Creating a Measurable Social Media Marketing Strategy: Increasing the Value and ROI of Intangibles & Tangibles for HokeyPokey (MKSC

Transcription

1 Creating a Measurable Social Media Marketing Strategy: Increasing the Value and ROI of Intangibles & Tangibles for HokeyPokey (MKSC R2) Web Appendix I

2 Figure WA-1: User Dashboard Web-Appendix 1 Users could login to a custom dashboard to view new creations that their friends were talking about, check their total points and redeem points for various goodies. The dashboard also doubled up as a Twitter client from which users could post and view all their Twitter activities. Figure WA-2: Admin Console The research team and HokeyPokey management could login to the admin console and view all the activities, including information about CIE and CIV, customer insights, and the management of rewards. II

3 Figure WA-3: Share Your Brownies Handbook A handbook was distributed to high CIE customers and seed users to give them a grasp of the game as well as excite them with tangible material. The details can be found in the following link : III

4 Figure WA-4.: Campaign Timeline - Media timelines and the Activities Phase Activity Jun -09..Aug- 09 Sep-09..Nov 09 Dec-09 Jan-..Mar Apr- May-..Jul1 0 Aug- Sep- Oct-..Dec Jan- 11..Mar 11 Apr- 11..Jul11 Market Research Platform Identification Forming initial set in Table A-1 Scoping and Penetration Identifying optimal platform choice Software Development Keyword Tracking Engine Stickiness Association Mapping Mappi ng associa tions Searching & categorizing WOM Spatial SI mapping Social Graph Construction Realtime CIE/ CIV Calculator Calc. α Calc. CIE Calc. CIV Social connection tracking and Storing & retrieving user meta Pre-test Share Your Brownies Management Console Constructing Messaging Strategy Analysis of Message Choice Implementation Nonincentivized Influence Tracking Calculating CIE Seeding message formats Estimating CIE drivers Measuring influence Constructing Ideal Message Format I

5 Phase Activity Seed User Analysis Jun -09 Aug- 09 Sep-09..Nov- 09 Dec-09 Jan-..Mar Apri- May Initial Campaing Construction..Jul1 0 Aug- Sep- Oct-..Dec - Seed launch (in Ahmedabad) Jan- 11..Mar- 11 Apr- 11..Jul11 Launch Incentivized Influence Tracking Tier-I Incentivization for top CIE customers Tier-II Incentivization based on future CIV Creations on the Wall In-store promotions Parlor Staff Training Campaign Deployment Share Your Brownies Campaign Sales data integration Handbook creation Operations staff training Trainin g video creation Key Performance Measurement Measuring baseline sales Measuring baseline buzz and awareness Collecting buzz and WOM data Customer feedback measurement Level & Type of Involvement Passive (observational) Offshore strategizing Offshore analysis, measurement & modeling Offshore development Onsite training Onsite setup Active (run-time data) Feedback Key II

6 Figure WA-5: Conceptual framework with the Drivers- Predicting CIE and CIV I

7 Web-Appendix 2: Calculation of CIE The calculation of CIE accounts for cyclic graphs as well as acyclic graphs and an explicit effort has been made in the current research to limit its scope to acyclic networks for 2 reasons: a. First, to provide a simple example for the readers an acyclic graph is used. For example, if a cyclic graph were used (A influences B and B repeats the message, influencing A, and so on) conceptually the calculation of CIE for A would result in a (decaying) geometric progression of the type: CIEa= SPREADa + INFa(CIEb) => CIEa= SPREADa + INFa(SPREADb + INFb(CIEa) and so on since influence is always <=1. In simpler terms, If A influences B with a certain WOM, and then B influences C, and C influences A again, what is the extent to which A's initial WOM influences himself? For example, if you were to tell a friend about why a certain car is great, and the friend talks to their friend, and subsequently this second friend talks to you about the same car, it would be a practically impossible to tease out the extent to which you have influenced yourself once again. b. The current research does not account for resonated influence (that is a user being influenced by someone repeating their WOM instance). Therefore we are focusing the current research on acyclic flow of WOM only. II

8 Web-Appendix 3- Rationale for Hypotheses Phase 1 Sub-Phase 1: Probability of Tie Existence In this study, the existence of a tie refers to the information path or a directed WOM channel between a pair of users. In Twitter, this can be interchangeably used to indicate the existence of a (single, unidirectional) follow relationship between the user pair. Specifically in the case of Twitter, the presence of a tie implies that a recipient follows a host. The existence of this tie is contingent upon characteristics of the host, the prospective recipient and the network characteristics that affect the recipients perception of the host. The factors that cause a user to follow another user are categorized into (1) Generosity, (2) Compatibility and (3) Host Clout. Generosity: Generosity factors are antecedents of tie formation that are caused due to a feeling of indebtedness or gratitude. The social norm of Reciprocity is the expectation that people would respond to each other similarly (Gouldner, 1960). The act of a followed user in Twitter returning the favor by following the follower is considered social etiquette by a considerable proportion of Twitter users. Secondly, since users may check their existing followers at any time, the act of establishing a tie with a user increases the visibility of the following user to the followed user. Therefore, probability of existence of a follow relationship between a pair of users increases if a reciprocal relationship exists between the pair of users. Accordingly, we propose the following: H1: The probability of user A following user B at time t increases if user B already follows user A at time t-1. Network Compatibility: The compatibility between a pair of users is defined in this research as the degree of similarity in the topics that they discuss in their Tweets. We further define the degree of similarity based on (a) the category match between the Tweets of the two users, (b) the extent of common topics discussed by the users and (c) the affiliation of the user pairs to the same list. The similarity in Tweets posted by a pair of users is the degree to which their SI values are correlated. A higher match in SI indicates that the users discuss similar topics in their Tweets, while lower and negative values indicate that the topics discussed by these users are dissimilar. Therefore we propose the following: H2: The probability of a tie relationship existing between a pair of users is directly proportional to the degree of match between their SI values. In Twitter, Tweets are often categorized (tagged) within topics by using a specific format known as the Hash Tag (any word preceded by the # symbol). These tags form searchable queries on this tag, thereby III

