Bridging the marketing-finance divide: use of customer voice in managerial decision-making

Abstract

Purpose

This study aims to provide more insight into how customer voice is captured and used in managerial decision-making at the marketing-finance interface. This study’s focus is on understanding how the customer voice, often communicated through online and social media platforms, is used in high-performing hotels.

Design/methodology/approach

This research is based on a case study of four high-performing Irish hotels. For each case, multiple informants, including marketing managers, general managers and finance managers, were interviewed and shadowed. Twenty seven decisions across the four cases were analysed to assess the use of customer voice in managerial decision-making.

Findings

Social media provides a stage that has empowered the customer voice because of the public nature of the interaction and the network effect. Customer voice is incorporated in managerial decision-making in three distinct ways – symbolically as part of an early warning system, for action-oriented operational decisions and to some extent in the knowledge-enhancing role for tactical decisions. While there is a greater appreciation among senior managers and the finance and accounting managers of the importance of customer voice, this study finds clear limits in its utilisation and more reliance on traditional finance and accounting data, especially in strategic decision-making.

Research limitations/implications

The cases belong to a highly visible open environment of hotels in an industry where customer voice has immediate and strong effects. The findings may not directly apply to industries characterised by a relatively more closed context such as banking or insurance. Moreover, the findings reflect the practices of high-performing hotels and do not necessarily capture the practices used in less successfully operating hotels.

Practical implications

While marketers need to enhance their ability to create a narrative that links the customer voice to revenue generation, finance managers also need to develop a skillset and adopt a mindset that appropriately reflects the influential role for customer voice in managerial decision-making.

Originality/value

Despite the linkage of marketing performance to business performance, there is limited research on the impact of customer information on managerial decision-making. This research provides insight into how customer voice is considered at the critical marketing-finance interface.

Citation

Saxena, D., Brady, M., Lamest, M. and Fellenz, M. (2022), "Bridging the marketing-finance divide: use of customer voice in managerial decision-making", Qualitative Market Research, Vol. 25 No. 3, pp. 361-382. https://doi.org/10.1108/QMR-09-2020-0113

1. Introduction

The customer-centric philosophy of business views marketing as the total company effort towards customer satisfaction at a profit (Kotler et al., 2019). One of the drivers of a dedicated customer focus is the increased ability of customers in communicating with firms through social media. This also relies on the ability of firms to develop core capability (Brown et al., 2019; Foroudi et al., 2017) to listen to, analyse and use customer voice in managerial decision-making. Studies have shown a strong relationship between customer satisfaction and business performance (Morgan, 2012), both at the macro (Morgan and Rego, 2006) and firm levels (Williams and Naumann, 2011). Customer satisfaction is shown to have strong links with repurchase intention, customer retention and positive word of mouth (Assaf et al., 2015; Lacey, 2012) and in turn, with overall revenue and the profitability of the firm (Aksoy et al., 2008). While prior studies on this topic (Lacey, 2012; Morgan et al., 2005; Rollins et al., 2012) have used solicited customer data (i.e. customers are invited to share their opinions), ubiquitous public social media platforms have increased the importance of unsolicited consumer-generated opinions (Diffley and McCole, 2019; Whiting et al., 2019), which have become a central influence on customers’ purchase decisions in many industries, especially tourism and hospitality (Litvin et al., 2018), where electronic word of mouth (eWoM) has proven to be of enormous importance by driving important outcomes such as online bookings (Ye et al., 2011) and increased occupancy rates (Viglia et al., 2016). While much of this research focuses on the impact of eWoM on customer decision-making (for recent reviews see Muritala et al., 2020; Nusair et al., 2019), from a marketing management perspective the important question is how this vast and varied data can and is used internally to improve the customer experience (Strong, 2014; Rageh et al., 2013; Velayati et al., 2020).

The satisfaction-profit chain (Anderson and Mittal, 2000; Slater and Narver, 1998) appears to be generally well appreciated and used by practicing managers, yet relatively less is known about how customer voice is used in managerial decision-making. Despite their stated focus on the customers, many organisations still struggle to embrace customer-oriented decision-making because of a dominance of finance and accounting perspective in managerial decision-making (Homburg et al., 2012; Mintz and Currim, 2013; Rust et al., 2010, 2016). The key challenge for marketing is to shift the perception of marketing as a cost centre to customer-centricity as a managerial philosophy manifested through all organisational systems, practices, measures and decisions (Hunt, 2018; Joshi and Giménez, 2014; Strandvik et al., 2014). While new technologies have contributed to increased organisational capabilities (Brown et al., 2019; Erevelles et al., 2016; Foroudi et al., 2017; Lamest and Brady, 2019) in processing vast amount of customer information, the way in which organisational actors use customer perspectives in their decision-making is less well understood, especially at the critical marketing-finance interface (Hanssens, 2019; Sidhu and Roberts, 2008). Such knowledge is crucial because customer orientation is shown more effective than competitor orientation in high technology service innovations (Van Riel et al., 2004). This study provides insight into this important topic by addressing the research question:

RQ1.

How do hospitality organisations capture customer voice, and how do they use it in managerial decision-making across the marketing-finance interface?

