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How price consulting is coming of age
HERMANN SIMON
Prof Dr. Dr. h.c. mult. Hermann Simon, Simon-Kucher & Partners E-mail: hermann.simon@simon-kucher.com
Abstract. This article describes and discusses the coming of age of price consulting from the perspective of the world's leading price consultancy, Simon, Kucher & Partners. Starting with modest theoretical and methodological beginnings, the price consulting field has evolved into many specializations, including price measurement, price model building, pricing structures, and vertical industry segments. Its current stage of development is marked by advanced integration of theory and practice. Despite significant progress in recent decades, there remains considerable growth potential for the future. Similar to a young adult, price consulting still has much to learn and can further contribute to the professionalization of price management. Keywords: price consulting, price response, conjoint measurement, stage of development.
как взрослеет консультационная деятельность в области ценообразования.
Г СИМОН
д-р, профессор, председатель правления «Саймон, Кухер энд партнерс стрэтэджи энд маркетинг консалтенс» в Бонне (Германия) и Кембридже (США)
Аннотация. В статье описывается и обсуждается становление консультационной деятельности в области ценообразования с точки зрения ведущей в мире компании по ценовому консультированию «Simon, Kucher & Partners». Начавшись как скромная теоретическая и методологическая область, ценовая консультационная деятельность развилась во многие специализации (ценовое измерение, ценовое строительство модели), оценивая структуры и вертикальные промышленные сегменты. Ее текущий этап развития отмечен интеграцией теории и практики. Несмотря на значительный прогресс за последние десятилетия, ценовому консультированию еще есть куда развиваться. Подобно молодому человеку, эта область имеет многое для того, чтобы набираться знаний и в будущем может способствовать профессиональному ценовому управлению. ключевые слова: ценовая консультация, ценовой ответ, объединенное измерение, этап развития.
The emergence of Price consulting
Where does price consulting stand today? I cannot speak for the price consulting industry in general but would like to illustrate the rather spectacular development of this special consulting branch through the case of Simon, Kucher & Partners or Simon-Kucher for short. I cofounded this firm with Dr. Eckhard Kucher and Dr. Karl-Heinz Sebastian, my first and second doctoral student respectively. Today Simon-Kucher has 700 employees (over 550 professionals) working out of 27 offices in 22 countries. Simon-Kucher is generally considered the global market leader in price consulting ("Simon-Kucher is world leader in giving advice to companies on how to price their products,» (Ewing 2004); «No firm
has spearheaded the professionalization of pricing more than Simon-Kucher & Partners,» (Poundstone
2010)). In the following pages I will describe important steps in the e, ermergence of price consulting. Necessarily my view is strongly influenced by my experience with Simon-Kucher. Thus, I will not talk about fields such as pricing software, revenue management or other areas which have a relation to pricing but where Simon-Kucher is not active. When we started in 1985 the world of pricing was very different from what it is today. This definitely applies to information, data, models, and methods (Ehrhardt,
2011). It applies less to decision making processes and implementation — areas which still leave huge potentials for innovation and growth of price consulting.
Econometrics for Pricing
As an economist, demand or so-called price response functions played a major role in my studies, especially in microeconomics. But these price response functions were treated as purely theoretical concepts. Actually they were alluded to as «conjectural» implying that they exist only in the imagination or in theory but have no practical or empirical significance. In the late 1960s and early 1970s, the first researchers started to econometrically calibrate price response functions for individual products using empirical data. The data were few and generally of poor quality These empirical studies attracted my interest and I started to apply econometric methods to calibrate price response functions for individual products or brands. Due to the availability of data most of my work at that time was in fast moving consumer goods and in pharmaceuticals. Naturally my first doctoral students worked in the same field with similar methods. However, they had access to the first scanner data which made econometric methods much more meaningful.
When we founded Simon-Kucher our vision was to use econometrics to support and improve price decisions. Econometric methods draw on historical data: for example, sales volumes, market shares and prices of a company and its competitors are compiled and analyzed, factors influencing the sales or the market share of the product in question are estimated, and the findings are applied to the decision at hand. A fundamental assumption of the econometric approach is that effects and behavior remain constant over time; only then can past developments form a basis for assessing future price effects. This basic assumption of econometrics does not take into account that, in reality, the majority of price decision-making situations is triggered by structural disruptions. These may come in the form of a new competitor or a shift in a competitor's behavior. Unexpected disruptions can also originate from retailers. Such situations require decisions to be made — but historical data that assume continuity and stability are of little value here. In most cases that do not involve a structural disruption, however, no major decisions are required or invoked by managers.
