Data at your service! : A case study of utilizing in-service data to support the B2B sales process at a large information and communications technology company

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Linköping University | Department of Management and Engineering Master’s Thesis, 30 ECTS | Industrial Engineering and Management – Industrial Marketing Spring term 2021 | LIU-IEI-TEK-A--21/04075—SE

Data at your service!

– A case study of utilizing in-service data to support the

B2B sales process at a large information and

communications technology company

Ingrid Wendin

Per Bark

Supervisor: Martin Hoshi Larsson Examiner: Jakob Rehme

Linköping University SE-581 83 Linköping, Sweden +4613-28 10 00, www.liu.se

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Abstract

The digitalization of our society and the creation of data intense industries are transforming how industrial sales can be made. Large volumes of data are generated when businesses and people use digital products and services which are available in the modern world. Some of this data describes the digital products and services when they are in use, i.e., it is in-service data. Furthermore, data has during the last decade been seen as an asset which can improve decision-making and has made sales activities become increasingly customer specific.

The purpose of this study was to explore how knowledge from in-service data can serve B2B selling. To realize this purpose the following three research questions were answered by conducting a single case study of a large company in the information and communications technology (ICT) industry. (RQ1) How does a company in a data intense industry use knowledge from in-service data in the B2B sales process? (RQ2) What opportunities does knowledge from in-service data create in the B2B sales process? (RQ3) What challenges hinder a company from using knowledge from in-service data in the B2B sales process? RQ1: This study has concluded that, in the context of a data intense industry, throughout the steps in the B2B sales process, knowledge from in-service data is actively used by the sales team, however, to varying degrees. In-service data is used in six categories of sales activities: (1) to understand the customer in terms of their technical and strategical needs, which enables lead generation and cross-selling, (2) to make information from in-service data available through data collection, storage, and analyses, (3) to nurture the relationship between buyer and seller by creating understanding, trust and satisfactory offers to the customer, (4) to present solutions with convincing arguments, (5) to solve problems and satisfy the customer’s needs, and (6) to provide post-sale value-adding services. Moreover, three general resources which are used in the activities were identified: An audit report which presents the information of the data, a plan which presents strategic expansions of the solution, and simulations of the solution. Furthermore, four general actors who are performing the activities were identified: the Key Account Manager (KAM) who is responsible for conducting the sales interactions with the customer, the sales team, and the presales team who both support the KAM, and the customer. In addition to the general resources and actors, companies may use step-specific resources and actors.

RQ2: Four categories of opportunities were identified: knowledge from in-service data (1) assists KAMs in discovering customer needs, (2) guides the KAM in creating better customer specific solutions, (3) helps the KAM move the sale faster through the sales process, and (4) assists the company in becoming a true partner who provides strategic services, rather than acting as a supplier.

RQ3: Finally, four categories of challenges were identified: (1) organizational, (2) technological, (3) cultural, and (4) legal & security. Out of these, obtaining access to the data was identified as the greatest challenge to use in-service data. The opportunities and the challenge to access data are deemed to be general for companies in data intense industries, while the other challenges are depending on the structure, size, and culture of the individual company.

The findings of this study contribute to a general understanding of how companies in data intense industries may use knowledge from in-service data, what opportunities this data create for their B2B sales process, and which challenges they face when they pursue activities which use the knowledge from in-service data. To conclude, in-service data serves B2B selling especially as a source of customer knowledge. It is used by salespeople to understand the customer in terms of its technical and strategical needs and salespeople use this knowledge to conduct various customer-oriented sales activities. In-service data creates several opportunities in B2B sales. However, several challenges must be overcome to seize the opportunities. Especially the question of data access.

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Table of figures

Figure 1 The evolved sales process as described by Moncrief and Marshall (2005). The evolved sales process consists of seven steps which are not bound by a sequential order. ... 7 Figure 2 The analytical framework used to study the use of knowledge from in-service data in the B2B sales process. ... 20 Figure 3 The analytical framework contains the markers used to guide identification of opportunities the company faces when using knowledge from in-service data. ... 21 Figure 4 The analytical framework used to study the challenges the company faces when using knowledge from in-service data. ... 21 Figure 5 The steps taken when conducting the study. The approach builds on Lekvall & Wahlbin (1987) and was modified to include an abductive approach. ... 23 Figure 6 The concept of systematic combining, as described by Dubois and Gadde 2002. ... 24 Figure 7 An isolated view on the method of analysis used in the study. ... 28 Figure 8 The method of analysis as part of the matching process proposed by Dubois and Gadde (2002). ... 29 Figure 9: Model of the identified general actors, resources and activities in the in-service data driven sales process. ... 52 Figure 10 A summary of the activities, actors, and resources used in the sales process step customer retention and deletion. ... 54 Figure 11 A summary of the actors, activities, and resources in the sales process step database knowledge management. ... 57 Figure 12 A summary of the actors, activities, and resources used in the sales process step nurturing the relationship. ... 60 Figure 13 A summary of the actors, activities, and resources used in the sales process step marketing the product. ... 62 Figure 14 A summary of the actors, activities, and resources in the new sales process step solving problems & satisfying needs. ... 67 Figure 15 A summary of the actors, activities, and resources used in the sales process step customer relationship maintenance. ... 69

List of tables

Table 1 The three groups of roles interviewd during the pilot study. ... 25 Table 2 A description of the conducted interviews of the main study with anonymized roles, date of interview and the length of the interview. ... 25 Table 3 The markers and codes used to guide the analysis to identify an opportunity. ... 29 Table 4 The respondents of the main study. ... 32 Table 5 A description of the tollgates which the sale must pass to continue in the sales process and the activities which are conducted to reach the tollgate. ... 33 Table 6 A short presentation of the internal actors in the sales process in the context of in-service data. . 35

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Table 7 Summary of the result of RQ1. ... 70 Table 8 Summary of the result of RQ2. ... 74 Table 9 Summary of the result of RQ3. ... 79

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1 Introduction

In the following section, the subject of data in sales is introduced and the case company’s situation is presented, followed by a specification of the perspective the authors of this study use of data and the knowledge derived from it. The section concludes with the study’s purpose and research questions.

1.1 Digitalization drives the creation of data and changes the business landscape

In the last decade, the amount of data generated in the world has increased exponentially (Statista.com, no date). In 2020 the volume of data being created and consumed on a global scale reached fifty-nine zettabytes, a steep increase from two zettabytes in 2010 (Statista.com, no date). The surge of data, sometimes called “big data” is driven by the adoption of mobile devices, the use of social media platforms, and the integration of data-generating sensors into products and manufacturing tools (The Economist, 2011). The generation of data seen today data is predicted to bring change: The Economist (2011) conclude in their report on big data that a data-driven revolution is underway, and it is transforming the landscape of business.

