DSS 2.0 – Supporting Decision Making with New Technologies Gloria Phillips-Wren et al. (Eds.) IOS Press, 2014 © 2014 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-61499-399-5-533

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Achieving Organisational Benefits with Social Media Analytics Nargiza BEKMAMEDOVA a, Graeme SHANKSa,1, Sven CARLSSON b a The University of Melbourne b Lund University Abstract. Social media analytics (SMA) involves the analysis of vast amounts of social media data to provide insights to organisational decision-makers. Many organisations are investing in SMA but few describe themselves as effective adopters. In this paper, a framework for achieving benefits with social media analytics is presented, based on SMA motivations, the resource-based view (RBV) and dynamic capabilities theory. Concepts in the framework are clearly defined and two propositions are developed, based on relationships in the framework. Finally, we discuss implications of the framework for researchers and practitioners and suggest directions for future research. Keywords. Social media analytics, resource based view, dynamic capabilities.

Introduction Social media, or web-based technologies that mediate human communication, have transformed the way people communicate, collaborate, and consume and radically changed the way organisations engage with the marketplace and society [1]. Social media is defined as a “group of Internet-based applications that build on…the foundations of Web 2.0, and allow the creation and exchange of user generated content” ([2], p. 61). Widely used examples of social media include Facebook, Twitter and YouTube together with many other blogs, social news sites, virtual communities, and online reviews. There are now nearly 850 million active users on Facebook and the time spent on social media in the United States of America increased by 37 percent to 121 billion minutes in 2012 [3]. The use of social media creates very large amounts of data, including consumer opinions, experiences and sentiments towards brands, products and services that are potentially of great value to business [4, 5]. Social media analytics (SMA) involves the use of analytics-based capabilities to analyse and interpret vast amounts of semi-structured and unstructured data from social media sources. SMA provides businesses with insights into customer values, opinions, sentiments and perspectives on brands, marketing campaigns and new product and service opportunities [6, 7, 8]. It presents a unique opportunity for organizations to treat the market as a ‘conversation’ between organizations and customers instead of the traditional B2C approach with one-way communication [9]. Although there is much research published in the more technical areas of SMA, there is currently little research published in the management of SMA and the value of SMA to organisations. 1

Corresponding Author.

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There are three motivations for our research. First, although many organisations are engaging with SMA, few describe themselves as effective adopters [10]. An understanding of how to gain benefits from SMA would help these organisations use SMA more effectively. Second, organisations are increasingly investing in SMA technologies to better understand their customers and consumer perceptions of their brand and market positions [8]. The use of SMA can potentially enable organisations to reach their customers with unprecedented scale and low cost. Third, there is currently no systematic and theoretically-based understanding of how organisations can achieve benefits using SMA. The research question we explore in this paper is: How can organisations achieve benefits with social media analytics? To answer this question, we extend our previous work [11] and propose a theoretical framework, based on SMA motivations [9], the resource-based view (RBV) [12] and dynamic capabilities theory [13]. The framework provides researchers and practitioners with an understanding of the motivations that explain why organisations use SMA, and the SMA resources that explain how they achieve benefits. The theoretical framework is developed using a conceptual study, involving a critical analysis of relevant literature and a synthesis of key concepts and their interrelationships [14]. The paper is organised as follows. First we discuss the background to the study, including organisational use of SMA, resource-based view and dynamic capabilities theory. We then describe the framework for achieving benefits with SMA, and define key concepts and relationships in the framework. We then discuss implications of the framework for researchers and practitioners and suggest directions for future research. 1 . Background Social media have the potential to transform the way organisations interact with consumers. There is currently little understanding of how organisations should manage social media and understand and measure benefits achieved [1]. In this section, we discuss the organisational use of SMA, the resource-based view and dynamic capabilities, and the organisational benefits that can be achieved with SMA. 1.1. Organisational Use of Social Media Analytics Users generate social media content by sharing their opinions, experiences and knowledge on a wide variety of issues. Vast amounts of data are created using social media that are potentially of great use to organisations. Social media data may positively or negatively influence consumer attitudes, perceptions and buying decisions [15]. Organisations can use social media to better engage with their customers and as a low cost marketing channel to improve customer awareness of brands, products and services [7, 8]. SMA involves the storage, analysis and interpretation of social media data to provide insights for decision-makers. It has been widely used for customer and market intelligence, in product development and supply chain, and in politics. It has influenced and improved customer relationships and brand awareness [15, 17]. From a technical perspective, SMA is related to a number of fields including social network analysis,