In terms of price elasticity — the focus of most econometric studies — there is another problem. Using econometrics, price elasticities based on market data are difficult to measure reliably. Telser, then a professor at the University of Chicago, pointed this
out as early as 1962 (Telser, 1962). Telser's argument goes as follows: High price elasticities in a market allow only for minimal price differences, because the competitors are forced to align their prices. In other words, they cannot afford to maintain significant price differentials (the independent variable in the price response consequently shows a variation that is too small). With low price elasticities, the prices differ more widely, but this does not have a significant effect on the dependent variable (the market share or the sales volume) and thus the price elasticity cannot be reliably quantified (the dependent variable has too little variance).
In one of Simon-Kucher's earliest projects we applied econometric analyses to a well-known brand. The analyses were carried out by a consultant who had a PhD in statistics, was very familiar with response models, and had a sound understanding of all methodological subtleties. The results were essentially useless because the situation had changed due to the entry of generic products, a hitherto unknown category in that market.
In the several thousand price consulting projects Simon-Kucher has conducted since the mid 1980s, econometric models and methods have been applied in less than one hundred cases. Even in the relatively few projects where the method was applied, the results were not always useful. I do not want to imply that other marketing researchers and consultants haven't had different experiences with econometrics. But in Simon-Kucher's case, the expectations of econometric modeling were never fulfilled. The basic flaw is that data from the past is often not relevant for the future or the decision at hand. Our initial hope to build a price consultancy on econometric modeling didn't materialize (for a deeper discussion see Simon, 2008).
expert Judgment
If econometrics didn't supply us with valid and practically useful price response functions how could we generate such curves, which are indispensable to price optimization? The answer was expert judgment. We borrowed this idea from John D.C. Little (1970) who presented the method in a seminal article and called it «decision calculus». The expert judgment approach to determining price response functions involves asking internal or external experts familiar with the relevant market to estimate product sales or market share at different price points. The information is typically gathered during a workshop, with
Figure 1. Elements and structure of a decision support system
participants entering their responses into worksheets or directly into computers. Generally two types of sales estimates are investigated: one set assuming no price reaction on the part of the competition, the other assuming such a price reaction. Expert judgment is an inexpensive method, and generally is more valid than the frequently used gut feeling approaches. Its main weakness is that it relies on internal judgment and does not involve the customer.
Expert judgment has become an important and frequently used method in Simon-Kucher's consulting practice. It is mostly employed in combination with other methods such as conjoint measurement to cross-verify price elasticities. Over the years personal computers have been increasingly used for the estimations. This renders consideration of various scenarios of customer and competitive reactions to price alternatives easy.
conjoint Measurement
I am often asked «what is the most important aspect in pricing?» My answer is «value-tocustomer». Price and the willingness to pay are only a reflection of the value perceived by the customer. Value and price should therefore always be seen together or «conjointly». This is essentially the core and the great contribution of conjoint measurement. Conjoint measurement simultaneously measures perceived value-to-customer and price elasticity. Instead of being asked direct questions about price and willingness to pay, the customer is required to make choices, so-called trade-offs, between various
offerings, which include different levels of product attributes and price. Conjoint thus avoids the weakness of direct questioning about price, which usually sensitizes respondents to price and overestimates price elasticities.
Conjoint reveals which product attributes are particularly important to the customer and how much he or she is willing to pay for them. Conjoint measurement is undoubtedly the most important development for price consulting in the last 30 years and has proven its worth in thousands of cases. Often, conjoint measurement data forms the basis for another method that has proven successful in our consulting practice: decision support systems (see below).
When we started to apply conjoint measurement in the 1980s we used paper questionnaires, which were, of course, very inflexible. The introduction of adaptive conjoint and the availability of portable computers marked an important innovation. Over time all of the innovations of conjoint measurement, such as discrete choice modeling, were integrated in our work. In the first decade Simon-Kucher had its own market research department because the highly specialized surveys we needed were not supplied by market research vendors. In the late 1990s we closed the department and today the data collection is outsourced. This allows us to focus on our core competency, price consulting.
