Social Media Metrics: Practices of Measuring Brand equity and Reputation in Online Social Collectives

Andreina Mandelli

Cosimo Accoto Alessandro Mari

Contact: Andreina.mandelli@sdabocconi.it Abstract This paper presents a preliminary study on the professional practices of brand-related measurements and reputation in social media. We reviewed both practitioner-based literature and professional monitoring platforms and methodologies. We analyzed these procedures, and drew preliminary conclusions about a more general framework, within which they can be better understood and developed. We did it equipped with the insights provided by the academic literature on branding in online environments and reputation management, and our more general vision of markets as mediated conversations, which informs the research program to which this study is part of. Keywords Internet, social media, conversations, brand equity, reputation, metrics.   

Social Media Metrics: Practices of Measuring Brand equity and Reputation in Online Social Collectives

Introduction We present here a preliminary study on professional practices of brand equity and reputation measurement in social media, based on the review of the most relevant literature and professional services (monitoring platforms and methodologies) available in the field. We introduce the analysis, and the discussion of findings, with relevant definitions and information about social media and branding in these new communication environments. Branding in social media Social Media (or Web 2.0) has been defined by Costantinides and Fountain (2008) as "a collection of open-source, interactive and user controlled online applications expanding the experiences, knowledge and market power of the users as participants in business and social processes. These applications support the creation of informal user's networks facilitating the flow of ideas and knowledge by allowing the efficient generation, dissemination, sharing and editing/refining of informational content. While the mass-market literature has widely adopted the label of social media (“social web” or “Web 2.0”), the academic literature use different labels such as “social computing” (Liu et alii 2009) or “social software” (Hatzipanagos and Warburton, 2009, ed.; Candace Deans, 2009). “Social media markets” are conceived as made by interactions and stories between people who use digital social platforms as a new form of communication media and social environment. This transforms people into content producers and therefore into media players, not only conversational agents. From the implementation of social technologies in applications (Bell, 2009) such as, to name a few, blogs, social networks, virtual worlds, content sharing, and wikis, it is possible to readily identify the main conceptual foundations of social media: the concept of “Generativity” (Zittrain 2008). The generative dimension of social media introduces the idea and practices of user media production. Social media agency is ultimately a human process through which an instance of collective action takes place in the form of a wiki page, forum thread, blog comment, shared video, virtual playing or networking update. Consumer agency emerges from a networked production and consumption process (Bruns, 2008). Social web identifies an “online place where people with a common interests can gather together to share thoughts, comments, and opinions” on products or brands (Weber, 2009). For some authors like Chaffey and Smith (2008), this means that clusters or collectives of customers with similar tastes and interests are connecting with each other to form new global niches, segments and electronic tribes (Adams and Smith 2008; Cova and Cova, 2002). In this perspective, the ideal customer for companies is no longer the one that buys the product, but the one who contributes to a service or product over time, shares information and becomes a partner, exchanging useful feedback and contributing to the brand project in a continuous process of distributed value production. These actions, through social media, are putting consumers closer to companies so they can be conceived as part of the organization (Mandelli and Vianello, 2009). If the customers of a firm spend significant amounts of time online, interacting with the company and with other customers, it creates an unexpected and unprecedented opportunity for businesses to empower the traditional customer information systems and to enrich the consumer online research methods (Fielding et alii, 2008.). Consumers bring their needs and passions online, but also their skills and intelligence. Consumer experience online builds around conversations and stories produced and planned by occasional encounters with brands and other users. These kinds of relationships can provide learning opportunities for the organization to increase sales, create positive “word-of-