The exponential creation of data is caused by the ongoing digitalization in the world and is transforming our society (Gupta et al., 2017). Throughout the previous centuries, certain technological discoveries have been made and brought with them paradigm shifts in how our society functions (Lasi et al., 2014). Digitalization is such a paradigm shift (Lasi et al., 2014), and is characterized by an ongoing computerization of human tasks, where the boundary for what computers can do is constantly pushed forward (Gupta et al., 2017).

Resulting from the emerging digitalization and the connected products made available from it, is the large volumes of data people and organizations generate in their everyday actions (Manyika et al., 2011). In the digitalized society data are created as a byproduct of activities between actors who communicate with each other (Manyika et al., 2011). For instance, the sensors which are installed in devices in the physical world (e.g., automobiles, smart energy meters, and industrial machines) sense, create, and communicate data when they are in service (Manyika et al., 2011). Moreover, electrical devices for consumers allow users around the world to actively and passively generate data when these devices communicate with each other (Manyika et al., 2011). The data which is passively created can be surfing history and location data (The

Economist, 2011). Furthermore, companies are not excluded from this data generation, as they create large

volume of data when they use digital products to interact with other businesses or individuals (Manyika et

al., 2011).

It is thus clear that large volumes of data are generated by people's actions in a digital society. This data can either be actively created by the user when he or she use digital products or be created by sensors integrated into the systems which the user operates. In the next part, the value of data in business will be further discussed.

1.2 The potential value of data in business

Data and information have for a long time been used by companies to aid in deciding on a course of action (Rothberg and Erickson, 2017). However, with the new volume of data gathered, increased storage and access capacity, together with stronger analytical tools, the range of possibilities for companies have been extended (Rothberg and Erickson, 2017).

Data, and especially big data, is seen by many as a valuable resource in business to increase profitability and aid in decision-making (Manyika et al., 2011; McAfee and Brynjolfsson, 2012; Gupta et al., 2017). The McKinsey Global institute (Manyika et al., 2011) studied the potential of big data and found that with

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2 the right investments (1) European governments could use big data to reduce costs in administrative work to a value of at least €150 billion, (2) manufacturing firms could globally use big data to decrease product development time by 20 to 50 percent, and (3) personal location data can bring $100 billion in new value to service providers. Moreover, the data created today contains valuable opportunities for companies, such as access to better information (Gupta et al., 2017), which can be used in decision making, as described by McAfee and Brynjolfsson (2012). They argue, that the contribution of data, and especially big data, on business performance is that it enables managers to make informed decisions based on data rather than intuition (McAfee and Brynjolfsson, 2012).

Companies are increasingly interested in data and have begun adopting modern data-driven measures to seize the opportunities of big data ((McAfee and Brynjolfsson, 2012; Phillips-Wren and Hoskisson, 2015). With data being estimated to contribute to improved financial performance, companies have begun to perceive data as increasingly important and pursue data-driven business practices (Phillips-Wren and Hoskisson, 2015). Furthermore, the trend towards using data in business seems to be driven in part by the volume of data being generated and collected by companies: As companies collect more data, they have begun to consider how the data can be used to generate more value in business departments, such as marketing and sales (McAfee and Brynjolfsson, 2012). However, despite excitement pertaining to the potential in data, few empirical studies have been conducted where the potential of these new data streams is assessed and to understand what opportunities are created from the data (Fosso Wamba et al., 2015). To conclude, data is a resource which brings excitement in both the academical field and in business. It is seen as a source of financial opportunities and there is a belief that value can be extracted out of previously collected data. The next part addresses the attention the growing data streams have gained from the sales field.

1.3 The sales departments’ interest in data to increase sales performance

Data has had a key role in the sales field even before the data streams began to grow as they do from big data (Ryals and Payne, 2001; Swift, 2001). Within customer relationship management (CRM), data has had a key role for marketing and sales as a source of detailed customer information and has been used to implement improved relationship marketing strategies (Ryals and Payne, 2001). For instance, customer information has been obtained from customer data and made businesses able to create comprehensive views of customers, which in turn enabled businesses to improve service personalization and customization to better fit with the customer’s individual needs and demands (Anshari et al., 2019).

It is evident that significant changes towards data-driven sales have been made within B2C with promising results. For instance, the Economist (2011) reported that the British retailer Tesco collected 1.5 billion potentially valuable data points every month and used the data to adjust prices and promotion content. Moreover, Amazon claimed that 30 percent of their sales were generated by their recommendation engine, which gives the customer suggestions about additional items he or she might want to purchase (The

Economist, 2011). Furthermore, Harley Davidson’s New York office used artificial intelligence (AI) and

customer data to identify the characteristics of high-value customers and their behavior, which could be used to identify high-value customer “lookalikes” as sales leads, and test innovative marketing campaigns (Power, 2017). The trial resulted in an increased knowledge of how to create effective formulations in the Harley Davidson marketing campaigns, which resulted in increasing sales, and a 2930 percentage increase in identified leads after three months, where 50 percent of the leads were high-value customer “lookalikes” (Power, 2017).

With the accelerating generation of data caused by the digitalization, the volume of data, the speed at which it is generated, and the variety of its type means that more information can be obtained and used to generate

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3 business insights and better understanding of the customer (McAfee and Brynjolfsson, 2012). The opportunities of big data have led to a need of understanding and identifying ways of leveraging big data in sales, such as through CRM systems (Phillips-Wren and Hoskisson, 2015; Zerbino et al., 2018), and how to extract valuable insights from it (Phillips-Wren and Hoskisson, 2015). Furthermore, since sales and sales management have historically responded to changes in the macro-environment and changed themselves with them (Syam and Sharma, 2018), it is thus prudent to understand how salespeople respond to the increase in customer data and how sales organizations are adapting to the new data streams.

1.4 The case company’s interest in data to improve sales, and defining data intense

industries

The company studied in this research is a global actor within the information and communications technology (ICT) industry and will hereinafter be referred to as the case company, or the company. The case company manufactures and sells complex products, which often consist of a combination of products and services, to a small number of long-term business customers. The small number of customers in the market is the result of the market being developed and having high barriers of entry, where few opportunities for the acquisition of new customers arise because of the few actors in the market. The complex products are used to build a technical system of many units, often a network, and the solutions are often considered by the customer as large investments due to their high cost and complexity. The case company prides itself with building long-term B2B sales relationships where helping the customer is part of the purpose of the sales teams’ activities.