Decision support systems
Decision support systems for marketing were first proposed by John Little (1979, for a review on
Figure 2. Volume and profit effects of a price change in the range of +/-8 percent applied
to all models of a product line.
applications see Wierenga et al., 1999). These systems have become very important tools for Simon-Kucher's price consulting. They offer clients a low-cost, rather risk-free method to objectively test market reactions to alternative prices and various courses of action before implementation.
In price consulting, decision support systems are used to answer a large variety of questions such as:
• What is the relationship between price and volume; and what is the price elasticity?
• What impact will various product and price changes have on revenue and profit? How will individual customer segments react to price variations? And can segment specific pricing measures be developed on the basis of this information?
• Which combination of product/service/price features will maximize profits?
• What is the optimal price, given customers' willingness to pay, competitors' offerings and possible competitive product or price reactions?
• What is the optimal reaction to competitors' price and other actions?
• What is the optimal product lineup?
Apart from addressing product or service specific issues, decision support systems can also be used to resolve conflicts between objectives. By helping to calculate the trade-off between volume, market share and profit, these systems create a sound basis for rational strategic decision-making. Moreover, by
generating quantified conclusions, they significantly contribute to more objective, cross-functional coordination, as for example between sales, marketing, production and finance. This facilitates buy-in from decision-makers, and thereby creates the conditions necessary for a successful implementation.
Support systems for price and other decisions share some common elements, and a structure (see Figure 1). Using expert judgment, the behavior of both the company's customers and of its rivals is modeled. The system is fed with quantitative information reflecting consumer preferences and needs, the buying process, market structure, and other relevant marketing elements such as communication and distribution channels. This information is aggregated, producing a forecast of volume and revenue based on the price and other marketing parameters entered. If costs are incorporated into the system, it can also determine profit.
The primary goal is to capture the purchase decision situation of the individual customer as realistically as possible. This requires a thorough understanding of each buyer's individual characteristics. The level of detail necessary in the information-gathering process depends on management's questions and the defined objectives for the decision support system. For example, is the price level for a whole product line to be determined? Or, is the goal to fine-tune the price levels of individual product
Figure 3. Applications of decision support systems: industries and main decision parameters
variants within this line? In general, the finest level of segmentation determines the system's complexity; and, therefore, the quantity of information necessary to feed it.
A decision support system helps management to better estimate the consequences of product and price decisions. Does management want to maximize profit? Short-term or long-term? Or does it strive to attain both adequate profit and a specific market share (a goal frequently encountered in real life)? Or does management want to increase penetration in certain customer segments? The decision support system helps select the measures that will most likely result in these and other objectives being met.
In complex and mature markets a decision support system can be very helpful in making better decisions. Consider the automotive market. Each segment of it comprises numerous brands, models, engine derivatives and body types. This makes it very difficult to quantify the effects of product and pricing measures on a subjective basis; complex interde-pendencies make it all the more necessary to employ a decision support system.
Figure 2 shows how a range of price changes (-8 percent to +8 percent) affects volume and profit of a model line of a premium car. The highest profit increase is achieved when the price is increased by 4 percent. The figure also shows, however, that the
profit increase is associated with a decline in volume of 2,200 units, or 13 percent.
Figure 3 summarizes the application of 600 decision support systems developed by Simon-Kucher & Partners. These decision support systems have been applied in many industries for the purpose of evaluating a broad range of decision parameters. Pricing issues occupy first place, followed by product-related issues. Frequently the system is used with respect to a combination of parameters (Note: The percentages for the main decision parameters add up to more than 100 percent, because some systems had more than one main decision parameter). Pricing applications are dominant for two reasons. First, pricing is well suited to the quantitative nature of decision support systems. Second, pricing is an enormous profit driver, making the return on investment for a pricing decision support system particularly attractive.
