Quantifying online buzz and its impact on product launch
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Quantifying online buzz and its impact on product launch Quantifying online buzz and its impact on product launch Document Transcript

  • Measuring Buzz in the context of Apple Inc’s iPhone launch Justin Thonzakhup, T Ginvanglian IIM Bangalore Abstract - This paper seeks to quantify online ‘buzz’ about a consumer product launch and measure its value before, during or after the event. We developed a model that enables us to measure ‘buzz’ (also called ‘word-ofmouth’ equity). We then used our model to measure Iphone’s word-of-mouth equity over the years since its first version was launch in 2007. It was found that wordof-mouth equity subsides over the course of months and at different rates for different iphone versions like iphone 3G, 3GS. There seem to be a correlation between word-of-mouth equity and unit sales and also share price. When compared to a competitor like HTC which manufacture mobile phones before apple did, HTC’s word-of-mouth equity increases 4.5 times during its product launch (measured from one month prior and one month post launch), with corresponding 32% increase in sales within the next 3 quarters. By contrast Apple’s WOM Equity increases only 1.7 times but the corresponding sales increase within the next 3 quarters was 750%. This strongly indicates about the significance of the magnitude of the initial ‘buzz’ that is relevant to consumer interest that can generate product purchase. Index Terms – Word of mouth, iphone, News, Apple, HTC. 1. INTRODUCTION Rumours are powerful agents that can convey powerful information sometimes affecting our day to day life. Rumours about war in the Gulf drive oil prices up; rumours about a possible financial bankruptcy can de-value a company’s stock price within a few seconds. With no geographical boundaries, online rumours that spread at the click of a mouse have gain more and more prominence and more so increasing number of people beginning to have access to the internet. As of March 2011, the number of internet users stood at about 2.095 billion people (~30% of the world’s population)1. Over the years, huge volumes of content about a product, a service, a book, or an idea etc. have been generated over the Internet. This paper deals with the kind of rumour specifically about a particular consumer product, the mobile phone. In the Internet medium, such information is generated through online news, company websites, discussion forums, blogs, product reviews. We choose Apple Inc. because it has been traditionally 1 http://www.internetworldstats.com/stats.htm associated with generating huge excitement in the media when launching its latest products. Even before the revolutionary iPhone was launched, a great deal of information about the product was generated over the Internet about the expectations, speculations, predictions etc. These buzz existed in the form of speculations about possible launch dates, the features that could be incorporated in the product and so on. Quantifying such buzz around a product is the first step towards having a sensible discussion about it and its significance. Giving a quantitative value to ‘buzz’ is always a challenge faced by managers and marketers. This paper present a novel approach of how to quantify the buzz about a product based on a model which takes into account two factors namely: the relevance of buzz and on how influential the source is. 2. THEORY BACKGORUND 2.1 Concepts of buzz and WOM Emmanuel Rosen in ‘The anatomy of buzz’ defines buzz as simply the hum of everything that we tell each other about products, movies, people, books, and ideas. Buzz is a result of the Word-of-mouth marketing. Buzz can be either a relatively commercial variant or a seemingly less commercial kind of buzz, which is created by passionate individuals with no expectation of commercial gains or benefit in return (Larceneux, 2007). Several studies have shown that word-of-mouth (WOM) communications often exert a strong influence on judgments of products . For example, consumers frequently rely on WOM when considering the purchase of a new product or service (Arndt 1967; Brown and Reingen 1987; Reingen and Kernan 1986; Richins 1983). Word-of-mouth (WOM) has a stronger influence than those printed communications . Positive word of mouth has higher influence than negative word of mouth . Consumer networks exhibit decay, possibly due to the dissipation of involvement. This phenomenon was noticed by Holme (2003) in his study of dating networks. He noticed that ties decay exponentially as time goes on because of decreasing contact. Bart et al. (2005) note that community features are a factor driving trust in Web sites, especially those characterized by information risk (the risk associated with revealing personal August 2011 Term 4 CCS