mouth,” generate more effective market segmentation, increase website traffic, create stronger brands, and create better product support and customer service for the company (Vianello and Mandelli, 2009). Why it is important to measure brand-related phenomena in Social Media According to the extensive literature on brand equity in marketing (see Fuduric and Mandelli, 2009 for a recent review) and reputation management in corporate communication (Fombrun, 1996) brands make the difference in markets. Brand equity influences firm’s reputation and performance. Measuring their value has been since long an important endeavor in the field. In the social media environment, the mechanisms and tools for this practices offer some relevant improvements. In particular, digital interactive environments offer the capability to gather in an automatic, unobtrusive, and steady way a considerable amount of consumer behavior data. Furthermore, this data is collected in real-time and at a fraction of the cost of traditional market research. “The Web can be viewed as an enormous distributed laboratory for studying human behavior in a virtual ‘digital environment” (Baldi et alii, 2003). From a data analysis viewpoint, the Web provides rich opportunities to gather observational data on a large-scale and to use this data to construct, test, reject, and adapt models of how humans behave in the Web environment. As also stated in Breakenridge (2008), “a company should use the flexible 2.0 research that’s available on the internet – from the free search engines to the paid service providers with amazing technological advances – to allow its brands to more than just survive, but to thrive in fast-moving, highly competitive, and constantly changing environment.” These types of models are of broad interest across a wide variety of fields such as: the social scientist who wishes to better understand the social implications of Web usage, the human-machine interface specialist who wishes to design better software tools for information access to the Web, the network engineer who seeks a better understanding of the human mechanism that contribute to aggregate network traffic patterns, and the e-commerce sales manager who wants to better predict which factors influence the Web shopping behavior of a site-user. The Internet (and the social media, in particular) can be thought of like “a massive focus group with uninhibited customers offering up their thoughts for free” (Scott 2009). Companies that do not care about reviews and discussions of their products or services found on social media are living dangerously. In fact, customer-generated (or expert-generated) information about a specific brand should be considered important and valuable as more formal market and media research. In addition, in the fast-changing and turbulent markets of today, it is particularly important to conduct brand research on a regular basis because consumer behavior can change much faster than any formal market research program. Li C. and Bernoff J. (2008) identified two relevant listening strategies on the web: either set up your own private community or begin brand monitoring in blogs, micro-blogging, social networking, video sharing, etc.. In fact, many web resources are available to help firms monitor messaging and editorial coverage in Web 2.0. Social media measurement (also, “social media analytics”) is still in its infancy stage, but it enables, for instance, a company to evaluate whether conversations on a topic are increasing in size or decreasing, if the number of participants in a particular conversation compared to others is bigger or smaller, if the conversation on the negative side increases or if there is positive buzz around a brand or a product. In fact, the newest linguistic models allows one to analyze the content and the tonality of a story, even revealing whether the speaker is male or female (Breakenridge, 2008). However, there also are significant limits to these models. They are both technical and conceptual and suffer from problems like privacy issues, incomplete raw data, oversize amount of information, abstractions and assumptions, semantic complexity, and the difficulty

connect data to actions (the problem of the actionable metrics, see Burby and Atchison, 2007). From the conceptual standpoint, what is missing from this system is a robust framework which allows companies to 1) better understand what brands can do in social media, for improving their equity through their participation to these social collectives; 2) and how these new market encounters and narratives can help build better knowledge and support for action. As Mandelli (2005) suggests, “A more integrated and communication based research agenda should be developed, so to uncover the complex relationship between the processes of learning and brand building in consumer communities.” Our study This paper presents a preliminary study on the use of brand-related metrics by marketing and communication practitioners in social media. We reviewed both practitioner-based literature (from 2007 to 2009) and actual monitoring platforms and methodologies. A selected list of the texts reviewed is provided in the bibliography. We build our knowledge of the methodologies adopted in the field, out of the analysis of the most important platforms and procedures proposed and adopted by major companies and brand analysts. We based the selection of these platforms on the list contained in the Forrester Wave Listening Platforms Study Q1 09 and Forrester Wave Web Analytics Q3 09 study, two authoritative reports on global best practices in the field (fig. 1 – Appendix 1). We reviewed all these procedures with the help of the insights provided by the academic literature on marketing in online environments (Hoffman and Novack, 1996; Mandelli, 1998; Christodoulides and de Chernatony, 2004) and our more general vision on markets as mediated conversations (Mandelli and Snehota, 2008; Mandelli, 2010). At this stage of our project we have not done a quantitative/systematic analysis of these platforms. The approach we used is exploratory and preliminary to a more in-depth analysis of these methods and instruments. Our aim was to find commonalities in the different methodologies proposed and used, and start building a list of the most important metrics, classifying them in relevant categories, according to the different objectives and methodologies adopted. This should help us link back these practices to a more conceptual framework that we are building, with the help of academic literature on brand equity metrics, and original theorizing on the new nature of markets (Mandelli and Snehota, 2008), and brand-customer relationships in markets as mediated conversations (Mandelli, 2010). Monitoring the conversations: practices in the field Here we describe the metrics adopted in the professional procedures examined, and classify them in relevant categories. We warn that these categories are not so distinct, and somewhat overlap: 1) Customer engagement The concept of “engagement” is used to describe the involvement of customers in the online environment. Practitioners use Customer Engagement (CE) as a metric for measuring the degree and depth of visitor interaction against a clearly defined set of goals. Originally referring to the rich media internet applications, more recently the concept has shifted to the social media environment (see IAB, 2009) to monitor and track the practices of social agency. Objectives and metrics The general aim of customer engagement measurement is to describe and evaluate the online consumer interaction with brands, beyond the first-order metrics (for instance, page views and unique visitors), and according with some defined business objectives (Jackson, 2009). Various attempts to overcome the original audiometric level (page-centric and click-centric) has been made to address the new dimensions of consumer engagement. The measurement

