started with dynamic pricing is to test and monitor your software’s changes. Learn more about testing the effectiveness of your online pricing.
Frequently Asked Questions about dynamic pricing models for electronic business
What is dynamic pricing model?
Dynamic pricing is a pricing strategy that applies variable prices instead of fixed prices. Instead of deciding on a set price for a season, retailers can update their prices multiple times per day to capitalize on the ever-changing market.
What is dynamic pricing in digital marketing?
Dynamic pricing, also called real-time pricing, is an approach to setting the cost for a product or service that is highly flexible. The goal of dynamic pricing is to allow a company that sells goods or services over the Internet to adjust prices on the fly in response to market demands.
What are 4 examples of dynamic pricing?
Examples of dynamic pricingPrice setting for Uber taxis – where the company advertises the price will vary depending on demand. … Tickets for professional sport. … Price of flights Easyjet, Ryanair – prices are constantly being revised depending on how well they are selling.Google Ads. … Electricity companies.Jun 12, 2019
