World Cup 2010 Sponsorship Effectiveness: ESPN XP Cross-Platform Research
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World Cup 2010 Sponsorship Effectiveness: ESPN XP Cross-Platform Research

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Sponsoring the World Cup. How Pointlogic helped ESPN demonstrate effectiveness

Sponsoring the World Cup. How Pointlogic helped ESPN demonstrate effectiveness

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World Cup 2010 Sponsorship Effectiveness: ESPN XP Cross-Platform Research World Cup 2010 Sponsorship Effectiveness: ESPN XP Cross-Platform Research Document Transcript

  • ESPN XP Cross-Platform ResearchAuthor: Iris Donkers, Senior Analytical ConsultantE: donkers@pointlogic.comAuthor: Nicole Alexander, VP Global MarketingE: alexander@pointlogic.comNovember 2010BACKGROUNDESPN is an American cable television network dedicated to broadcasting and producing sports-related programming 24 hours a day. They also own the rights to broadcast World Championship Soccer ‘10 in South Africa. 10 different brands participated in sponsoring the World Championship Soccer ’10 at ESPN’s media platforms. Different kinds of research has been done during the broadcasting periodESPN has tracked the effectiveness of sponsoring of 10 major brands: Sony, Adidas, AT&T, Budweiser, Bud Light, Cisco, EA Sports, Hyundai, Powerade, and Nike. They have done various surveys with various companies such as an effectiveness study with OTX for Sony, Adidas and AT&T measuring Sponsorship awareness, Affinity and Purchase intent; an effectiveness study with Knowledge Networks for all brands measuring Brand Awareness, Sponsorship awareness, Slogan Recall, Slogan Attribution, Consideration, Purchase intent; audience measurement of TV and online with Nielsen for Sony, Adidas and AT&T measuring exposures for 46,000 respondents in a fused TV and online dataset; lastly a WOM tracking study by Keller Fay for all brands measuring the amount of WOM. The sponsoring for these brands was done trough TV (in match, commercial breaks, etc); Radio; Magazine (ESPN magazine); Mobile internet; Online (video, banners, and homepage roadblocks). Pointlogic was tasked with combining these different researches and determining sponsorship effects for the media sponsors and implementing the effects in a user friendly tool that allowed advertising budget allocations to be optimized. We happily took this challenge on and worked with all of the different research providers to align the data and implement that data into Pointlogic’s Chorus platform.<br />Sources & Data<br />The key challenge was that no single source contained all information necessary for gaining insights in the real effect of the sponsorships. Knowledge Networks contained the core data however we calibrated the data for hard measurements from Nielsen data which improved the overall quality. In summary by taking the data from these multiple sources Pointlogic was able to combine the data sets in order to gain the required objective: learning about the effectiveness of sponsorships and optimize the media plans appropriately based on these insights.<br />TRACKING DATA<br />The Knowledge Network survey contains questions about sponsorships of the World Championship Soccer 2010 in South Africa. Respondents were asked about how much and where in which media they were confronted with the tournament. Based on this information, media consumption figures have been created for each respondent. The survey also included brand metrics for the (media) sponsors of the tournament like ad recall, sponsorship recall, slogan attribution, brand awareness, brand consideration/purchase intent, affinity etc. Respondent level media consumption estimates and brand metrics have been combined to shape effect curves for the sponsorships: per brand, per effect, per channel. <br />SYNERGETIC EFFECTS<br />The OTX survey contains questions about sponsorships of the World Championship Soccer 2010 in South Africa. Respondents were asked about where, through which media, they were confronted with the tournament. Based on this information, respondents were assigned to different groups like TV only, TV & online, etc. The survey also included 3 brand metrics (sponsorship recall, purchase intent and affinity) for 3 different media sponsors. OTX used this data to calculate stand alone effects of the different channels and synergetic effects of a combination of channels.<br />WORD-OF-MOUTH MEASUREMENT<br />Keller Fay conducts a weekly study about daily conversations. The purpose of the study is to learn what America is talking about. Respondents indicate what they talk about which product categories, which products and which brands. They also register other information about the conversation – positive or negative? Related to any media source? Propensity to take action? Information about media consumption and WOM has been combined to learn about the relationship between advertising around the World Championship Soccer ’10 and consumers talking about the brand.<br />National People Meter<br />Nielsen registers people’s actual media behavior. Data has been provided by Nielsen for TV and online behavior. The data has been fused by Nielsen for it to be one total set including both TV and online behavior. This hard, measured data is used to calibrate the self-reported media consumption estimates from the tracking data. The calibrated dataset is used in the back end of Chorus, also called the respondent set. It is used for taking into account the overlap of channels with regard to media usage. <br />The Results<br />Predictive models have been created based on the data from Knowledge Networks, OTX and Keller Fay for each of the sponsorship brands. Depending on the brand, there are models for ad recall, sponsorship recall, slogan attribution and/or brand awareness. <br />The results of the models are called effect curves which represent the relation between the number of exposures people have with a channel and its effect on an effect metric. This means that an effect curve has been created for each combination of effect metric and channel. These effect curves make it possible to do several key things. Learn about the maximum effect a channel may have; gain insights in the effective frequency range; learn about diminishing returns; optimally allocate budgets across different channels; and advantageous allocation of budgets across different tasks.<br />