Customer Data-driven Business Models: A Case Study in the Retail

Industry

Maider Elorza and Eduardo Castellano

Faculty of Business Studies, Mondragon Unibertsitatea, Oñati, Spain

Keywords: Customer Data Monetization, Strategy, Business Model, Retail Industry, Case Study.

Abstract: In today’s data era, the retail industry has increased the possibility of acquisition of a large volume of customer

data, becoming more achievable its monetization. This paper develops a literature research and an empirical

study identifying the different strategies that organizations perform and their instantiation by a concrete

retailer. In each of these strategies, there have been identified the reasons for the case study to implement

them as well as the specific instantiation performed. The research enriches the literature twofold; (1) by adding

the Retail Media strategy as an indirect customer data monetization strategy; (2) by identifying relevant

elements of the investment-cost-revenue structure for the different customer data monetization strategies.

1 INTRODUCTION

In recent years, companies are beginning to

investigate the possibility of exploiting their data to

improve their operating accounts (Parvinen et al.,

2020). In fact, numerous authors point out that in

2016, 30% of businesses worldwide begun to carry

out data exploitation (Moore, 2015).

The retail industry in Spain is no stranger to the

changes that are taking place at a global and sectoral

level in terms of data exploitation (Teijelo, 2019).

Nowadays, the Spanish retail market is very

competitive (Teijelo, 2019). The existing pressure

due to the price war in the sector, coupled with the

heavy investments being made to gain or at least not

lose market share, have caused a significant drop in

profitability, forcing retailers to a permanent

innovation on the search of different alternatives to

generate new revenue streams (Forrester, 2019).

However, a report made in 2019 shows that only 15%

of retailers globally were able to use insights to

generate new revenue through data monetization

(Forrester, 2019). Aspects such as the management of

the data sale price strategies are activities that are

currently quite unknown by companies, since until

now the data has not had a relevant strategic value

and, therefore, there was no express concern about it

(Castellano and López, 2020).

The literature to date has largely focused on

theoretical and descriptive studies about the terms

data monetization (Gartner, 2020; Shukla and Dubey,

2014), monetization strategies and models (Laney et

al., 2015; Moore, 2015; Walker, 2015; Woerner and

Wixom, 2015). However, how organizations design

their cost and revenue structure for monetizing

customer data directly and indirectly has attracted less

attention in the academic literature. The few studies

that do discuss the issue (Brinch, 2018; Najjar and

Kettinger, 2017; Yu and Zhang, 2017) operate at the

conceptual level or do not focus on the monetization

of customer data.

Therefore, in this paper we focus on better

understanding possible data-driven business models

that retail companies can use to obtain new revenue.

To do so, we study the different strategies in which

retail organizations monetize customer data. Our

interest lies in the reasoning companies apply when

choosing from different monetization business

models and how they have designed their cost and

revenue structure.

To do so, an empirical study has been carried out

in one of the most important commercial distribution

companies for consumer goods and services in Spain,

which has its headquarters in the Basque Country,

Spain (hereinafter referred to as the Retail-Business).

The organization of the paper is as follows:

Section 2 overviews the identified scientific literature

about data monetization, its strategies and revenue

and cost structure; Section 3 describes the

methodological approach followed; Section 4

Elorza, M. and Castellano, E.

Customer Data-driven Business Models: A Case Study in the Retail Industry.

DOI: 10.5220/0011138800003280

In Proceedings of the 19th International Conference on Smart Business Technologies (ICSBT 2022), pages 101-110

ISBN: 978-989-758-587-6; ISSN: 2184-772X

101

presents the results of the study; Section 5 discusses

de results, and; Section 6 details the conclusions.

2 LITERATURE REVIEW

The trend towards digitization, the emergence of new

and improved technologies (e.g. Internet of Things,

big data, cloud computing, data analytics, …) and

factors such as increased connectivity and user

mobility have risen the possibility of obtaining a

significant amount of quality data, connected in real

time and available at any time (Brown et al., 2012;

Manyika et al., 2011; Spijker, 2014; Yousif, 2015).

