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.

1. Monetising Social Media Fan Engagement for
Sponsorships and Events.

2. Live Tweets have changedthe rules of the
game
Do you recognize the level of engagement with your brand at
Sporting events?
Apart from brand awareness, how can you be sure you are
capturing what fans are really saying about your brand?
By understanding live social media engagement, future
sponsorships investments can be better allocated.

3. Our approach is based on Stance-Shift Analysis
1. Uses the Social Media conversations about your Brand as
input
2. Applies linguistic principles of sentiment and tonality to
uncover the deep subtleties of what’s said during live
events.
3. Converts the conversations into a quantitative metric
called the Semantic Engagement Index (SEITM)
We use Linguistics to uncover what is really meant

4. We use Linguistics to uncover what is really meant
The SEITM measures the degree to which
fans are engaged with the sports and
sponsors, as it transpires through the
language used on social media.

5. The SEITM showssuperior correlations to brand sales
compared with off the shelf sentiment metrics
82.9%
14.8%
9.9%
7.7%
5.9%
2.8%
-3.2%
-20% 0% 20% 40% 60% 80% 100%
SEMANTIC ENGAGEMENT INDEX POS/NEG RATIO
METRIC 5 POS/NEG RATIO
METRIC 1 POS/NEG RATIO
METRIC 4 POS/NEG RATIO
METRIC 6 POS/NEG RATIO
METRIC 2 POS/NEG RATIO
METRIC 3 POS/NEG RATIO
Comparison of correlation to sales for the SEI TM versus the six leading sentiment metrics
5
The SEI TM performs better than other off
the shelf sentiment metrics only because
we take into account the nuances in
language which is altogether different from
volume based approaches.

6. Client Case: Monetising Sponsorship via SEITM
• Client invested 65% of its marketing budget into sports
sponsorships
– Traditional marketing mix models treat these as dummy variables and measure only the
sponsorship 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 of
how consumers were emotionally and personally engaged with these sports as fans. The SEI
measures 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 resource
allocation. Using the SEITM is a much more effective way of translating fan engagement with
the true ROI measure of the property.
• Brand growth accelerated from +3% to +9% the following year.

7. SEITM to assess ROI of sports Sponsorships
Sponsorships generated 13% of
sales, but sponsorship media only
accounted for 1% of sales.
Our approach of using SEITM engagement to measure sponsorship yields a
significantly greater impact that traditional approaches of only measuring
sponsor-tagged advertising. 7

8. Alpha’s primary opportunity is to invest more in high yield (ROI), high growth
properties, especially NFL Football, in order to maximize brand growth in the
coming year.
Investment decision matrix to guide future
allocations.

9. Michael Wolfe
CEO
Bottom Line Analytics
E: mjw@bottomlineanalytics.com
M: 770.485.0270
www.bottomlineanalytics.com
Masood Akhtar
Partner, Analytics (EMEA)
Bottom Line Analytics
E: ma@bottomlineanalytics.com
M: +44 7970 789 663
www.bottomlineanalytics.com
David Weinberger
CMO
Bottom Line Analytics
E: david@bottomlineanalytics.com
M: 770.649.0472
www.bottomlineanalytics.com
Simon Brock
Client Services Director
Bottom Line Analytics
E: simon@bottomlineanalytics.com
M: +44 (0) 7824 305325
www.bottomlineanalytics.com
London & EMEA USA