# Pricing Models for Data Products in the Industrial Food Production

Calvin Rix, Jana Frank, Volker Stich, Dennis Urban

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Calvin Rix, Jana Frank, Volker Stich, Dennis Urban. Pricing Models for Data Products in the Industrial Food Production. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.553-563, 10.1007/978-3-030-85914-5_59. hal-03897876

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# Pricing models for data products in the industrial food production

Calvin Rix<fnref n="1" />, Jana Frank<fnref n="1" />, Volker Stich<fnref n="1" />, Dennis Urban<fnref n="2" />

**Abstract.** In the food industry, a very large potential of data ecosystems is seen, in which data is understood, exchanged and monetized as an economic asset. However, despite the enormous economic potential, companies in the food industry continue to rely on traditional, product-oriented business models. Existing data in the value chain of industrial food production, e. g., in harvesting, logistics, and production processes, is primarily used for internal optimization and is not monetized in the form of data products. Especially the pricing of data products is a key challenge for data-based business models due to their special characteristics compared to conventional, analog offerings and multiple design options. The goal of this work is therefore to solve this issue by developing a framework that allows the identification of pricing models for data products in the industrial food production. For this purpose, following the procedure of typology formation, essential design parameters and the respective characteristics are derived. Furthermore, three types for pricing models of data products are shown. The results will serve not only stakeholders in the food industry but also manufacturing companies in general as input for an orientation of their data-based business models.

**Keywords:** Pricing models, Data products, Food production, Typology

## 1 Introduction

The increasing use of networked machines is generating ever greater volumes of data [1]. In the manufacturing industry the value was 3.5 zettabytes in 2018 and is estimated to grow to 21 zettabytes by 2025 [2]. Against a backdrop of evolving capabilities in the areas of data analytics and storage, as well as artificial intelligence, this data holds continuously growing economic potential [3]. The value of the European data economy, for example, is estimated to grow to 827 billion euros in 2025 [4]. This data is generated across the entire food production value chain: starting with the selection of raw materials, through international transport and production activities, to real-time market analyses [5]. Through intelligent aggregation and processing by data-analytic services, this data can deliver significant added value and become an economic good itself, the so-called data product. As a result value creation is increasingly shifting to data-driven business models that provide unique value to the customer [6]. Nevertheless, the cross-manufacturer use of data in a data economy is still in its infancy [7]. Despite a focus on

<footnote n="1">¹Institute for Industrial Management (FIR) at RWTH Aachen University,
Campus-Boulevard 55, 52074 Aachen, Germany</footnote>

<footnote n="2">²Lindt & Sprüngli Germany GmbH, Süsterfeldstraße 130, 52072 Aachen, Germany</footnote>
data monetization of manufacturing companies today, few mechanisms exist to price
data products [3]. This can be attributed to characteristics that complicate the choice of
a pricing model compared to tangible goods [8]. It is difficult to accurately determine
the value of data prior to purchase as the value lies in the derived information, which is
intangible and difficult to measure [9]. In addition, data products have a unique cost
structure: The fixed costs of building the infrastructure and aggregating the first data
product are often substantial ("first copy cost") while the marginal costs of copying and
disseminating are negligible [10]. The provisioning costs are composed of the specific
value creation process: data extraction, data preparation, information extraction, infor-
mation provision and information usage [11]. Data products can develop very hetero-
geneous values for buyers depending on the value creation stage and the specific type
[12]. They can be sold directly, as a supplement to existing products and services or as
enabler of an entire new value proposition composed of a bundle of services [13].

Due to the above mentioned characteristics and design options, data product pricing
is a central challenge of digital business models [14]. Traditional cost- and competition-
oriented pricing methods are not sufficient to achieve optimal pricing for data products
[15], so that innovative pricing models have become established [8]. Since there can be
no universally valid price model for all data products and price models are basically
composed of several elements, we addressed the following research question in this
paper: *How can pricing models for data products in industrial food production be de-
signed?* Therefore we provide a design framework for the price parameters of data
products in the industrial food production. Therefore, we performed a typology for-
mation through workshops with pricing, digitalization and food production experts.

2 State of Research

Since pricing of data products has attracted lot of attention, there has been an increase
in recent publications. Nevertheless, research in this area is still in its infancy.