Thomas and Leiponen (2016) point out that data

ecosystems will profoundly disrupt businesses in

nearly every consumer-centric industry. Other

authors recognise the potentially broad impact of big

data across multiple industries (Chen et al., 2012;

Mayer-Schönberger and Cukier, 2013).

In particular, in the retail industry, due to the

characteristics of organisations and their consumer

orientation, a large volume of heterogeneous

customer data is collected. This volume of data

implies deeper knowledge and detailed information

on the individual-customer level, e.g. what do they

consume, how and where do they do it, when do they

do it, with whom do they consume, etc. (Dekimpe,

2020; Erevelles et al., 2016; Hofacker et al., 2016).

Therefore, the use of big data technologies is core for

the management of this large amount of data, as it

allows companies to achieve greater transparency,

more possibilities for collaboration, better

personalisation of products/services and more

evidence-based management (Brown et al., 2012;

Manyika et al., 2011; McAfee and Brynjolfsson,

2012).

It is also worth mentioning that technological

progress has drastically reduced the cost of data

acquisition and storage (Carrière-Swallow and

Haksar, 2019; Liu and Chen, 2015), ensuring that the

variety of data is not the bottleneck for data

exploitation (Spijker, 2014).

Moreover, Carrière-Swallow and Haksar (2019)

and Najjar and Kettinger (2017) argue that the change

in business environment has been driven not only by

the vast amount of data, but also by advances in

analytical techniques enabling more advanced

processing to analyse in real time the value from the

available data. In addition, the increased capabilities

for analysing, exploiting and sharing data have made

possible new strategies to monetize data (Najjar and

Kettinger, 2017; Parmar et al., 2014).

It is true that the concept of reusing, sharing and

exchanging data is not new (Moore, 2015; Parmar et

al., 2014). However, a critical analysis of the

scientific literature on data monetization shows that

there is a lack of specificity in its definition (Woerner

and Wixom, 2015). Some authors mention that data

monetization is a value creation process (Najjar and

Kettinger, 2017; Prakash, 2014; Shukla and Dubey,

2014) which improves the competitiveness and

differentiation of the organization (Mohasseb, 2014).

Likewise, many academics agree that data

monetization is the process of using data to obtain

quantifiable economic benefits, either through

internal use of data (indirect data monetization) or

external use of data (direct data monetization)

(Gartner, 2020; Wixom and Ross, 2017).

To take advantage of data monetization,

companies should create and develop new

appropriate data-driven business models, adopting

and designing different strategies to create additional

value (Hartmann et al., 2016). According to some

authors, companies can pursue, at the same time,

more than one strategy when monetizing data (Laney

et al., 2015). However, in practice, the adoption of

each strategy requires specific organizational changes

and specific data and technology management

upgrades. Therefore, some authors propose to

identify the most viable opportunity and start from

there (Wixom and Ross, 2017).

In the scientific literature, different classifications

and types of customer data monetization strategies

can be identified depending on the authors (Laney et

al., 2015; Walker, 2015; Wixom and Ross, 2017;

Woerner and Wixom, 2015), but all of them have the

same approach, which is reflected in the study carried

out by Gartner (Moore, 2015). In this study two types

of strategies can be differentiated:

• Direct data monetization: This strategy occurs

when the company sells or exchanges the

customer data, giving third-party companies

access to this asset in exchange of a price agreed

between the parties (Moore, 2015). The main

objective of this strategy is to obtain new revenue

streams from selling raw or processed data

(Moore, 2015).

• Indirect data monetization: In this case, the

company uses its own customer data internally, to

improve processes, efficiency, decision-making,

or even offer new products or services to

consumers (Moore, 2015). Therefore, in this case,

the data itself may not be sold, but the main

objective of this strategy is still to obtain new

revenue from internal data processing in order to

reduce costs and increase revenues through selling

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new products and services (Wells and Chiang,

2017).

