MSI Proposal Conditions for Owned, Paid and Earned Media effectiveness: The Performance Impact of online Customer-Initiated actions for Better versus Lesser Known Brands of Search and Experience Goods Ceren Demirci Koen Pauwels Ozyegin University, Istanbul, Turkey Shuba Srinivasan Boston University Gokhan Yildirim Carlos III University of Madrid, Spain March 28, 2011 Executive Summary Within the growing variety of online marketing, customer-initiated communication (CIC) such as search and social media stands out in terms of current spending and future growth potential. Many companies have invested in “paid” CIC, such as paid search and affiliate marketing, “earned” CIC such as Facebook and Twitter campaigns, and “owned” CIC, such as search engine optimization leading to higher organic search results. However, the relative effectiveness of these different customer initiated actions remains an unanswered question. This proposal takes a contingency perspective by considering customer initiated digital media in relation with brand strength and the search versus experience nature of the category. We hypothesize and demonstrate how these conditions favor different types of customer-initiated online media. In particular, owned CIC should be more sales effective for known brands, while paid CIC should be more sales effective for lesser known brands. Likewise, earned CIC should be more important for experience goods than for search goods. Combining these hypotheses results in a 2x2 matrix, which yields specific predictions for managers running a better/lesser known brand in a search/experience good category. Our empirical analysis uses Bayesian Vector Autoregressive (BVAR) models to estimate long-term sales elasticities, while imposing restrictions to avoid over-parametrization and wrongly-signed coefficients. Preliminary findings yield results consistent with the hypotheses in two cases, and Marketing Science Institute’s support will enable us to investigate the remaining cases. This proposal touches on all three key areas of the MSI call, in particular the questions: “How does the impact of advertising vary by context?”, “How can firms compare the effectiveness of diverse marketing communication activities?” and “What is the role of the brand in an environment characterized by the emergence of social media and complex, multiple touch points between brands and customers?” 1 Research Statement Since the introduction of the first banner ad in 1994, online advertising has redefined the global advertising landscape. Spending in the sector has continued to grow, reaching $55.2 billion globally in 2009. Expectations are for this expansion to continue, reaching $61.8 billion in 2010 and up to $96.8 billion by 2014 (EMarketer, 2010). However, managers still struggle to quantify the return on investment for activities as different as banner ads and social media. Moreover, the current hype for new media makes it difficult to establish boundary conditions for success stories. Which online media work best for which kinds of brands and products? Several studies have started to explore the sales effectiveness of online advertising. One emerging consensus is that customer-initiated actions; such as search and social media, have a higher sales elasticity than firm-initiated actions, such as banner ads and email (Gartner, Inc., 2008). Indeed, Manchanda et al. (2006) found that banner ads have a lower elasticity (0.02) than offline advertising for a health and beauty product, while Wiesel et al (2010) found that paid search has a higher elasticity (4.35) than offline advertising for an office furniture supplier. Likewise, improvements in “earned” (i.e. social) media metrics such as Facebook likes may substantially increase sales (Pauwels et al. 2010). Finally, many companies have invested substantially in “owned” media, including search engine optimization to rank higher in organic search. These forms of paid, earned and owned media have in common that potential customers initiated the online search and/or conversation. However, the relative effectiveness of these different customer initiated actions remains an unanswered question. The purpose of this proposal is to understand how the effectiveness of paid, earned and owned online media varies by brand and category context. Our hypotheses in Table 1 focus on brand strength, and on the search versus experience nature of the category. 