There are publications examining the selection of pricing models as well as the eval-
uation of prices on data marketplaces [8, 14, 16, 17]. However, these works focus pri-
marily on pure information goods and leaves out data-based service bundles that gen-
erate direct added value for the customer. Furthermore, no design factors for the respec-
tive data products are shown. Frohmann [15] presents five overarching pillars of pricing
models of digital products in his work. The framework provides a precious basis, but
refers to digital products in general, whereby the results are on a high level and im-
portant design factors for the pricing of the addressed data products in this paper will
not be considered. In addition, Buxmann and Lehmann [18] present a model for the
pricing of pure software products. Even though many of the design factors can also be
applied to this work, it does not cover the entire spectrum of data products due to the
focus on pure software products. Liang et al. [8] categorize pricing strategies in terms
of different market structures and identify their limitations as part of the Big Data
lifecycle. The focus is on setting an appropriate price for the data product. However, as
pure digital offers are prioritized, the results are of limited use. In addition, there are
scientific approaches that address a systemic overview of design factors of data-based
business models and services as well as data marketplaces in particular [3, 19, 20]. In
these works, the business models are considered holistically and thus no detailed attention is paid to the respective pricing models.

This shows that even if the current state of research provides building blocks to answer the research question, no framework for the design of pricing models of data products in the industrial food production has been developed yet. Accordingly, there is a research gap not only for the food industry, but also for the entire manufacturing sector.

## 3 Methodology

Due to the heterogeneous characteristics of data products and diverse design options of pricing models, the method of typology formation is used for designing and building a framework to describe characteristics of data product pricing models within the industrial food production. The development process is based on the well-established approach provided by Welter [21]. With the help of typology an area of investigation can be systematized and thus made comprehensible. Furthermore, it serves to uncover correlations and to support design recommendations [21].

The typification is ideally suited to structure the area of investigation and to offer an application-oriented tool. This includes all relevant characteristics, which are presented holistically as well as the subsequently developed types. When identifying the features as well as their characteristics, the following criteria must be taken into account in order to ensure that the types are meaningful and meet the requirements [22]: First of all, each characteristic must have at least two expressions, whereby an upper limit is not prescribed. In addition, each characteristic must have a certain meaningfulness, which means that there must be a causal and a preferably relationship between the purpose of the examination and the characteristic. Further the differentiability requires that only characteristics that contribute to the differentiation of types are used. The procedure for the formation of the typology is based on a five-step approach (see Fig. 1).

<table><caption>Fig. 1. Procedure of the type formation process [21]</caption><thead><tr><th>Step 1</th><th>Step 2</th><th>Step 3</th><th>Step 4</th><th>Step 5</th></tr></thead><tbody><tr><td>Delimitation of the investigation area</td><td>Selection of suitable features</td><td>Determination of meaningful feature characteristics</td><td>Formation of types by combinations of characteristics</td><td>Graphical representation of the obtained types</td></tr></tbody></table>

In the first step, the definition and appropriate generalization of the given problem takes place, in which primarily the area of investigation is delimited and discussed. Subsequently, the derivation of suitable features (step 2) as well as their characteristics (step 3) takes place. These were derived factually and logically through the experience of the authors and the experts of the EVAREST project team, considering existing literature approaches and requirements from operational practice. This is followed by the identification of typical feature combinations (step 4). Following Grosse-Oetringhaus [22], a combination of progressive and retrograde typing is chosen as an iterative procedure. For this purpose, relevant literature was used as a basis. In addition, consistent combinations of the characteristics were determined according to the configuration theory. Afterwards, the developed combinations were validated by case studies from the food industry. Finally, the results are presented graphically (step 5).
# 4 Results

The results were developed during the research project EVAREST (see Chapter 6). In the following the steps of typology formation and the achieved results are described.

## 4.1 Investigation Area (Step 1)

In addition to the delineation of the scope of the study shown in chapter 1, the retrograde approach requires the consideration of the targeted types in advance. In the literature different approaches can be found that describe and categorize data products.

Tempich [23] defines three different types: Firstly, “Data as a Service”, i.e. data are made available by providers and used to generate direct revenue (data * price = revenue). Secondly, “Data as Insights” where data is used to improve product marketing and achieve higher economic results. Thirdly, for “Data-enhanced Products”, data en-rich physical or virtual products. Hereby, increasing revenue of the enhanced physical product corresponds to the revenue generated by data. Wixom and Ross [24] distinguish data products as follows: “Selling data”, “optimize existing products or service” or “im-proving internal processes”. Liozu and Ulaga [13] add “new business models and rev- enue streams” to these types. Laney [25] defines data products based on economic value which they capture for businesses: “direct exchange with goods, services or monetary resources”; “use to increase income, or reduce risks and expenses”. To date, there is no consensus on the definition of data products, so a distinction of data product types has been made using the approach described in Chapter 3.

## 4.2 Development of the framework (Step 2 to 3)

Within step 2, the following features could be derived: price determination, price dis-covery, measurement unit, payment flow, timing of price determination, bundle com-ponents, bundling type, degree of integration, differentiation, price dynamic, value cre-ation. The features and feature characteristics (step 3) developed are described below.