When an organization monetizes customer data

directly, it must be decided to what extend the data

will be transformed through analytics, how the data

will be visualised, distributed, and to whom (Laney et

al., 2015). The methods of direct data monetization

include:

• Sale or exchange of raw data: The most direct way

to monetize data is to sell it (Spijker, 2014). Under

this strategy, companies such as PatientsLikeMe

sell their data to suppliers (PatientsLikeMe,

2019). Other companies from different sectors,

such as telecommunications (Vodafone and

Telefónica) or automotive (Toyota) are beginning

to explore the sale of data to third-party

companies, both to companies from different

sectors and to advanced service companies which

are specialized in combining and analysis of data

from multiple sources (Lewis and Mckone, 2016;

Thomas and Leiponen, 2016).

• Sale or exchange analysed data: Although the

previous option is usually the first approach of

companies in the direct monetization of data, once

they mature their analytics competencies, most of

the companies evolve to this second option in

order to develop a more valuable proposition

(Spijker, 2014; Thomas and Leiponen, 2016).

Companies like Tesco, Kroger, etc. are nowadays

carrying out this type of strategy (Dunnhumby,

2019). Normally, under this option, companies do

not usually provide access to the data to all data

consumers, or at least not in the same way (Najjar

and Kettinger, 2017).

Regarding the indirect monetization, the

important aspect of this type of monetization is to

measure the accrued economic revenue, since without

this measurement, it is difficult, at best, to say that the

customer data is really being monetized (Laney et al.,

2015). The methods of indirect monetizing include:

• Maintain data ownership: In this case, data is used

to increase the quality of decision-making and

efficiency and improve operational processes and

customer experience (Laney et al., 2015; Walker,

2015). The main objective of this strategy is to

reduce costs (Wells and Chiang, 2017).

Companies such as Tesco, Carrefour, Kroger,

Amazon are carrying out this type of strategy

(Dunnhumby, 2019; Rus, 2020; Using Beacons,

2015).

• Use of data for the prescription of third-party

products/services: This typology consists of

companies taking advantage of customer

knowledge and segmentation capacity to offer

consumers new products/services, not included in

the core business, which are operated by third-

parties. Thanks to this relationship, between the

company and the third-party, the client gets

product/service which are improved compared to

those offered on the market (e.g. price offers,

features), the third-party accesses a new market

with high precision and the company can get fees

for the transactions that are carried out (Laney et

al., 2015; Walker, 2015). For example, Carrefour

is working with this type of strategy (Carrefour,

2021).

• Information-based products or services: This

typology is also known as data-wrapping, which

consists of incorporating customer data-based

information into the current products or services,

or develop new products/services, to increase

customer loyalty (Wixom and Ross, 2017). For

example, BBVA offers its customers a

personalized financial health tool that is integrated

into its app (Wixom et al., 2020). The servitization

phenomenon is also be included in this typology

(Opresnik and Taisch, 2015).

The results from direct and indirect data

monetization stem from a clear data monetization

strategy, combined with the investments that an

organization has to make in terms of data

management (Wixom and Ross, 2017). The

investments made to enable data monetization must

be balanced with respect to the expected revenue

generation, because, otherwise, data monetization

business models would not be economically a viable

option. Therefore, the development of a data

monetization strategy, in part, depends on the current

technological (e.g. hardware, software, and

networking capabilities) and analytical (e.g.

mathematical and business analytical knowledge and

skills of the employees) situation in the organization

(Najjar and Kettinger, 2017). For some organizations

data monetization does not suppose huge investments

as they already have the necessary technological

capabilities, and may only require reallocation of

current workers and talent acquisition (e.g. data

managers, data scientists, quality specialists,

technical engineers, analysts, statisticians)

(Hanafizadeh and Harati Nik, 2020; Opher et al.,

2016). Moreover, the investments may vary

depending on the chosen strategy (Yu and Zhang,

2017).