A prime criterion for assessing a decision support system's usefulness is the quality of its forecasts or to what extent the forecasts can predict the actual outcome. There is little research on this topic as companies are reluctant to give insight into their marketing practice (Wierenga et al, 1999). We have access to both qualitative and quantitative results for about 80 percent of systems developed by Simon-Kucher. From a scientific viewpoint, a high degree of consistency should be expected if the framework
under which the decision support systems were developed and under which the strategic measures have been implemented are largely comparable. This is typical for the automotive market. For such markets, the systems regularly achieve a forecast validity of ± 5 percent. Decision support systems for product innovations or for products in high-growth markets on average have a reduced level of forecast quality; however, more detailed forecasts seem implausible given the high pace at which such markets tend to evolve. Interestingly, clients usually would not even expect such high levels of forecast quality.
Another interesting finding is how the management's and the user's confidence in price decision support systems can be increased. We have noted over the years that these systems are met with considerable skepticism at first. This understandable and advisable skepticism, however, usually loses ground to a much more positive attitude in more than 90 percent of cases after some practical experience has been gained and the utility of such a system has been recognized. In order for sufficient confidence to be built, no implausible forecasts concerning volume and profit effects should be made. Such results, however, can occur if the users want to put the system to the acid test by using extreme prices. This should only be done if the database on which the system relies covers such value ranges. Drawing on our own experience, it is not advisable for the manager — as the decision maker — to use the decision support system without the assistance of the consultant who developed the system, for only an informed and conscious use of such complex systems will lead to valid results. Ideally, an experienced expert uses and updates the system, often the consultant who originally developed the system. Only when it is certain that for a given problem the appropriate parameters are chosen, the resulting values are correctly interpreted and proper conclusions are drawn. This procedure is particularly advisable if the decision support system is intended for long-term use. It is wrong to assume that there is a system that solves all problems by pressing a button. The systems must be fed and interpreted with great diligence (for a deeper discussion of marketing decision support systems see Engelke and Simon, 2007).
Multidimensional Price Structures
Usually one thinks of a price as a one-dimensional construct. A cup of coffee costs $1.50, that's it. One-dimensional pricing is, however, a very confined
concept. It does not always allow the full exploitation of customers' willingness to pay. One of the most important developments in price consulting's coming of age are increasingly complex multidimensional price structures. A milestone in Simon-Kucher's work was the introduction of the BahnCard (Railcard) in 1993 for the German Railroad Corporation (Deutsche Bahn). Before 1993 the railroad company had only offered a one-dimensional price system; tickets were priced per kilometer. The main competitor of the railroad, the car, has, however, an implicit two- or multidimensional price structure, consisting of fixed or semi-fixed components such as insurance, depreciation, and variable components such as gasoline. The problem for the railroad is that consumers tend to neglect the fixed components when they decide between rail and car. Thus, the one-dimensional (full cost) price structure of the railway posed a serious competitive disadvantage. We solved the problem by adopting a two-dimensional price structure, consisting of the price for the BahnCard and a reduced price per kilometer. Today the Bahncard costs €498 ($672) for the First Class and €249 ($3336) for the Second Class. It gives the owner a discount of 50 percent on all tickets for the duration of one year. The BahnCard became a huge success and more than 5 million people have this card today. With the discount of 50 percent the railway is highly competitive to the car, especially with increasing oil prices.
Price bundling is another extremely successful method for extracting customers' willingness to pay and maximizing profits. Here the customer buys a bundle of products rather than a single item. The CEO of a large bank recently stated that banks sell their customers only 2.1 products on average. This is primarily due to an ineffective approach; the number can be significantly increased through bundling. The same applies to multi-person pricing, which is particularly effective in the tourism and banking industries. Today, in about one quarter of Simon-Kucher's projects we apply multidimensional price structures. They include nonlinear pricing, price bundling, multi-person-pricing, multi-product pricing, combinations of product and service prices, and multi-country price arrangements. These structures can be applied in myriads of industries, for example telecommunications, e-commerce and the Internet in general (Amazon's $78 offer for free shipping for a year is an interesting recent example), media, health care, consumer and industrial services.