  • information). They propose that “shared consciousness and a sense of moral responsibility and affinity enhance the consumer’s level of trust” and may make consumers more confident in acting on information gained from online communities. WOM Equitymedia = ∑of all websites (Influence of website x Relevance of content) = ∑of all websites (I x R) 2.2 WOM measurement Where, I = Influence of website R= Relevance of the content to the context Several attempts have been made in earlier research on how to measure buzz. Schillewaert et al (2007) proposes an approach for measuring buzz. Research shows that 59% of communications about brands happen at home. There is a high percentage of buzz at work or school, a good amount of buzz at shops, food establishments, and public transport. Online forums, company websites, blogs, chat sessions; newspaper articles are some of the online media through which consumers are informed about brands. Google provides an option to choose the medium ‘News’ and also allow us to fix the range of dates say between Oct 1, 2007-Dec 31, 2007 (Q1-Financial year 2007). This is shown below. Figure 3a Google search is used with specific keyword for the search along with sorting by date (not relevance) Bughin et al (2010) proposes a new way to measure wordof- mouth marketing. The impact of word of mouth is called word of mouth equity. The research proposed that word of mouth equity is a product of the volume of buzz and the level of impact. 3. METHODOLOGY AND MODEL Our model is a direct descendant of the one proposed by Bughin et al (2010) with the important difference being we decided to take only two of the four factors they proposed that influence consumer decision to purchase. Also, we devise our own meanse to measure those corresponding factors. The model is discussed below. 3.1 Factors influencing word-of-mouth The following are the four factors influencing worth of mouth equity namely2: 1. 2. 3. 4. Network: Where are they talking? (+) Close/trusted, (-) Large/dispersed Message content: What are they saying? (+)Relevant key buying factor, (-)Irrelevant key buing factor Sender: who is talking? (+)Influential, (-) Non-influential Message source: What is the trigger? With regard to point 1, our measuremnet is based soley on online ‘News’. Of the remaining, our model considers message and sender as the most important factors and we ignore the last point 4, since our interest is about quantifying buzz and not necessarily about a specific type of buzz that may necessarily result in consumer decision to purchase. From now on we use ‘buzz’ and word-of-mouth equity also called WOM Equity interchangeably. According to our model, for N number of websites: 2 McKinsey Quarterly: A new way to measure word-of-mouth marketing, April 2010, Bughin et al (2010) August 2011 Term 4 CCS
  • 3.2 Influence (I): We quantify the influence of a particular website by its page rank. In our particular model we use the website’s alexa ranking3. The highest rank being 1 will have the maximum influence and greatest contribution to the WOM Equity. We fix the lowest rank to be 1,000,000 and convert the alexa rank for its contribution to WOM Equity using the formula below. I = 1-(alexa rank/1,000,000) I varies between 0 and 1. i.e., a website like ww.google.com which have alexa rank=1 is considered to have maximum influence since, I = 1 – 1/1,000,000 ~ 1. 3.3 Relevance (R): A page analysed in the context of a product launch like ‘iphone’ is considered irrelevant if its falls in any of the following categories: 1. 2. If the product is shown in negative light (see Appendix Table A.1 for examples). It is mentioned out of context e.g. in the context of other product (see Appendix Table A.1 for examples). In contrast, a website which is relevant to ‘buzz’ about the iphone is exclusively about the product. It may even be a neutral article about the iphone, as long as it is not negative about the product. If other products are mentioned, it should be in the context of the iphone and not vice versa. Table 3.4 Both rank information and content relevance is collected per website Oct 200x to Dec 200x Google Page no. Link for each page (From top) Alexa rank I R 1 1 1 10 6 2 10,000 70 4673 0.9900 0.9999 0.9953 1 N (results)=16,400 1 1 No. of websites 547 547 547 WOM Equity 541 547 544 The WOM Equity for ‘N’ results for media ‘News’ now becomes: WOM EquityNews = ∑30 (I x R) x (N/30) 3.5 Data sources For this paper, we sample a total of 49 quarters, 41 quarters for Apple Inc. and 8 quarters for HTC Corp. i.e., we individually-manually analysed a total of 49*30 = 1470 websites for its relevance and ranking. Each sample analysed was documented giving the google page number and position in the search page, keeping open the possibility for third party verification. All data is attached in Appendix at the end. 4 RESULTS We give the weights, R = 1, if the content is relevant to the product. = 0, if the content is not relevant to the product. 3.4 WOM Equity for N websites: The earlier example for ‘iphone’ News search returned 16,400 results. And it is impossible to analyze each individual website for its influence and relevance of their contents. So, we sample randomly 30 websites and took their individual alexa rank and analyze each of them for relevance based on the above framework. For instance, if the 1st website analyzed is relevant and has 10,000 alexa rank, its WOM Equity is =IxR= 1x (1-10,000/1,000,000) =0.99 We extrapolate the results of these 30 samples to the whole 16,400 results i.e., we assume that for every page like the one we analyzed there are other 16,400/30 ~ 547 websites which are both relevant and are equally influential. Such 547 website has WOM Equity = 0.99*547 = 541. We repeat the same procedure for the remaining 29 samples. 