Basically, there are three different ways for the *price determination* of data products [12]: cost-based, competition-based and value-based. In the traditional cost-based ap-proach, the supplier costs are calculated and a price is determined for the customer by adding up an amount (cost-plus) [26]. The cost-plus approach is considered ineffective due to the aforementioned cost structure, as the customer's willingness to pay can sig-nificantly exceed the costs including the target margin, especially for digital offers and services [15]. Nonetheless, there is widespread use for digital products, due to higher acceptance, simple and quick determination as well as no necessary data regarding de-mand structures and willingness to pay [12, 27]. In competition-based pricing, expected or observable price levels of the market serve as the main source of pricing [28]. Thus, competition-based approaches have severe limitations due to the lack of data-based of-ferings, as it requires the existence of an active market where prices can be continuously observed and compared [12]. Basically, cost and competitive situation are important influencing factors for any pricing model, but the isolated use of both methods is neither sufficient to achieve optimal pricing for analog nor intangible assets [15, 27]. It is also
necessary to assess the actual value of data products to the customer especially in the industrial sector [27]. Thus, in the value-based approach, the price is no longer based on the provider's internal variables (e.g., costs) or competitive prices, but on the added value that the offered solution generates in the customer's business environment [28]. The added value of the data product is determined on the basis of the benefits that arise over the entire data product lifecycle [29]. Even if value-based pricing is considered to be an superior approach, paradoxically, cost-based and competitive approaches continue to play a dominant role in industrial pricing [30]. One of the main problems in business practice is to gain access to essential data to quantify the value of the offers to the customers [31]. Another aspect is the so-called fixed-pie bias. Even though value-based pricing has the potential for win-win situations, the dominant assumption is that what the company gains, the customer loses and vice versa (zero-sum game) [32].

*Price discovery* is a key function of marketplaces as it allows suppliers and demanders to set a price at which both agree to the transaction [33]. Firstly, it can be determined by one of the two sides, supplier or demander [20]. In this case, the other side only has the option to accept or reject the offer at this price. Secondly, the price can be determined by the platform provider. Therefore, provider and buyer must accept the determined price to be part of the marketplace [20]. A third option are negotiations between buyers and sellers, which are primarily relevant for goods of higher value [20].

The *measurement unit* of a price model specifies the service the customer pays for [15]. A basic distinction is made between usage-independent and usage-dependent pricing models [15]. In addition to the one-time payment, subscription payments count as usage-independent measurement unit. Usage-dependent pricing models are dependent on the usage phase of the customer. These pricing models are becoming increasingly important, especially for digital goods. The customer pays for the actual use or even the outcome or economic success achieved by the data-based solution [15, 34].

There are three variants for the *payment flow* [15]: single payment, recurring payment and a hybrid combination of both variants.

The *timing of price determination* can be either ex-ante or ex-post the service provision [26]. Ex-ante market pricing is particularly relevant for standardized products and services [15]. Ex-post price determination is based on the actual performance provided by the supplier and is particularly advantageous for individual services [15].

Another elementary part of the pricing model are the *components* that are bundled into the offer [35]. For the considered data products, the data itself, the analytic services used, and the enhanced products and services are the main items to be considered. Furthermore, a holistic data-based solution can also be offered.

Depending on the *bundling type*, three categories are possible [18]: unbundling, mixed bundling and pure bundling. In unbundling, products can only be purchased individually [36]. If the customer can choose whether to purchase the entire bundle or the included products individually, this is termed as mixed bundling. For pure bundling, the products are offered exclusively in the bundle defined by the provider.

Furthermore, the products in the bundle can also be described in terms of their *degree of integration*. The partial services can be independent to each other or have a substitutive or complementary relationship to each other [37].

*Price differentiation* is based on the optimal exploitation of heterogeneous customers' willingness to pay through different prices [37]. Price differentiation is particularly
important for suppliers of purely digital goods, as inexpensive modification greatly fa-
cilitates the application [18]. Basically there are three different forms of price differen-
tiation [38]: self-selection, segmentation and willingness to pay. With self-selection,
the customer decides for himself which product-price combination he wants to choose
[39]. A distinction is made between quantity-based, time-based and performance-based
self-selection [40]. In this context, versioning is considered to be very advantageous for
digital goods [41]. Often, data product providers offer extensive versions and likewise
offerings with reduced functionalities to achieve better market penetration, also for less
solvent customers. In segmentation, the provider decides how to differentiate prices on
its own criteria [37]. The price can be determined, for example, based on the size of the
customer's company, the country or region in which the customer operates [40]. Finally,
there is the differentiation that tries to skim off the customer's actual willingness to pay,
which, however, is the biggest challenge of the differentiation types [42].