Figure 4. a pricing process for an industrial product
Multidimensional price structures pose complex challenges with regard to understanding, measurement, and implementation. They require very experienced consultants who need both a very solid knowledge of theory and the ability to develop practical solutions. Only by understanding the theoretical rationale for the optimality of a specific model can the consultant be sure to choose the right structure. The underlying complexities cannot be mastered by common sense, experience or gut feeling alone. Moreover, with these structures it is critical to measure price elasticities and thresholds very precisely and validly. In order to capitalize on the profit potential, willingness to pay must be exploited to the fullest. Even a slight inaccuracy can cause strong adverse effects on profits. While much progress has been made in this area, there is still considerable potential to exploit sophisticated price structures. Multidimensional price structures have become an important region in Simon-Kucher's price consulting landscape.
International Pricing
It is well known that prices can differ strongly across countries. It is less obvious what companies can or should do about this phenomenon. In Simon-Kuch-er's consulting practice the first international pricing projects originated from the emergence of parallel imports in pharmaceuticals and the introduction of the Euro. With the disappearance of trade barriers in Europe in the early 1990s the incentives to buy pharmaceuticals in countries where they were cheap and resell them in high price-countries became very
enticing. Arbitrage costs for pharmaceuticals were low and high margins could be earned. This phenomenon was not confined to the pharmaceutical sector, but was most prevalent there. The introduction of the Euro in 2002 increased price transparency across countries and led to parallel imports in numerous other industries (consumer goods, cars, household appliances, many B2B sectors). Many companies reacted by introducing uniform European prices. But this uniformity is hardly ever optimal because it ignores differences in customer behavior, trade margins and a company's market positions in different countries. Multinational companies were forced to realign their price positions. Pricing projects, previously confined to one country now became international and analytically much more complex. The allocation of price decision-making power between corporate center and country subsidiary introduced a new organizational aspect.
Consequently, price consulting expanded its organizational scope. On the consultant's side multinational teams were required to cover the various markets involved. This gave a strong push to the internationalization of Simon-Kucher, because it became a necessity to have local resources for studies in key markets. Or conversely, if you were not present there, you would not get a project involving the respective country. Even today international pricing continues to pose complex challenges for companies and attractive opportunities for consultants. A recent study found that in several sectors the Euro has not led to a narrower spread of prices. For some products
(e.g. washing machines) the price differences actually increased (Deutsche Bundesbank, 2009). This surprising result shows that even after the introduction of a common currency there remains room for international price differentiation. We estimate that in the automotive industry 25 percent of profits come from this differentiation.
Pricing Processes
I have to admit that it took me 20 years of research and consulting work to fully recognize the outstanding importance of pricing processes. Especially the B2B -world is dominated by pricing processes rather than price optimization in the microeconomic sense. The reason is simple: in these transactions most prices are negotiated. In spite of their extreme relevance in practice, pricing processes have hardly been the subject of academic research. As opposed to price optimization, pricing processes address the tricky issue of price implementation. This field encompasses everything related to price realization, such as pricing information, who is responsible for pricing decisions (such as approving special conditions and discounts), and training in negotiation and price defense. Price-controls and price- or margin-oriented incentive systems for the sales force also play a critical role in this context. Figure 4 illustrates the five phases of a typical pricing process for an industrial goods company.
Reorganizing pricing processes requires an interdisciplinary approach. Aspects such as organizational structures, responsibilities of corporate functions, customer behavior, incentives, and controls, etc. must be included. Different functions such as marketing, sales, accounting, finance, and human resources must be involved because the pricing process may affect them. Based on Simon-Kucher's experience with more than 1,000 projects, pricing process reorganization typically increases profit margins by around two percentage points, for example from 5 to 7 percent, or from 10 to 12 percent of revenue. Very different factors have to be taken into account in each case. However, it is important to point out that in practice this extremely valuable approach has profited less from academic research than from the accumulation of practical experience. Pricing process reorganization is also quite taxing on the consultants because they may have to work for months on the client's site. The information comes directly from
the client's employees and has to be collected in a tenuous, politically charged process. This also explains why pricing processes are so difficult for academic researchers to access. Even from a consulting perspective with substantial experience in reorganizing pricing processes, generalizations are extremely difficult because these processes are highly industry and company specific.