The complex products sold by the company are hardware and software heavy and generate large volumes of active and passive data when used by the customers’ customers. Actively generated data is created when the customers’ customers use the products to send information. Passively generated data is concurrently created and contain system information such as the performance and capacity of the product system. The size of each piece of information actively sent in the system can be small, in the range of megabytes to gigabytes. However, as the number of end users sending data is in the range of thousands to millions of people, the aggregated data volume is thus enormous. The case company is hence active in an industry where large volumes of data are being generated by the system made of the products, solutions, and services which they have sold. In this study, we call an industry which can be characterized by this description, i.e., consisting of companies whose products, solutions, and services generate large volumes of data when they are in use, as a “data intense industry.” The name data intense industry captures the fact that these industries are characterized by large volumes of data generation.

Through initial interviews with a handful of employees within and outside the sales organization, it has become clear that individuals within the case company believe that actively and passively generated data contain potential value for business. Moreover, they have argued that the case company is in the position to potentially be able to collect vast amounts of the data. Data are already used within sales, nevertheless, many respondents want to pursue a change within the organization to further increase the usage of data within sales, which emphasizes their interest in the role data can play in sales. Furthermore, formal, and informal processes to obtain customer knowledge from data generated by the complex products have already been started in various parts of the company on a local scale, though mainly from an R&D perspective.

The company is an interesting case to study to understand how companies within data intense industries use data in B2B sales, and to gain insights into the challenges they are facing in their endeavor to become more data-driven, as this is an ongoing process. In the pilot study interviews concerns were raised that the sales process, by increasingly focusing on data, could risk moving away from the company’s successful

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4 relationship selling approach. Moreover, additional concerns were expressed by respondents regarding how to leverage the data and specifically pointed at the considerable number of recourses which would be needed to process the data, would it be processed without a specific goal in mind.

This study is particularly interested in the data which is created by digital products and services when they are in service, hence the concept of in-service data. The definition of the term is handled in the following part.

1.5 Specifying the term in-service data

The term in-service data is discussed in this part to bring clarity to the meaning of the concept by first studying how the concept is used in extant literature, followed by our definition of the term.

Igba et al. (2015) studied wind turbines. They use the term in-service data (synonym: field data) to describe all information which is generated or produced by the wind turbine during the period it is in the use and support stage of the product life cycle (Igba et al., 2015). In the case of wind turbines, large volumes of in-service data are created and can be, but are not limited to, operational, environmental, failure, in-service and maintenance or logical data (Igba et al., 2015). James et al., (2002) use in-service data to assess the reliability of electrical products by studying how they operate when used. The authors (2002) suggest that in-service data can help electrical engineers understand how their products work in the field and why they fail, and therefore concern product performance to a significant extent.

In this report, we define the term in-service data as data generated by the product or service when it is in operation. In-service data can thus be both actively generated by the system user and passively generated by the system in response to the activity by the user.

On an ending note, data are not insights on their own, and need to be processed for valuable and actionable insights to be attained (McAfee and Brynjolfsson, 2012). The question of obtaining value from data will be considered in the following part.

1.6 Using data processing to obtain knowledge from data

There seems to exist a mutual understanding that value lies in information and knowledge rather than in data (see for instance (Rowley, 2007)). Swift (2001) suggest that detailed customer knowledge is what successful companies use to acquire and retain customers, not raw data. Data exist inherently without meaning and value, as it is both unstructured and unprocessed (Rowley, 2007). However, data may be a source of value: companies can obtain information by structuring data through data processing and applying the data in a meaningful context (Rowley, 2007; Rothberg and Erickson, 2017). Furthermore, when humans apply their understanding and experience on the information, then they can turn the information into knowledge and business insights (Rowley, 2007; Rothberg and Erickson, 2017).

The rise of big data has caused several challenges to emerge (Chen, Chiang and Storey, 2012). Traditional data processing was used on structured data of limited types, however, with the new high volume, high velocity, and high variety creation of data, the methodologies used in traditional marketing analytics have become inefficient to manage the new data streams and obtain knowledge from them (Chen, Chiang and Storey, 2012). To realize competitive advantages from data, companies must generate valuable insights and share them with key stakeholders, however, for this to be possible, they must develop high-level skills to be able to handle the data in an effective way, not only capabilities in data analytics (Fosso Wamba et al., 2015). Data have different informative values; hence companies need to consider what data are needed to obtain knowledge of use for the business, and thereafter analyze that set of data (Chierici et al., 2019).

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5 In essence, data is not valuable on its own and needs to be structured, applied in the right context and with understanding to become meaningful in the form of information and knowledge. Furthermore, to obtain information of high quality, data quality becomes important and the information value of the data.

1.7 The research purpose and research questions

While an increasing amount of research focus on how big data analytics impacts business performance (McAfee and Brynjolfsson, 2012), fewer studies concern how the knowledge can be used in the firm’s sales process to increase sales efficiency and create competitive advantages, with a few notable exceptions (e.g. (Phillips-Wren and Hoskisson, 2015; Hallikainen, Savimäki and Laukkanen, 2020)). However, with the increasing attention sales organizations have on information from data, and the potential value the data contains, it is thus prudent to study the usage of data within the sales organization to understand how knowledge from data is used in a sales context. Nevertheless, data is a broad concept, and this study thus limits itself to study knowledge from in-service data.

The purpose of this research was to explore how knowledge from in-service data can serve B2B selling. To realize the aim of the study, the following research questions were answered:

RQ1: How does a company in a data intense industry use knowledge from in-service data in the B2B sales process?

RQ2: What opportunities does knowledge from in-service data create in the B2B sales process?

RQ3: What challenges hinder a company from using knowledge from in-service data in the B2B sales process?

The structure of this report is as following: first a review of extant literature is presented, concerning the industrial sales process, opportunities, and challenges regarding using insights from data, and the ARA model. Then the methodology of the study is presented, which is a case study using an abductive approach. Thereafter is the description of the empirical material presented, which was collected through interviews, followed by the analysis of the empirical material and the study’s conclusions and a discussion.

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2 Literature review

In the following section the theories used in the report are presented. The theory provides empirical observations with support and context for the analysis.

2.1 The sales process guides industrial selling

In the following part, the sales process of industrial selling is described.