2 Research Background and Hypotheses Differences among Online Customer-Initiated Marketing Actions Customer-initiated actions (CICs) differ from Firm-initiated actions in that they require (potential) customers to actively search for and/or engage in online conversations about the firm’s offerings (Gartner 2008, Hoffman and Fodor 2010; Wiesel et al. 2010). For instance, emails sent by firms to consumers are regarded as firm-initiated, while customer click-through on paid search is regarded as customer-initiated. In terms of spending, customer-initiated search took 47.1% of $22.7 billion US online advertising market (EMarketer, 2010), while customerinitiated social media is expected to grow from 6% to 18% of the online marketing budget in the next 5 years (Alinean 2011). Despite this current size and growth projections, sales and profit effects have yet to be extensively quantified, with Shankar (2008) singling out “spending on unmeasured media such as search marketing” as an emerging research area. Paid customer-initiated media include affiliate marketing and paid search. Affiliate marketing involves merchants sharing a percentage of the revenue when a customer arrives at the company’s website by clicking the content in the merchant’s website (Gallaugher, Auger and Barnir, 2001). Already 11 years ago, Hoffman and Novak (2000) found a low effectiveness of online banner ads, and proposed affiliate marketing as a more efficient way of customer acquisition. More recently, paid search has gained popularity, with US companies spending more than 40% of the total online advertising dollars for paid search (Animesh et al. 2010). In paid search like Google’s AdWords, advertisers bid for to be in a place closer to the top in the listing of the paid search results which are displayed on the top or side of organic search results. 3 Owned media includes the online assets owned by the company, such as its websites and their search engine optimization qualities. The strength of owned media shows up in the company’s ranking in organic search (Yang and Ghose 2010), and in the amount of ‘direct visits’, i.e. visitors that type the company’s name directly into the URL. Such ‘type-in’ traffic may include loyal, repeat customers and late-stage buyers who have already visited the site through other means but needed time to make the purchase decision (Bustos 2008). Earned (social) media for a brand is created, initiated, circulated and used by consumers (Blackshaw & Nazzaro, 2006). Social media activities include blogging, microblogging (e.g. Twitter), cocreation, social bookmarking, forums and discussion boards, product reviews, social networks (e.g. Facebook) and video- and photosharing (Hoffman and Fodor 2010). Consumers are motivated to participate in these activities due to their desire to connect, create, control and consume (ibid). Foresee Results (2010) reports that, while search brings more potential customers to company’s websites, purchase conversion rates are higher for visitors coming from social media. Recently, the medium has drawn criticism given the poor sales results of Burger King and Pepsi, despite social media campaigns that scored in terms of traffic and engagement (Baskin 2011). However, one needs to control for other sales drivers: for a brand losing share due to higher prices and lower distribution, Pauwels et al (2010) found that earned media (brand engagement on Facebook) helped stem these losses. Conditions that favor paid, owned and earned media effectiveness Brand strength: Better known brands versus lesser known brands From an economic perspective, consumers search or sample as long as the marginal benefits of doing so outweigh the costs (Ekelund et al., 1995). Known brands have rich associations in 4 consumer minds (Keller 1993) and thus carry less perceived risk and less costs for search. As they are more easily accessible in consumer memory, known brands are more likely to obtain direct traffic to their site (Bustos 2008). Likewise, search engines have included authority and trust into their organic search ranking algorithms, making it easier for known brands to raise to the top of the organic search listings (Crandall 2009). Because the organic search rank is a strong predictor of both click-through and sales conversion (Yang and Ghose 2010), known brands have the advantage in organic search effectiveness. In this context, paid search gives better marginal opportunities for lesser known brands, such as Inofec (Wiesel et al. 2010) to reach potential customers. Based on an engaging title and message, paid search links are evaluated higher than organic search links by users in terms of relevance (Jansen 2007). By the same token, affiliate marketing with a popular merchant should be more beneficial to lesser known brands. In contrast, several known brands have cut back on affiliate marketing, as they believe it cannibalizes site traffic they would have obtained anyway (NewMediaAge 2010). H1: Owned media (organic search, direct visits) are more effective for better known brands than for lesser known brands H2: Paid media (affiliate marketing, paid search) are more effective for lesser known brands than for better known brands. As for social media effectiveness, brand strength appears a double-edged sword. On the one hand, known brands have an easier time gathering