industry nowadays uses two audiometric approach: (1) “time” and (2) “event” (or some combinations of them). On one hand, the idea is that the amount of time spent on a site (or page, content, application and so on) can be a significant measure of the engagement of the customer. On the other hand it is assumed that the tracking of specific application based inter-actions (with video players, widget applications, instant messenger and so on) can give a more precise idea of this consumer-based phenomenon. Engagement can also be conceptualized (and measured) as a process, with subsequent steps, in a so-called “engagement funnel”. A relevant problem, in this metric discourse, is the capability to correctly assign the “attribution” of engagement conversion to specific steps (in the engagement funnel process), and to calculate, consequently, cost per and return on investment of specific brand engagement-stimulating actions. This is framed in the more general discussion about the need for developing better “actionable metrics (Burby and Atchison, 2007). To quantify the user engagement, a pletora of metrics (especially in form of “key performance indicators” or KPI) is used (for ex. Video uploads, see the list in table 1- Appendix 2), according to the business goals and the typology or steps of events tracked. Tools and techniques Social media practitioners use “web analytics” techniques (Peterson, 2005, 2006; Kaushik, 2007,2009) for measuring Customer Engagement. In particular, digital publishers use the log files to track and analyze (Jansen et alii, 2009) or use more sophisticated content “tagging” instruments. In addition, the online marketers collect most of the data from online metered panels (Bermejo, 2008), for socio-demographic profiling and competitive benchmarking. 2) Buzz Buzz or “Marketing Buzz” has been defined as “the interaction of consumers and users of a product or service, that serve to amplify the original marketing message” (Greg, 2006). The aim of buzz marketing is creating positive word of mouth around a product by turning selected consumers into spontaneous carriers of the message (Salzman M.L., Matathia I. and O’Really A., 2003; Hughes M., 2005; Rosen E., 2002). In this respect, the term buzz monitoring indentifies the task of tracking the consumer responses to commercial services and products. Many firms and researchers operating in social media are trying to develop instruments and algorithms to monitor, track, and report (quantitatively and qualitatively) what they call the “voice of customers”. To monitor these customers, companies have to be constantly informed about the talks around the brand on the web (such as posts, comments, reviews, etc.) and, more recently, in terms of the so-called “opinion mining and sentiment analysis”. According to Pang And Lee (2008), this refers to “computational treatment of (in alphabetical order) opinion, sentiment, and subjectivity in text. Such work has come to be known as opinion mining, sentiment analysis, and/or subjectivity analysis. The phrases review mining and appraisal extraction have been used too. These techniques and algorithms can also have a relevant role in enabling other social technologies like “recommendations systems”. These analyses can be performed also at a very granular level, for specific targets or topics (see the list in table 1 –Appendix 2). Tools and techniques - Enterprise Listening Platforms: The measurement of buzz can be made with the help of software, that takes into consideration the discussions on the web, in order to monitor the impact of social media campaigns on consumer conversations (Have the customers received the message? How are they responding to it? What reactions does the message generate?) These tools are called Enterprise Listening Platforms, so-called ELP. Companies using the dedicated buzz monitoring procedures (usually using a complex system of crawling, indexing and retrieval of information tools) can