2.1.1 New technologies and philosophies have changed the method salespeople use to sell

In 1980/1981 Dubinsky (1981) proposed a sales process called the seven steps of selling, which describes the sales process of personal selling as consisting of (1) prospecting, (2) pre-approach, (3) approach, (4) presentation, (5) handling objections, (6) the close, and (7) follow-up. The process is linear, product focused, and characterized by not being changed, or only changed to a small degree, by salespeople when the salespeople apply the process in sales with different customers (Moncrief and Marshall, 2005). Moncrief and Marshall (2005) states that the “seven steps of selling” proposed by Dubinsky (1981), is one of the oldest and most widely accepted paradigms in industrial sales. However, they also observe that the sales environment has changed and prompted the field of selling to evolve, hence Moncrief and Marshall (2005) draw the conclusion that the seven steps of selling must change to better reflect the current sales practices. The changes which have taken place are created by technological development and is a change in the philosophy of sales (Moncrief and Marshall, 2005). Marshall, Moncrief and Lassk identified in 1999 that innovative technologies within telecommunication and computer science had resulted in new sales activities being practiced by salespeople. For example, communication with customers through email, creating sales pitches from customer information in databases, using software to aid in researching a customer’s background, and using laptops as a presentation tool in the sales presentation (Marshall, Moncrief and Lassk, 1999). Furthermore, Wotruba (1991) describes how the salespeople’s sales objective have changed, from the sellers being product providers and persuaders to taking a customer-centric role and creating a solution tailored to the specific needs of the individual customer. Marshall, Moncrief and Lassk (1999) observed the same change when they noted that adaptive selling, value-added selling, and consultative selling had become popular selling jargons since 1986.

The change in sales orientation towards a consultative sales approach was forecasted by Sheth and Sharma (2008) to lead to the end of the traditional seven steps of selling. They argued that consultative selling will change the sales steps to revolve around problem identification, presentation of solution, and continued customer support, and observed that while no new sales process has been created, some initial progress has been made (Sheth and Sharma, 2008). Moncrief and Marshall (2005) argue that an overreliance on the traditional sales process may be hindering for many sales organizations that pursue building customer relationships. Their argument is based on Dawson’s implication (1970) that the traditional sales process is simplistic and lacks customer orientation.

In short, we note that a shift in the sales field has taken place over the years towards a consultative sales approach and that new technology has been adopted by the sellers, which has resulted in Moncrief and Marshall (2005) proposing an evolved version of the traditional seven steps of selling. In the following part the evolved sales process will be reviewed and complemented with additional material from extant literature.

2.1.2 A sales process which includes current customer-oriented sales practices

An updated version of the seven steps of selling has been proposed by Moncrief and Marshall (2005). Their proposed sales process differs from the traditional sales process in that the salespeople are not assumed to follow the steps in sequential order, nor are the salespeople assumed to go through each step during each

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7 sales call. Rather, Moncrief and Marshall (2005) assume that the salespeople will conduct all sales steps over time and the steps will involve multiple people in the company who cooperate to close the sale and build lasting relationships with the customers. The steps in the evolved sales process (Moncrief and Marshall, 2005) are (1) customer retention and deletion, (2) database knowledge management, (3) nurturing the relationship, (4) marketing the product, (5) problem solving, (6) adding value & satisfying needs, and (7) customer relationship maintenance. See Figure 1. In this study, the sales process is seen as having the same structure and qualities as described by Moncrief and Marshall (2005). In the following parts, each step is presented and summarized.

Figure 1 The evolved sales process as described by Moncrief and Marshall (2005). The evolved sales process consists of seven steps which are not bound by a sequential order.

Sales process step: Customer retention and deletion

Customer retention and deletion is the evolved version of the prospecting step and concerns the salespeople’s activities both to acquire new customers and to maintain existing customer relationships with the right customers (Moncrief and Marshall, 2005).

The methodology to acquire new customers is described by the prospecting step of the traditional sales process. In traditional prospecting, the salespeople identify potential buyers as people who have the need, authority, willingness, and ability to buy (Dubinsky, 1981). To identify potential customers, salespeople ask current prospects for referrals, respond to phone, or email enquiries from potential prospects, make cold calls (Dubinsky, 1981), or research potential customers’ websites (Marshall, Moncrief and Lassk, 1999), to name a few. However, it is expensive for companies to assign traditional prospecting activities to their salesforce, which have led to more companies outsourcing the activity and assigning customer retention and deletion roles to their salespeople (Moncrief and Marshall, 2005). The marketing department are increasingly aiding salespeople in customer prospecting, by providing salespeople with names of people who responded to the company’s marketing initiatives, such as advertising, public relations, or trade shows (Coe, 2004). The customer Customer retention and deletion Database knowledge management Nurturing the relationship Marketing the product Problem solving Adding value & satisfying needs Customer relationship maintenance

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8 In addition to acquiring new customers, Moncrief, and Marshall (2005) have included the activities to retain and delete customers in the sales process step. The authors (2005) state that as sales organizations have found that 80 percent or more of their business comes from 20 percent or less of their customer base, they have begun investing more time and resources into retaining valuable customers. This is supported by Rodríguez, Svensson and Mehl (2020), who found that salespeople selling complex products preferred to spend time maintaining the relationship with existing customers compared to searching for information to identify new customers. The salespeople’s reason was twofold (Rodríguez, Svensson and Mehl, 2020): first, searching for information on potential customers compared to existing customers is more time consuming for the salesperson, since information on the existing customer has often already been gathered by a salesperson. Second, existing customers could hold additional sales opportunities which have not yet been identified by the salesperson, making existing customers generate on average two thirds of the sales revenues (Rodríguez, Svensson and Mehl, 2020).

According to Moncrief and Marshall (2005), customer deletion is a consequence of the sales organization realizing that the purchases of some small customers do not cover the expenses the company generate to retain them. The concept of customer deletion is connected to customer retention through customer profitability, and the thought within CRM that customer relationships have different profitability, and a long-term relationship should thus only be sought with appropriate customers (Reinartz and Kumar, 2003). Ryals and Payne (2001) suggest that firms should calculate the customer’s or customer segment’s lifetime profitability to determine whether the relationship with the customer is of an appropriate type, a suggestion which Moncrief and Marshall (2005) agree with. The company should then adapt their interaction with the customer to fit the level of profitability the customer generates for the firm, where customers with high lifetime profitability should be rewarded for their loyalty, while customers with low lifetime profitability should be outsourced and firms should reduce investment into these customer segments (Reinartz and Kumar, 2003).

An emerging area within sales is for companies to use artificial intelligence (AI) to aid with customer acquisition, retention, and deletion. Syam’s and Sharma’s (2018) review of extant literature suggests that AI could help with customer segmentation through automated clustering techniques, estimating customer demands and forecasting customer profitability, as well as determining whether the prospect will result in a sale. The contribution these techniques have brought to sales is so far mostly theoretical, but empirical results are beginning to surface. For example, empirical results within the B2C segment demonstrate that AI can be useful to identify qualified prospects by comparing potential customers with the characteristics of the firm’s high-value customers (Power, 2017).