a large social media following (Alinean 2011). On the other hand, the performance benefits of such following are unclear: does social media really generates additional sales for these brands? As one example, Burger King has been receiving several social media ad awards (eg for ‘Subservient Chicken’ and ‘Facebook Whopper Sacrifice’), but fails to see sales increase (Ries 2010). In contrast, lesser 5 known brands consider social media as a low-investment tool with great potential payoffs. The CMO Survey (2011) showed that smaller companies devote the largest part of their budget to social media. Examples of successful media campaigns by lesser known brands include “Destroy Your Printer” by Expert Laser Services (Mueller, 2011) and Crème Brulee Cart in San Francisco (Rao, 2010). Thus, whether better or lesser known brands achieve higher sales returns from social media actions remains an empirical question we aim to investigate. Product category: search versus experience goods Consumers can evaluate the quality of search goods prior to purchase, but can only determine the quality of experience goods after purchase (Nelson 1970). This higher uncertainty makes consumers more likely to search for additional information for experience goods (Nelson, 1974). In the online world, consumers spend more time evaluating experience goods and favor interactive mechanisms (Huang et al. 2009). Moreover, consumers should prefer peer-based recommendations for experience goods, but rule-based recommendations for search goods (Agarwal and Vaidyanathan 2003). While Agarwal and Vaidyanathan (2003) failed to find a preference for collaborative filtering for experience goods, they attribute this to the low source credibility of virtual recommendation agents. In contrast, today’s social media is among the most trusted information sources (Hoffman and Fodor 2010) and the shared usage experiences of other consumers should matter especially when deciding to purchase experience goods. H3: Earned media is more effective for experience goods than for search goods. Combining our hypotheses with informed speculation yields the 2x2 matrix in Table 1, which offers specific predictions for managers in each cell, and helps putting the few previous 6 findings into context. For instance, Wiesel et al. (2010) report the rather high elasticity of 4.35 for paid search. The company in question is Inofec, a supplier of office furniture (search good) which does not invest in brand building (low brand strength). Given the above hypotheses, paid search should be especially beneficial in this situation, and different brand/category combinations are likely to reflect systematically different effectiveness. Moreover, our framework suggests that earned media should be more effective than owned media for this company, and thus receive a higher priority in future experiments. 3. Methodology and preliminary findings Bayesian Vector Autoregressive Models Dynamic system models such as Vector Autoregression (VAR) have been a popular tool to analyze both short-term and long-term marketing effectiveness for offline activities ranging from new product introductions to price promotions, distribution and communication (Bronnenberg et al. 2000; Dekimpe and Hanssens 1999; Pauwels et al. 2004). Moreover, VARmodels appear especially relevant in an online context, given the multiple touchpoints brands have with consumers over time (Trusov et al. 2009; Wiesel et al. 2010). In addition to such ‘dynamic synergies’, we also allow for same-period synergies by adding the interaction terms among paid, owned and earned media. For one, sales may increase when potential customers are exposed to both paid and organic search listings for the brand (Yang and Ghose 2010). Typical issues with VAR-models include overparametrization and wrongly-signed coefficients (Ramos 2003). We can address both through shrinkage, which imposes restrictions on the parameters of the VAR model. Such Bayesian Vector Autoregressive (BVAR) models are formulated in Litterman (1986) and Doan, Litterman and Sims (1984), but have seen little 7 application in marketing (Ramos 2003). Among the possible priors, the Minnesota (Litterman) prior has the advantage of a simple posterior inference involving only the normal distribution (Koop and Korobilis, 2010). Using Doan et al.’s (1984) formula for the uncertainty of the Minnesota prior means, we can specify individual prior variances for a large number of coefficients in the model using only a few parameters (LeSage, 1999). These parameters , and w(i,j) represent the overall tightness, lag decay and the weighting matrix respectively. We estimate the BVAR model through the “mixed estimation” technique developed by Theil and Goldberger (1961). This method involves supplementing data with prior information on the distributions of the coefficients (Ramos, 2003). A typical unrestricted