“Managing Pricing: How managers
exert control when a Dynamic Pricing
Strategy is used”
Master Thesis
Maurice Buitenkamp S3845583
University of Groningen
MSc Business Administration – Management Accounting & Control Focus Area Digital Business
Supervisor: A. Bellisario
ABSTRACT
TABLE OF CONTENTS
1. INTRODUCTION 4
2. LITERATURE REVIEW 7
2.1 Dynamic Pricing and management control 7
2.2 Management Control Systems and the Levers of Control framework 9
3. METHOD 15 3.1 Case background 15 3.2 Data collection 16 3.3 Data analysis 17 4. FINDINGS 18 4.1 Goal achievement 18 4.2 Experimentation 24 5. DISCUSSION 29 6. CONCLUSION 32 REFERENCES 34 APPENDICES 37
APPENDIX I – Example questions of interviews 37
1. INTRODUCTION
In today’s online business setting, organizations are confronted with the opportunity to make use of an abundance of sales related data. This data offers possibilities to exploit knowledge about customer behavior, and firms can use this knowledge to their advantage for several important functions in the organization. A function that can severely benefit from this knowledge is pricing (den Boer, 2015). In recent years, many firms in various industries have adopted dynamic pricing strategies. In contrast to traditional pricing where firms keep their prices fixed for most of the products’ selling horizons, the prices in a dynamic pricing strategy adapt continuously to changing circumstances. The goal of dynamic pricing is to charge the “right” price to the customer at any moment with the use of data and technology (Elmaghraby & Keskinocak, 2003). Therefore, it can be seen as a study in which
organizations try to strive for optimal prices that deliver the highest value to the company, and in which learning is an important aspect (den Boer, 2015). A dynamic pricing strategy offers possibilities for the organization to match supply with demand more effectively. This offers possibilities to improve revenues and profits extensively (Chen & Chen, 2015). As many sales are performed online and margins decreased due to stronger competition, adopting a dynamic pricing strategy could be a companies’ most powerful source to gain a competitive advantage on its competitors (Hinterhuber & Liozu, 2014). However, dynamic pricing is a complex strategy that could ruin the reputation of the firm and hurt customers when it is not properly managed. Therefore, organizations need adequate control structures in order to make dynamic pricing successful (Liu, Pang, & Qi, 2020).
This helped to shift the understanding of control systems as ‘fixed’ systems into a role in which control is seen as an action-driven process which fosters organizational learning and innovation as well (Batac & Carassus, 2009). An important framework that supports this view is the Levers of Control (LOC) framework by Simons (1995). Simons (1995) defines MCS as “formal, information-based routines and procedures managers use to maintain or alter patterns in organizational activities”. His framework describes a MCS as four levers which act together to exert control over the achievement of the objectives of the organization while providing support for innovation. As the strategic objectives of dynamic pricing strategies are interrelated with the broader objectives of the organization, and learning is an important aspect of dynamic pricing, MCS literature has the potential to explain how managers exert control when a dynamic pricing strategy is used.
However, literature that examines management control in light of dynamic pricing is scarce. The majority of studies around dynamic pricing focus on the development of
mathematical models to determine optimal pricing policies (Bitran & Mondschein, 2016; Gibbs, Guttentag, Gretzel, Yao, & Morton, 2018; Liu et al., 2020). A study by Hinterhuber & Liozu (2014) provided some insights of the consequences for the internal organization when a pricing innovation is adopted, such as dynamic pricing. The authors argue that such an adoption should lead to the establishment of a dedicated pricing function, centralization and decentralization of responsibilities, and the development of organization-wide pricing capabilities. As these factors have a direct influence on the control structure of the
2. LITERATURE REVIEW
The first paragraph discusses literature about dynamic pricing strategies and focuses specifically on challenges for management control. In the second paragraph, the Levers of Control framework by Simons (1995) is introduced as a conceptual framework for this study. Recent insights on this framework by various authors have been added to ground the study on state-of-the-art knowledge.
2.1 Dynamic Pricing and management control
Dynamic pricing is a strategy that firms can adopt to determine optimal selling prices of products and services in settings where prices can easily fluctuate (den Boer, 2015). A dynamic pricing strategy comprises the continuous adaptation of product prices to available information at any moment, by using digital technologies (Hinterhuber & Liozu, 2014). This information includes a wide range of data about customers and competitors (den Boer, 2015). Literature about dynamic pricing is primarily focused on the description of effective pricing policies under specific circumstances. For instance, Bitran & Mondschein (2016) study optimal pricing strategies for retail firms that sell perishable products. Papanastasiou & Savva (2017) describe what influence Social Learning and Strategic Customers have on the choice of adopting different pricing plans. Although studies about dynamic pricing and control are scarce, previous literature indicated that the adoption of pricing innovations has implications for management control. For instance, Hinterhuber & Liozu (2014) describe several
decisions (den Boer, 2015). In general, this digitalization of the organization has been linked to specific control challenges (Verhoef et al., 2019).
First, digital transformation requires flexible structures in the organization. Firms should be digitally agile to continuously modify its assets and capabilities in order to respond to changing customer needs, new technology, and intensified competition. This implies that standard, hierarchical structures with a top-down approach are not sufficient and
decentralized decision-making is necessary in order to respond fast(Verhoef et al., 2019). Decentralized structures have benefits, such as the effective use of local knowledge and conservation of management time. However, decentralization could also lead to several control challenges, such as incentive problems, failure of coordination and less effective use of information that is stored centrally (Ecker, van Triest, & Williams, 2013). In light of dynamic pricing, managers need to balance this trade-off and make decisions about centralization and decentralization of pricing related responsibilities. Moreover, control systems are needed that effectively address the costs of decentralization. Second, digital transformation requires the addition of metrics and goals that measure digital performance. Outcome related metrics like ROI, profitability and revenue growth remain relevant.