3 Alexa has been crawling the web since early 1996. It currently gathers approximately 1.6 terabytes (1600 gigabytes) of web content per day. (http://www.alexa.com/company/technology) The search item for analyzing Iphone’s Word-of-mouth Equity is ‘iphone’. Data is analysed per quarter from FY 2007 Q1 till FY 2011 Q2. Also, with the release of other Iphone versions like the Iphone 3G, Iphone 3GS and Iphone 4, we separately analyzed their WOM Equity just on the eve of their launch spanning about 6 quarters from 2007 Q1. Among others, we tried to compare the results with Apple’s EPS (Earnings per share). This makes sense because by Q4 2008, the Iphone had become a significnat revenue earner for Apple contribution to more than 30% of the company’s gross margin. We also analyzed our findings from the perspective of the unit sales of Iphones (and other related products and services), the data being publicly available on Apple’s website. We continue our analysis by comparing Apple’s WOM Equity with that of Apple and how their respective WOM Equity changes with time. The idea behind choosing HTC Corp. is not by accident but because HTC Corp. was an already existing player selling smart phones and the launch of HTC’s new smart phone HTC Touch was the same month, June 2007. The search item for HTC Touch’ WOM Equity is ‘htc touch’ with ‘News’ as the media for our analysis, the same as for the ‘iphone’. Again, sales figures for HTC Touch shipments are publicly available on the company’s webstie. August 2011 Term 4 CCS
  • Figure 4.5. Iphone’s WOM Equity over the years 4.5 Apple’s iphone launch Apple's long-awaited entry into the mobile phone market was the biggest news of 2007. Apple’s CEO Steve Jobs described the iPhone as a "magical device" that would "revolutionize the industry". During the launch weekend of 29 June, more than 270,000 were sold in US stores. The speculation about the iPhone generated an estimated $400m free advertising for Apple4. Table 4.5 Product launch dates: typically a 1 yr. gap Launch dates June 29, 2007 July 11, 2008 June 19, 2009 June 24, 2010 Products iPhone iPhone 3G iPhone 3G iPhone 3GS iPhone 3GS iPhone 3GS iPhone 4 4.6 WOM decay: Iphone 3G Figure X cleary shows how the WOM Equity of Iphone 3G subsides over the years. Launched on July 2008, its WOM Equity continues to subside during FY 2009 down to about 500 and it is not affected by the ‘buzz’ of the Iphone 4 launch during 2010 Q3. This decay in WOM Equity Figure 4.6 Iphone 3G 5 years after it was launch Variations 8 GB 16 GB 8 GB 16 GB 8 GB 16 GB 32 GB In fact the WOM Equity of Iphone increased from 4067 in Jan 2008 to 11088 by September the same year; a 1700% increases. It reached its peak by December reaching a value of 13386. Refer Appendix for exact values. Also from 4 Figure 4.5, there appears to be a correlation between unit sales and WOM Equity. (http://goliath.ecnext.com/coms2/gi_0199-7029582/Non-food-Apple-siPhone.html) This decay is analogous to what Holme (2003) recognized in his study of dating networks mentioned earlier. Whether the decay is exponential or not is left to speculation but like in exponential decays, Iphone 3G’s WOM Equity hardly August 2011 Term 4 CCS
  • reaches zero, floating around a steady value of around 500. In fact during our website analysis, such remainder WOM Equity is contributed by countries like China where ‘buzz’ happens even at somewhat at a later stage far from the time of its initial public launch. correlation between a particular quarter’s WOM Equity and the next quarter’s EPS had been observed, we decided to check the correlation of all WOM Equity (relevant + irrelevant) and the next quarter’s EPS. The two resulting plots are shown in Figure x and figure y. 4.7 Iphone 4 Figure 4.8a WOM Equity vs. Next quarter’s EPS where irrelevant content is not ignored Figure 4.7 Huge spikes on Iphone’s WOM Equity just prior to launch of the Iphone 4 Figure 4.8b WOM Equity vs Next quarter’s EPS where irrelevant content is ignored IPhone 4 has abnormally high buzz. In fact iPhone 4, released on June 24, 2010 in the U.S., Britain, France, Germany and Japan, sold a record 1.7 million in just three days after the launch, making it the most successful start in the company’s history in terms of single product sales5. This corresponds to a dramatic shoot in WOM Equity by 4.6 times from 8447 in Q2 to a peak 39482 in Q3 2010. What is interesting though is the significant decrease in WOM Equity from the last quarter of 2009 to a very low value 8447 prior to the launch of the Iphone 4. In fact, the last time Iphone’s WOM Equity went lower than this was way back in Q3 2007 (a value of 6426). There is no clear cut explanation for this.One strong possibility is due to the launch of the Ipad in January 2010. Many search results during this period often return the Iphone in the context of