Pricing and the Internet
Already in Internet 1.0 we have seen many pricing innovations. A notable case was the «name your own price» model, most prominently exemplified by Priceline.com. Auction mechanisms were employed on a large scale in B2C, C2C, and B2B settings, with eBay as the most popular contender. Price transparency increased dramatically through all kinds of search and price comparison engines. Internet 2.0 took pricing to new heights. Google introduced highly sophisticated pricing models such as Ad-words. The pricing of digital content continues to pose a difficult challenge, because the vast majority of content is (still) offered free on the Internet (see Simon 2013 for more details). These developments have opened new opportunities for price consulting. Simon-Kucher, especially its Silicon Valley office in Mountain View, California, has worked for most contenders in the Internet space. But the coming of age of Internet marketing and pricing still lie in the future. We are at an early stage. We can expect many pricing innovations to be developed in cooperation between entrepreneurs and consultants rather than academics.
Pricing for Big Projects
Big projects pose one of the most difficult pricing challenges. How do you successfully negotiate prices for a power plant, an automotive supply contract which runs over five years and may have a value of several billion dollars, a global service contract for jet engines, or for large scale IT-outsourcing projects? These are not fields where the classical price response function and profit maximization according to the marginal cost equals marginal revenue-principle are particularly helpful. Rather both the vendor and the consultant need an extremely deep understanding of aspects such as:
• The relative power between vendor and customer (and competing vendors),
• the value chain processes of the vendor's customer,
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• the buying center structure on the customer side,
• the history of transactions between vendor and customer,
• the personalities of the key people on both sides.
At Simon-Kucher, pricing for big projects has become a focus and an area of innovation in recent years. Time and again, experience shows that executives are insufficiently prepared for their really big deals and make suboptimal decisions. The key additional value that a pricing consultant brings to the negotiation table is a neutral, quantitative view on the power balance of this particular deal. Decades of psycho-babble have led people to believe that tricks help in big deal pricing. If there is one trick that works, it is solid, detailed preparation in anticipation of the vendor's opening offer, target price and walkaway price. The consultants who work on pricing for big projects have to be very experienced and senior, for two reasons. First, they must be able to both analyze very complex situations and evaluate the people who are involved in the pricing process. Second, they typically deal with top management and therefore need a strong personal standing. The stakes are very high in this area, not only with regard to price as a determinant of the profitability of a project, but also regarding the risk of losing a project.
Behavioral Pricing
It may well turn out that the new field of behavioral economics will revolutionize economics. In 2002 Daniel Kahneman and Amos Tversky received the Nobel prize for their original work on prospect theory. Since then numerous researchers like Richard Thaler or Dan Ariely have expanded our knowledge on seemingly «irrational» price effects. Since about five years behavioral economics is finding its way into price consulting at Simon-Kucher. Dr. Enrico Trevisan, partner in the Italian office of Simon-Kuch-er, has authored a book based on his experiences with applying behavioral pricing concepts, especially in the financial services sector (see Trevisan 2013). Most of the findings reported by the academic researchers proved true in Simon-Kucher's practice and led to interesting new pricing concepts. Anchoring effects, product line extensions, new temporal structures which systematically exploit the new knowledge produced significant profit improvements in several projects. One limitation of behavioral economics is that generalizations are risky. The reason
is that the findings rely on specific experiments and are not derived from theoretical assumptions which specify the conditions under which the effects occur. Applying the concepts to specific price decision situations thus requires new experimentation in each project. But over time and with more applications Simon-Kucher is optimistic to build a knowledge which allows for more, though cautious generalization. In any case the new field of behavioral pricing holds huge potential for future profit improvement.
Increasing Industry specialization
When I started as an academic researcher in the 1970s I never would have imagined how varied pricing practices in different industries are. As a consequence of this insight Simon-Kucher developed ever increasing industry specialization. As of the early 1990s industrial sectors became the dominant organizational dimension and our divisions are primarily organized along industry sectors. The senior people in the divisions are pronounced industry experts. Today we have pricing experts for pharmaceuticals, medical technology, banking, insurance, chemicals, building technologies, energy, engineering, automotive, technology, telecommunications, Internet, logistics, hospitality/leisure and several other sectors. In countries where our office teams are still small the degree of specialization is naturally less pronounced. Therefore these teams need and get support from the specialized divisions. According to our experience the clients highly value deep industry expertise of price consultants. Simon-Kucher is convinced that it has a clear competitive advantage in this regard.