However, in order to determine how well the digital business model is delivering value, it is important to implement “digital” metrics as well. This implies that managers need to
determine effective measures for digital performance (Verhoef et al., 2019). Third, there is a need for digital resources in the organization (Verhoef et al., 2019). Digital resources of the firm include ownership and control over digital assets and capabilities. Digital assets include the IT-infrastructure, the storage of data, and technologies to compete digitally.
The ability to effectively collect and analyze data for decision making is a key challenge for firms. Despite the ease of collecting data nowadays, firms struggle to use data effectively for decision-making (Verhoef et al., 2019). The use of data for decision-making brings risks of information overload, as it can be challenging for managers to understand which data is relevant for them (Raffoni, Visani, Bartolini, & Silvi, 2018). Moreover, the use of data for decision-making could lead to making wrong decisions faster, as transparency of what is and what is not included in the data is low (Quattrone, 2016). Therefore, assuring data reliability present a key control challenge for managers (Bhimani & Willcocks, 2014).
Their findings illustrate that it can be difficult to internalize the processes around the technology in the organization. More specifically, it can be difficult to make employees understand what the digital processes entail and how they are linked to the functioning of the company. Moreover, wrong entries in the system led to wrong decision making and
correcting these entries resulted in excessive consumption of management time (Teittinen et al., 2013). In addition, one of the key features of dynamic pricing, learning, indicates that there exists another control challenge. Dynamic pricing strategies are focused on determining optimal selling prices, by exploiting knowledge from data and use this to change prices to fast changing circumstances (den Boer, 2015). Pricing is a complex activity which covers
multiple functions in the organization, such as sales, marketing and IT (Cetin, Demirciftci, & Bilgihan, 2016). In order to be able to learn, knowledge needs to be shared through the company, and control systems are needed that leave room for learning, while also providing tight control in order to achieve the goals of the pricing strategy. This interaction between tight control and organizational learning is vital for many organizations and presents a key challenge for managers (Batac & Carassus, 2009). In sum, these findings indicate that there are important control challenges and implications for management control when
organizations use dynamic pricing strategies. The next paragraph elaborates on Management Control Systems (MCS) and the Levers of Control framework by Simons (1995), which help to understand how managers exert control in organizations.
2.2 Management Control Systems and the Levers of Control framework
Management Control Systems (MCS) comprise mechanisms in an organization that managers use to be in control. Simons (1995) defines MCS as “formal, information-based routines and procedures managers use to maintain or alter patterns in organizational activities”. MCS fulfill two independent roles that complement each other. The first role is the attainment of the key objectives of the firms’ strategy. The second role is to enable employees to solve problems and to search for new opportunities (Mundy, 2010). The Levers Of Control (LOC) framework by Simons (1995) is adopted in this study to explore how control is exerted by managers when a dynamic pricing strategy is used. The LOC framework describes MCS as four control systems or ‘levers’ that operate together: the diagnostic
appropriate to explore the subject of this study because of three reasons. First, the framework presents a holistic view of MCS (Simons, 1995). During the years, there has been growing consensus that control systems should be considered in entirety. Especially for the Levers of Control, there exist complementarities between control systems that are important to
understand how managers attempt to guide, direct and control activities in the organization (Kruis, Speklé, & Widener, 2016; Mundy, 2010). Therefore, using a framework that covers the control systems in totality seems necessary to adequately determine how managers exert control in dynamic pricing. Second, the LOC framework is concerned with the use of control systems by managers in achieving strategic objectives, rather than only describing their technologies, existence, structure or design (Mundy, 2010). Therefore, the scope of the framework matches with the objective of this study: determining how managers exert control in dynamic pricing. Third, the LOC framework is concerned with the use of control systems to exert control over the achievement of strategic objectives while simultaneously driving innovation (Simons, 1995). As learning is a key feature of dynamic pricing, the framework can be used to address control systems for this purpose as well. In addition, recent
developments that other authors have made with the framework seem promising to address the control challenges of dynamic pricing. In the next paragraph, the properties of each lever will be shortly described, and additional insights of recent literature are included. Moreover, an explanation is provided of how each lever can help to address control challenges for dynamic pricing that were described in the previous paragraph.
of the organization, by measuring operational risk and uncertainty through precise measures. Although a diagnostic use of controls is proposed by Simons (1995) as having a constraining force on behavior, recent studies showed that they can also motivate managers and enhance organizational learning. Diagnostic controls highlight problems that managers can solve to achieve their goals, sometimes through novel means. Moreover, information on performance indicators provide important inputs for discussion and debate that can help to determine new ways for the strategy of the organization (Mundy, 2010). In addition, Bedford (2015)
underlines that diagnostic control systems can be helpful in light of learning. His findings illustrate that organizations which use an exploitative mode of innovation benefit from emphasizing diagnostic controls. Exploitative innovation entails the refinement of existing markets and technological capabilities. Greater emphasis on diagnostic control enables
managers to exert tight control over business activities while providing room to experiment in order to refine existing strategy, although in a limited manner compared to explorative
innovation. Heinicke, Guenther, & Widener (2016) found that larger firms tend to emphasize diagnostic controls. As the scale of operations in large firms tend to be more extensive, there are greater coordination needs in light of control as well. This requires tighter forms of control compared to smaller firms, as more employees are involved in operations and it becomes more difficult to control performance. Diagnostic controls are used to provide formal information systems that allow managers to adequately monitor performance, without excessive involvement with subordinates. With these features, diagnostic controls have the ability to address several challenges in light of dynamic pricing. First, they allow managers to exert control over the objectives of the pricing strategy, by monitoring deviations between goals and performance measures. Second, diagnostic controls allow for tight control over these measures with the use of formal feedback systems, which managers can use to make adjustments to the performances of subordinates. Third, diagnostic measures can be used interactively through discussions to enhance organizational learning in dynamic pricing.