VAR with n endogenous variables and p lags can be written as: Focusing on a single equation of the model: where is the vector of observations on , the matrix X represent the lagged values of and the deterministic components, the vector A stands for the coefficients of the lagged variables and deterministic components and is the residual vector. Prior restrictions for this single equation model can be written as: Where prior mean prior, and is the prior mean and is the standard deviation of the Minnesota . Using Theil and Goldberger (1961), we rewrite equation (3) as: 8 Then, we can find the estimator for a typical equation by using the following formula: In order to find the optimum values for the parameters , and w, we minimize the log determinant of the sample covariance matrix of the one-step-ahead forecast errors for all the equations of the BVAR (Doan et al. 1984). Preliminary findings We applied the above model to analyze the effectiveness of marketing actions by Knewton, a relatively new player (since 2008) in the GMAT test preparation market. Instead of the traditional face-to-face interaction, Knewton uses adaptive learning software to provide maximum flexibility and individual customization to each student’s progress (Di Meglio, 2009). The experience nature of the category is evident: customers will only be able to evaluate the quality of this service after they have experienced it. This situation thus corresponds to the unknown brand/experience good quadrant in Table 1. Consequently, our prediction is that social and paid media would be more effective than owned media. The available marketing data include price, paid search, organic visits, direct visits and online banner ads (i.e. we miss earned media actions). Our dependent variable is the number of customers that sign up for the service. Consistent with our hypotheses, we find that paid search has a higher sign-up elasiticity (2.32) as compared to owned media, in the form of organic search (0.56) and direct visits (0.97). Online banner ads have the lowest elasticity; from 0.004 on a general interest site (Amazon) to 0.11 on a special interest site (US News, which provides annual MBA school rankings). 9 4. Support Needs and Time Table While our pilot study gave preliminary support to some of our hypotheses, we need data in the remaining quadrants of our framework. Especially helpful are data on customer-initiated activity by known brands, many of which are MSI members. Therefore, we appreciate any support in identifying and contacting data providers. In many cases, additional information is needed, and funding will enable us to buy such data, to clean it and get it ready for analysis. The Bayesian VAR estimation also involves substantial efforts in program coding and debugging. Last but certainly not least, we are looking forward to the feedback and advice of the expert review panel on this proposal. Table 2 below details this timetable and summarizes our support needs. 5. Expected Outcomes and their Relation to MSI call priorities The results of these proposed studies are expected to shed new light on how the elasticity of owned, paid and earned media differs according to brand strength and product category dimensions. We aim to address MSI’s challenge to provide insights on how to manage an expanded marketing communications mix that includes multiple media and channels, and rich marketing communications. While the effectiveness of traditional firm-initiated communications is relatively well understood, the sales and profits effects of new customerinitiated media are much debated. This proposal touches on all three key areas of the MSI call, in particular the questions: (1) How does the impact of advertising vary by context?, (2) How can firms compare the effectiveness of diverse marketing communication activities?, and (3) What is the role of the brand in an environment characterized by the emergence of social media and complex, multiple touch points between brands and customers? 10 Table 1: Conceptual framework and hypotheses Search Good Experience Good Owned > Paid, Earned Earned > Owned > Paid Example: to be collected Example: to be collected Paid > Owned, Earned Earned > Paid > Owned Example: Inofec office furniture Example: Knewton test preparation Known brand Unknown brand Table 2: Support Needs and Time Table Budget Item Time of completion Cost 1 Complementary data collection September 15, 2011 $ 3,000 Cumulative Total $ 3,000 2 Program coding and debugging 200 hours @ $25 per hour October 15, 2011 $ 5,000 $ 8,000 December 1, 2011 $ 10,000 $ 18,000 3 Data analysis and verification results 400 hours @ $25 per hour Total funding requested from MSI $ 18, 000 Target completion interim report December 15, 2011 Target completion date for paper March 15, 2012 11 References Agarwal P. and R. 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