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machine learning, information retrieval and natural language processing. Although SMA is closely related to ‘big data’ analytics, we argue that it is a distinct and emerging discipline [10]. Big data analytics involves the data storage, management, analysis, and visualization of very large and complex data sets [40], and focuses on new data management techniques. While SMA uses large volumes of data, it focuses primarily on the interpretation and use of insights from social media data to achieve organizational benefits [14] and uses different techniques (e.g. text mining) [30]. SMA provides organisations with an ‘intimate’ means of better connecting with customers and understanding and shaping their perceptions of brands, products and services using social media platforms. SMA techniques and tools enable organisations to better target marketing campaigns, provide more responsive customer service, analyse consumer sentiment and identify key influencers [7]. 1.2. Resource-based View and Dynamic Capabilities Theory The RBV is currently the dominant strategic management theory and has been extensively used within the information systems (IS) discipline to understand and explain how investments in IS lead to organisational benefits and competitive advantage [18, 19, 20]. An organisation may be conceptualised as a collection of resources that enable it to succeed and compete. Resources comprise assets, including hardware, software, data and people and capabilities, including competencies (skills) and practices (routines). To be of strategic importance and achieve competitive advantage, resources must be valuable, rare, inimitable and non-substitutable (VRIN) [12]. Resources, including both assets and capabilities, are a critical determinant of firm performance [18]. Different types of IS investment at various levels within organisations, including business processes, business units and organisation-wide initiatives have been related to different types of benefits [18]. There has been a longrunning debate over whether IT contributes to business value and whether the impact is direct. The RBV theory has extensively contributed to management-based IT business value research by examining the efficiency and effectiveness of specific organisational resources. The RBV has been criticised as being static in nature, particularly in volatile business environments [21]. Dynamic capabilities were created in response to this criticism. They reconfigure and renew an organisation’s existing resource base to adapt to rapidly changing technology and business changes [13]. Organisations invest in and reconfigure resources and learn how to use them over time by developing skills and practices. 1.3. Social Media Analytics and Organisational Benefits Much is known about the benefits that organisations can achieve with business analytics (BA) including increased customer profitability, reduced customer attrition, and increased response rates from marketing campaigns [22]. SMA is a subset of BA, with a focus on analysing large amounts of unstructured social media data. Although there is strong evidence that SMA can bring benefits to organisations, particularly in the practitioner literature [5, 7, 8, 16]. Returns from SMA investments cannot always be measured using traditional financial indicators. Some benefits are measured using customer behaviors and perceptions. Organisational benefits from SMA include

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increased awareness of the organisation and its products and services, more favourable perceptions about the organisation, the ability to monitor what is being said about the organisation, and better understanding of customer perceptions of brands [10]. However, there is no systematic, theoretical explanation as to how and why SMA brings benefits to organisations [11]. We address this research gap by proposing a theoretical framework for achieving benefits with SMA, which is described in detail in the next section of the paper. 2 . A Framework for Achieving Benefits with Social Media Analytics In proposing the theoretical framework we focus on the management of SMA within organisations. This includes the motivations that explain why organisations use SMA, and the SMA resources that explain how they achieve benefits. The framework is based on SMA motivations [9], the resource-based view (RBV) [12] and dynamic capabilities theory [13].