Throughout the years, the features of interactive control systems have been extended to clarify the type of usage that characterize interactive controls. Bisbe, Batista-Foguet, & Chenhall (2007) introduced five super codes that relate to properties of interactive control systems: 1) intensive use by top management, 2) intensive use by operating managers, 3) a pervasiveness of face-to-face challenges and debates, 4) a focus on strategic uncertainties, 5) a non-invasive, facilitating and inspirational involvement (Bisbe et al., 2007). Where other levers are focused on the internal organization, interactive control systems are used to scan the external environment by addressing competitive uncertainties. Therefore, managers use those control systems interactively that focus the organization on the specific set of
information that helps to resolve these uncertainties(Garcia Osma, Gomez-Conde, & de las Heras, 2018; Widener, 2007). Plesner Rossing (2013) demonstrated how managers employ interactive control systems for organizational learning in light of transfer pricing practices. In his study, interactive systems were employed and used in an inspiring way to stimulate learning about the tax environment and to fine-tune practices around transfer pricing. The author enriched the LOC framework by urging to take into consideration the underlying information and knowledge flows that precedes interactive control. This can help to better understand how organizational learning enriches formal MCS routines. Moreover, Arjaliès & Mundy (2013) showed how interactive processes helped organizations to identify and exploit opportunities to develop their CSR practices. Their data indicates that formal processes for sharing good practices are a key means through which interactive processes are activated, as they facilitate the exchange of knowledge between employees throughout the organization. In turn, this enables managers to identify innovations and uncertainties for the strategy.
Moreover, regular meetings between senior managers and operational employees were used to debate assumptions underpinning the companies’ plans as well as the choice of targets and measures. In addition, Bedford (2015) found that the value of interactive control systems may differ in the ‘mode’ of innovation in organizations. Especially in the context of exploratory innovation, firms could enhance performance when they emphasize interactive processes. The main argument for this is that more active roles of accounting are better suited for contexts where there is uncertainty around the consequences of actions. As explorative innovation is focused on the development of new technology and the search for new and emerging markets, consequences of actions are less clear and involve more ambiguity.
Top managers need to involve themselves with subordinates personally and regularly to select those initiatives that propose the most potential for competitive advantage,
in order to determine which initiatives should receive resources to develop further (Bedford, 2015). In light of dynamic pricing, interactive control systems could help to address the control challenge related to organizational learning. Interactive controls allow information sharing between managers and subordinates. This enhances the sharing of knowledge about the pricing strategy throughout the organization. Moreover, managers can use interactive systems to signal innovations and uncertainties for the pricing strategy through active debate, which they can use to innovate the pricing strategy.
Belief systems include control systems that communicate core values of the
organization. They inspire and motivate employees to search, create, explore and to expend their effort to engage in actions that are important to pursue the firms’ objectives. The definitions of values are communicated in broad terms and do not prescribe behavior to the letter. This enables employees to engage in spontaneous and responsive actions. Any system that provides employees with information about the organization’s values can be determined as a belief system (Simons, 1995). With these features, belief systems have found to be central both in driving change and in maintaining a focus on the key priorities of the organization. The purpose is to inspire employees in the search and discovery for new opportunities in line with the values of the organization. Moreover, they help managers to establish a shared vision around the firms’ strategy and to unite employees around a set of values. Managers use belief systems to signal strategic objectives to subordinates in uncertain conditions. This helps organizational members to match their behavior with outcomes that are desired, even if the belief systems are not directly relevant to particular business units or employees (Mundy, 2010). Belief systems are mobilized around other control systems, such as diagnostic and interactive controls, to reinforce the key message about strategies and to communicate the values to as many employees as possible (Arjaliès & Mundy, 2013). Heinicke, Guenther, & Widener (2016) found that the emphasis on beliefs control in organizations is directly related to the extent of a flexible culture. While beliefs control is important for all firms, they point at the similarities between flexibility values and the open-ended scope of belief systems. Belief systems communicate the standards and ideals of the organization, and they match with values of teamwork, cohesion, and social ties that
represent flexibility. In light of dynamic pricing, belief systems could help managers to exert control in several ways. First, they can help to motivate employees to search for new
environment where circumstances change rapidly, belief systems could help managers to signal the strategic objectives of the organization while employees search for new directions. As learning is an important element of dynamic pricing and the adoption of digital strategies has been linked to a greater emphasis on flexibility in the organization, beliefs systems could have a significant role for control in light of dynamic pricing.
Boundary systems are used by managers to bring employee behavior in line with the strategic objectives of the organization, by providing explicit guidance on acceptable and unacceptable activities. Any control system that sets out guidelines for behavior can be classified as a boundary system (Simons, 1995). Arjaliès & Mundy (2013) found how
boundary systems were used to manage internal risks and to ensure compliance with external standards in light of a Corporate Social Responsibility (CSR) strategy. Boundaries were used to manage internal risks by formalizing standards for behavior in the form of a codes of conduct. These codes of conducts included rules in line with external regulations as well. This helped to prevent employees from engaging in behaviors which were not in line with the CSR strategy, during daily operations and in the search for new opportunities. Their findings illustrate that boundary systems are not only based on internal risks but can be used by managers to manage risks around external influences as well, such as legal frameworks. The findings of Kruis et al., (2016) indicate that the emphasis on boundary controls is less when there is a higher level of centralization in organizations. The reason for this is that
3. METHOD
In order to understand how managers exert control in a dynamic pricing strategy, I chose to adopt a qualitative research approach with a single case study design. Case studies are explicitly useful to study phenomena in its natural setting and help to generate theory based on observations of practice. Moreover, a single case study is especially useful to investigate how and why questions in situations where variables are still unknown or where the
phenomenon is still not completely understood (Eisenhardt, 1989; Voss et al., 2002). Since specific literature about dynamic pricing and control is scarce, a case-study design provided the opportunity to explore the subject and to build theory simultaneously. Moreover, this allowed me to study the subject based on the experiences of managers in an organizational setting. In order to achieve triangulation, I used a total of three data collection methods for my study (Voss et al., 2002). These consisted of observations of the pricing application, company materials in the form of training slides about pricing, and semi-structured interviews which were the main source of my data.