To conclude, in this study the evolved prospecting step in the sales process hence contains three areas for the selling organization to handle: the acquisition of new customers, the retention of existing valuable customers, and the deletion of customers with low profitability.

Sales process step: Database knowledge management

Database knowledge management is the evolved version of the pre-approach step and has to a significant extent the same goals of the traditional pre-approach (Moncrief and Marshall, 2005). The difference in the database knowledge management step is the usage of databases to store and manage the large volumes of customer data (Moncrief and Marshall, 2005).

The database knowledge management step and the pre-approach aim for the same result, to increase customer knowledge and become better prepared in interactions with the customer. In the traditional pre-approach, the goal is to further collect customer information, with the specific goal to identify the prospect’s needs and goals before the prospect is approached (Dubinsky, 1981). By having customer information, the

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9 salesperson can further qualify the prospect, better understand how to approach the prospect, and develop a tailored sales presentation to the prospect (Dubinsky, 1981). The database knowledge management step aims similarly to gather customer information, such as purchasing history, past and present needs, and sometimes anticipations of future benefits for the customer, to build customer knowledge (Moncrief and Marshall, 2005). The importance of customer information for salespeople, is supported by Blocker, et al. (2012) and Haas, Snehota and Corsaro (2012) who argue that having knowledge of customers’ expectations on value creation is important for salespeople also in the initial sales interactions as they need to act appropriately to satisfy these value creation expectations.

Though the aims of the traditional and evolved steps are similar, the activities have however significantly changed to be digital and involve database management (Moncrief and Marshall, 2005). Companies have invested in computers and databases to strengthen the salespeople’s capabilities within customer knowledge management (Moncrief and Marshall, 2005). Nowadays, the information salespeople gather on prospects are commonly stored in databases as lead profiles (Swift, 2001), and researchers have begun considering how these profiles can be analyzed with computers to increase lead management performance and aid in lead segmentation (Espadinha-Cruz, Fernandes and Grilo, 2021). Salespeople have also received support from the sales organization and other parts of the firm to gather customer information, resulting in both more information to analyze and more time for the salesperson to spend on other sales activities (Moncrief and Marshall, 2005). Furthermore, the digitalization enabled mobile and web-based means for the sales organization to contact the customers with, making information gathering easier (Syam and Sharma, 2018). Furthermore, new communication technologies have enabled salespeople to use digital channels to gather customer information (Rodríguez, Svensson and Mehl, 2020). Moncrief and Marshall (2005) argue that the salespeople’s access to customer records, buying history and extensive personal information have made them the best-informed sales generation in history. However, this argument might be more of a logical reasoning of possibilities than the absolute truth of the current state. Rodríguez, Svensson and Mehl (2020) found that salespeople perceived using digital information channels, such as the internet, to gather information as faster than traditional analogue channels. However, they also found that salespeople thought the quality of the information collected through digital channels was of lower quality compared to the information collected through analogue channels (Rodríguez, Svensson and Mehl, 2020).

In short, in this study, database knowledge management is the step where the selling company aims to develop an understanding of the customer through extensive data collections using digital tools such as computers and databases. Moreover, database management and technology enable salespeople to have quicker access to customer information. However, the quality of the information is still an issue to consider.

Sales process step: Nurturing the relationship

Nurturing the relationship is the evolved version of the approach step and involves building, maintaining and nurturing the business relationship with the potential or existing customer (Moncrief and Marshall, 2005).

The focus of the evolved step, nurturing the relationship, is the salespeople’s usage of the relationship selling strategy and how relationship selling affects salespeople’s interaction with the customer (Moncrief and Marshall, 2005). Fundamental in relationship selling is a long-term perspective of value creation (Peck

et al., 1999) and the cooperation among business parties to reach common goals (Sheth and Sharma, 2008).

In the evolved approach step, salespeople are interacting with the customer to build such a long-term relationship which aims for creating common goals (Moncrief and Marshall, 2005). The ideal situation, according to Moncrief and Marshall (2005), is when the salesperson has developed the customer relationship to a degree where the salesperson knows which service or solution to present to the customer

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10 for best long-term results. When the ideal situation is achieved approaching the customer becomes undramatic, as the salesperson already understands the customer (Moncrief and Marshall, 2005).

The creation of every relationship begins with a first interaction where no prior experience with the other party exists. In the case of a new potential customer, where no previous interactions can guide the salesperson’s approach, the salesperson should focus his or her attention on building a relationship foundation (Moncrief and Marshall, 2005). In the first interactions with the customer, the salesperson should aim to understand the customer’s organizational structure, needs, and issues (Long, Tellefsen and Lichtenthal, 2007), so that they later, after several interactions with the potential customer, can approach the customer, propose a well-fitting solution, and initiate the sales discussion (Moncrief and Marshall, 2005). Relationship selling is hence facilitated by several rounds of interactions between the salesperson and the potential customer before the salesperson approaches the customer to initiate a sale (Moncrief and Marshall, 2005).

Companies that practice relationship selling must also consider factors outside the direct sales interaction, as made evident by Leigh and Marshall (2001) and Williams and Attaway (1996). Leigh and Marshall (2001) note that factors such as how salespeople are rewarded may affect the relationship between salesperson and customer, and consequently the salesperson's ability to practice relationship selling. Even broader, Williams and Attaway (1996) found that the culture of the selling firm affects the salespeople’s customer orientated behavior, which in turn affects the development of the buyer-seller relationship. They found that salespeople who possess customer-oriented capabilities can develop and maintain better relationships with buyers, compared to salespeople who are lacking these skills (Williams and Attaway, 1996). Results which are supported by Cross et al. (2007). In the concept customer-oriented capabilities, Williams and Attaway (1996) include the salesperson’s attempt to uncover the customer’s needs through discussions with them, uncover the solution which would help the customer most, and answering the customer’s questions as truthfully, among other actions which are aimed to understand and respond to the customer’s needs. The view of customer orientation used by William and Attaway (1996) is supported by Saxe and Wietz (1982) and frequently used by others (see for instance (Slater and Narver, 1995; Cross et

al., 2007))

To summarize, in this study, the step called nurturing the relationship is where the salesperson uses relationship selling and initiates a sale through a sales approach. To practice relationship selling, the salesperson must have previously built a relationship with the potential customer which may take several customer interactions. When a buyer-seller relationship exists, the relationship needs to be nurtured through the salesperson expressing consideration for the long-term goal of the customer in their interactions. This can be made through cooperative activities to create common value and reach common goals.