9 providing a ground for users who speak about these tags to be visible to those who search these topics. We may argue that users who discuss more common topics are more likely to have a follow relationship between themselves than those who discuss fewer or no common topics. Therefore we hypothesize the following: H3: The probability of a tie relationship existing between a pair of users is directly influenced by the extent of common topics discussed by users. Finally, Twitter features user lists, through which users may affiliate themselves or others into groups. Users who belong to the same group are both more visible to each other and have a common linkage (affiliation to the group). Therefore we propose the following: H4: The probability of the existence of a tie between a pair of users increases if the users are affiliated to the same user lists. Host Clout: The clout or perception of superiority enjoyed by one user over another is an important driver to the latter user following the former. The number of followers that a user has, or the in degree of the host, is visible to all other users in Twitter. Superficially, the number of followers that a user has is often perceived by others as an indicator of the popularity of the user. An example is the famous Follower War between the Hollywood celebrity Ashton Kutcher and Turner Broadcasting (CNN) to be the first to reach over a million followers. Therefore, the in degree connections that a user has at a time period has a positive feedback on itself in the next time period. Popular users habitually attract more followers. Therefore we propose the following: H5: The probability of a user (user A) following another (user B) at time t is directly proportional to the number of followers that user B has at time t-1. The normalized number of followers that a user has at a point of time is known as the in degree centrality of the user. This value is also significant from the information spread perspective, since it refers to how central a user is to information flow within the network. In other words, the number of followers that a user has indicates the number of people the user can directly influence through her/ his Tweets. The degree of triangulation between the user pair is another measure of clout that serves as an antecedent to the formation of a follow relationship. Triangulation effect refers to the situation where user A follows users B 1, B 2 etc. who follow user C, which leads user A to follow user C. Theoretically, there are numerous reasons justifying this hypothesis. Primarily, this measure draws from basic constructs in crowd psychology such as peer pressure and the bandwagon effect and specifically the concept that a IV

10 friend of my friend is also my friend. Second, if a number of people who user A follows also follow user C, the Tweets posted by user C are likely to be of interest to user A. Finally, user A receives information from user C through multiple intermediaries, thereby enhancing user A s perception of user C (C s clout on A). In other words, we argue that the probability of user A following user B is directly proportional to the number of users that A follows that also follow B. Therefore we propose the following: H6: The probability of a user following another is directly proportional to the degree of transitive triangulation between the two users. Phase 1 Sub-Phase 2: Probability of Seeing WOM Instance Once a tie relationship is established in the form of a prospective receiver user following a host user, the receiver gets all the Tweets posted by the host. However, since a user may not be assumed to be active on Twitter at every time instance, she/he may not see all received Tweets. Further, upon signing in to Twitter, the number of Tweets that users see immediately from their followers is limited to twenty. Therefore, even if a prospective receiver followed a host, an instance of WOM does not influence a receiver who has not seen it. Since Twitter is a real-time platform, Tweets posted by a user are visible to their followers instantly. Therefore, for user A (host) who is followed by user B (receiver), user B is more likely to see user A s Tweets if the two users are active on Twitter at the same time. The match in the window of activeness between the pair of users is a predictor of whether or not B sees A s Tweets. The attributes responsible for this match between a host- receiver pair is called the Activeness Compatibility factor. Activeness Compatibility: Twitter users often follow the updates of a number of other users. Therefore, if user B followed user A, but also followed a number of other users, A s Tweets may be lost in the crowd of Tweets that B receives, and may not be seen. Therefore, the probability of user B seeing user A s instance of WOM depends upon user A s share of user B s received Tweets. In other words, the chances of user B seeing any Tweet sent by A are driven by the proportion of A s Tweets amongst the total Tweets that B receives from all of his/her other followers. A greater match in the activeness window between a host and a receiver could be argued to translate to an increased probability of the host s message being seen by the receiver. Therefore, we propose the following: H7: The relationship between the probability of a host s Tweets being seen by their followers, and the match in the activeness windows of the two users is moderated by the share of the hosts Tweets among all the Tweets that a follower receives. V