Qualifications of Price consultants
It goes without saying that price consulting requires a solid and deep understanding of the underlying theories. This applies equally to basic concepts such as price elasticity, measurement, optimization and to complex price structures such as nonlinear price, price bundling, product line- or multi-person prices. Accordingly most of Simon-Kucher's professionals graduated in business or economics, the vast majority from leading universities and colleges in their respective countries. Driven by the need for industry specialization we increasingly hired experts with different backgrounds. Today we employ physicians, engineers, psychologists, mathematicians, physicists, pharmacists, biotechnologists, computer scientists and consultants from other disciplines. The reason is that we have to understand the value of our clients'
offerings in order to get the price right. A physician, a pharmacist or a biotechnologist is better prepared to analyze the true value and the competitive advantages of a new high-tech medication. Our scientists work in teams with the consultants with business backgrounds to arrive at solutions that equally observe the technical and the economic aspects of pricing. On our clients' side we often encounter scientists and engineers. They highly appreciate if a consultant from their own field is on our team. We expect that experts from various technical and scientific fields will over-proportionally grow in our work force.
summary and conclusion
Could one have expected this development of price consulting in the 1970s? Most likely not. Yes, price consulting is coming of age. But it is not yet there. Most companies still price based on rules of thumb, cost plus considerations and gut feeling. Therefore the growth opportunities for price consulting are virtually unlimited. We will see a penetration of more professional pricing into industries and companies that have remained untapped. The same applies to fast growing regions, especially emerging markets. I know that today few pricing projects are carried out in these fast growing economies. Relative to other consulting fields price consulting is a late comer. The reason is obvious. Companies first have to get their fundamentals such as product quality, supply chain, and physical distribution in order before they start to optimize their marketing instruments, and especially their prices. And sophisticated pricing requires good data that is not always available in emerging economies.
Another growth path for price consulting lies in new methods, tools and applications. In this regard I expect a lot from the Internet. With regard to selling and pricing digital content, and also exploiting the pricing potential of social networks or locational services, we are at a very early stage of development. The Internet holds unseen opportunities with regard to behavioral data, price differentiation, addressability of segments and even individuals, and will become a gold mine for sophisticated pricers.
In summary, I see price consulting today as a young adult with a great future.
References
1. Deutsche Bundesbank (2009), Konvergenz der Preise im Euro-Raum, Monthly Report, March, 39-50.
2. Dolan, Robert J. & Simon, Hermann (1996), Power Pricing — How Managing Price Transforms the Bottom Line, New York (NY): The Free Press.
3. Ehrhardt, Annette (2011), Then and Now: 25 Years of Pricing History, The Journal of Professional Pricing (First Quarter), 28-30.
4. Engelke, Jan & Simon, Hermann (2007), Decision Support Systems in Marketing, Journal of Business Market Management, 4, 289-307.
5. Engelke, Jan & Simon, Hermann (2008), Decision Support Systems in Pricing, Journal of Professional Pricing, Vol. 18 (Second Quarter), 24-28.
6. Ewing, Jack (2004), Hidden Champions — The Little-known European Companies that Are Conquering the World, Business Week, January 26, 42-44.
7. Little, John D. C. (1970), Models and Managers: The Concept of a Decision Calculus, Management Science, 16 (April), 466-485.
8. Little, John D. C. (1979), Decision Support Systems for Managers, Journal of Marketing, Vol. 43 (3), 9-26.
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10. Poundstone, William (2010), Priceless: The Myth of Fair Value, New York (NY): Hill and Wang.
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12. Simon, Hermann (2008), The Impact of Academic Research on Business Practice, Experiences from Marketing, Journal of Business Market Management 4, 203-218.
13. Simon, Hermann (2013), Preisheiten — Alles, was Sie über Preise wissen müssen Frankfurt am Main: Campus.
14. Telser, Lester G. (1962); The Demand for Branded Goods as Estimated from Consumer Panel Data, The Review of Economics and Statistics, 44, 300-324.
15. Trevisan, Enrico, The Irrational Consumer, Farnham (UK): Gower.
16. Wierenga, Berend, van Bruggen, Gerrit H. & Staelin, Richard (1999), The Success of Marketing Management Support Systems, Marketing Science, Vol. 18 (3), 196-207.