Social Media Analytics Motivations Create awareness Persuade

lead to

Social Media Analytics Resources IT Assets SMA Capabilities competencies practices Dynamic Capabilities

lead to

Social Media Analytics Benefits Financial Benefits Behavioural Benefits Perceptual Benefits

Figure 1. Framework for Achieving Benefits with Social Media Analytics

The framework comprises three main concepts: SMA Motivations, SMA Resources and SMA Benefits. SMA motivations are the goals that an organisation pursues and guide the subsequent actions of that organisation. Typical SMA motivations include better customer engagement, increased brand awareness, and greater market value: these explain why organisations adopt and use SMA. SMA resources include effective combinations of IT assets and SMA capabilities that take time to develop, and require significant of learning and optimisation. Typical SMA resources include technologies for large-scale unstructured databases (e.g. Hadoop), text mining and social network analysis tools, people with relevant skills to be able to extract insights from SMA, and processes that integrate SMA insights within existing business processes: these explain how organisations adopt and use SMA to achieve benefits. SMA Benefits may be measured using financial, behavioural and perceptual indicators. Typical SMA benefits include increased revenue from more effective marketing campaigns, increased customer satisfaction with the organisation and key brands and improved customer engagement. In the following sections we define each of the concepts and their interrelationships.

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2.1. Social Media Analytics Motivations In an organisational context, motivations may be defined as the goals an organisation pursues, that subsequently guide the actions of that organisation [23]. SMA provides organisations with a unique opportunity to monitor, analyse and influence consumer interactions on social media platforms. Larson and Watson [9] identify three motivations for organisations to use social media: awareness, persuasion and collaboration. The awareness and persuasion motivations are also relevant motivations for SMA, as analytics can play an important role. These motivations reflect the current potential of social media that organisations may exploit, including ’listening’ to customer conversations, discovering new ideas (e.g. topics, participants, sentiments), segmenting the market (e.g. influencers, prospective users) and embedding data insights (e.g. associations, behavioural sentiments) into organisational decision-making (e.g. planning of a marketing campaign via social media channels). However, Larson and Watson [9] acknowledge that the collaboration motivation is not yet well enough understood for rigorous analysis and thus, we focus only on awareness and persuasion as two commonly recognised and widely used motivations. Definitions of the two motivations in the context of SMA follow, and Table 1 below shows the goals and actions related to each SMA motivation. Awareness involves the collection and analysis of social media data in order to increase organisational understanding of issues discussed in customer-empowered social media environments [9]. The two main goals of awareness motivation are to provide insight into customer’s values and behaviours [8] and the effectiveness of online marketing campaigns [24]. A third goal is the discovery of new ideas for brand reputation and engagement purposes, and gathering feedback from online initiatives [25]. Persuasion involves the collection and analysis of social media data for advertising, marketing and sales purposes [9]. The main goals of persuasion motivation are to directly promote brands, products and services and to identify social influencers to provide ‘word-of-mouth marketing’ to their peers and increase sales [5]. Table 1. Social Media Analytics Motivations SMA Motivation

Awareness

Persuasion

Goals

Actions

Gather customer insights

Analyse customer values, preferences, behaviors and demographics to identify customer sentiment and adverse events [16, 26].

Assess online marketing campaigns

Measure effectiveness and return on investment of marketing and outreach initiatives [24, 27].

Discover new ideas

Monitor brand, product and service reputation and track competitiveness [6, 25, 29].

Identify social influencers

Market to and persuade people who can influence others [5, 7, 27].

Identify popular social media channels

Promote sales initiatives and generate sales leads [5, 27].