11 Phase 2 Sub-Phase 1: Probability of Information Spread Once a channel for information flow is established and a receiver has seen the instance of WOM from the host, the receiver becomes influenced by the message contained in the WOM instance. There upon, the receiver may either choose to further spread this instance of WOM, or not take any action. The act of forwarding a Tweet in Twitter is known as ReTweeting. ReTweeting may be superficially seen as the metric to measure the spread of a message. However, receivers may to add their beliefs to the instance of WOM, reword the instance, or propagate the contents in their own words, at a later time. The spread of an instance of WOM by a user refers to the propagation of the contents and/ or context of the message and not just the message itself. The factors that drive a receiver to spread a particular message from a host are categorized into (a) Message stickiness to receiver, (b) Talkativeness of receiver and (c) Perceived host expertise. Message Stickiness: The stickiness of any information content refers to the degree to which the received message appeals to the receiver. The probability of a receiver further spreading an instance of WOM would therefore depend upon the attributes of the instance itself, such as how closely the message contents match the receiver s beliefs and conversations usually spoken about. The match between the contents of the WOM instance match with those of the receiver is measured as the degree of association between the SI of the Tweet and the SI of the user. A high match between the two SI s indicate that the contents of the Tweet match the contents of Tweets usually posted by the user, thereby translating to a better fit of the instance of WOM with the user s beliefs. While the formation of a follow relationship is driven by the degree of similarity in SI across all Tweets sent by the two users, the spread of an instance of WOM is dependent upon the SI match between the receiver and the actual Tweet itself. Therefore, we propose the following: H8: The probability of a receiver spreading an instance of WOM is directly proportional to the match between the SI of the contents of the instance of WOM contained in the Tweet and the SI of the receiver across all global keywords. Talkativeness of Receiver: A proportion of users in many social networking platforms act as silent observers, while some limit their posts to their own views. In other words, not all users within a network actively indulge in spreading information that they receive even if it they like them. Therefore, intrinsic characteristics of the receiver, such as the receiver s tendency to propagate or communicate or Talk within the network further the probability of her/ him propagating the message. Some users have a greater tendency to spread messages than others, denoted by their ReTweeting habits. Finally, some users may not specifically ReTweet a message, though they may reword the contents or add their thoughts to it. Adding information to an existing thought flow within the network also takes place through the use of VI

12 hashtags. Users frequently contribute to hashtags in topics that interest them, once they realize the existence of such a conversational thread through the hosts Tweet. Therefore, we propose the following: H9: The probability of a receiver further spreading an instance of WOM by forwarding the contents of a Tweet to their followers is directly proportional to the overall ReTweet frequency of the receiver. H: The probability of a receiver spreading an instance of WOM by adding their own contributions to the conversation thread is directly proportional to the number of hashtags that the receiver references within their Tweets. Perceived Host Expertise: Finally, host attributes such as the perception of superiority and clout enjoyed by the host with the receiver are drivers to a receiver spreading a WOM instance. Host expertise factors driving the spread of a message are similar to the Clout factors that lead to tie formation. However, the superiority of a host, with respect to the spread of a message, is observed in three levels - (a) The receiver perceives the host as an expert within a specific domain, (b) The receiver is a fan, or has a general preference to the host, and (c) The receiver believes in a sense of superiority by forwarding and mentioning the host s Tweets to their followers. The perception of the host as a domain expert leads to increased credibility of the host in that subject. Therefore the receiver has a greater confidence in the information posted by the host, within the subject of the host s expertise. For example, popular technology blogs such as Mashable and Scobleizer are perceived to have more authentic industry information and analysis within the technology industry. Therefore, WOM of these hosts are spread widely by followers who perceive them as experts within the domain. Followers sometimes are fans of certain users. Celebrities such as Ashton Kutcher and Trent Reznor enjoy a fan base amongst eager followers. These followers generally tend to spread Tweets from these hosts probably to feel a connection with their icons and stars. We measure the effect of fan followers by providing a dummy variable to indicate the star status of a user. Finally, the position of the host within the network topology contributes to the hosts messages being spread by a follower. Networks such as Twitter are inherently driven towards completeness (reference). As is common in real-world peer networks, users feel a sense of belonging to a group by discussing common friends and contacts. In the context of Twitter, a receiver has a greater motivation to spread and discuss the contents of an instance of WOM propagated by a host if the receiver s followers form a clique or a dense sub-network with the host. In other words, a receiver spreads the host s message if a large number of the receiver s followers also follow the host. Therefore the degree of transitive triangulation is also a driver of a receiver spreading a message. Therefore we propose the following: VII

13 H11: The probability of a receiver spreading an instance of WOM of a host is directly proportional to the match between the SI of the message and the SI of the host (perception of expertise in the domain). H12: The probability of a receiver spreading an instance of WOM depends upon the celebrity status or external clout that the host has. H13: The probability of a receiver spreading an instance of WOM is directly proportional to the degree of transitive triangulation between the host and the receiver. References: Gouldner, Alvin W. (1960), "The Norm of Reciprocity: A Preliminary Statement," American Sociological Review, 25 (2), VIII