Section 2 reviews extant research on customer voice captured via traditional and social media data. It highlights and discusses the continuing dominance of finance and accounting data in managerial decision-making and its implications for the use of customer voice. Section 3 outlines the case research methodology used in this study. The findings are presented in Section 4, noting the empowerment of customer voice and its diverse usage in managerial decision-making. Section 5 discusses the evolving role of customer voice and how managers use (or aspire to use) it for managerial decision-making, often by creating a financial narrative. Finally, the paper concludes by noting the implications for managers and the limitations of this study in Section 6.

2. Literature review

2.1 Customer voice, big data and managerial decision-making

Traditional capture of customer voice is based on data actively solicited from customers (Price et al., 2015), for example in the form of focus groups, satisfaction surveys, comment cards, loyalty programs and the data arising from sales and marketing. Consequently, because of the discrete nature of the interaction, businesses own the data and determine its format, control access and use. Social media has altered this situation by allowing customers both greater visibility and more flexibility in terms of both content and form (Hennig-Thurau et al., 2013). This customer power is further enhanced through network and crowd effects (Labrecque et al., 2013) that amplify customer views and provide these interactions with more contextual significance. Online or eWOM is found to be more effective than traditional marketing referrals (Trusov et al., 2009; Whiting et al., 2019). Positive or negative customer voice increases or decreases sales volume and share (Lacey, 2012), with a particularly high impact observed in the hotel sector (Assaf et al., 2015; Diffley and McCole, 2019; Viglia et al., 2016; Ye et al., 2011; Zhang, 2019). Peters et al. (2013) characterise this loss of managerial control over customer voice as game changing, as it is the customers who decide which information to provide and engage with publicly. While companies retain some control in closed platforms and owned social media, the real power of consumers comes to the fore in open platforms or earned social media where customers initiate the communication (Colicev et al., 2018). This shift has been driven by the proliferation of general social networks (e.g. Facebook, Twitter and Instagram) as well as domain-specific social platforms sites such as Amazon, TripAdvisor or Yelp. Because of this shift of power towards customers, social media has become an important but managerially difficult part of organisational decision-making (Brown et al., 2019; Foroudi et al., 2017; Lacoste, 2016; Ma et al., 2015). Coupled with other data sources, such as web-traffic data and companies’ own data sets, customer voice has become a key element of the big data revolution (Peters et al., 2013; Strong, 2014; Erevelles et al., 2016), opening up a plethora of opportunities and challenges for managers and firms. This dynamic is particularly keenly felt in the hotel and tourism industry where evidence suggests a large, positive impact of big data utilisation on firm performance (Yadegaridehkordi et al., 2020). Big data driven smart tourism promises to transform the hospitality landscape by enhancing the decision-making process and by providing more efficient and personalised services (Buhalis and Amaranggana, 2015; Del Vecchio et al., 2018).

Scholars (Davenport and Bean, 2018; Erevelles et al., 2016) underscore the importance of managerial decision-making based on big data analytics. Similar enthusiasm is echoed by consulting firms, for instance by EY declaring that big data analytics “can eliminate reliance on ‘gut feel’ decision-making” (Martin and Golsby-Smith, 2017, p.129). However, Côrte-Real et al. (2019) find that rather than the intentions, actual use of big data is the most significant factor in achieving sustained business value from big data analytics. Consideration of actual use is crucial because organisational and managerial decision-making process can be quite messy in nature (Rollins et al., 2012; Lindblom, 1959). As Wedel and Kannan (2016) note:

It is not yet sufficiently clear which types of analytics work for which types of problems and data […] or how companies and management should evolve to develop and implement skills and procedures to compete in this new environment (p. 97).

While the majority of available research focuses on justifying the value of listening to customer voice and the potential of big data analytics for firms, insights into their impact on managerial decision-making processes and practices is rather limited (Holmlund et al., 2020). Senior managers in particular seem to have a less than adequate understanding of social media and big data usage (Lacoste, 2016) and are potentially hampered by a relative lack of technical knowledge (Merendino et al., 2018; Velayati et al., 2020). This is compounded by cognitive overload (Martin and Golsby-Smith, 2017; Saxena and Lamest, 2018) caused because of the high variety, volume and velocity of big data.

Interestingly, the recognition of this underutilisation of marketing data in executive decision-making was noted even before the age of social media (Mintz and Currim, 2013; Strong, 2014). For instance, Gantz and Reinsel (2012) state that much of the rich insights, particularly into unstructured data, is lost in organisational decision-making processes and estimate that less than 1% of available data is actually analysed. Other observers claim that this rate may be as low as 0.5% and declining as more data is collected (Guess, 2015). With the volume, variety and velocity of customer voice, it is difficult for managers to determine what data is needed, in what form and when and how it should be used (Pauwels, 2015). In this complex and uncertain context managers often try to fill the uncertainty gap by relying on traditional finance and accounting data and frameworks, which is the focus of the following section.