Sales process step: Marketing the product

Marketing the product is the evolved version of the presentation step and marks a shift of the salesperson’s role from being pure sales activities to involve more marketing aspects (Moncrief and Marshall, 2005). In the step, marketing the product, we will see that the line between marketing and sales activities are blurred. Moncrief and Marshall (2005) argue that the role of the salesperson is expanding to not only concern a physically conducted sales presentation in a one-to-one interaction with the customer, but also involve marketing activities. This argument by Moncrief and Marshall stems from Leigh and Marshall (2001), who observed both an increased use of selling teams in sales interactions and that the sales presentation takes place over several meetings. They consequently concluded that salespeople have become more active in marketing (Moncrief and Marshall, 2005). The conclusion that salespeople have become more involved in marketing is supported by Cespedes (1995). Leigh and Marshall (2001) elaborate on the

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11 salespersons extended marketing role, when they observe how salespeople can take an active role in market segmentation and product and market development and diversification, in addition to sales activities which support sales, such as database management and data analytics.

According to Moncrief and Marshall (2005) sales presentations are still used by salespeople, but they have changed as sales has been computerized. In a traditional sales presentation, the salesperson presents the product-offer to the potential customer in a one-to-one meeting, with the aim to make the potential customer excited about the product (Dubinsky, 1981). The presentation has traditionally been made by demonstrating the products strengths through a visual tool such as a pamphlet or through a physical demonstration of the product, and explaining how the product can help the customer, which can be made by providing comparisons to competitors’ products (Dubinsky, 1981). With advanced presentation technologies becoming available to salespeople, the sales presentations can now involve multi-media demonstrations of product and service functionality in the buyer’s office, hence removing the reliance on tradeshows or on-site visits for an informative demonstration (Long, Tellefsen and Lichtenthal, 2007).

With digitalization, additional communication channels are available to salespeople (Moncrief and Marshall, 2005). Moncrief and Marshall (2005) propose that as customers can obtain up-to date product and sales information through digital channels such as websites, and email newsletters, the sales call becomes less about the solution presentation. This is supported by Long, Tellefsen and Lichtenthal (2007), who found that selling organizations can provide detailed product and service information via their websites, hence reducing the burden on the sales presentation as the sole medium for product demonstration. Moreover, with computers, salespeople are less reliant on the sales presentation to share information and can have a continuous dialogue with the potential customer through e-mail correspondence to share information outside the presentation meeting (Long, Tellefsen and Lichtenthal, 2007). Salespeople of complex sales are nevertheless not removing sales presentation as a source of information and are instead integrating digital elements into face-to-face meetings to make them more efficient and effective (Rodríguez, Svensson and Mehl, 2020).

Many authors describe that the salesperson’s access to information pertaining to the potential customer is important in sales presentations and is made possible by the new communication technologies. The salespeople’s usage of laptops has enabled them to have instantaneous access to customer or product information during the sales presentation, which is important in complex sales as the salesperson’s need for relevant and accurate information is perceived as higher the more complex the product is (Rodríguez, Svensson and Mehl, 2020). Moreover, Spiro & Weitz (1990) argue for the importance of salespeople adapting their sales approach to the customer to tailor the sales message to the needs of the individual. To enable the tailoring of the sales communication, the sales representative is required to gather information pertaining to the customer to become knowledgeable of which alterations the sales approach needs (Spiro and Weitz, 1990).

In this study, the evolved sales process step called marketing the product is thereby an extended version of the traditional presentation step. Salespeople are still conducting sales presentations; however, their role has expanded to include having the responsibility over product and service marketing activities in addition to delivering sales presentation. Moreover, sales presentations have become aided with computers. Furthermore, salespeople have changed the weight of the sales presentations in customer interactions: in addition to the sales presentation digital channels are now also used to deliver the information to the customer.

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Sales process step: Problem solving

Problem solving is the evolved version of the handling objections step and marks the change in the salesperson’s selling approach from being product-oriented to becoming customer-oriented and taking the role as a valuable business partner to the customer (Moncrief and Marshall, 2005).

In the traditional sales process step, the salesperson should be ready to handle any objections or buying resistance emitted from the potential customer with the goal to convince the potential customer to buy (Dubinsky, 1981). Dubinsky (1981) found that salespeople often use four categories of techniques to overcome the prospect’s objections: create strife, offset objections, clarify objections, and miscellaneous techniques. He then points out the risk of creating a contentious atmosphere in the meeting with the prospect but does not seem to consider alternative aims of the sales step to mitigate the tension between buyer and seller (Dubinsky, 1981).

In comparison to Dubinsky’s method (1981), Moncrief, and Marshall (2005) argue for salespeople taking a consultative approach in sales to solve problems. The consultative approach is sometimes also referred to as solutions selling and is where the salespeople’s aim to understand the customer’s issues, needs, and requirements to provide a customer specific solution rather than a pre-defined product or service (DeVincentis and Rackham, 1999). Moncrief and Marshall (2005) point out that the reason the potential customer might have objections is because the offer does not meet their needs, hence the salesperson should provide alterations to the offer, rather than argue for the correctness of the original offer. Furthermore, they also argue that salespeople by taking a consultative role can premier creating and maintaining long-term customer relationships (Moncrief and Marshall, 2005). This argument is supported by Liu and Leach (2013) who emphasize the importance of the salespeople being perceived as trustworthy and having a high degree of expertise to establish long-term relationships.

Rather than ensuring a short-term sale, the aim of the problem-solving step is to become a valuable business partner (Moncrief and Marshall, 2005). Moncrief and Marshall (2005) point out that the philosophy of the evolved sales process is based on relationship selling, which means that the long-term sales which are win-win situations trump short term sales where the salesperson has argued for a short-term sale. To avoid objections, listening abilities and the ability to ask questions becomes important for salespeople to succeed as a business partner which ensures that the company offers the customer what it needs (Marshall, Goebel and Moncrief, 2003; Drollinger and Comer, 2013). Nevertheless, the salesperson still wants a successful completion of a sale, the difference is, however that the aim of each sales call or interaction is not a close (Moncrief and Marshall, 2005).

However, some authors recommend caution when deciding on a sales orientation. Plouffe et al. (2016), suggests that the world is more complex than identifying a one-size-fits-all approach and point to their findings which showed that different stakeholders may be susceptible to different influence approaches. Moreover, Terho et al. (2012), also observed issues with implementing value-based approaches such as consultative selling either when the customer had a short-term objective, were reluctant to share information, or when the customer had moved into a formal tendering process and defined highly restrictive definitions for the purchase.

