University of Twente Student Theses


Designing a dashboard to support the decision process of dynamic pricing

Chapparadalli, N.L (2019) Designing a dashboard to support the decision process of dynamic pricing.

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Abstract:The wholesalers' market field is changing drastically and constantly evolving due to technological developments, changing demands and customers' wishes. In addition, the position of wholesale companies in the current market field is becoming more challenging. For this reason, many companies test their pricing strategy continuously for relevance and accuracy to compete with others. There are many ways of determining pricing such as on pricing tools (Omnia), but these tools are not smart and easily understandable for many of the price decision managers. Therefore, introducing artificial intelligence models and designing dashboards, the situation of price decision making can be improved. This increases the company’s profit margin. Purpose: The goal of this study is to do empirical research on the requirements for a smart dashboard to improve the decision process of dynamic pricing of wholesale companies. Methodology/Approach: The main research methods applied were literature reviews and multiple case studies that resulted from semi-structured interviews. Based on that, the design rules were defined, i.e. the decision processes underlying the design of the dashboard. The dashboard was used as a prototype to have concrete feedback from interviewees. To validate the prototype, an extensive evaluation process was conducted with six different experts, which included pricing managers, wholesale directors and business analysts. Results:From literature study, we abstracted five pricing strategies (Value-based, Competitors-based, Cost-based, Micro-marketing and Algorithmic pricing). In addition, methods (such as Regression and Bayesian), techniques (Machine learning algorithm technique) and approaches (Conservative approach) applied for those strategies have been identified. However, the research on dynamic pricing for wholesale companies is still scarce and specific design rules (decision processes) to wholesale companies are hardly mentioned. The findings of this study implicate that companies want to apply a value-based pricing strategy. Moreover, the interview results show that the main aspects needed for decision-making by wholesale companies and therefore the main drivers of the dashboard are: price elasticity, customer groups, sales, and gross margin. More importantly, it should be simple enough to understand. From these interviews, we also found that each company has a different ways of executing their pricing strategy. To incorporate literature studies and requirements of the wholesale companies, we defined the design rules. In order to define the rules and to support the decision process of pricing, we found Balanced Scorecard (BSC) to be a suitable framework. This framework has been used to define the design rules in four perspectives (Customer, Learning&Growth, Internal Process and Financial). In addition, the requirements of the dashboard from the interviewed companies are covered in these perspectives. Furthermore, from the five identified pricing strategies, we adopted the value-based pricing strategy and regression methods to calculate price elasticity, revenue and gross margin. Recommendations: Based on the interviews and an additional literature study, we provide design rules with four perspectives and simple mathematical models, of which the following are of direct value for wholesale companies and can be implemented easily. Firstly, group the customers in combination with the relevant products or product groups. This helps to identify the groups who have similar pricing behavior. Secondly, learn about how those identified customer groups value, in addition to the various product attributes and/or service(s) in relation to the price. More importantly, identify whether the company is operating in a red ocean or following a blue ocean strategy. "Red ocean" is a situation in which multiple vendors offer essentially the same product and thus mainly compete on price. In a "blue ocean" situation the product is sufficiently different from competitors' products to create an uncontested market space. If the companies are approaching red ocean strategy, then they should convert it to blue ocean strategy. This is because competition between the companies following red ocean strategy, makes them to set their prices as low as possible which results into lowest profit. However, companies can create and capture a new demand by setting their prices high in blue ocean. Furthermore, this way of learning makes it simple to determine the price elasticity and revenue combined with customer group or product group. This shows the optimal price at which revenue will be maximum. In addition, based on these calculations, we identify the key value items (KVIs) which are also called as leading products. Thirdly, for additional value services, understand the touchpoints for those customer groups and which actions at these touchpoints are most valued by the customers. For example, discount strategy, delivery time etc. Lastly, in the fourth perspective, optimize prices with gross margin and profit margin per distributed channel. Besides the above-mentioned points, we found three important points which will become important for wholesale companies in the near future. 1. Implement the price elasticity with the logistic model instead of linear regression model. This helps to determine the outliers. These outliers are the variables such as promotion price, discount price etc. 2. Integrate a designed dashboard within the current business of the company. 3. In addition, expand the design of the dashboard to price setting platform to change the suggested optimal price directly in the pricing system.
Item Type:Essay (Master)
E-tail Genius, Rotterdam, Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:83 economics
Programme:Business Information Technology MSc (60025)
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