2.2 Dominance of finance and accounting

Research suggests that companies that use customer data outperform their counterparts in financial terms (Morgan et al., 2009; O'Sullivan et al., 2009). The ability of companies to transform data into information and usable knowledge is a critical factor for differentiation and competitive advantage (Brown et al., 2019; Erevelles et al., 2016; Foroudi et al., 2017; Peters et al., 2013). In this regard, the marketing function has unique challenges not only as an external boundary spanner but also in terms of working with other internal functions in facilitating complex decisions (Joshi and Giménez, 2014). However, research and practice show a dominance of financial impact for decision-making with financial and accounting (F&A) data central to managerial decision-making in general and the net present value for capital allocation in particular (Graham et al., 2015). Homburg et al. (2012) observe that firms are “systematically biased towards financial measures” (p. 71). Available empirical evidence suggests that irrespective of the managers’ designation, decision-making favours a focus on F&A data, especially within firms with a cost and analytical focus (Mintz and Currim, 2013). Rust et al. (2010) report that the two dominant mental models among business managers are revenue-focussed and cost-focussed – both reflecting an F&A orientation (Sidhu and Roberts, 2008).

Thus, even though marketing is often claimed to bring the customer focus into the room (Grönroos, 2003), an apparent top management bias against marketing reflects in under-spending on marketing both during high growth (Mintz, 2016) and recession periods (Currim et al., 2018). Simms (2008) notes that less than 16% of the discussion time in the boardroom is spent in discussion of customer related issues. This is despite the evidence that firms that have a marketing representative in the top management team – a proxy for a customer-centric focus – have significantly higher long-term returns (Abernathy et al., 2013). In general, however, there is a perception that such marketing directors often “don’t have the authority and responsibility to be effective” (O’Brien et al., 2018, p.1), which renders the influence of marketing and the presence of customer voice in the boardroom indirect at best (Strandvik et al., 2014). Evidence suggests that boards often pay lip service to the customer relationship aspect (Rust et al., 2010, 2016), and the marketing perspective struggles to gain recognition and influence at the top table (O’Brien et al., 2018).

The contrast between the decision-making role of customer voice data and F&A data reflects two different worldviews (Sidhu and Roberts, 2008), with the latter often challenging, limiting or even ignoring the link from marketing actions to increased firm value (Abernathy et al., 2013; Hanssens et al., 2009). Finance remains the dominant mental model guiding decision-making despite the limiting nature of F&A data (Mizik, 2010) because of its focus on past trends and its limitation to the quantifiable measures (Strandvik et al., 2014; Mintz and Currim, 2013; Rust et al., 2010, 2016). At the same time marketing and customer perspectives often struggle for recognition and relevance as F&A perspectives often frame them as expenses (Abernathy et al., 2013; Mizik, 2010). Thus, marketers need to prove their value to the firm but can usually do so only within a dominant F&A mindset that severely limits their validity and impact (Hanssens and Pauwels, 2016). Against this backdrop, research on the marketing-finance interface (Hanssens, 2019; Sidhu and Roberts, 2008) is critical because these functions often work in silos (Velayati et al., 2020), reflecting a practice of myopic management (Mizik, 2010).

3. Methodology

Linking the two strands discussed earlier, this study investigates the following research question:

RQ2.

How do hospitality organisations capture customer voice, and how do they use it in managerial decision-making across the marketing-finance interface?

The case study was deemed the most appropriate methodology because the research problem was studied “in context,” the researchers had no control over the studied events and the research question is a “how”-type question (Farquhar, 2012; Yin, 2017). The hospitality sector is particularly apt as a research context because evidence points to the significant positive and negative impact of customer voice, especially eWoM, on firm performance in this sector (Assaf et al., 2015; Diffley and McCole, 2019; Viglia et al., 2016; Ye et al., 2011; Zhang, 2019) because of the proliferation of sector-specific social media platforms (e.g. Booking.com, Expedia and TripAdvisor) and the sensitivity of customer behaviour to such information. To support the theory building from our cases (Eisenhardt and Graebner, 2007), four high performing Irish hotels with TripAdvisor ratings at least 4.5 out of 5 were selected as instrumental (Stake, 1995) cases (Table 1) to study the best practices in using customer voice. All four cases are highly active on social media, have similar technological systems reflecting the norm in this industry and all have interactive dashboards with similar metrics supplied by external service providers (e.g. Medallia).

Primary data was collected from 14 respondents in the form of semi-structured interviews, subsequent review of transcript interviews and workplace shadowing. These were purposefully selected informants to ensure that each case included interviewees representing senior management, marketing and F&A perspectives (see Table 2). The interviews focused on the use of customer voice in decision-making processes and the information sharing and use between marketing and finance. Appendix 1 includes a list of typically asked questions during the interviews. Individual respondents in this paper are referred to as CnX format, where n refers to the case number and X refers to their level. For instance, C3S refers to the senior manager from Case 3. Secondary data was also collected in the form of documentation and observation. Documentation collected for the study included comment cards, customer satisfaction reports, screenshots of marketing dashboards, web traffic and social media reports and relevant reports created by the accounting system. To obtain a holistic impression of the practices under review, the researcher visited each hotel site and made observations on the use of customer data. As noted in Table 2, observation primarily focussed on the review activity and onsite monitoring of the dashboards and property management system used by managers.