Additionally, when an organization implements a

data monetization strategy, different variable costs

are generated (Mohasseb, 2014; Yu and Zhang,

Customer Data-driven Business Models: A Case Study in the Retail Industry

103

2017). These costs correspond to the process that a

company must follow throughout the lifecycle of the

data, i.e. data generation, data acquisition, data

storage, data pre-processing, data analysis and data

visualization (Faroukhi et al., 2020b; Li and

Raghunathan, 2014).

The technical and analytical capabilities needed

will depend on the strategy or strategies that the

company wants to follow, since depending on it the

process would be different (see Figure 1), and also the

cost that it has to be assumed (Najjar and Kettinger,

2017). Indeed, if the company wants to monetize raw

data, it does not necessarily need any processing

tools, thus only data acquisition and storage

infrastructures is needed (Faroukhi et al., 2020a). In

general, higher-quality data implies more data

processing and higher costs (Yu and Zhang, 2017).

Figure 1: Lifecycle of data for its monetization according to

Faroukhi, El Alaoui, Gahi, and Amine (2020a).

In addition to this costs, depending on the

company situation or chosen strategy, other costs may

arise, such as subcontracting of companies

specialized in data management, cost for preparing

contracts and NDAs with suppliers and third-parties

(Najjar and Kettinger, 2017).

According to the revenue, as mentioned before,

the main objective of data monetization is to obtain

new revenues. In the case of the direct data

monetization, different price models are established,

e.g. freemium models (data consumers have limited

access to data for free and pay for the premium

services), packaging models (data consumers buy a

certain amount of data at a fixed price), pay-per-use

models (data consumers pay for data services based

on their usage), flat-fee models (data consumers pay

a subscription fee in return for access to data

services), two-part-tariff models (data consumers pay

a fixed basic fee that becomes supplemented by an

additional fee when their usage exceeds some

predefined quota) (Hartmann et al., 2016; Yu and

Zhang, 2017). However, there are some common

weaknesses in existing data-pricing mechanisms,

since there is a lack of a standardized pricing model

(Yu and Zhang, 2017).

As for the indirect data monetization, the revenue

generated is usually not so evident, since it is not

normally perceived directly (Laney et al., 2015).

Moreover, the existing literature mentions that by

using this kind of strategy an organization can obtain

indirect revenue by improvements in customer

experience and loyalty, processes, efficiency,

decision making (Brinch, 2018; Woerner and Wixom,

2015), but does not establishes how an organization

can measure the revenue obtained due to the indirect

data monetization.

3 METHODOLOGY

3.1 Research Approach

Research on data monetization and data-driven

business models has primarily appeared as conceptual

papers (Moore, 2015; Prakash, 2014; Shukla and

Dubey, 2014; Wixom, 2014; Woerner and Wixom,

2015), single-case studies (Najjar and Kettinger,

2017) and multiple-case studies (Parvinen et al.,

2020). Even so, to date, there are few scientific

studies on business models from the perspective of

consumer data monetization in the retail industry

(Moore, 2015; Parvinen et al., 2020; Walker, 2015)

and none identified by the authors in the Basque

Country (where the headquarters of the retail under

investigation is located). Additionally, there is a lack

of scientific literature regarding the revenue and cost

structure of these models (Brinch, 2018; Najjar and

Kettinger, 2017; Yu and Zhang, 2017). Thus, in order

to develop an empirically more comprehensive

perspective on this new phenomenon, this research

has adopted a qualitative research approach. Given its

exploratory nature, qualitative methods allows an in-

depth study in a real and concrete and tangible context

on a specific place (Maxwell, 2005). In this study, it

have been combined indirect analysis of internal and

external documentary evidence as well as direct semi-

structured interviews techniques to provide a wide-

ranging view of data monetization practices in the

retail industry (Maxwell, 2005).

