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Information Technology Strategy Incorporating Dynamic Pricing in the Business Model of the Future

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 214)

Abstract

With the continuous development of Information Technology towards the Consumer Electronic area, consumers are provided with invaluable and powerful information for consumption purposes. This has imposed strangle-hold competitive pressures on businesses, especially retailers. The proposed Dynamic Pricing Model discussed in this research will provide the supply chain business partners of industry a strategic weapon to counter-balance the increased consumer competitive power. The main thrust of the model is predicated on the use of Information Technology to massively collect consumer data (Big Data) and apply pertinent Business Analytics to develop appropriate Consumer Utility-Value in the form of an index. This complex index can give businesses, especially retailers the ability to price their products/services according to the utility value it can generate based on the real-time desires/necessities of the consumers. By such practice, it is perceivable that additional revenues can be obtained without increase in costs, with the exception of the Information Technology and Business Analytics efforts.

Keywords

Information technology Dynamic pricing Utility value Business analytics 

1 Introduction

The intent of this research is to conceptualize a business model of the future where ubiquitous Information Technology in devices and corresponding applications dominate the world. Depending on geographic location, there are many industries globally that face the difficult situation of competitive and, at times, limited growth. This is especially true in the US and Europe where population growth is rather stagnated and many industries have reached their mature stage [1]. With competitive and investor pressure, it becomes imperative for these industries to transform themselves so as to be in position for the coming years [2, 3]. With pricing and volume growth as limiting factors, these businesses are increasingly focused on exploring opportunities to re-invent themselves and further streamline their supply chains in order to generate additional value for investors [3]. More than ever before, it is imperative for businesses to protect their profit margins in most of their products or services.

The main culprit of the erosion of profit margins in the past decade is the aggressive development of Information Technology especially in the areas of consumer electronics. In the beginning, it was the availability of the Internet in the hands of consumers, which allows consumers to become much better informed of choices and options of products and retail channels. Then with the movement in the past years in the direction of mobile devices, it further enhances the ubiquitous availability of information to consumers anytime anywhere. These recent developments in Information Technology continues to accelerate the fundamental shift of competitive powers in the supply chains of most businesses from upstream side of decades past to today’s situation where the downstream side harness majority of the competitive powers. In other words, consumers are in position to continue to impose price pressures to retailers, which in turn deal with this via their own cost controls by becoming more efficient with their suppliers networks. Often times this means additional price pressures further upstream in the supply chains. An example is the ability of a consumer with a mobile device to check purchasing options and prices via a smart phone in real time in a retail business, in addition to being able to do that online for a decade or more already at home.

An opportunity for business to counter this recent erosion of profit margins is the utilization of consumer information that are being captured increasingly via the retailers, online merchants, Web services providers like Google, and manufacturers as defined by the Information Technology industry as Big Data. With the availability of such massive amount of data, the race in business is to develop business analytics in various functions of themselves in order to further streamline their business operations. This includes applications in inventory control, supply chain synchronization, gaining consumer insights in their product lines, etc.

One direction of applications that is ambitious but unavoidable in the future is the use of the Big Data to generate consumer profiles that will allows retailers to implement the Dynamic Pricing concept in mass [4]. In many ways and in much minor scale, this is already under development. Except that most dynamic pricing efforts are based on a category of consumers and a pre-determined timeline instead of down to a segment of one and in real-time. In addition, it is certainly to the best interest of businesses to incorporate consumer valuation via the development of utility functions for such efforts. The optimization of utility function is a discipline in Management Science that is well-developed in the 1950 through 1980s. But the major challenge is the analysis of individual consumer profile that can allow businesses to determine the value of their product or service to the consumer in real-time [5, 6]. The progression of such development must be gradual. It is imperative that businesses continue to assess the acceptance by consumers of such practice. But certainly with such a revolutionary concept, roadblocks, especially from the public, will be severe. It has been proven in the past that the key to such implementation is the timing of the tiered introduction of the concept in order to facilitate consumer buy-in [7]. This Information Technology Strategy incorporating Dynamic Pricing concept has the capability of allowing businesses to counter the continuing increase in competitive power by consumers via Mobile technology. And it certainly can allow businesses to protect, if not improve on, their gross profit margins on their products/services.

It is the intention of the authors to provide the concept of Dynamic Pricing in detail, including rationales for and potential roadblocks. It would be imperative for businesses, and inevitable, to move in this direction in order to further protect the profit margins of products.