Aligned with the realist research philosophy and abductive reasoning, the analysis for this study was a combination of inductive and deductive logic (Boddy, 2019; Miles et al., 2017). Following the deductive logic, Dedouse software was used to perform multiple iterations of coding to group the text and other data into primary codes. The interviews and other secondary data yielded usable data on a total of 27 distinct decisions across the four case organisations. Appendix 2 presents the 27 decisions analysed across the four cases. Based on the scope, these decisions were classified as operational, tactical and strategic[1]. Based on prior studies (Menon and Varadarajan, 1992; Morgan et al., 2005; Rollins et al., 2012), the use of marketing data was classified as action-oriented, knowledge-enhancing and symbolic. The next section presents the major findings associated with the use of customer voice in managerial decision-making.

4. Findings: customer voice and its use in managerial decision-making

4.1 Customer voice in the symbolic role

All case companies have marketing departments that experience a flood of customer data. Moreover, the public nature of customer voice on open platforms and the resulting external and internal visibility requires that senior managers and marketing managers take such comments very seriously. Consequently, their response to the customer voice on such platforms is quicker. The managers report feeling “bad,” “worried” or “frightened” in case of lost business because of negative publicity on social media:

The opportunity costs in terms of people who, on reading reviews about your site go “Ah, I don’t like the sound of that, we choose not to book it”. I mean that’s the huge costs of not managing that seriously, of not taking that seriously (C1S).

Traditional forms of customer surveys (e.g. customer comment cards) are viewed as “private conversation” (C3F). Such data are usually available monthly and often not systematically aggregated. In contrast, the digital data allows for frequent customer reports available to senior-level management facilitating wider and quicker dissemination. Minimisation techniques such as dashboarding are used where aggregated customer data or the customer voice is converted into a small number of metrics. All four cases focus on a small set of relevant key metrics, for instance number of visitors, conversions, pay per click, bookings and revenue. There is also a focus on selective social media and review/booking sites because of the “overwhelming,” continuous and “unmanageable” flood of data that has become a critical stream of customer information.

Aggregated data derived from customer voice on social media is symbolically used by marketing managers as an early warning system of potential problems to help them take remedial action immediately and with a sense of widespread urgency:

If we got a lot of 3.0's, we'd see that as being bad and we'd take serious corrective action (C2S).

Well, TripAdvisor is […] everybody sees that, they see the ranking and that's important. We all get a fright if we get off number one. High alert, everybody needs to […] you know (C3S).

Here the symbolic nature of such high-level information may be noted because the focus is more on managing the rankings and perceptions in the public domain. This is in contrast to managerial response to comment cards that are not in the public domain, thus not resulting in a sense of urgency. Moreover, negative (or less positive) sentiments expressed by the customer invite more attention from the managers. For example, they were “upset” and “strongly disagreed” in cases where a customer supposedly commented negatively without a comprehensible reason. The public nature of the ranking and comments makes the managers sensitive to customer voice and promotes ad hoc decision-making to “fix” the issue. This contrasts with the systematic handling of the finance and accounting data, that is discussed next.

4.2 Role of finance and accounting vis-à-vis marketing

All case hotels keep a highly detailed track of transaction-based F&A data with the finance/accounts department considered to be associated with numbers, rules and recordings. They are viewed as a “watchdog” who “keep an eye” (C1F) and act as a “control mechanism” (C2F). They make sure that “things go normal” and that no anomalies are present in the financial metrics from diverse areas of the business. However, there is also an acknowledgment of the time lag or the backward-looking nature of F&A data because of its focus on the past trends. “I can just give the information and tell them [other departments] what's happened, but it's too late when I see it” (C4F). The creative role, in contrast, is attributed to “other” departments including marketing:

Financials were always there, they're the rules we live by, but the finances don't create anything, they're recordings and analyses. Marketing is creativity and development (C3S).

They understand customers […] they're the creative people and sales and marketing, you know (C4F).

The F&A reports provide a detailed insight into the financial performance based on past values, yet providing little information on the underlying performance drivers. F&A managers recognise the importance of customer voice but highlight that these metrics often need a narrative to make them relevant for managerial decision-making. Customer metrics are seen as less straightforward compared to financial metrics and accounting statements:

There's no point in saying to somebody […] 'Okay, you're 9.2 on Booking.com’ […] they don't know what that means, you need to explain 9.2 is an excellent result on Booking.com, it relates to x, y, z, you know, so I think that's very, very important that there's a verbiage behind it and it's something that I think […] it's much easier for me to produce a Profit and Loss Account and leave it at that (C4F).

In response, the non-F&A managers underscore the importance of customer voice and associated metrics in terms of more business and revenue but struggle to quantify that:

The higher the satisfaction scoring, the more profitable your property should be, by the sheer fact that your customers are going to be repeat customers (C1S).

Managers note that apart from a relative delay in observing trends in financial matters, F&A data does not provide direction for possible responses in terms of service decisions. Depending on the level of aggregation, fluctuations in financial metrics such as average daily rate or revenue per available room are not attributable to any one service element. The observation of financial metrics thus frequently triggers marketing managers to know more and to raise open questions in the form of why, what and how:

Why are we slipping, why is somebody else gaining business? (C3M).

Basically, it's to see the current month, how you're getting on and then future months and how you're going to react and how you're going to sell the rooms […]. That's when decisions are made saying […] 'I've a lot of availability mid-week for the month of May, we need to act on that now, so what do we do about it?' (C2M).