To achieve this purpose, it has been performed an

investigation in the context of a case study within a

retail company, enabling the analysis and

understanding of the relevant factors that take part in

the monetization of customer data.

3.2 Data Collection

For data collection, a triangulation methodology has

been used to increase the credibility and validity of

the research findings (Eisenhardt, 1989).

On the one hand, both internal and external

documents have been analysed. Regarding the

internal documents such as reports, studies and files

of the organization (e.g. web pages, presentations and

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images) have been examined. Regarding the external

ones, specialized publications and reports have been

analysed.

On the other hand, to go more deeply in the

empirical investigation, there have been conducted 11

semi-structured interviews with directors of Digital

Transformation and Innovation, Data Office,

Marketing and Customer areas, and with the six

managers of the projects identified in the case study.

The sample was defined based on selective or

intentional sampling (Schatzman and Strauss, 1973),

covering key managers in relation to business models

transformation, digital transformation, data

monetization and marketing management, in order to

gather a broad range of business perspectives.

The interviews followed a semi-structured

guideline that included the following topics:

• Data culture in the Retail-Business.

• Data monetization strategies currently carried out

in the Retail-Business; reasons and description.

• Revenue and cost structure of the implemented

models.

• Data monetization strategies not carried out in the

Retail-Business: reasons.

The interviews were documented by audio-

recording after obtaining participants’ consent to

collect and recording.

3.3 Data Analysis

The analysis started as soon as the first research

evidence emerges; documents and interviews. This

phase consists on the registration and classification of

the evidences that have been obtained from the

multiple sources in a database of the case.

For the categorization of all the evidences, Atlas

tool was used to codify and categorize the large

volume of written materials and interviews

transcriptions, as well as to identify patterns. The data

was categorized into the main four topics established

in the interview guide.

4 RESULTS

Under the digital plan of the Retail-Business, since

2017 the Digital Transformation and Innovation area

have been exploring different alternatives for

generating new revenue streams based on the

exploitation of customer data. In this way, the first

steps in the development of new business models are

being carried out.

Moreover, due to the interest in understanding

how to extract value from data, the need for data

governance, and becoming a data-driven

organization, the Retail-Business has a Data Office

area and a Customer area. Specifically, in these two

areas are carried out the exploration and

implementation of the different monetization models.

Additionally, the Management Board is firmly

committed to developing these types of models and,

therefore, has provided resources (e.g. people,

budget) to the projects. Likewise, the Retail-Business

points out the importance of experimentation, through

piloting specific use cases. In other words, it

emphasizes that before start making large

investments in technology, processes, and personnel,

prototypes of use cases must be progressively

launched and validated.

The utilization of data as an asset to generate new

revenue is not new for the Retail-Business, however,

there is still a long way to go to establish new business

models that can extract the maximum benefit from

customer data. The following points detail which of

the direct and indirect consumer data monetization

strategies, identified in the literature, are being carried

out in the Retail-Business and which are not, in

addition to specifying the cost and revenue structure

of each of them.

4.1 Direct Data Monetization

4.1.1 Sale of Raw and Analysed Data to

Suppliers

According to the sale of raw and analysed data to

suppliers, the Retail-Business started commercialize

a collaborative platform in 2019, where information

about customers’ consumption patterns is offered to

suppliers. Through this platform, suppliers benefit by

being able to make more accurate demand predictions

based on the analysis of customer data, and, therefore,

the Retail-Business sales have also increased.

Moreover, this service allows the Retail-Business to

develop a collaborative commercial strategy with

suppliers by sharing knowledge and advancing

together in the management of clients, with the aim

of improve customer experience.

The service is offered though a subscription plan,

distinguished in two different levels of data

granularity and reporting capability:

• Basic level: Aimed at unusual suppliers or those

with less information analysis capabilities. There

is offered basic information to know the diagnosis

situation of the brand by product category and

clients.