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2.2. Social Media Analytics Resources SMA motivations lead to the development, adoption and use of SMA resources. SMA resources are mutually reinforcing systems of IT assets (including IT infrastructure, data warehouses, SMA data and SMA tools) and SMA capabilities (including SMA competencies and practices) and dynamic capabilities. IT assets include IT infrastructure, data warehouses, SMA data and SMA tools. They provide a foundation for shared SMA services that may be integrated with other organisational systems and enable the development of new SMA-based systems [18]. IT assets relevant to SMA include data storage technologies for large-scale unstructured databases for social media data (e.g. Hadoop), and SMA tools (e.g. social network analysis tools). These tools contrast with traditional BA technologies including data warehousing and reporting tools, which are based on structured data. Some traditional BA technologies include built-in SMA tools, for example SAP Social OnDemand and Google Analytics [27]. While many IT assets including IT infrastructure, data warehouses and SMA data are important to both the awareness and persuasion motivations, some SMA technologies are relevant to a specific motivation. SMA technologies relevant to the awareness motivation include text analysis and sentiment analysis. SMA technologies relevant to the persuasion motivation include social network analysis and influencerating metrics [30]. SMA capabilities include the competencies (skills) and practices (routines) that enable organisations to successfully adopt and use SMA technologies. For example, in order to gather customer insights, an organisation needs to develop and allocate people with relevant SMA competencies, including text and sentiment analysis. In order to take appropriate actions, SMA-based insights need to be embedded into organisational practices. Table 2 below shows the SMA capabilities relevant to each of the two SMA motivations. SMA Competencies are the skills embodied in individuals or teams that actively manage or accomplish organisational tasks [31]. They are developed through learning and the repeated performance of related activities [32]. Competencies relevant to SMA include general competencies related to IT, business and management and specific SMA competencies. General competencies are important to the success of SMA as SMA insights need to be embedded within other organisational systems and SMA initiatives need to be well managed and aligned with business strategies [18]. In particular, SMA initiatives need to be strongly aligned with sales and marketing strategies [15]. SMA specific competencies include management of large, unstructured data sets (big data), natural language processing, text mining, sentiment analysis and social network analysis. These competencies are prominent in the emerging role of the data scientist [33]. Some SMA specific competencies are relevant to both SMA motivations, while others are relevant to a specific SMA motivation. SMA practices are the means by which organisations take actions, and are also mechanisms for storing and accessing knowledge about how to accomplish those actions [18]. Practices and competencies support and complement each other. Practices help individuals and teams develop competences with particular ways of working, while competencies are necessary for the effective execution of organisational practices toward specified goals. When practices reach a threshold level they become routinized and reliable [32]. SMA practices include evidence-based decision-making [34],

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information management practices [35] and sales and marketing organisational processes. For example, SMA practices within Starbucks include the use of SMA competencies within brand management, campaign planning and sales lead generation (using My Starbucks Idea) within the public relations, marketing and sales units. Table 2. Social Media Analytics Capabilities SMA Motivation

Awareness

Goals

Capabilities

Gather customer insights

• Sentiment analysis: sentiment polarity, behavioural sentiments [8] • Text mining and web analytics: customer behaviors, intentions, and preferences [25]

Assess online marketing campaigns

• Real-time market intelligence: revenue growth, product usage, marketing success and brand mentions [27]

Discover new ideas

• Trend analysis and crowd intelligence: new insights and innovations (products, services) [27] • Weak-signal analysis: emerging trends early [26] • Competitor analysis: tracking of competitive brands and products [5]

Identify social influencers

• Influence analysis: identifying influencers for marketing and sales [26] • Social network analysis: mapping of relationships between online users, communities [5]

Identify popular social media channels

• Data mining and machine learning: identifying ‘popular’ buys and building smart ‘wish lists’, recommendations [36] • Channel optimisation and propensity modeling: identifying profitable social media platforms and influencing buying decisions [8]

Persuasion

Three particular organisational practices are important for success with SMA, particularly the transformation of insight from SMA into actions that generate benefits: customer management, process management and performance management [35]. Customer management is an organisation’s ability to understand the expectations and preferences of its customer base and the characteristics of its market [37]. SMA enhances customer management by providing market intelligence. Process management is an organisation’s ability to attain flexibility, speed, and cost economies through the effective design and management of key processes [35]. SMA insights need to be embedded within relevant business processes to enable their effective use together with educating organisational stakeholders about the value of social media data. Performance management is an organisation’s ability to design and manage effective performance measurement and monitoring systems to support managerial decision-making and communicate performance to appropriate stakeholders [35]. SMA insights can inform performance management. Furthermore, measures of the business impact of SMA should be implemented. This is a vital capability that needs to be nurtured as senior managers still struggle to justify the return on investment of SMA initiatives [10]. Dynamic capabilities provide a mechanism for SMA resources to be renewed and reconfigured. They are particularly important in volatile business environments [13]. While IT assets and SMA capabilities are conceptualised as first order constructs,