3.1 Case background
I conducted my case study in a large Netherlands-based retail company which sells a wide range of products, online and through physical stores, in a total of three countries in Europe. The company uses a dynamic pricing strategy, which is executed through an own-developed pricing application that has been in use for years. In the organization, pricing related
decisions are made at two levels. First, there is a dedicated pricing function. This function is responsible for the management of the pricing application. Second, there are several category teams, which are organized around equal groups of products. An example is ‘Category Team Laptops’. These teams include Product Managers who are responsible for all figures around sales and returns of a specific product type. For instance, within ‘Category Team Laptops’, there are three Product Managers based on the operating systems. Product Managers can be seen as ‘owners’ of a range of products and they are concerned with day-to-day pricing decisions. I selected this specific company because the employed managers have considerable experience with dynamic pricing technology and the use of data analytics. My procedure to search for informants started with asking recommendations from employees at the
with this person. I asked for recommendations for other informants who could help me with the subject after each interview. This allowed the initial informant to make the best decisions for recommendations, as the content of the interview was already known. I spoke to Product Managers that work with dynamic pricing applications at a daily basis, and with managers and Business Controllers which are concerned with control activities around dynamic pricing. This allowed me to get a comprehensive understanding of the control systems that were used, as both the insights of managers and subordinates were included. I stopped performing interviews when theoretical saturation was reached, in the sense that no new significant findings were coming in anymore.
3.2 Data collection
Data collection has been primarily performed by conducting interviews with informants that are involved with pricing activities. In addition, I used information from company documents that related to the dynamic pricing process and I observed how the pricing application worked. This allowed me to visualize the terms that my informants introduced, and it helped to gain a better understanding of the answers I received. For the interviews, I contacted my informants via e-mail first to introduce myself, to introduce my study, and I mentioned who provided me their recommendation. When there was no response at first, I phone called the person to ask whether an interview could be arranged. This
provided me with an opportunity to ask whether the person in question was knowledgeable about my subject. The interviews were planned, and I sent my interview questions a few days before the actual meeting to my informants in order to allow them to prepare the interview if necessary. The interviews were performed via a videocall. Due to the Corona pandemic, it was not possible to meet my informants in person. I started each interview with a quick introduction of my topic and asked whether it was allowed to record the session for
whether all questions were discussed with the interviewee. After each interview, I
reconsidered my questions and added additional questions when necessary. Table 1 below provides an overview of my informants and the duration of each interview.
TABLE 1
Schedule of interviews
Interviewee Department Duration of interview
Pricing Manager Pricing 45 min
Pricing Specialist Pricing 45 min Business Controller 1 Business control 1 hour Business Controller 2 Business control 35 min Product Manager 1 Category team 1 45 min Product Manager 2 Category team 2 35 min Category Manager Category team 3 45 min
3.3 Data analysis
I based my data analysis on the description of Gioia, Corley, & Hamilton, (2012). I first transcribed my interviews. I used the Dutch language for my subscriptions, as the interviews were performed in the Dutch language as well. This allowed me to stay as close to the meaning of words as possible since Dutch my native language. I used Excel for the coding processes, and I started with collecting quotes that may be helpful to answer the research question. In the first order analysis, I added codes to each quote that adhered to the terms of the informant (Gioia et al., 2012). For instance, this is one of the quotes that I coded during my analysis: ‘I think on the one hand, you have the technical challenge of having the right
data, is it on time. For example, we can buy competition data four times a day, but we may want to do this ten times a day. So that's a challenge.’. As the interviewee talks here about a
challenge related to data, I used the code data challenge. This allowed me to compare any similarities between other quotes with a quick overview over the first order codes.
4. FINDINGS
In this chapter, I present the findings of my study. I aim to provide a deeper understanding of how managers exert control when a dynamic pricing strategy is used. Based on the themes that I found in my data, I created two separate sections. These sections relate to two activities in light of dynamic pricing and control.
4.1 Goal achievement
The first set of controls that managers use focus on the achievement of the goals of the pricing strategy. The case firm uses several IT-systems for its pricing activities. Their main system is a dedicated pricing application that is managed by the pricing department. This application is used by Product Managers for day-to-day pricing activities. The
application displays information about current prices, the standardized strategies which are followed, and it provides a recommended price based on various input data, such as
competitor prices. Product managers have the possibility to implement prices automatically or manually. They can set this for each product individually by choosing between
A Product Manager explains how a business rule can be set to limit the fluctuations of product prices on different levels of detail based on a margin setting:
“You can specify a percentage per product type, per brand, per segment, which margin you
want to keep and it [the margin] will not fall below that. “
As the Product Manager explains, business rules are an important tool to control pricing decisions. As these rules are set in line with the objectives of the pricing strategy, they help to achieve these objectives. This applies both for automated as manual pricing decisions. In this way, they form an extension of the MCS for the achievement of goals in a diagnostic way, as they passively monitor if pricing decisions lead to deviations from the goals of the strategy. The selection and monitoring of business rules are decentralized to the Product Managers. This allows them to use specific knowledge about the markets for which they are responsible to set these business rules.