the newly introduced Ipad. 4.8 Earnings per share and WOM Equity Till this time, in our discussion about WOM Equity, w e had incorporated in our model only relevant factors i.e., we give zero weightage to irrelevant news. To check the significance of this we decided to change all weightages to 1, whether the information is relevant or not. Since some kind of The resulting plot seems to suggest a closer relationship between WOM Equity and EPS when the weightage of irrelevant websites is given zero (which was followed in our model). This is not to say that a one to one correlaton exists between the WOM Equity of our model and EPS but it gives some justification for bothering to distinguish between relevant and irreleant contents out of the hundredes of websites we analyzed. 5 (http://www.zacks.com/stock/news/36222/Apple%92s+iPhone+4+Sets+Rec ord) August 2011 Term 4 CCS
  • 4.9 HTC Touch vs. Iphone: Unit sales Launched the same month as the Iphone, shipped units of HTC Touch was soon overtaken by the Iphone’s sales as shown in figure below. with corresponding increase in its units sales within the next three quarteres being 750%. However, the initial January ‘buzz’ for HTC Touch was a mere 250 compared to Iphone’s 4067. Figure 4.10b Huge ‘buzz’ build-up for the Iphone prior to its launch Figure 5. Quarterly Units shipped in thousands 4.10 HTC Touch vs. Iphone: WOM Equity HTC Touch was launched globally on June 5, 20076. 5 Concluding remarks Figure 4.10a Weak buzz before launch HTC Touch’s launch date 5.1 Summary of findings & implications for marketing management Our goal for this pape was to quantify ‘buzz’ in the context of product launch. To summarize. (1) There appears to be a correlation between unit sales of iphone and the buzz around it. How are they actually correlated is beyond the scope of this paper but examined against HTC Corp., bigger ‘buzz’ seem to favour better sales. ‘Buzz’/WOM Equity of HTC Touch during June, the month of its launch was less than WOM Equity of Iphone 3G 1 year after its launch (see Figure 4.6), a value of 912 for Iphone 3G against 902 for HTC Touch. Also, during the first three quarters of 2007 covering before and after its launch, HTC Touch’s WOM Equity increases 4.5 times and corresponding increase in unit’s shipped within the next three quarters was 32%. In contrast, within the same time, Iphone’s WOM Equity increases 1.7 times 6 HTC Corp. (press release), 5 June 2007 (2) ‘Buzz’/WOM Equity decays over time. Hence the need to launch new versions. It is interesting though to note that ‘Buzz’ of Iphone 3G after 1 year could still be greater than that of HTC Crop one month inside its launch date. ‘Buzz’ decays very slowly over time, perhaps made possible by promotion/launch in other geographical locations. However, the sacrifice of magnitude of ‘buzz’ by not launching in simultaneious geographical locations has to be examined against possible future launch opportunity. And there seem to be a strong case about the significance of the magnitude of the initial ‘buzz’ in relation to initial product sales. (3) Prior to product launch, it is not the percentage increase in ‘buzz’. What counts is the magnitude of ‘buzz’. (4) With the advent of newer technologies and fast changing fads in consumer products like the iphone, August 2011 Term 4 CCS
  • companies like Apple Inc. have only a small window of opportunity to make the greatest sales. Launch of Ipad and Iphone 4 around the same time could affect the buzz around either of the product. This in turn could significantly affect the huge initial sales of either of the two products that are fed by the huge buzz around it. Companies have to strategize accordingly. 5.2 Future research opportunities Of particular interest is the strong correlation between ‘buzz’ and the next quarter’s EPS. To dig deep into this topic further, a thorough regresssion analysis could be the way forward. The same could also be done between ‘buzz’ and unit sales with different time periods like weeks, months apart from the present quarterly analysis. Also, the huge data collected amounting to 1470 websites with their rankings and relevance to a particular product could be leverage from another research perspective. 6 ACKNOWLEDGMENT The authors thank Dr. Manaswini Bhalla, Assistant Professor, Economics & Social Science area for the guidance and support that they received for completing this project. It would not have been possible without the continuous feedback and guidance from her. The motivation and encouragement that they received during the course of the project was of immense help. REFERENCES [1] Herr, Paul, M; Kardes, Frank, R; Kim, John "Effects of Word-ofMouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective ", Journal of consumer research. Inc. vol. 17 March 1991, pp. 454-462 [2] East, Robert; Hammond, Kathy; Lomax, Wendy, " Measuring the impact of positive and negative word of mouth on brand purchase probability ", Intern. J. of Research in Marketing 25 (2008) pp. 215– 224 [3] Bughin, Jacques; Doogan, Jonathan; Vetvik, Jorgen, Ole, “A new way to measure word-of- mouth marketing”, McKinsey Quarterly, April 2010 August 2011 Term 4 CCS