rely on a dynamic database, with information coming from a variable number of social media (blogs, social networks, groups, boards of discussion, forums, and other consumer-generated media platforms). - Text Mining Tools The text mining tools activate algorithmic procedures to mine large volumes of unstructured data (Song and Wu, 2009), using methods derived from data mining, machine learning, natural language processing, information retrieval, and knowledge management. Text mining technology can be used to assist in finding relevant or novel information regarding the brands the company wants to monitor. We have to say here that, even if in the illustration of the procedures this is not always made clear, it is important that machine-based analyses are supported by human-based, qualitative and interpretive analysis of the conversations and texts, for better guiding the application of the simplified software interpretation rules. - Site Analytics The “web analytics” tools can be applied to track the activity of the company on different social media channels (Croll and Power, 2009). Companies do not surf the web searching for discussions about the particular brand. These tools operate by looking at the various responses to the company activities and compare the results obtained for each social media channel. It is then possible, by setting the right research parameters, to understand the response rate of the customers in terms of leads, transactions and content consumption (Murdough, 2009). 3) Brand advocacy Not all products may generate enthusiastic sentiments among consumers, even if some products stimulate consumer passions (Mandelli, 2005). The brand advocacy models respond to the necessity of the firms to understand who are the customers that positively talk about the brand, and how likely they are to promote it. These instruments can be useful to understand the level of affection toward the brand and also the possibility of positive word-of-mouth generation. They are usually linked to buzz analysis procedures. See the list of metrics in table 1 (Appendix 2). Tools and techniques These analyses made following a qualitative approach trademarked with different labels (Net promoter Score, Advocacy Index, Customer Focused Insight Quotient, etc.). Through this method, companies can measure and capture not only advocacy, but also negative opinions and competitor recommendations. The method used to measure brand advocacy is not equal for all the operators. Some ask directly to their customers (using a survey-like approach) about the willingness to suggest a brand, while others try to analyze which brands the customers have already suggested to their peers. There are currently several discussions circulating among professionals on the validity of these methods, also compared to other procedures, for ex. Customer satisfaction analysis (Keiningham et alii, 2008). Many consider the first approach to be incorrect because it is based only on the question of “Would you suggest this brand/product to a friend?” which measure intent, but not action. A more popular method is becoming the monitoring of the conversations, passively observing the number of customers sponsoring certain products. At this moment the discussion about the validity and reliability of each of these methods is open and still lacks theoretical support from the academic world. 4) Network influencer The “influencer” analysis is based on the concept that not all the talkers (fans as well as detractors) have the equal importance in the discussion. Some peers (the influencers) guide discussions and act as opinion leaders. In a socio-metric perspective, the industry uses the well-established “social network analysis” techniques in order to mine, map and measure the