Customer Data-driven Business Models: A Case Study in the Retail Industry

105

• Premium level: Aimed at suppliers that have

incorporated the use of information in their

decision making. They are offered complete and

detailed analyses with data up to the store level

and the possibility of customer segmentation,

identifying growth opportunities in the Retail-

Business.

In order to carry out this model, an initial

investment was made in terms of the design and

development of the platform. On the one hand, the

hardware infrastructure was prepared to

accommodate the installation and integration of the

software. On the other hand, work was done on the

installation of the software, which is made up of

several software packages in charge of satisfying

different functions such as data acquisition, automatic

exportation, preparation, storage, and aggregation.

Likewise, it was necessary to invest in the design and

development of predictive models and dashboards

with relevant indicators that facilitate descriptive

analysis. All this was carried out with the

collaboration of an expert company in Artificial

Intelligence. Concerning the current costs, they come

from preparing contracts and NDAs, personnel cost

(audit and monitoring of the project) and the

subcontracting of the expert company in Artificial

Intelligence for the technical and analytical tasks e.g.

preparation of the data infrastructure, predictive

analytics and visualization. Regarding the current

direct revenues, they are obtained by charging

providers for the annual subscription to the platform.

For the subscription plan it has been established a

tiered pricing model, which consist on differentiating

the price depending on the contracted service, i.e.

basic level (cheaper) or premium level (more

expensive). In addition, this model has led to growth

in the Retail-Business annual retail sales.

4.1.2 Sale of Raw and Analysed Data to

Third-parties

Regarding the sale of raw and analysed data to third-

parties, currently, the Retail-Business avoids selling

customer data to companies from other sectors or

advanced service companies, in order to protect this

valuable assets, as well as due to security and privacy

concerns and potential reputational risks.

4.2 Indirect Data Monetization

4.2.1 Maintain Data Ownership

Regarding this strategy, the Retail-Business is

carrying out two principal projects. One of them has

the aim of creating a personalized omni-channel

relationship with the customer, developing a global

view of the customer throughout the customer

journey. The Retail-Business wants to interact with

customers in a personalized and seamless way across

channels to offer relevant customer purchase

experience and thus increase the annual sales. This

project is currently in a discovery phase, with a

company specialized in customer data science, so the

costs and revenues of this model are yet to be

estimated. The technological base that the Retail-

Business currently has to carry out this strategy is not

suitable, thus the initial investment in technology and

systems seems to be relevant.

The other project consists of improving the

efficiently the cashiers planning, predicting the

number of open cash registers needed in the store at

any time. The objective of this strategy is to improve

the efficiency of the point of sale processes. In order

to make these predictions, the variables used in

advanced analytics are e.g. purchase receipts, number

and types of purchases made by customers by time

slot. This model is being carried out internally

without the collaboration of third-parties, so the

associated costs are both technical and analytical,

assuming the costs of acquisition, processing,

storage, analysis and visualization of data. In terms of

revenue, no direct or indirect revenue are associated

with it, but thanks to a better planning of the need of

cashiers at any time, personal costs are expected to be

reduced.

4.2.2 Use of Data for the Prescription of

Third-party Services

Considering the use of data for the prescription of

third-party services, the Retail-Business since 2021 is

taking advantage of its knowledge about its customers

and its segmentation capability, reaching an alliance

with a telecommunication company to offer to the

customer members services operated by the third-

party. This model is based on a win-win-win

principle, where:

• Customers get telecommunication service with

price advantages regarding those offered on the

market.

• The telecommunication company accesses to a

new market of potential customers with high

precision.

• The Retail-Business obtains new revenues and

offers services that are not currently covered in the

company core business.

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It must be taken into account that in no case the

Retail-Business provides the raw data of customers to

the third-party, but rather it gives access to the

telecommunication company to a new market.