F&A data generally does not provide guidance on the causes of performance outcomes. It raises questions rather than providing answers on what operational activity could be undertaken to address performance issues. Aggregated focused customer voice data in contrast provides substantive guidance about possible solutions and thus provides input to managerial decision-making. This is explored in the following sections.

4.3 Action-oriented role of customer voice in operational service decisions

Table 3 notes the action-oriented role of marketing data in managerial decisions identified across cases. It can be inferred from the nature of decisions that the action-oriented role of marketing data is primarily confined to operational decisions.

Evidence suggests that the aggregated customer voice is powerful and results in action-oriented service decisions on multiple instances:

You know, some people say the mattress is hard […] and if this kept coming through, if we saw more, then we would say we have a problem with mattresses […] this is recurring, this is a trend, so you know […]. You might keep it in the back of your head, the housekeeper might (know) […] and you might check the room (C3M).

Interestingly, managers’ increased focus on public customer voice data often resulted in ad hoc service decisions based on small numbers of qualitative comments. The findings indicate the willingness of managers to act immediately in response to vivid customer input by “auctioning,” “fixing” and “rectifying” issues close to the time they arise. Such responses are often proactive in nature and bypass the traditional metrics reporting and administrative processes such as monthly meetings. The findings show a heightened concentration on the customer and focused on implementing changes, “we listen and take action” (C1M1) to all research but quicker for the digitalised customer voice:

The service issues are dealt with immediately. We don't wait for the information though that's on a bigger spreadsheet (C3S).

So, if on a continuous basis people are complaining about the coffee in the bar, then you have got to change the coffee (C1S).

Customer voice plays a key role in operational service decisions that require relatively little investment and fall within operational budgets. Examples of such decisions in the case organisation include addressing a service issue with the quality of the breakfast, changing the parking signage or adding a low-cost service element (e.g. shower caps). However, for service decisions that require more investment, customer voice has less direct influence on the final decisions and is relegated to a more informative or knowledge-enhancing role.

4.4 Knowledge-enhancing role of customer voice in tactical decisions

Customer voice plays a knowledge-enhancing role when it is used to inform the development of more comprehensive investment decisions. In other words, the knowledge-enhancing role of marketing data primarily relates to tactical decisions. In such cases, customer voice data informs the decision, but F&A data provides the main justification for final decisions. The analysis presented in Table 4 suggests that particularly for service development decisions, a combination of quantitative and qualitative customer voice data and quantitative financial metrics are used.

In the knowledge-enhancing role, the marketing personnel need to take the customer voice data and provide a revenue narrative that explicitly links customer experiences with F&A data by attaching a monetary value to aggregate customer metrics. This was done, for example, by estimating opportunity costs to pressure F&A to support the decision or to justify action as opposed to inaction:

Each complaint that we get you can say has a cost of x value to it. And at the end of the month, we can kind of say, costs of complaints this month was €1,000 (C1S).

Evidence suggests that marketing managers had to persuade F&A managers who struggled in aligning their own and the customer perspective. For example, a large investment demanded by customers for in-room tea and coffee facilities, the finance director asked, “why is it coming up so many times?” (C1F). In this case, the real time, publicly visible and continuous customer voice trend was used to provide a narrative to F&A managers:

It doesn't make sense. It might make sense to us, but it doesn't make sense, obviously, to the customer, because it kept coming up […] this will impact on our rating if we don't address it, which will, in turn, have a bigger impact financially than the €40,000 in the food and beverage department. They know now, they're going to change, but that's taken a good six months (C3M).

So, if on a continuous basis people are complaining about the coffee in the bar, then you have got to change the coffee (C1S).

Findings suggest that while major investment decisions are F&A dominated, they would not have materialised without the inclusion of the customer voice in a usable aggregated and interactive form. The data is used internally to increase marketing’s and general management ability to influence and persuade by consistently showing the financial impact of customer issues. This is done via aggregated outcome metrics, customer comments and revenues (through web traffic metrics), thereby providing actionable insights:

We’re going to have to do it because […] they [senior managers] can see it, so rather than me telling […] in the old days, we would say “Oh, I think the customer wants this.” [Now] they can see it themselves that it's unsustainable (C3M).

A major reason identified for the prevalence of web-traffic data utilisation is the ease of establishing its direct link with the financial transactions. Through the availability of digitalised customer voice and metrics, managers in all case hotels create compelling narratives. The web-traffic data arguably provides accurate metrics linked to online marketing activities aligned with customer online behaviour, customer purchasing behaviour and subsequent revenues and financial returns. The resulting transparency increases marketing’s ability to account to internal stakeholders for cost and return and such F&A based arguments attract higher impact in decision-making. This increases the acceptance of marketing proposals within finance and decision-making input at board level.

5. Discussion: evolving role of customer voice in managerial decision-making

Our findings support the argument (Hennig-Thurau et al., 2013; Labrecque et al., 2013; Trusov et al., 2009; Whiting et al., 2019) that the emergence of social media has contributed to the power of the customer voice. While there is a general consensus among marketing, F&A and general managers on the importance of listening to customer voice, this study shows that the role of customer voice in managerial decision-making in the hotel industry is evolving with time (Figure 1).