type of relationships between nodes in networks in terms of proximity and density (see the list of metrics in table 1- Appendix 2). A “referral” approach to determine the value of influencing power among peers (customer referral value) is proposed by Iyengar et alii (2009) in “Do Friends Influence Purchases in a Social Network?”. It starts with three questions: (a) Do friends influence purchases of a user in a social network? (b) Which users are more influenced by this social pressure? (c) What is the impact of this social influence in terms of changes in sales and revenues? The authors outlines a complex and evolving dynamics in social consumer behavior. Although many tools have been developed to measure this important aspect of the conversation, some experts seem to be skeptical about them. As Gillin (2007) pointed out, “What is sure is that influence is hard to measure; so many variables are involved that even the most astute measurement firms cannot give definitive guidelines to measure it.” Tools and techniques Using different methods such as link analysis, percolation theory and epidemic models, social network analysis seeks to mine and describe the so-called FOAF ecology (friend-of-a-friend). This describes networks of relations and ties, pulling out the prominent patterns in such relationships, tracing the flow and direction of information (and other resources), and discovering the power and velocity of influencing. The principal methods for analyzing the influencers has been introduced by Razorfish: The “SIM (Social Influence Marketing) Score”. This score is the result of “net sentiment” on “share of voice”. This is first obtained through the data received by an ELP (in this case TNS Cymfony) and by an offline buzz measurement tool. These methods are a good example of the integrated, but often also overlapping, nature of most of the procedures adopted. Besides the analysis of the most relevant platforms, we explored the professional literature, in order to complement the analysis of the user-centric methods (adopted in the listening and web analytic platforms) with the illustration of monitoring procedures and metrics applied at different levels of analysis; these might be useful for starting build a more general framework of how to monitor communication and brands in social media. Beyond customer-centric procedures: organization-centric analyses Most of the metrics we examined so far measure phenomena at the individual level (for ex Customer Engagement). Few others (for example: volume of discussion) consider the network as a unit of analysis. Through our exploration we realized that there are several procedures for measuring social media brand-related phenomena and reputation at the brand/organization unit of analysis. We are here referring to the methodologies of data collection and analysis that more or less agree on the labeled terminology of: - Brand engagement analysis - Reputation analysis Brand engagement analysis (see for ex. The methodology Engagement DB at www.engagementdb.com) evaluates and rank the activism and social media presence of the top 100 global brands, through interviews and web analysis. This analysis offer a rich account of best practices in social media, but also a brand-engagement segmentation, with groups based on the level of activism: “mavens”, “butterflies”, “selectives” and “wallflowers”. This analysis, when conducted at the industry level, provides useful competitive information. Reputation analysis is, instead, the study of the social value of a brand/corporation, measured through the analysis of media coverage, stakeholders’ esteem and corporate participation to social life (social responsibility). With the explosion of social media these analyses have required to be updated and supported by technology-enhanced procedures. Many communication and PR agencies offer these services to their clients. They include these procedures in stable/continuous monitoring programs of corporate reputation in social media,

but also in procedures for alerting companies about the need for managing reputation “crisis” or social threats online. Of course some of these analyses build on conversation monitoring, typical of the consumer-centered procedures described above (customer engagement, buzz, brand advocacy and influencer analyses), and – even – the influencer analysis (when focused on bloggers) can be considered an updated version of media coverage evaluation. But what changes here is the breadth of monitoring goals, units of analysis (individual, organization, network level), program (business partners, investors, and civic society are strategic actors in the game), and perspectives (organizations monitor corporate reputation from the brand, the industry or a more general social perspective). The problem with these practices seem located in the conceptual and methodological weakness of the procedures used. There is a diffused confusion between brand image and firm reputation, and a confusion between brand image and conversation about brands. Brand advocacy cannot be considered as an operationalization of brand image nor as a proxy of firm reputation. It is not possible to equate brand-related talk with brand perceptions, and any of them with the aggregated attitudes of different stakeholders, which form reputation (Dowling, 2001). Comments and Conclusive remarks The analysis shows a picture of the practices in the field, which demonstrates a greater maturity compared to just a couple of years ago, when brands and technology providers started to consider social media metrics an interesting area of application and management (Forrester, 2009). We now see that there are several technology-supported methodologies and a growing set of metrics which can be applied to measure relevant phenomena in social media spaces. We classified these metrics into 4 categories: customer engagement, buzz, brand advocacy and influencer analysis. We consider this a useful first effort in the project of analyzing systematically what actors in the field are doing for monitoring and measuring relevant communication and brand-related phenomena in social media. This analysis and classification also helps us build conceptual frameworks, which can support discussion in the field and a more robust development of methods and tools supporting brand management. As we said, there are several limits not only in these practices but also in our analysis. The most evident weakness regards the temptative and preliminary nature of our classification. As also the practitioners often recognize, many of these metrics and methodologies are redundant and at least partly overlap, because they are developed in different contexts and for serving different knowledge and strategy goals. We still consider them a valuable basis for future work on the subject, because they help us start building the link between adopted metrics and the identified goals, promoted by Murdough (2009). The author illustrates the main phases of the social media measurement process (see figure 2 – Appendix 1), linking strictly the decisions about metrics to social media strategy. The phases are: 1) concept (which defines the general goals for the brand), 2) definition (outlining strategy), 3) design (enumerating social tactics and measurement programs); 4) deployment (implementation), 5) optimization (identifying actionable opportunities for adaption. Murdough (2009) also includes a useful first classification of units of analysis for social media monitoring, that are: • Reach (network level) • Discussions (individual level) • Outcomes (individual level) Fuduric and Mandelli (2009) summarize this framework in table 2 (Appendix 2). Advancing the objective of establishing a clear link between measurement and brand objectives, we find useful to include the new consumer-centric metrics, that we have reviewed