Specifically, based on the target audience that the

company is looking for, the Retail-Business identifies

in its database the customers who belong to this target

and contact them to ask if they would be interested in

being contacted by the telecommunication company

to obtain the services cheaper than on the market. In

the case that the client is interested, the person is put

in contact automatically with the telecommunication

company. Therefore, the retailer works as a market

broker for leads generation.

Regarding the cost structure, the Retail-Business

must assume, on the one hand, the technical costs of

data acquisition, storage and processing, in order to

identify possible potential customers taking into

account the target audience of the telecommunication

company. On the other hand, it has internal personnel

costs for data management and project monitoring, in

addition to the costs of preparing the contract and

NDA with the telecommunication company. Lastly, it

also bears the cost of launching the offer and the

recurring communications. Concerning the revenues,

the telecommunication company pays to the Retail-

Business a fixed income for each lead converted into

client, an annual fixed income for advertising and a

recurring commission which is calculated based on

the invoice generated by the client. One part of this

commission is deposited directly in the Retail-

Business and the other part is added to the loyalty card

of the customer, considering indirect revenue for the

Retail-Business. This action increases customer

loyalty.

4.2.3 Information-based Services

According to the information-based services strategy,

the Retail-Business has a program launched in 2017

with the main objective of increase customer loyalty

by offering them a free balanced diet personalized

plan. Moreover, through this program the Retail-

Business wants to enhance the differentiation of the

brand and reinforce the positioning as a company that

helps customers lead a healthier life. Customers of the

Retail-Business can register on the website to access

the program and become members to obtain their

personalized nutritional diagnosis.

The program compares the purchases made by the

consumer with the nutritional criteria recommended

by the scientific community. As a result, the program

generates a monthly report, which consists of a

nutritional diagnosis taking into account the products

purchased by the client during the last three months.

For the diagnosis, the composition of the household

(i.e. number of members, adults and children) and the

frequency of meals at home are taken into account.

Likewise, the members are able to see, in a visual and

understandable way, the evolution of their diet over

time and compare their results with the average of the

consumers participating in the program.

In this way, consumers are able to know what their

nutritional profile is and improve their habits towards

healthier eating. Along with this assessment, there are

also provided other resources such as nutritional

experts’ consultations, healthy recipes, product

promotions and nutritional information based on the

conclusions of the diagnosis.

In 2016, in order to carry out this program, an

initial investment was made in terms of the design and

development of the service, accomplished with an

information technology and analytic services

company, with which the program is currently being

developed. In terms of associated costs, the main cost

is the subcontracting of the information technology

and analytic services company. Currently, this

company makes the nutritional reports that are sent

monthly to the members. In order to make this

possible, the company has access to the database of

customers subscribed to the program. Likewise, the

program has internal personnel costs, both for

monitoring the program and for the nutrition part e.g.

recipes, nutritional consultations. However, at

present, the objective of the program is not to obtain

new revenue, but rather to position the brand in the

awareness of food health. That is why the service

offered to the customers is free. Therefore, direct

revenue is not imputed to this model, but indirect

revenue through increased sales of healthy products

is assigned.

4.3 Retail Media

The Retail-Business is also, since 2020, carrying out

the strategy known as Retail Media. This strategy

consists of selling advertising spaces to brands taking

advantage of all the retailer's points of contact with

the consumer, exploiting the retail's analytical and

personalization capabilities. In other words, it is about

generating a new model equivalent to developing a

media agency, which exploits utterly the retailer’s

both online (e.g. e-commerce, web, app, newsletter)

and offline (e.g. store, magazine, discount vouchers)

channels.

This strategy is based on a win-win-win principle,

where:

Customer Data-driven Business Models: A Case Study in the Retail Industry

107

• Customers receive relevant and personalized

communications, improving their shopping

experience.

• Brands get the chance of advertising their

products near the point of sale, which makes it

easier for consumers to convert. Moreover, they

have the possibility of carrying out integrated and

personalized campaigns thanks to the reports that

the retailer provides to the brands. These reports

visually show campaigns’ performance, interest

levels, shoppers’ satisfaction, and customers’

spending.