To conclude, in this study, the problem-solving step of the sales process is where the salesperson listens to the customer’s objections and asks questions to ensure that the right corrections can be made to the offer to fulfill the customer’s needs. Moreover, as the salesperson’s objective is long-term sales which benefit both the seller and the buyer, the salesperson avoids short-term sales and can have multiple interactions with the customer where the goal is not to close the sale but to further refine the offer and solve problems.

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Sales process step: Adding value & satisfying needs

Adding value and satisfying needs is the evolved version of the closing step. In this step, the salesperson ensures that value is created for the customer, often through a co-creative element, which leads to the customer becoming satisfied with the offer and decide to purchase (Moncrief and Marshall, 2005).

The step to add value & satisfy needs concerns the closing of the sale and builds on the changes which the introduction of consultative selling brought to the sales process (Moncrief and Marshall, 2005). The key goal of the adding value & satisfying needs step is the realization of the shared goals in the buyer-seller relationship at that moment in time (Moncrief and Marshall, 2005). By satisfying the buyer’s needs through value-added selling and tailor the solution to the customer’s specific needs, the salesperson can close the sale with a customer who have received reasons to return (Swift, 2001). As with the previous steps, nurturing the relationship, marketing the product, and problem solving, this step may also involve several interactions with the customer, since the closing of the sale does not happen until the customer’s needs have been catered to and both parties have obtained their objectives (Moncrief and Marshall, 2005). Nevertheless, in some cases the traditional method to ask for an order is still used to close the sale (Moncrief and Marshall, 2005).

As we will see, value creation can be done in cooperation between the buyer and the seller and involves both technical aspects and financial factors. Moncrief and Marshall (2005) state that in the step of value adding & satisfying needs, the seller and the customer are working together to obtain the shared goals. This is supported by Vargo and Lusch (2008), who argue that value in business relationship is created when the buyer and the seller are interacting to produce a solution, rather than through a transaction. Consequently, to create value for the customer, the salesperson needs to consider the sale’s effect on the customer’s financial results in addition to the technical requirements of the solution (Terho et al., 2012). Lastly, Terho et al. (2012) suggest that salespeople who base their selling on building customer value (value-based selling) should clearly communicate the seller’s role in improving the customer’s business financials, in terms of customer specific value quantifications, in addition to considering customer fulfillment and long-term satisfaction, or selling in a way which fits the customer.

To create and deliver communication which addresses the value of the solution, many authors describe that the salespeople need to prove their value claims. Anderson, Narus and Rossum (2006) observe that the salespeople’s ability to demonstrate and document the value proposition is significant to make them persuasive. Moreover, Terho et al. (2012) suggest that it is particularly important for the salespeople to have knowledge of the customer and the customer’s business model to create clear and persuasive value communication. Value-based sellers can communicate effectively when they provide persuasive evidence of their value claims through value quantifications or examples of previously demonstrated results (Terho

et al., 2012). Moreover, when selling higher-value, higher-price solutions, it is especially important for

sellers to provide value evidence to increase the purchasing intentions for the offers (Anderson and Wynstra, 2010). Value quantification can be value calculations, value studies, simulations, return-on-investment studies, lifecycle calculations, or knowledge about the value created for the customer, moreover, all calculations should be customer specific and co-creation makes the calculations more effective (Grönroos and Helle, 2010; Terho et al., 2012). To create value quantifications, the selling organization needs to collect data, which is often obtained directly from the customer, however in cases where such collection is not possible, then the data may come from external sources such as industry studies (Anderson, Narus and Rossum, 2006).

In short, the traditional close step has evolved to be less focused on how salespeople can make the close and more focused on how salespeople can facilitate and ease the customers’ decision-making process to buy. In this study, we define the step of Adding value & satisfying needs as the step where salespeople

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14 facilitate and ease the customers’ decision-making process to purchase. Moreover, when the seller adds value to the solution and satisfies the customer’s needs, at the same time as the seller's goals are fulfilled, the sale will naturally go towards closure. Adding value and satisfying needs can be co-creative process and salespeople should consider how the value can be communicated effectively to the customer in their interactions, for instance by providing value evidence.

Sales process step: Customer relationship maintenance

Customer relationship maintenance is the evolved version of the follow-up step and is where additional sales opportunities can be discovered. In customer relationship maintenance the salesperson not only-follow up the sale with a thank you but consider the customer’s new situation and what consultation they are further in need of to evolve their business (Moncrief and Marshall, 2005).

The last step in the evolved sales process is the customer relationship maintenance step, which marks the end of the sale Moncrief and Marshall (2005). According to Moncrief and Marshall (2005), the customer relationship maintenance step is the step in the evolved sales process with the most similar goal to its traditional counterpart. Traditional follow-up is described by Dubinsky (1981) as a step where the salesperson handles the buyer’s post-purchase concerns which ensures a satisfied customer and potential future purchases. These activities can be grouped into three categories: (1) customer service activities, such as advice on where to install the product; (2) customer satisfaction-oriented activities, such as adjusting the product if it did not meet the buyer’s expectations; and (3) customer referral activities, which is to ask for sales leads (Dubinsky, 1981). With digitalization the activities are now to a significant extent handled with computers, which has increased the effectiveness of communication (Moncrief and Marshall, 2005). Though the customer relationship maintenance step marks the end of the sale, it does however not mark the end of the buyer-seller relationship (Moncrief and Marshall, 2005). When the sale is closed the selling firm assigns an individual or a team, who is not necessarily the seller, to maintain the business relationship with the customer (Moncrief and Marshall, 2005). Long, Tellefsen and Lichtenthal (2007) argue in support of Moncrief and Marshall (2005), that post-purchase follow-up is essential for the salesperson to ensure that the buyer is satisfied and will likely return for repeated purchases in the future. This is supported by Burger and Cann (1995). They further argue, yet again in line with Moncrief and Marshall (2005) and Burger and Cann (1995), that post-purchase follow-up conducted by the selling company, which is both continuous and well-executed, is crucial for the creation of a long-term business relationship with the customer (Long, Tellefsen and Lichtenthal, 2007).

In short, in this study, customer relationship maintenance involves the post-purchase care of the customer. This step emphasizes the importance of the sales process to involve activities which care for the customer after the purchase and consider the building of long-term customer relationships.

2.2 Defining opportunity in sales

In this part, the meaning an opportunity has in this study is clarified. To create a definition which is not limited to the initial view of the authors of this study, and which captures several aspects of the concept, we first study extant sources before deciding on the final definition which will be used in the study. The Cambridge Dictionary defines opportunity as “an occasion or situation that makes it possible to do

something that you want to do or have to do, or the possibility of doing something”(Cambridge University

Press, no date).