In the case firm, formal reports are used by managers to inform themselves about performance measures of the pricing strategy. These formal reports take the form of dashboards which can be adjusted to personal preferences. For pricing, two types of dashboards are used that differ in the information that is included. The pricing department monitors performance measures on technical aspects of pricing, such as data reliability, model accuracy and the extent to which pricing decisions are automated. Business
Controllers and Category Managers monitor commercial performance. A Category Manager explains how these dashboards are used to make adjustments on a daily basis, by focusing on negative outliers:
“The manager of each category team gets a winners and bleeders report every morning. And then you see which products have turned the most margin and which products have made a
loss. That last list is a reason to investigate "Hey Product Manager, why did we sell this product nine times with a five-euro margin loss each?” ”
Here, the Category Manager illustrates how formal reports are used to monitor whether actual performance of pricing activities is in line with the strategic objectives of the company. As daily pricing responsibilities are decentralized to the Product Managers, control systems are needed that inform managers adequately about their performance in order to involve
A report that shows negative outliers allows for direct interventions with subordinates which are underperforming. Based on the performance measures of the pricing application, they form a diagnostic control system through which management by exception is performed.
In addition to these formal reports, regular meetings are organized to discuss performance. Category Teams, which consist of multiple Product Managers that have responsibility over related product types, have weekly ‘stand-ups’. The Category Manager, Product Managers, and various other colleagues from other functions, such as purchasing, attend these meetings. One of the main topics is to discuss performance on pricing related metrics, as the Product Manager explains:
“Every Monday we [Product Managers] have a week start, a stand-up, where everyone briefly explains the sales, the products sold, your primary margin and your transaction margin for their product type. And if there are crazy things in it then you always have to
explain them.”
Here, the Product Manager explains that these meetings are used to share information on performance with a specific focus on abnormalities. The stand-ups encourage Product Managers to declare why these deviations in performance occurred. However, the focus of these meetings is not to discuss any underlying assumptions of the pricing strategy in broader terms, as the Category Manager explains:
“You try to keep it short [during the stand-up]. That everyone is aware. […] It is team-wide, all disciplines are included, so you don't want to make it too technical.”
Here, the Category Manager clarifies the scope and duration of these meetings. They are not focused on challenging underlying assumptions of the pricing strategy. Rather, they are used to discuss exceptions in performance and to share information which is relevant for daily operations. Therefore, these meetings are used diagnostically by managers to inform themselves about the performance of subordinates.
Each quarter, meetings are organized between Product Managers, purchasers and the Category Manager. During these meetings, Product Managers present their targets to the Category Manager. Moreover, they engage in discussions to determine whether it’s a coherent story. A Product Manager explains how this target setting unfolds and how these targets are discussed:
“We present it, me and my purchaser to the Category Manager for any uncertainties. As soon as we have an agreement on it, he can send it to the management.
[…]
I only do that [setting targets] for my product types. Of course, my manager will check that too. He checks if it's a logical story, if it's related to our strategy. For instance: okay, we want to capture market share, then it doesn't make sense if you take strategy X and go the average selling price. So it must be a coherent story. And then He [Category Manager] has
to present it to the management again, and we need a ‘go’ on that.”
As the Product Manager explains here, the activity of setting targets for pricing is decentralized to the product managers. Higher level managers and top management are involved in the process of ‘approving’ these targets after discussions of the underlying assumptions. These discussions focus primarily on the underlying assumptions of targets that are not in line with the pricing strategy. These meetings provide managers with the
opportunity for double loop-learning, as they become informed about the motives of these targets, changed strategies, and strategic uncertainties.
In the case firm, controls are implemented that influence behaviour. In order to prevent employees from engaging in pricing activities that may be harmful for the
organization, codes of conduct are in place that specifically focus on the prevention of price fixing activities. A Category Manager explains how these codes of conducts are established in the firm and why price fixing is a serious risk that should be mitigated:
“There is a legal training so that people know clearly what is allowed and what is not allowed. In this sense, many things are not allowed. It is no joke. If they [Authority for Consumers & Markets] suspect you [of price fixing], there is the reverse burden of proof. […] There are certainly rules for this [in the organization]. You must also sign for this that
Here, the Category Manager explains the necessity to avoid price fixing behaviours at all costs. The legal training and codes of conduct are key tools that managers use to prevent employees from engaging in price fixing activities in their daily operations. These systems form the boundary system that help managers to align employee behaviour with the strategic goals in relation to pricing, in order to achieve the goals of the strategy.
In addition to these control systems, various challenges in light of dynamic pricing and control were mentioned as well. First, there is a challenge of creating trust in the pricing application. The manager of the pricing department explained that more than hundred people work in the pricing application every day. Ensuring trust in this application represents a key challenge for control, as one of the goals of the dynamic pricing strategy is to automate as much decisions as possible, as this saves time of Product Managers which can be used for other responsibilities. In order to cope with this challenge, the pricing manager emphasized that it is important to explain people how the pricing application works and how its various settings should be used. This creates trust in using the pricing application to automate pricing decisions. In turn, this helps to achieve the goals for dynamic pricing as well as more
extensive knowledge helps employees to automate pricing decisions as much as possible. Moreover, data quality issues imposed challenges for control as well. These include
signalling wrong entries and errors in input data. Product Managers explained that errors in data scrapings of competitor prices are common. These prices are used as an input for the pricing application. They signal these errors themselves through the use of price indexes to find extensive outliers, as there are no ready-made controls for this in the pricing application or at the company which provides these scrapings. A Product Managers explains how this works:
“You can't actually see that [an error in the data scraping] unless you know yourself; I don't think this is correct, and then you check it. So I don't think there is any safety behind that.
Except the product manager himself with the knowledge you have of the market.“
In addition to these challenges, a Business Controller mentioned that it is difficult to manage the trade-off between speed and quality of control, especially in light of dynamic pricing:
“I find it very difficult to guarantee the playing field between quality and speed in light of control. Because we have, of course, automatically deployed a number of controls. For
example, that function X may only change a certain price and function Y must agree. […]
An operation that is very focused on sales, on volume, finds that difficult. But from a Business Control perspective, I think: speed is important, but we also need to ensure that we indeed
make the right decisions and therefore go through the controls so that we don't make mistakes. And that sometimes gives friction.”