• The Retail-Business obtains additional revenue

and increased sales thanks to the

commercialization of the advertising spaces that

exist in the different channels, e.g. in the e-

commerce, web, and app (sponsored products,

banners, order of prioritization of products, brand

suggestion in the search engine), in the

newsletters (banners) in the stores (stoppers, cards

media, advertising stickers, alarms media, doors

media) and in the magazine (full pages,

advertorials).

Today, the Retail-Business has a relevant Retail

Media business. However, although certain internal

coordination is carried out when selling advertising

spaces, there is not a centralized strategy. Coming to

carry out the current media commercialization

management in silos and without having the focus on

the needs of each client. Which leads to

inconsistencies between channels, generating a high

risk of a disjointed customer experience. In order to

deal with these gaps, the Retail-Business is

considering reaching an alliance with a customer data

science company expert in the Retail Media strategy.

Regarding the current cost structure, since most of

the sales of spaces are currently made with direct

agreements with the brands, without automation, the

main cost to be assumed by the Retail-Business is the

cost of the commercials and the preparation of

contracts and NDAs. Concerning the revenues, they

come from the sale of advertising spaces to the

brands. In addition, this model generates an increase

in annual retail sales.

5 DISCUSSION

According to the results obtained, it has been

confirmed that the Retail-Business has been

exploring, for some years now, the different strategies

identified in the literature review in order to generate

new revenue streams through the exploitation of

customer data. This is mainly due to the fact that the

Retail-Business encourages data-driven business

models and, mostly because the Management Board

of the organization supports data monetization

strategies with enabling resources.

In addition, a new indirect monetization strategy

for customer data, which does not appear in the

scientific literature analysed, has been identified, i.e.

the Retail Media strategy.

As observed in the results, the main reasons to

implement one or another monetization model vary

depending on the chosen strategy (see Table 1).

The Retail-Business studied has moved and

continues moving step by step throughout the

implementation of different consumer data

monetization strategies, experimenting with new

models and improving the implemented ones. By

doing so, the organization is developing the required

capabilities to innovate in the implementation of

different monetization strategies.

Table 1: The relationship between strategies and the reasons for implementing a monetization model.

Direct Data

Monetization

Indirect Data Monetization

Sale of raw and

analysed data to

suppliers

Maintain data

ownership

Use of data for the

prescription of

third-party services

Information-

based service

Retail

Media

Obtain direct revenue X X X

Obtain indirect revenue X X X X X

Reduce costs X

Improve customer purchase

experience

X X X

Improve efficiency X

Offer services that are not

covered in the core business

Reinforce brand positioning X

Increase customer loyalty X X

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Also, the Retail-Business shows concerns in

relation to data privacy and security, which is why it

moves with caution in strategies where the data is sold

directly to third-parties.

Moreover, it has been observed that, currently, the

organization does not have the necessary technical

and analytical capabilities internally and therefore

often subcontracts or collaborates with companies

specialized in data management, to scale without

significant capital investment.

6 CONCLUSIONS

This research is focus on better understanding the

possible business models that retail companies can

implement to obtain new revenue streams monetizing

customer data, both directly and indirectly.

A literature research and an empirical study have

been developed identifying the different strategies

that organizations perform and their instantiation by

a concrete retailer. In each of the strategies, there have

been identified the reasons for the case study to

implement them as well as the specific instantiation

performed.

Moreover, the research has identified the Retail

Media strategy as an indirect customer data

monetization strategy to be added in the literature.

Finally, the research enriches the current literature

by identifying relevant elements of the investment-

cost-revenue structure for the different customer data

monetization strategies identified. This preliminary

analysis will be further developed in future research,

following a quantitative approach, in order to make

an in depth analysis of their economic viability and

profitability.

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