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dynamic capabilities are conceptualised as second order constructs, as they are not directly involved in the production of goods or provision of services. Rather, they build, integrate, and reconfigure IT assets and SMA capabilities [13]. Dynamic capabilities comprise routines for searching for new opportunities, selecting and funding those opportunities that have the potential to provide organisational benefits and then asset orchestration to renew and reconfigure IT assets and SMA capabilities [13]. Dynamic capabilities are particularly important in the context of SMA as they provide a means of identifying new business opportunities enabled by SMA, developing new IT assets and SMA capabilities as required and integrating them into existing organisational practices. They also require the establishment of effective governance mechanisms with clear guidelines for innovation, decision rights and accountabilities. Dynamic capabilities are themselves organisational practices that have been developed over time through organisational learning [20]. 2.3. Social Media Analytics Benefits SMA motivations lead to the development, adoption and use of SMA resources, which in turn lead to SMA benefits. There are three types of SMA benefits: customer-related benefits, financial-related benefits and organisational effectiveness benefits [19, 20, 35]. SMA benefits may be measured in three ways: financial (e.g. revenue, costs), perceptual (e.g. customer satisfaction) and behavioral (e.g. use of SMA insights within business processes) [20]. Behavioual measures reflect the current situation, perceptual measures are often forward looking and financial measures may not be apparent for some time. Together, they provide a good measure of SMA benefits [19]. Customer-related benefits include increased customer understanding and engagement, improved customer satisfaction, and better customer service. In particular, SMA leads to better understanding of customer sentiments about brands, products and services, and customer trends and issues. These benefits are usually measured using perceptual and behavioural measures. Organisational effectiveness benefits include reduced time to market, higher levels of innovation, improved flexibility in production and supply chain management and improved marketing campaign success [24, 35]. For example, the use of some social media platforms (e.g. blogs, Twitter) might prove to be more effective and less costly for brand promotions, and crowdsourcing might bring innovative ideas about products and services to organisations [25]. These benefits are also usually measured using perceptual and behavioural measures. Financial-related benefits relate to actions resulting from SMA insights that lead to increased revenue, reduced costs and improved profits [18, 35]. These benefits are often indirectly related to customer-related and organisational effectiveness-related benefits and may take some time to occur. Table 3 below shows the SMA benefits relevant to each of the two SMA motivations and their goals and related capabilities. There are two relationships in the framework for achieving benefits with social media analytics: SMA motivations lead to SMA Resources, and SMA resources lead to SMA benefits. We noted earlier that SMA resources comprise IT assets, SMA capabilities and dynamic capabilities. Many of these resource elements are common to all SMA motivations, for example many IT assets and dynamic capabilities. Therefore, we focus on SMA capabilities, which differ considerably between different motivations.

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Table 3. Social Media Analytics Benefits SMA motivation

Goals and related SMA capabilities

SMA benefits

Gather customer insights

•

Better customer engagement [28]

• •

Better customer understanding [8] Customer service improvement [38]

•

Marketing strategy improvement, improved insights about target markets and activities [26]

•

Better predictive modeling and business planning [24]

•

Increase in new business [8]

•

Better brand reputation management [38]

•

Increased customer base [26]

•

Increased sales leads generated (e.g. click rates, social e-commerce sales, conversion rates) [17]

• •

Awareness

Sentiment analysis Text mining and web analytics

Assess online marketing campaigns •

Real-time market intelligence

Discover new ideas • Trend analysis and crowd intelligence • •

Weak-signal analysis Competitor analysis

Identify social influencers • • Persuasion

Influence analysis Social network analysis

Identify popular social media channels • Data mining and machine learning • Channel optimisation and propensity modelling