The most immediate use of the customer voice is in managing the public perception in terms of ranking and public comments. The public nature of such customer feedback (Hennig-Thurau et al., 2013; Labrecque et al., 2013) prompts ad hoc decision-making from the managers. In such situations, customer voice is symbolically used as an early indicator to identify issues in the service delivery (Diffley and McCole, 2019; Hammami et al., 2018). The reason for such heightened sensitivity to managers to publicly visible data (ranking/comments) is that there may be an undue focus on the negative feedback, i.e. when ranking drops or if an overly critical comment is posted. This is in line with the observation that firms prioritise addressing negative comments on social media (Ma et al., 2015). In many situations, the negative sentiment may not be representative and ad hoc decisions may not be desirable or required in all circumstances (Hall, 1996; Ho-Dac et al., 2013; Huifeng et al., 2020). However, because of its symbolic value, managers still attend to such negative comments with utmost urgency.

A more systematic use of the customer voice is to take action-oriented operational decisions (Menon and Varadarajan, 1992; Morgan et al., 2005; Rollins et al., 2012), for instance website upgrades or adding shower caps, where huge investment is not required. These decisions are within the discretionary budget of marketing and operations managers. Thus, such service decisions need not become part of cross-functional strategic decision-making processes nor require input from F&A managers.

Increasingly, customer voice is also being used to enhance knowledge and understanding among senior decision-makers to frame discussions on tactical service improvements (Brown et al., 2019; Velayati et al., 2020) such as restyling or refurbishments. Because such initiatives require a significant budget, the customer voice plays a knowledge-enhancing role (Menon and Varadarajan, 1992; Morgan et al., 2005; Rollins et al., 2012) with F&A data providing the primary basis. When managers observe a repeating trend of customer complaints in a certain area, they want to resolve the problem by investment in a solution. F&A must then be convinced so they provide the narrative aligned to the F&A data such as return on investment and other metrics to support this, reflecting a dominance of cost and revenue mental models (Mintz and Currim, 2013, 2016; Rust et al., 2010, 2016). Hence, tactical decision-making is now supported by customer voice, albeit tentatively and with persuasion by the marketing or general manager. The ability of marketing managers to create such a link and to frame the customer voice data as part of a financially relevant narrative is the key driver for F&A and the method by which customer voice is used in managerial decisions.

Our findings suggest that while top management appreciates the role of customer voice as a central element of the strategic decision-making processes, it remains aspirational at present, especially in case of large-scale investment decisions. This might be because of the fact that the board level managers are more used to decisions based on the trends observed in the structured F&A data (Graham et al., 2015). This also reflects the streetlight effect (Du et al., 2021), suggesting that readily available data is used more often because of the ease of measurement and use.

Following a service-dominant logic, a possible way to incorporate customer voice in strategic decision-making would be service co-creation. This co-creation may take the form of co-ideation, co-design, co-evaluation, co-production and so on (Oertzen et al., 2018; Tran and Vu, 2021). Specific forms of engagement would also depend upon the type of the hotel and their target customer segments. For instance, Oyner and Korelina (2016) find that while low price hostels engage in active co-creation, high-price hotels use customisation and co-production more often. This, in turn, would also depend upon strategic fit and technology readiness of the hotels (Cabiddu et al., 2013; Fellenz and Brady, 2008).

6. Implications, limitations and future work

A key contribution of our study is demonstrating the impact of customer voice on managerial practices and highlighting the finance-marketing divide when it comes to managerial decision-making. Despite the steady and recognised proliferation of increasingly rich data from social media, managers are still struggling to leverage this data and use it in decision-making to drive performance and growth (Du et al., 2021). Many theories and concepts like customer journey mapping, service dominant logic and co-creation in services all suggest the need to work with the customer. This study suggests how customer voice, in an evolving manner, may be used more centrally in operational and tactical and begin to be integrated into strategic decisions. Our findings indicate that F&A managers, who are often initially reluctant to accept marketing data as sufficient justification for investment decisions, are starting to embrace customer voice in the presence of narratives that explicitly link it to financial revenue and performance figures. Therefore, for the customer voice to be more present in strategic decision-making processes, the data need to be more formalised and structured. This would help aggregated customer voice fit better into dominant mental models (Mintz and Currim, 2013; Rust et al., 2010, 2016), that overwhelmingly reflect F&A perspectives. In this regard, increased aggregation, formalisation and transformation of customer voice via big data analytics and interactive dashboards (Davenport and Bean, 2018; Erevelles et al., 2016; Lamest and Brady, 2019; Peters et al., 2013) may play a crucial role in elevating the role of customer voice and in turn marketing, in the boardroom.

This study answers the calls for research on marketing-finance interface (Hanssens, 2019; Sidhu and Roberts, 2008) noting how the managers from two disciplines often have two different worldviews. This study demonstrates that the marketing function still needs to work hard to make customer perspectives heard as part of strategic decision-making processes. This suggests that top management teams and boards perhaps have not yet fully accepted that marketing and customer-oriented mind-sets are important for creating value for the firm (Abernathy et al., 2013; O’Brien et al., 2018; Simms, 2008; Slater and Narver, 1998). The findings have relevance for both highly ranked hotels who wish to retain their rank and also for the hotels who aspire to be more customer centric. The findings offer a framework to enhance their decision-making through the inclusion of customer voice in an evolving manner, for operational, tactical and (hopefully) strategic decisions. This also engages with the integration between the three perspectives of technology, customer/marketing and finance (Fellenz and Brady, 2008) or what Russo-Spena and Mele (2012) call the technology-driven perspective, customer-driven perspective and service-driven perspective.