The Merriam-Webster online dictionary defines opportunity as “a favorable juncture of circumstances” and as “a good chance for advancement or progress” (Merriam-Webster, no date).

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15 The Collins online dictionary defines opportunity “An opportunity is a situation in which it is possible for

you to do something that you want to do.”(Collins Dictionary, no date).

Based on the three sources, an opportunity can thus in essence be described as favorable or positive and may be connected to fulfilling the subject’s need or want; it is pertaining to a specific occasion or a situation and is bound by its specific circumstances. Moreover, it contains an aspect of positively charged uncertainty through ‘chance’ and ‘possibility,’ and results in a forward movement or action, such as ‘advancement,’ ‘progress,’ or the notion of ‘doing something.’

In this study, we define an opportunity as: A positive occasion or situation which enables the actor to do

something it wants to do.

Moreover, as the meaning of an opportunity is broad and the analysis can be challenging without clearer structure, we thus introduce four guiding aspects based on the qualities of an opportunity identified above. To identify an opportunity in the empirical material the following four aspects will be seen as markers for opportunities and guide the identification of them: positive situation, bound by circumstances, uncertainty, and resulting in a forward action.

To obtain an understanding of what opportunities in sales can be, we conducted a small study of extant literature. We found that future opportunities within sales are thought to be caused by disruptive technology. For instance, Syam and Sharma (2018) hypothesizes that a disruptive change in the sales function will occur due to emerging technologies and the applications of them. This hypothesis is only one among many of the impending technology-induced changes in business, where data is treated as a monetizable asset, and the capability of leveraging the information the data creates is a key competitive advantage (Marr, 2017). The opportunities lie within implementing technologies such as artificial intelligence, big data and machine learning which allow for further digitalization of the sales process, some non-digital era sales practices will cease as new data-driven practices take their place (Syam and Sharma, 2018). Moreover, as previously seen in the introduction of this study, McAfee and Brynjolfsson (2012) focus on the improvements which can be made within the quality of decision-making with the contribution of data, and the effect improved decision-making has on business performance. Their observation is that the opportunity for business lies in that data enables managers to make informed decisions based on data rather than intuition (McAfee and Brynjolfsson, 2012).

To summarize this part, though the concept of an opportunity is both vague and broad, we have decided on a definition of the term and identified four major characteristics. Moreover, the small literature review identified technology as a bringer of opportunities within sales. However, as we will cover in the next part, technology and data-driven practices are associated with four categories of challenges.

2.3 Defining challenges to use knowledge from in-service data in the sales process

One of the research questions of this study is to explore the challenges which hinder a company to use in-service data in the sales process. In the concept of challenge, we include everything which hinder the company’s aim to use in-service data in sales to capture opportunities. Since this definition is broad, we conducted a brief review of extant literature on challenges in digitalizing sales to identify a framework to guide the analysis.

Rodríguez, Svensson and Mehl (2020) present a framework with enablers and obstacles in data-driven industrial sales. We used this framework in the review of extant literature and later used it as an analytical framework in the analysis. We argue that we can study enablers and obstacles to understand the challenges a company can face, since a company without the required enablers and which is facing obstacles will be hindered, and thus, using our definition, faces challenges. Rodríguez, Svensson and Mehl (2020) identified

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16 four groups of enablers and obstacles mediating digitalization of the complex sales process: organizational, technological, cultural, as well as legal & security.

2.3.1 Organizational factors mediating success

Rodríguez, Svensson and Mehl (2020) describe the organizational enablers and obstacles to the digitalization of the complex sales processes, as concerning the internal structure of the industrial companies in the markets. They found that organizational factors influence the speed and quality of the data-driven practices the company pursues, such as process and information standardization, and communication between the sales department and other departments in the company (Rodríguez, Svensson and Mehl, 2020).

Organizational enablers and obstacles are of many kinds (Rodríguez, Svensson and Mehl, 2020) Communication is one such factor, where companies which communicate the rational and motives for new ways of working with its employees ensures a smoother transition with employees participating in enabling change rather than actively working against the changes (Rodríguez, Svensson and Mehl, 2020). Moreover, McAfee and Brynjolfsson (2012) argue that more or better data cannot substitute the need for leaders with visions and understanding of market behavior. Having the right leadership is critical as organizations which use information from Big Data but lack a leadership that provides clear goals, defines success, and ask the correct questions will nevertheless find themselves unable to reap the full benefits from the data (McAfee and Brynjolfsson, 2012). Furthermore, Fosso Wamba et al. (2015) found that organizational change and talent related issues are challenges frequently mentioned in literature. One such article is by McAfee and Brynjolfsson (2012) who found that talent acquisition for capabilities in big data analytics is critical and challenging for businesses.

2.3.2 Technological factors mediating success

Rodríguez, Svensson and Mehl (2020) describe the technical enablers and obstacles to the digitalization of the complex sales processes, as concerning the technological resources the companies can access and use. These resources can be optic fiber, technical devices, digital networks, as well as hardware and software (Rodríguez, Svensson and Mehl, 2020).

Old technology may contain challenges as they are not built for the current technological situation (McAfee and Brynjolfsson, 2012). For instance, legacy (old) systems might hinder alternative types of data sourcing, storage, and analysis, which are required to leverage information from data (Barton and Court, 2012; McAfee and Brynjolfsson, 2012). Two qualities in legacy systems can be problematic: First, legacy systems which cannot handle continuous flow of data will become an obstacle if such data flows are pursued (McAfee and Brynjolfsson, 2012). Second, existing data architecture might prevent the required information flow and perpetuate knowledge decentralization and siloed information (Barton and Court, 2012).

2.3.3 Cultural factors mediating success

Rodríguez, Svensson and Mehl (2020) describe the cultural enablers and obstacles to the digitalization of the complex sales processes, as concerning the traits of firms. These traits affect the firms’ adaption processes of digital tools and can be, but are not limited to, the employees’ perception of technology, and their flexibility towards changes in the technology they use (Rodríguez, Svensson and Mehl, 2020). Companies can enable a smoother transition to data-driven practices by considering the varying perception and attitudes towards technology of the employees (McAfee and Brynjolfsson, 2012). Taking the user into consideration was found to be a factor of success when implementing IT systems, such as CRM systems (Jayachandran et al., 2004). In successful cases the information needs, and processes of the user group were

Data at your service! : A case study of utilizing in-service data to support the B2B sales process at a large information and communications technology company