Managers deal with this trade-off through regular evaluations in which controls are reconsidered, as the Business Controller explained:
“Yes, this is evaluated continuously. What you notice is that we have a lot of "afterwards" controls. […] Suppose there is a negative margin. […] You actually want to make that known
in advance [before the decision is made] or have it clear. So, we are working on adjusting the reports. […] We are trying to get a better grip on beforehand versus afterwards.”
In sum, there are several control systems which managers use to exert control over the achievement of the goals of the pricing strategy. These control systems include business rules, formal reports, and meetings which are used diagnostically by managers. Moreover, performance measures are used as inputs for interactive control systems. These include the regular meetings to discuss quarterly targets which have been set by the Product Managers. This offers managers possibilities for double loop learning, as they can ask questions about underlying assumptions of these targets, and they get informed about changed strategies and strategic uncertainties. Boundary systems are in place to prevent employees in price fixing activities, which is a considerable risk in relation to pricing. Controls are employed in a decentralized structure that allow Product Managers, who are involved in day-to-day pricing decisions, to become experts in their markets. This helps to develop local knowledge which can be used in both pricing setting and target setting activities. Moreover, Product Managers use this knowledge to signal potential errors in the input data for their pricing decisions which are performed through the pricing application. Managers use the diagnostic control systems to manage by exception, by spotting deviations between actual performance and the strategic goals. They use these deviations to determine which subordinates need personal involvement. In light of control, there exists a challenge in finding a balance between speed and quality of control. Especially in an operation that is focused on speed, such as pricing, it is important to evaluate control systems regularly.
4.2 Experimentation
Experimentation seems to be an important activity in a dynamic pricing strategy. Product Managers and employees at the pricing function perform ‘tests’ in order to improve the pricing strategies that are used in the organization. The manager of the pricing department explains why testing is important in dynamic pricing:
“It is very difficult to track back if an increase in price let to an increase in revenues. […]
The cause [an increase in revenues] could also relate to an increase in marketing expenses, or you sold more of the product because there was an increase in temperatures outside.
That's why you want to test a lot.”
Knowledge about these relationships can be used to improve the pricing strategy, and it requires frequent testing in order to learn. In addition, experimentation with pricing is
essential to find a balance between opposing goals: following the lowest price to gain market share or increasing prices to gain margins. Balancing these goals requires continuous
adaptations to circumstances which change rapidly. A Product Manager explains the necessity to perform tests to find this balance:
“It's all about finding balance. On the one hand, you naturally want to go with the lowest
price. The balance is in: how do we keep enough margin, that we remain profitable, that we earn enough from the products, that we grow year over year and that we can also maintain
the market share. So that is always a search.”
Here, the Product Manager explains the necessity for experimentation in dynamic pricing for their day-to-day pricing decisions. As circumstances change fast, there is no single truth in the sense of an optimal price which remains stable for a longer period. Pursuing the goals of the pricing strategy requires testing in order to find this balance, by making use of the knowledge they possess about their specific markets.
Testing with pricing is performed in a decentralized manner by Product Managers, and in a centralized manner by the pricing function. The pricing function performs larger scale tests, which are called ‘optimization strategies’. In these tests, the strategies of products are exchanged between products with similar specifications in order to determine whether it delivers additional margins. The manager of the pricing function explains the scope of these tests:
“We work on new strategies at the pricing function, we call them optimization strategies. Then we want to know: that specific strategy works very well for Product X, but do we know
whether this works for Product Y as well? Then we will test if this works for Product Y. Or whether it works well for accessories, and we will test if it works well for a main model. So,
we are testing a lot and often to see if it has yielded more margin.”
“Product managers can test themselves. So, suppose you are Product Manager of product category X. You go and say: I will follow my competitor first [with 0% increase] and then I
will follow my competitor with + 3% [on the sales price] and then I will see what this does with the turnover. That is just a small test and we [Pricing Function] do not involve ourselves
with it. Everyone who is responsible for that product type can set up such tests.”
Here, the pricing managers explains that the scope of the tests is focused on refining existing pricing strategies, in the case of the pricing function as well as for Product Managers. The pricing application offers standardized pricing strategies which are complemented with business rules. By exchanging settings of the pricing application between products, the pricing function tries to uncover refined strategies that delivers additional margins. The tests of product managers are focused on refining the existing strategies as well, by trying different settings and determine if it delivered additional performance.
In order to share knowledge about experimentation, regular meetings are organized where knowledge about outcomes of tests are shared. These meetings are called demos, and Category Teams, Knowledge Centres, and Support Centres use these meetings to share information about important projects, including insights of tests that have been performed. One of the Business Controllers elaborates on the purpose:
“All domains and knowledge centres give a demo every two weeks. Based on this, the insights of what has been tested are shared. In those demos we show it, and we share it with each
other.”
Another Business Controller adds that there is the possibility to ask critical questions and that top management joins these demos regularly as well:
“Sometimes, the CEO and other top management join too, and you can ask critical questions during this demo.”
The demo’s form an interactive control system through which managers inform themselves about new insights in relation to pricing, and which helps to share knowledge throughout the organization as many functions join these meetings regularly.