above, into the conceptual framework proposed by Christodoulides and De Chernatony (2004), in their effort of building an integrated picture of brand equity offline and online. As shown in tab. 3 (Appendix 2) social media branding provide useful information to brands, at their different steps of evolution: differentiation, positioning, personality, vision and added value. Brand engagement and reputation analysis procedures (both organization-centric) enlarge the scope of our measurement possibilities, offering information on competitive social media presence of the brand, and the more general social value of corporations. We can include all the 6 types of brand-related social media monitoring procedures in a more general framework, considering the different units of analysis and focal perspectives involved. We summarize this last analysis in figure 3 (Appendix 1). Our study has pointed out different issues concerned with the current measurement methodologies and metrics practices. Sophisticated software has been recently developed, increasing the possibility to measure dimensions of social media conversations that were not possible to measure before. This possibility is translated into a large/differentiated offering by specialists in the field, and the proliferation of specialized tools and services. The operators claim the possibility of integration of these instruments, for giving a larger view on different aspects of the social media conversations. But this seems difficult at this stage, because of lack of standardization of audiometric concepts and technical terminology (more recently mitigated by the attempts of industry associations as IAB and WOMMA to set standard guidelines), and because of the overlapping scope of some of these methodologies. There is even a scarce reliability of these tools, giving the discrepancy of data collected with different measurement platforms. Another problematic issue regards the scarcity of new competences, which integrate marketing and PR knowledge with social media culture and practices. An example of how this competence gap can create validity problems in the measurement practices in the field is the confusion between conversations, brand image and reputation that we mentioned in the previous paragraph. A clear understanding of the difference (and relationship) between brand-related talk (interpersonal communication), brand image (perceptions) and reputation (aggregated attitudes and macro-level standing) is needed. Besides this, it is important to consider the complex role and reciprocal impact of different types and sources of communication. Social media conversations participate to a process of influence along with brand communication (marketing and corporate communication) and professional media. All these sources and activities impact on the image that consumers (and other stakeholders) form about the products and the firms (fig. 4). The last issue regards the difficulty of applying general measurement frameworks to specific business and brand contexts. Considering the technology load of most of these measurement techniques, we wish to conclude this study calling the attention of the reader on the possible danger that can originate from a mechanical adoption of the tools available, as often happens when innovative phenomena challenge established procedures and practices of managers. It becomes attractive, in the short-term, to rely on new procedures and practices incorporated in standards and ready-to-use technology tools. This does not help companies to successfully face the new market challenges. Data collection and analysis is expensive, even in the world of digital communication and overload of information puts strain on the efficiency of brand management. The integrated tools for data collection and analysis from social media should be considered, as they are, tools for supporting strategy making; they cannot substitute for it. This does not mean that we should include social media research into a rigid “plan and result” approach, since we know that behavioristic and planning-type versions of control, in complex and unpredictable social collectives, do not work well (Mandelli, 2010). We can, however, try to improve our control on performance through intelligence. If traditional branding was based

on a simplified model of the relationship between communication and consumer behaviour (in the so-called “communication effects” tradition), this simplification does not work well for branding in social media. The complexity of the system and the unpredictability of directions in conversations undermine this possibility. Metrics and monitoring should be considered, in these innovative branding environments (if not ever), as distributed and dynamic social intelligence, which build out - but also bridge - local interactions and conversations, and help the agents make sense of their past and future, in the organizing of their actions (Mandelli, 2010).