This research highlights how customer voice has found its way into more central roles in some managerial decision-making. Further work is needed, however, before such data is naturally accepted in a broader variety of decision-making processes at executive level. Narratives that link marketing and customer data to the dominant financial mental models and formalisation of data are a useful starting point to make the customer voice heard in organisational decision-making processes. However, this reflects a one-way adaptation (Boyce, 2000; Ward, 2004) that appears to be a far cry from more egalitarian approaches towards a customer-centric organisation (Kotler et al., 2019). At present the translation work is required from the marketing function to make their perspectives fit into the dominant F&A mental models, but the skillset to do this well is as yet neither evenly distributed nor universally available among marketers.

A key medium-term goal to progress the use of customer voice in the boardroom (Strandvik et al., 2014) would be to formally integrate and standardise relevant customer voice metrics that highlight their importance to firm revenue and cash flow (Hanssens and Pauwels, 2016; Sidhu and Roberts, 2008). Similarly, non-quantifiable aspects of customer expectations and experiences need to be woven into the overarching narratives that support executives in bringing customer voice into the boardrooms. Educational changes are also critical for both marketers and F&A professionals (Harrigan and Hulbert, 2011; Ward, 2004), as there is need for a financial acumen as a core competency to the marketing skillset. At the same time, F&A managers need to develop an appreciation for and understanding of marketing and customer perspectives (Boyce, 2000) beyond satisfaction metrics and financial trends. More importantly and perhaps more challenging, companies must also start to develop a mind-set that goes beyond the typical F&A focus that dominates many strategic decision processes. Ultimately, the way in which marketing actions directly drive business performance must be made clearer and the customer, through the use of customer voice data, must find a place in the conversations that take place in the boardroom.

Some limitations of this study need to be highlighted. First, the cases belong to a highly visible open environment of hotels in an industry where customer voice has immediate and strong effects. The findings may not directly apply to industries characterised by a relatively more closed context such as banking or insurance. Second, the findings reflect the practices of high-performing hotels and do not necessarily capture the practices used in less successfully operating hotels. However, we argue that our framework provides a possible evolution path to such hotels. Third, we could not find any evidence for marketing data use in strategic decision-making. Our findings also call for increased understanding of the strategic decision-making and value creation processes within service firms, so that customers as critical actors are more effectively exploited and managed for value co-creation (Tran and Vu, 2021).

Notwithstanding these limitations, we believe that the four case studies offer rich empirical insights on the growing power of customer voice and demonstrate how such data is used in managerial decision-making. Future research may focus on replicating the study in other sectors and the use of customer voice in specific strategic, tactical and operational decisions.

Figures

Figure 1.

Use of customer voice in managerial decision-making

Table 1.

Overview of case study organisations

Attribute	Case 1	Case 2	Case 3	Case 4
Location	City	Countryside	City	City
Star rating	Five	Three	Five	Five
Number of rooms	≈140	≈70	≈140	≈190
TripAdvisor*	≈2,000 customer reviews; 100% answered by senior level manager	≈400 customer reviews; 90% answered by senior level manager	≈2,400 customer reviews; ≈90% answered by senior level manager	≈1,500 customer reviews; ≈95% answered by marketing manager
Facebook*	≈400 customer reviews; ≈10,000 likes	≈ 300 customer reviews; ≈9,000 likes	≈450 customer reviews; ≈13,000 likes	≈600 customer reviews; ≈16,000 likes
Twitter*	≈2,600 tweets; ≈4,000 followers	≈ 8,500 tweets; ≈ 5,000 followers	≈1,000 tweets; ≈10,000 followers	≈4,500 tweets; ≈12,000 followers

Note:

*During the research period

Table 2.

Data collected for each case company

Interview respondents
Managerial level	Case 1	Case 2	Case 3	Case 4
General Manager or Senior Manager (S)	1	1	1	1
Marketing Manager(M)	2	1	1	2
Senior Finance and/or Accounting manager (F)	1	1	1	1
Observations
Property Management System	✓	✓	✓	✓
Dashboards	✓	✓	✓	✓
Work routines	✓	✓	✓	✓

Table 3.

Action-oriented role of customer data in managerial decisions

Decision	F&A data used	Marketing data used[2]
Easter ad campaign		Advertising budget
Newspaper advertising		Advertising budget
Phone campaign	Sales figures	Customer information
Introduction of mobile app		Cost of development, website conversion, web traffic
Online advertising: banner		Advertising cost, bookings, origin of business, website conversion, web traffic
Facebook claims campaign	Number of available rooms	Advertising cost, number of Facebook claims, web traffic
Website upgrade		Marketing budget, website conversion, web traffic
Checking and replacing mattress	Cost of investment	Verbal customer feedback and online reviews
Adding shower caps	Cost of shower caps	Verbal customer feedback and online reviews
New car park signage		Verbal customer feedback and online reviews
New staff training		Verbal customer feedback and online reviews
Service issue – breakfast		Verbal customer feedback and online reviews
Opening of the roof-top terrace	Total sales figure, payroll	Observations
Adding online distribution channels	Average daily room rates, occupancy	Budget, past booking data

Table 4.