The values of the company are communicated in several ways and are broadly supported by the organizational members. For instance, the company uses value statements, and the values are visible on the walls at the headquarters. A key value of the company is related to performing better every day, by taking small steps. A link was made between the values of the firm and the possibility to perform test at a small detail level. One of the Business Controllers explains the influence of the values on how control is exerted for dynamic pricing:
“In that we are flexible, in the sense of doing a little better every day and just doing it. We have arranged this in such a way that we can experiment. We can adjust things very specifically in a very small manner in a very small area of our organization. So, for example,
you could run a certain experiment on a specific product, and not, for instance like a more static pricing model, implement it only for the full range of products. We are able to adjust things in a small detail and see what the effect is. So that you do not make big mistakes and on the other hand not very big profits. When it is confirmed that it works, you can scale-up.”
The Business Controller explains how the values encouraged flexibility in the design of controls in the organization, in order to give employees the possibility to undertake
experimentation with pricing and to motivate them as well. Controls are organized in such a way that this experimentation can be performed on a small scale by Product Managers with a lot of freedom. Moreover, the values guide Product Managers to undertake experiments at a small scale. This helps to incur only small losses when experiments fail, and successful tests can be scaled-up into greater projects.
In sum, there are several control systems that are used by managers to exert control over experimentation with pricing. Experimentation activities are important to identify cause-and-effect relationships between financial measures and data inputs and to balance different goals of the pricing strategy in day-to-day pricing decisions. The purpose of experimentation is to refine the existing pricing strategies in the organization. The findings suggest that the control systems for experimentation, as an addition to the control systems for goal
achievement, are interactive and focus on the distribution of knowledge about pricing
and information about new features are shared. The possibility to ask critical questions helps to challenge underlying assumptions of the findings. As management attend these meetings as well, they get informed about new ways for the pricing strategy. The values of the company have an important role in experimentation as well. The values guide employees in their search for refinements, as they encourage to undertake experiments at a small scale. The flexible manner in which controls are organized motivate employees to undertake pricing strategies.
TABLE 2
Control systems that were used in the case firm for Goal Achievement and Experimentation
Goal Achievement Experimentation
- Performance measures (Diagnostic) - Business rules (Diagnostic) - Formal reports (Diagnostic) - Stand-ups (Diagnostic)
- Quarterly meetings to discuss targets (Interactive)
- Codes of conduct (Boundary)
5. DISCUSSION
In this section, the findings of the study are considered in relation to previous literature. The main purpose was to explore how managers of an organization exert control when a dynamic pricing strategy is used.
The findings indicate that managers use diagnostic control systems to exert control over the achievement of the goals in a dynamic pricing strategy. In the case firm, managers used performance measures for monitoring purposes, and to involve themselves with subordinates when performance was not in line with the preset goals. Responsibilities of pricing activities were widely decentralized. This allowed Product Managers, who were concerned with day-to-day pricing decisions, to develop local knowledge about the markets for which they were responsible. Managers needed effective controls that provided freedom for employees to make day-to-day pricing decisions individually and to experiment with pricing, while allowing for tight control over situations where performance was not in line with the objectives of the strategy. Managers used diagnostic controls to provide freedom to subordinates to perform their daily tasks in a decentralized manner, while maintaining tight control in case of underperformance. Therefore, the findings indicate that diagnostic controls are especially useful to exert control over subordinates who work in decentralized units. This finding extents the findings of Kruis et al. (2016) who found contradictory evidence for the usage of diagnostic control and decentralization in organizations. In addition, diagnostic performance measures were used interactively as well, as they provided inputs for meetings where targets for the pricing strategy were discussed. This finding is consistent with the findings of Tuomela (2005) who argues that performance measures are used both for
diagnostic and interactive control purposes. Moreover, this finding is consistent with Widener (2007) who argues that performance measurement systems should be used both
diagnostically and interactively to realize its full benefits. As targets were set by
In addition, the findings of this study contribute to management control literature by illustrating how business rules in digital technologies complement diagnostic control systems in the achievement of the goals a dynamic pricing strategy. Business rules were used to program automated pricing decisions and to guide manual pricing decisions. By providing boundaries based on various input data, business rules helped to accept and reject pricing decisions in relation to these data inputs. As business rules were set in line with the strategic objectives of the pricing strategy, they helped to exert control over the achievement of these objectives. The importance of taking business rules into consideration has been highlighted before in previous literature on dynamic pricing (Chen & Chen, 2015). However, the findings of this study show that business rules have a role for management control as well, as they extent diagnostic control systems in pursuing the objectives of the dynamic pricing strategy. This role has not been considered before in management control literature and the findings suggest that business rules could serve control purposes in strategies that rely on digital technology.
The findings of this study indicate that experimentation activities for dynamic pricing primarily unfold in an exploitative way. Organizational members in the case firm searched for opportunities to refine existing pricing strategies within the pre-defined boundaries of the strategic objectives. The findings indicate that interactive and belief systems are important control systems for experimentation activities in dynamic pricing. This finding contrasts with the findings of Bedford (2015), who argues that firms should emphasize diagnostic and boundary controls for exploitative innovation. The findings of this study indicate that key systems to control experimentation were beliefs and interactive control systems. In the case firm, the values had a key role in motivating employees to engage in exploitative
experimentation and to guide them to perform these activities at a small scale. Moreover, sharing knowledge about experimentation activities throughout the organization by using interactive control systems remained very important despite the exploitative character of this experimentation. The findings of this study extent the findings from Verhoef et al., (2019) by showing how managers deal with data reliability challenges through the use of local
The findings show that the use of local knowledge of subordinates can help to address data reliability issues. Data errors are a significant risk for digital processes that depend on data for decision making, as it could lead to wrong decisions and excessive use of
6. CONCLUSION
With this study, I investigated how managers exert control in a dynamic pricing strategy. I used a single case study design to explore the subject in a large, Netherlands based retail firm which sells their goods and services primarily online. The research design allowed me to investigate the subject while literature about dynamic pricing and control was scarce.