*Dynamic pricing* is based on adjusting the price over time [18]. For the addressed data products, the penetration and skimming strategy as well as supply & demand and result-orientation are of importance. The penetration strategy has the objective to use low prices in order to maximize market penetration [18]. The skimming strategy aims at skimming off customers with a high willingness to pay with initially high prices and then reducing prices to win further customers. Supply & demand models depend on the buying behavior of market participants [8]. It can be used to balance peak periods with very high demand and to make purchasing more attractive in less busy periods [8]. A strategy that is dynamically oriented towards the achieved result aims to continuously increase the performance for the customer and thus create a positive lock-in effect.

The way in which *value is created* by the data product can be distinguished by three
types: product/service sales, barter and achieved performance. A monetary added value
can be achieved through the traditional sale of products and services. In addition, bar-
tering plays an essential role for data products [25]. This means the exchange of gener-
ated data for added value, like products or better business conditions. The generated
and exchanged data represent an indirect value, as it can be resold to interested parties
in the ecosystem (e. g. drinking behavior for beverage producer) (see Section 4.3). In
addition, the monetary added value can also be based on the performance actually
achieved for the customer, accounted by a suitable measurement unit [34].

## 4.3 Pricing models for data product types (Step 4 to 5)

In the final step, pricing models were derived for the identified data product types and
presented graphically by using the developed framework (see Fig. 2).

The first type of data product is the *data product as insight (blue)*. It is composed of
raw data and analytical services aggregated into a data product that aims to answer
business-critical questions for the customer. The price of this data product is measured
by the potential added value that the additional insights bring to a business decision. In
addition to basic criteria such as quality and relevance in the business context, the de-
gree of analytical maturity plays a major role for the added value. The value increases
from a descriptive data product over a diagnosis to forecasts or even decision support
that shows direct guidance for action in the future [43]. Since it is not possible to clearly
allocate the added values for the customer, the value can be determined and estimated ex-ante based on of existing value attributes. The price can be paid once or on a sub-scription basis, e.g. for the purpose of updating the data. In food production data products as insight can add great value to a wide range of areas. In addition to classic price panels, sales forecasts are particularly valuable because they are very volatile and are influenced by various external factors such as weather, seasonality, constantly changing customer needs and political influences. This has a positive impact on inventory management throughout the food production value chain, from retailers to distributors to producers and farmers. Stocks can thus be reduced, and capacity limits improved, thereby reducing the loss due to overcapacity.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Typology visualized as a morphological box</figcaption></figure>

The next type is *data-enhanced product or service (red)*. Compared to conventional products and services, data connectivity enables various special features. In addition to the positive economic factors, such as the possibility of charging higher prices or implementing services more cost-effectively, data connectivity enables innovations for the pricing model. The complementary bundle of services can be billed on a usage basis and even supply & demand models are possible. Furthermore, the data generated can be used to achieve additional value through bartering. Data-based features also allow the customer to select performance levels with little effort, even for hardware products. There are countless implementation possibilities for the food industry. For example Celli Group's smart dispensing systems or Bizerba's innovative weighing technology can be used to directly analyze the consumer behavior in order to optimize inventories, reduce waste or directly measure the success of marketing campaigns for the food manufacturing companies [44, 45].

*Data product as performance (green)* goes beyond the mere provision of insights and uses prescriptive analytics as part of a holistic solution to generate concrete benefits for the customer. This requires both data analytics and industry-specific expertise.
Through a participative business constellation of provider and customer, this data prod-
uct opens the possibility of implementing performance-oriented pricing models and
transferring them into a contractual framework. For this purpose, an interactive price
determination with the customer is to be implemented, in which the price is ultimately
determined by the result. The timing of the pricing takes place ex-post to be able to use
the actual added values achieved and, optimally, to enter a long-term partnership with
the customer that is result-oriented and represents a win-win situation. The data gener-
ated in the process provides the opportunity to continuously improve the offer and
transform it into innovations.

5 Conclusion & Outlook

In this work parameters for pricing models of data product types within the industrial food production were derived. Three data product types were developed, determined and illustrated. The results aim to improve the systematization and classification of future research and extend the existing body of knowledge by specifying the understanding of data product pricing in the manufacturing industry. The research results are also subject to limitations. Due to the new domain of data products, the model requires frequent updating to stay relevant and to incorporate new dimensions and characteristics. Since the creation of the typology was developed with the EVAREST consortium researchers, other researchers could derive further dimensions and characteristics that they deem more significant. Although the framework of the pricing model is specified for industrial food production, further research could reuse the structure for other sectors and an extension could be made for the entire manufacturing industry.

6 Acknowledgements

The research and development project EVAREST that forms the basis for this report is
funded within the scope of the “Smart Data Economy” technology program run by the
Federal Ministry for Economic Affairs and Energy and is managed by the DLR project
management agency. The authors are responsible for the content of this publication.

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