Knowledge-enhancing role of customer data in managerial decision-making

Decision	F&A data used	Marketing data used
Refurbishing rooms	Investment sum, opportunity cost (closed rooms – six months loss of revenue	Segmentation variables (corporate clients, domestic market, etc.), customer comments, verbal feedback
Refurbishing the coffee bar	Coffee bar revenue, Cost of investment, projected price premium for coffee	Customer sentiment web tool score, Verbal customer feedback and online reviews
Refurbishing the restaurant	Restaurant revenue	Verbal customer feedback and online reviews
Restyling the room	Restyling cost and opportunity cost of closed rooms	Verbal customer feedback and online reviews
New windows	Cost of investment and projected rate premium (PRP)	Recovery cost of customer complaints, no negative reviews on particular problem
Adding a new distribution channel	Business on the books	Bookings coming into the city
Campaign for corporate market	Room nights, revenue	Number of bookings, competitor’s activities, customer feedback
Adjusting room rates	Sales figures, room rate and level of business	Market segmentation data, historical data on customer reaction to changes in room rates
Changing breakfast pricing	Breakfast revenue and rates – supplemental rates	Revenue referable to web traffic metrics, verbal customer feedback and online reviews
Increasing the staffing level	Payroll	Customer demographics, customer complaints, observation
Re-carpeting bedrooms	Cost of investment	Verbal customer feedback and online reviews
Renewing air conditioning	Cost of investment, opportunity cost	Verbal customer feedback and online reviews
New room-card system	Cost of investment, opportunity cost	Verbal customer feedback and online reviews

Decision	Decision type	Role of customer voice	Case 1	Case 2	Case 3	Case 4
Adding a new distribution channel	Tactical	Knowledge enhancing			✓
Adding online distribution channels	Operational	Action oriented				✓
Adding shower caps	Operational	Action oriented		✓
Adjusting room rates	Tactical	Knowledge enhancing			✓
Campaign for corporate market	Tactical	Knowledge enhancing				✓
Changing breakfast pricing	Tactical	Knowledge enhancing	✓
Checking and replacing mattress	Operational	Action oriented		✓
Easter ad campaign	Operational	Action oriented			✓
Facebook claims campaign	Operational	Action oriented	✓
Increasing the staffing level	Tactical	Knowledge enhancing				✓
Introduction of mobile app	Operational	Action oriented			✓
New car park signage	Operational	Action oriented				✓
New room–card system	Tactical	Knowledge enhancing		✓
New staff training	Operational	Action oriented	✓
New windows	Tactical	Knowledge enhancing			✓
Newspaper advertising	Operational	Action oriented			✓
Online advertising: banner	Operational	Action oriented		✓
Opening of the roof-top terrace	Operational	Action oriented				✓
Phone campaign	Operational	Action oriented			✓
Re-carpeting bedrooms	Tactical	Knowledge enhancing		✓
Refurbishing rooms	Tactical	Knowledge enhancing			✓
Refurbishing the coffee bar	Tactical	Knowledge enhancing	✓
Refurbishing the restaurant	Tactical	Knowledge enhancing			✓
Renewing air conditioning	Tactical	Knowledge enhancing		✓
Restyling the room	Tactical	Knowledge enhancing	✓
Service issue – breakfast	Operational	Action oriented				✓
Website upgrade	Operational	Action oriented		✓

Notes

1. We did not find the use of marketing data in strategic decision-making in our cases.

2. Once allocated, the decision regarding the use of marketing and advertising budgets are under the preview of marketing managers. Hence, advertising and marketing budget is considered under marketing data.

Appendix 1. Interview questions

General background

• Could you briefly explain your role in this company?

• What are your experiences in the hotel industry?

• What is your professional background?

• What is your academic background?

Collection of customer data

• What is the value of qualitative content in the context of quantitative measures?

• How can metrics transform qualitative content into measures?

• How are you making sense of qualitative content?

• How do you process unstructured qualitative content from the internet?

• What are the challenges faced in the context of the collection and use of qualitative data?

• How do you respond to a bad or good comment on a social media platform?

• How do you establish the connection between user generated content and social media-related metrics on the one hand and financial metrics on the other?

• How is the financial impact of social-media related activities justified and communicated within the firm?

Use of customer data in the decision-making

• In what types of decisions are metrics a routine input?

• How important are metrics for each of these decisions?

• In what types of decisions are metrics an infrequent input

• How and why are the available metrics used in decision-making?

• In what types of decisions is qualitative data a frequent input?

• How does qualitative data influence decision-making?

• How important is qualitative data for decision-making in contrast to quantitative metrics?

• How is this information used in the context of decision-making?

• Who will receive this information within the company?

• To what extent do social-media related metrics impact decision-making?

• Were financial metrics/marketing metrics used as decision aids when making the decision?

• What was the performance in terms of marketing, financial and/or overall outcomes?

• To what extent do you attribute this performance to the use of financial and marketing metrics respectively?

• For the future, is there room for improvement in terms of metrics input into similar decisions of this kind?

Appendix 2. Decisions instances across the cases