2.4. Relationships in the Framework SMA motivations define the goals that an organisation pursues and guide the subsequent actions of that organisation [23]. We argue that in order to achieve the goals of particular SMA motivations, organisations will develop, outsource, acquire and renew particular SMA resources. According to the RBV, it is these resources that enable organisations to succeed and compete [12]. We define two specific SMA motivations: awareness and persuasion. Each of these motivations will lead to particular SMA capabilities. For example, according to Table 2, the awareness motivation will lead to capabilities including sentiment analysis, text mining and web analytics, real-time market intelligence, trend analysis and crowd intelligence, weaksignal intelligence, and competitor analysis. These capabilities often require tools and skills related to a particular task at hand (e.g. perform sentiment analysis on a corporate brand using the Twitter platform). The resulting SMA insight then needs to be properly actioned (i.e. embedding the insight into current marketing processes and developing performance metrics) by business people. We name these ‘SMA awareness capabilities’. Therefore, two propositions may be defined for the first relationship in the framework. Proposition 1a: Greater SMA awareness motivation will lead to the development of greater SMA awareness capabilities.

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Proposition 1b:

Greater SMA persuasion motivation will lead to the development of greater SMA persuasion capabilities. According to the RBV, the development and use of SMA capabilities should lead to SMA benefits [18, 19]. In the framework for achieving benefits with SMA we define two groups of SMA capabilities, SMA awareness capabilities and SMA persuasion capabilities. Each of these groups of SMA capabilities will lead to particular SMA benefits. For example, according to Table 3, SMA persuasion capabilities will lead to better brand reputation management and increased sales leads generated. Therefore, two propositions may be defined for the second relationship in the framework. Proposition 1a: Greater SMA awareness capabilities will lead to greater SMA awareness benefits. Proposition 1b: Greater SMA persuasion capabilities will lead to greater SMA persuasion benefits The described relationships between the concepts provide consistent logic in guiding managers for developing portfolios of SMA capabilities in relation to the nature of SMA motivations. The scope and complexity of each of the motivation (e.g. a simple task of analysing sentiments via a single social media channel versus postevaluation of a product launch via multiple channels) will guide the degree of involvement of SMA resources required to perform a task at hand (e.g. one-off use of a sentiment analysis tool versus involvement of multiple social media tools and embedding results into existing CRM or other marketing system). As a result, benefits are expected to be different depending on what resources have been involved (e.g. inhouse development, customising SMA software, outsourcing) and how well the SMA insight has been actioned within the organisation. 3 . Discussion The framework will be of great value to decision support systems (DSS) researchers and practitioners. In particular, the goals, capabilities and benefits associated with the awareness motivation are highly relevant to decision-makers, particularly in marketing and product development. SMA provides an important opportunity for decision-makers to gain insight into customer sentiments and ideas, the effectiveness of marketing campaigns and competitor analysis. For researchers, the framework provides a theoretically based and parsimonious explanation of how organisations achieve benefits with SMA. The framework is based on a synthesis of organisational motivation theory, RBV and dynamic capability theory. This is an important contribution to knowledge, as noted by LePine and King’s [39]: “synthesizing theory to produce integrative theories may be worthwhile in helping readers to develop a broad understanding of a concept or a process” (p. 506). The synthesis of organisational motivation theory and RBV is novel in the information systems literature. It contributes to IT management research by taking an organisational-level focus when investigating the use of social media technology for business transformation [41]. Researchers may use the framework as a sound base for empirical work, in order to evaluate and enhance the framework. The framework provides case study researchers with a useful lens for conducting data collection and analysis, in order to develop a deeper understanding of concepts in the underlying

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reasons for the relationships between concepts. Further refinement and development of the framework is required before it is suitable for survey research. For practitioners, the framework provides a systematic means to understand the reasons SMA is used by organisations and the SMA resources that are necessary to achieve benefits. The framework may help practitioners to identify which SMA assets and capabilities are core and which are non-core and may be outsourced. It also may be used by practitioners to develop guidelines for development and allocation of resources in order to succeed with SMA. 4 . Conclusion SMA is an important, emerging phenomenon that provides organisations with new and novel opportunities to interact with consumers and achieve benefits. Social media technology has developed rapidly and is now ‘fused’ into organisational life. SMA also provides decision-makers with new sources of information and will become an integral component of future information-based DSS. The framework for achieving benefits with SMA provides a systematic and theoretically sound means of understanding and explaining how organisations may succeed with SMA. Further work is required to refine the framework and use it in empirical research studies. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]

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