Moreover, it provided the possibility to explore the subject based on managers’ experiences by conducting semi-structured interviews (Voss et al., 2002). Through observing the
functioning of the pricing application and with the use of company materials, I gained a deeper understanding of the concepts of dynamic pricing. I adopted the Levers of Control framework by Simons (1995) as a theoretical lens to make sense of the control practices and systems that were used by managers, and added recent insights of this framework in my theoretical background which helped me to guide the research on state-of-the art knowledge.
The findings of the study show that two main activities in dynamic pricing are relevant in light of control: goal achievement and experimentation. For goal achievement, managers relied on control systems that were primarily diagnostic in nature. The diagnostic control systems included performance measures and business rules in the pricing application, formal reports, and meetings that were focused on sharing information about performance. Many responsibilities around pricing were decentralized, in order for Product Managers to be able to develop local knowledge about their markets. This had several benefits for day-to-day pricing decisions, experimentation activities and to address control challenges related to data reliability. Performance measures served as inputs for interactive meetings to discuss
quarterly targets for the pricing strategy. These meetings were used to challenge the underlying assumptions and to check whether these targets were in line with the broader strategic objectives. In light of experimentation, interactive systems were used to share knowledge around pricing throughout the organization, and therefore, they were primarily used for learning. Beliefs systems motivated organizational members to engage in
experimentation, while providing guidance at the same time to perform tests at a small scale. Overall, experimentation seems to be an important activity in dynamic pricing in order to learn about cause-and-effect relationships between data and prices, and to balance goals of increasing margins and the attainment of market share in circumstances that change rapidly. In the case firm, experimentation was focused on improving existing pricing strategies, rather than developing new strategies in the sense of exploration. Therefore, the type of
managers pointed several challenges. There exists a trade-off between speed and quality of controls that is especially relevant for dynamic pricing. Moreover, creating trust in the digital processes around dynamic pricing and ensuring data reliability were mentioned as key challenges. The use of local knowledge of subordinates seemed key to mitigate the risk around errors in input data.
This study has its limitations. First, there was one case selected to explore the subject. This implies that the findings may not be generalizable for other organizations, for instance organizations that are of different size or sell in a smaller set of markets as the case firm from this study. As dynamic pricing strategies are used in various industries nowadays, other firms may face different challenges compared to the retail sector in which this study was employed (Elmaghraby & Keskinocak, 2003). Second, as data was primarily based on semi-structured interviews, the findings are based on the opinions and experiences of managers. As different people share different views on the same subject, it could be that the views incorporated in this study may differ from the views of other managers that were not included in the data collection. Practical implications of this study follow from the findings related to the importance of using local knowledge in dynamic pricing. As the findings illustrate, human judgement remains important even in strategies that are focused on automation and which depend on digital technology. Managers should be aware of the role of human judgement in digital strategies and organize control in such a way that local knowledge can be generated and used effectively for decisions making by subordinates.
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APPENDICES
APPENDIX I – Example questions of interviews
General questions:
- What challenges do you see with regard to pricing and control? - How do you deal with these challenges control?
- What are the main features of dynamic pricing?
- What do you think are the reasons for using a dynamic pricing strategy? - What are the challenges of a dynamic pricing strategy?
- How do you deal with these challenges? Diagnostic control systems:
- How are targets for the pricing strategy set? - How are KPIs for the pricing strategy determined?
- How do you monitor whether the performance is in line with the objectives? - How does management monitor whether performance is in line with its objectives? - How are deviations between actual performance and targets communicated? Interactive control systems:
- How are results discussed with each other? - How is top management involved in this? - How are pricing strategies improved? - Which systems are used for this? - Which processes are present for this?
- To what extent are you free to experiment with pricing decisions? - In what way is top management engaged with dynamic pricing? - How are best practices shared?
Beliefs systems
- How are the company's core values communicated?
- How do the company's core values contribute to motivating employees? - How do the core values of the company affect control in dynamic pricing? Boundary systems
- What risks do you see with regard to dynamic pricing? - How are these risks dealt with?
- How is undesirable behavior prevented?
APPENDIX II – Core quotes per theme
TABLE 3
Core quotes that relate to concepts of goal achievement
Concept Interviewee Quote
Business rules Business Controller 2
Pricing Specialist
Product Manager 1
Business Controller 1
“You can include certain rules in the application. For example, you want to stay between a certain bandwidth in terms of margin.”
“You have a number of lines in that tool that you can check. That is limited in a way. Suppose we want to follow the market, then that is still with the limit until we, for example, still grab a 4% margin. So there are several rules used to determine that. So that you actually guarantee that you never go towards that negative margin, unless you really make a conscious decision, and that you don't have to price all those pieces separately.”
“There are just two limits within which the price can fluctuate, and these can be set by the product managers.” “So, what you can also do is make it even more complicated by expanding it with more rules. You can also choose if something has been around for a long time to discount it. But you set all these things, and on every day we have four moments when we calculate an optimal price again. Then that optimal price first passes through the minimum, through your maximum, and then it becomes your recommended price. If your product type is automated, it will be adjusted immediately and you do not have to accept anything. But if you have it on manual, you always have to adjust it yourself.”
“So, we set a strategy and then we have a minimum and a maximum price check. So, what we do is, based on the strategy, we arrive at a certain optimal price. You just saw this one. But then the optimal price first goes through a check; if this optimal price is smaller than the minimal price, in this case it was, then we go to the minimum price. If that is not the case, we will see whether the optimal price is greater than the maximum price, if so, it is also limited by the maximum price. If this is not the case, then the optimal price is the recommended price.”
“You can also say; I want to have everything followed automatically so that I don't have to look at it, but you can also set everything to manual, which you have to approve yourself before the price goes down. Then the system will simply give recommendations.”
“Another strategy is that we should in principle not have a negative transaction margin. Every time a pop-up appears on the screen asking: “Are you sure you want to implement a negative expected margin based on this price?”