APPENDIX 1

Fig. 1 - List of platforms contained in the Forrester Wave Listening Platforms Study Q1 09 and Forrester Wave Web Analytics Study Q3 09.

 

Source: Forrester, The Forrester Wave™: Listening Platforms, by Suresh Vittal, Q1 & Q3, 2009.

Figure 2 - The main phases of the social media measurement process

Source: Murdough C. (2009).

Figure 3 - Social media metrics: a general framework for the analysis

Focal perspective

Unito

fanalysis

Individual

Organization

Network

Brand Industry/economy

Society

Brandengagement

Buzz analysis

Buzz analysis

Buzz analysisBrand advocacy

Consumerengagement

Buzz analysis

Buzz analysis

Reputationanalysis

Reputationanalysis

Buzz analysis

Consumerengagement

Customerengagement

Brand advocacy

Reputationanalysis

Reputationanalysis

Reputationanalysis

Reputationanalysis

Reputationanalysis

Reputationanalysis

Reputationanalysis

Source: Our elaboration

Fig. 4 Conversations, brands image and reputation

Brandimage

ReputationConversations

Brandcommunication

texts

Mediatexts

Source: Mandelli, 2010

APPENDIX 2 Tab. 1 - List of Metrics

Metrics Customer engagement

- Average time played - Numbers of start, reward, stop of player - Completion rate - Conversion rate - Volume of video uploads - Numbers of widget/application installs - Widgets placements - Configurator actions (personalization) - Details views (zoom-in, zoom-out) - Video views and reviews - Registrations or option-in - Content downloads - Posts, tags, revisions - Recency, frequency - Video engagement rate - Cost per engaged visit

Buzz measurement

- The volume of discussions around the brand - Post volume, occurrences, comments - Volume per author - Mentions per social media channel - Conversation index (ratio posts/comments) - The reach level obtained by the social media presence - Buzz competitive performance analysis - Audience composition/segmentation; - Quality of the discussion around the brand/sentiment - Brand perceptions and quality of the attributes - Topics and tonality of comments

Brand advocacy

- Numbers of recommendations - Products reviews and feedback - Embedded contents - Sharing rate - Adoption rate

- Fan contributions/creations (pages, groups,…) - Number of new product ideas - Supporting proneness - Customer loyalty/retention - Bookmarks, votes, ratings

Network influencer

- Numbers of followers - Ratio followers - Numbers of re-twitters - Pass-along rate - Velocity of spread of the idea/meme/message - Acceleration effect - Number of incoming links to a profile, resource - Numbers of invites - Numbers of bookmarks - Credibility/Authority - Rss subscribers - Relevance and Trust - Popularity

Source: Our Elaboration Table 2 – Framework

Phase Activity Output

Concept 1.Mapping measurement objectives 2. Identifying key KPIs 3.Establishing performance benchmarks

1. Goals 2. Objectives 3. Metrics

Definition 1.Itemize insight questions 2.Illustrate the analysis approach 3.determine the frequency of performance evaluation and timing

1. Reach 2. Discussions 3. Outcomes

Design 1.Establishing performance data sources and/or methodologies 2.Enumerating specific technical tracking hooks and manual interventions 3.Set up, configure or customize performance reporting tools

Samples of social media insight tools and data sources to be used

Deployment 1.Conducting quality assurance of data collection methods 2.Validation of performance reports 3.Building data infrastructure

Optimization Reporting and insight

Source: Elaboration from Murdough (2009) in Fuduric M., Mandelli A. (2009).

Table 3 – Old and New Brand Equity Metrics

Source: Our elaboration, starting from Christodoulides and De Chernatony (2004)

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