Monetising Fan Engagement from Sponsorships and Sports Events


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Using tagged media within predictive econometric models is no longer sufficient to monetise sponsorship campaigns. Our Semantic Engagement Index (SEI) decodes the fan engagement from conversations during live sports events. This helps to understand the sponsorship effectiveness is whole new light. Packaging the SEI into an econometrics approach helps to better account for sponsorship and steer future allocations.

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Monetising Fan Engagement from Sponsorships and Sports Events

  1. 1. Monetising Social Media Fan Engagement forSponsorships and Events.
  2. 2. Live Tweets have changedthe rules of thegameDo you recognize the level of engagement with your brand atSporting events?Apart from brand awareness, how can you be sure you arecapturing what fans are really saying about your brand?By understanding live social media engagement, futuresponsorships investments can be better allocated.
  3. 3. Our approach is based on Stance-Shift Analysis1. Uses the Social Media conversations about your Brand asinput2. Applies linguistic principles of sentiment and tonality touncover the deep subtleties of what’s said during liveevents.3. Converts the conversations into a quantitative metriccalled the Semantic Engagement Index (SEITM)We use Linguistics to uncover what is really meant
  4. 4. We use Linguistics to uncover what is really meantThe SEITM measures the degree to whichfans are engaged with the sports andsponsors, as it transpires through thelanguage used on social media.
  5. 5. The SEITM showssuperior correlations to brand salescompared with off the shelf sentiment metrics82.9%14.8%9.9%7.7%5.9%2.8%-3.2%-20% 0% 20% 40% 60% 80% 100%SEMANTIC ENGAGEMENT INDEX POS/NEG RATIOMETRIC 5 POS/NEG RATIOMETRIC 1 POS/NEG RATIOMETRIC 4 POS/NEG RATIOMETRIC 6 POS/NEG RATIOMETRIC 2 POS/NEG RATIOMETRIC 3 POS/NEG RATIOComparison of correlation to sales for the SEI TM versus the six leading sentiment metrics5The SEI TM performs better than other offthe shelf sentiment metrics only becausewe take into account the nuances inlanguage which is altogether different fromvolume based approaches.
  6. 6. Client Case: Monetising Sponsorship via SEITM• Client invested 65% of its marketing budget into sportssponsorships– Traditional marketing mix models treat these as dummy variables and measure only thesponsorship tagged media.• ROI was negative and the credibility of these estimates were suspect.• Our approach was to leverage SEITM measurement of the sponsorships, a true indication ofhow consumers were emotionally and personally engaged with these sports as fans. The SEImeasures the fan engagement or “passion” towards the sponsorship property or sport.• Results found a much more robust and positive ROI estimate, with clearer focus on resourceallocation. Using the SEITM is a much more effective way of translating fan engagement withthe true ROI measure of the property.• Brand growth accelerated from +3% to +9% the following year.
  7. 7. SEITM to assess ROI of sports SponsorshipsSponsorships generated 13% ofsales, but sponsorship media onlyaccounted for 1% of sales.Our approach of using SEITM engagement to measure sponsorship yields asignificantly greater impact that traditional approaches of only measuringsponsor-tagged advertising. 7
  8. 8. Alpha’s primary opportunity is to invest more in high yield (ROI), high growthproperties, especially NFL Football, in order to maximize brand growth in thecoming year.Investment decision matrix to guide futureallocations.
  9. 9. Michael WolfeCEOBottom Line AnalyticsE: mjw@bottomlineanalytics.comM: 770.485.0270www.bottomlineanalytics.comMasood AkhtarPartner, Analytics (EMEA)Bottom Line AnalyticsE: ma@bottomlineanalytics.comM: +44 7970 789 663www.bottomlineanalytics.comDavid WeinbergerCMOBottom Line AnalyticsE: david@bottomlineanalytics.comM: 770.649.0472www.bottomlineanalytics.comSimon BrockClient Services DirectorBottom Line AnalyticsE: simon@bottomlineanalytics.comM: +44 (0) 7824 305325www.bottomlineanalytics.comLondon & EMEA USA