2 Dynamic Pricing Model

The proposed Dynamic Pricing Model discussed in this research will provide the supply chain business partners of industry a strategic weapon to counter-balance the increased consumer competitive power. The thrust of the model is predicated on the use of Information Technology to massively collect consumer data (Big Data) and apply pertinent Business Analytics to develop appropriate Consumer Utility-Value in the form of an index. This complex index can give businesses, especially retailers the ability to price their products/services according to the utility value it can generate based on the real-time desires/necessities of the consumers. By such practice, it is perceivable that additional revenues can be obtained without increase in costs, with the exception of the Information Technology and Business Analytics efforts (Fig. 1).
Fig. 1

Supply chain of a typical original equipment manufacturer

The development of such an index has been gradual, usually from a segmentation approach of classification strategy ultimately to a Segment of One in the future. Even if the ultimate goal of Segment of One cannot be achieved, the concept will certainly allow the business entities to capture significant additional revenues. There is precedence of such practices that are more subtle in the past. For example, the idea of the airline industry that price future ticket sales by the buckets, representing different duration of time before each flight, and continuously adjusting the pricing based on more current information. It is not down to a segment of one, but there is no reason why it cannot in the future if more pertinent information is made available to assess. It is important to recognize that pricing differently for various segments or even down to one consumer is much more feasible with online sales than in-store sales. But that does not mean it is not appropriate for in-store sales. For example, it is common practice for retail store chains to issue different levels of coupons or incentives to various segments. Making discounting available to selective groups is a form of Differential Pricing. But of course these practices are not in real-time, as is proposed in this research work.

A traditional mathematical programming model is the base of the generation of the Consumer Utility Index.
  • Optimize F(X)

  • Subject To
    • G(X) S1

    • X ∈ S2

  • X is a vector of decision variables.

  • X is chosen so that the objective function F(X) is optimized (e.g., maximized or minimized).

  • In choosing X, the choice is made subject to a set of constraints.

  • G(X)  ∈  S1 (generally the utility restrictions)

  • X  ∈  S2 (limits the range of values on X due to various assumptions of the respective individuals)

The general principle of the model is to allow businesses to collect pertinent behavioral data on individual consumers based on their historical individual preferences, economic status, buying behaviors, and any other important profiling information about the specific individual [8, 9, 10]. Then utilize such data set to generate a specific Consumer Utility Index that express the potential value of what a certain specific business may mean to that individual consumer. Obviously it is expected that each individual consumer will likely be represented by a different value, whereby some of the values may even be determined based on the timing of the evaluation. This can be in terms of a certain season, month, week, or day. That is because individual consumer’s desire or preference of a various products and/or services fluctuates due to different circumstances. But it is not as important that this portion of the assumption of the Dynamic Pricing model be in real-time. But certainly that should be the direction of future enhancement of such model. It is important for businesses that employ such a concept to keep the model as simple as possible, develop according to the individual consumer data available, which should become richer as the society’s data collection mechanism continue to evolve over time due to continuing improvement of the sophistication of Information Technology trends.

3 The Role of Information Technology

Certainly the role of Information Technology in the Dynamic Pricing model is not as much on the algorithmic portion of the model. Conventional solution principles of such a model are well developed over the past decades. There is no need for further elaboration or development in such front for this research. And current computing technology is more than capable of handling such efforts even when further development will move the utilization of such model to near real-time. That is due to the intentional simplicity of the Dynamic Pricing model in mathematical programming terms. Additional complexity will not add much value or precision to the model.

The major contribution of the utilization of Information Technology is towards the data collection portion of the Dynamic Pricing model. The growth of such capability is unprecedented and if history is any guide, will only continue to evolve in hard-to-expect timeline. So it is only fair to assume that whatever necessary data set for such model can generally be acquired via direct internal collection or the purchased of specific portion from external sources. Generally most participating business will acquire such data set via a data exchange/collection facility with a reasonable fee. At least that would be the conventional approach to such business practice. But there is no reason that the participating businesses would not develop additional data collection mechanism from their internal Information Technology entity, especially on proprietary individual consumer data that are applicable only to their own customers. This can easily be accomplished utilizing their everyday interaction with their own customers either from their portals or Point-Of-Sales systems [11, 12]. As a matter of fact, many of such potential participating businesses would have engaged in such activities anyway, even if at a very minimal level.

It is worthwhile to re-elaborate that the evolution of the Dynamic Pricing model has to be gradual and intentionally keep simple at the beginning. That is because this is going to be a learning process for the businesses participating [13]. The learning curve is not in the Information Technology aspect of the model, nor the mathematical programming algorithmic aspect of the model. It is in the in-depth understanding of the behavior of the business’s consumer base, individually or collectively.

4 Conclusion

A potential Dynamic Pricing model for businesses to counter the explosive growth of competitive power of the ultimate downstream side of most supply chains, the individual consumer, is not just an enhancement, but a necessity due to the erosion of profit margin such power impacted. The main core of this model is the mathematical programming that establishes a Consumer Utility Index and conventional algorithmic efforts for such model, and the Information Technology efforts in the data collection portion, both internal and external. The value of such model is its simplicity in development, and then the enhancement that follows over time. But the thrust is the capability to allow businesses to develop an improved understanding of their individual consumers through evolution of the model. That will allow further enhancement in product and/or service offerings by such business with an evolving learning curve that will no doubt strengthen the customer relationship overtime [14].

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.University of La VerneLa VerneUSA
  2. 2.California State UniversitySan BernardinoUSA

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