This document discusses FanAI's approach to attributing real-world outcomes to sponsorships. It covers FanAI's goals of measuring brand affinity lift and spend lift. The presentation outlines FanAI's data collection process including first party records, Twitter data, Twitch data, TV viewership data, location data and demographic data. It then discusses FanAI's methodology for segmentation, control groups, and ensuring statistical confidence. The presentation concludes with an overview of FanAI's final analysis and reporting on sponsorship attribution.

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Attributing Real-World
Outcomes to Sponsorships
DataCon LA 2020 | @KarimVarela

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Agenda
1. Intro to FanAI and sponsorship attribution
2. Goals with sponsorship attribution
3. Data collection
4. Data enrichment
5. Segmentation
6. Control groups
7. Statistical confidence
8. Final Analysis
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$60MM
Annual sponsorship spend
$500k
Spend per sponsorship

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$89BGlobal sponsorship spend

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$89B
Global sponsorship spend
Impact?
LTV?
Sales?
Transactions?
Mindshare?

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Attribution: What is it?
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In marketing, attribution, also known as multi-touch attribution, is the
identification of a set of brand exposures that contribute in some manner to a
desired outcome, and then the assignment of a value to each of these events*
*IAB Digital Primer

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Online Attribution is Solved
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Offline Attribution is Hard
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The Solution
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FanAI improves sponsorship and media
effectiveness by measuring the real-world
outcomes that result from sponsorships.
So marketers can better allocate,
optimize, and measure their spend
across ALL their marketing
channels

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“Real world outcomes?”
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Goal 1: Brand Affinity Lift
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Definition:
Shared followers between a brand and team
As a brand, I want to gain followers on social
channels as a result of my sponsorship.
I expect that the percentage of followers I
share with a team will increase, since followers
of the team are being exposed to my brand.

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Brand Affinity Example
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3.7%
3.2M
Only follow Coca-Cola
2.0M
Only follow TSM
200k
Follow both
59.3% 37.0%
5.4M
Total followers in combined audience

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Goal 2: Spend Lift (aka assisted sales / ROI)
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Definition: The increase in how an exposed
audience spends compared to a control group.
As a brand, I expect sales, transactions, and
transactors to all increase as a result of my
sponsorship.

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How are fans actually spending at a Brand?
(Over time and relative to a control group of a brand’s customers)
Data contained in this visual are for illustrative purposes only and do not reflect actual FanAI customer data

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McDonalds & the NY Mets
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Sponsorship Spend:
$5MM
+
Exposed Period:
2018-2019 Home Games
Exposed Audiences:
1. Season Ticket-holders
2. Fans w/ Mobile devices at stadium
3. Smart / Streaming TV viewers
Exposure Method(s):
1. Signage in arena
2. Commercials on TV broadcast

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Sponsorship Outcomes Analytics Process
Fan Data
Device ID
Merch
Newsletter
Streaming
Tickets
Smart TV
Customer
Purchase Data
Family Dining
Real Business
Outcomes
Assisted Revenue Lift
Assisted Transaction Lift
Mindshare Uplift
Demographics

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Data team
requests
location
data
Backend
serves
spend data
to client
Initiates
Property
Tracking
Views
spend data
in platform
Spend Uplift Enrichment Process
Data team
requests
TV data
Sends data to
payment
service
provider
Calculates
spend data
Returns spend
data to FanAI
Matches
personal info
to spend ids
Location data
provider returns
device IDs
TV data provider
returns
household IDs
Data team
segments
data

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3-Party Matching Model
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Data Collection
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PII:
● Email
● Name & Address
● Phone number
FanAI Tech:
● Google Storage
● Separate project
● Separate bucket per client
● Least privilege access
65MM+
First Party Records (PII)

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Firehose API details:
● Tier 2 Follower Graph
○ Followers
○ Friends
○ Users
● Enables us to download followers of
~20k Twitter handles on a weekly basis
● Used to build up social following
audiences
FanAI Tech:
● Google Cloud Platform (GCP)
● Google Kubernetes Engine (GKE)
● PubSub (queueing)
● BigQuery (data warehouse)
62TB
Twitter Data

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API details:
● Free access to streams, streamers,
viewers, etc...
● Enables us to download viewers top ~5k
streams every 3 minutes
● Used to build up audiences of who’s
watching esports
FanAI Tech:
● Google Cloud Platform (GCP)
● Google Kubernetes Engine (GKE)
● Google Cloud Functions
● Google Cloud Scheduler
● PubSub (queueing)
● BigQuery (data warehouse)
6TB
Twitch Data

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https://kinetiq.tv/
Details:
● “World’s largest unified TV intelligence
network”
● 15MM devices
● Deliver files full of household / device ids
● Used to build up audiences of people
who watched live events on TV
FanAI Tech:
● Google Cloud Platform
● AWS S3
● BigQuery

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Details:
● 12M+ US POI locations,
● Used to build up audience of people
exposed to sponsorship campaigns at
live events
● Also we can determine who attended
event at stadium AND went to store
FanAI Tech:
● GCP
● Deliver .parquet files full of
MAIDs to GCP storage buckets
● Google Functions (serverless)
● BigQuery

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Details:
● Demographic enrichment service
● API driven
● Given email, phone, or Twitter handle,
returns full demographic info (age,
gender, location, education, etc…)
● Used to segment audiences before
further enrichment
● Could also look at how demographics are
changing since Covid
FanAI Tech:
● GCP
● GKE
● PubSub
● BigQuery

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Fan Insights
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1:1 Control Group Definition
A perfect control group would be a 1:1 mapping of
people who have the same demographics, lifestyle,
interests, and income as the exposed group, but were
not exposed.
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I.e. if the exposed group is people who attended home
games at the Mets stadium, we should try to find people
who live in NY, and share as many common
characteristics of the folks who attended the Mets games,
but actually did NOT attend the Mets games.

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1:1 Control Group Methodology
Spend data providers have pertinent data
For each person in exposed group:
1. Find most similar person NOT in exposed group based on demographics and / or spending habits
2. DO take into consideration how they spend in many different categories
3. Do NOT take into consideration spend in spend category in question (e.g. QSR) since we’d like to
see a change in spend in that category
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Notes:
1. Can utilize an algorithm like closest distance in the multi-dimensional
space to find most similar person.
2. Can use dimension reduction to reduce thousands of dimensions (e.g.
spend in thousands of categories) to ~30 key dimension

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Do we really need a 1:1 control group?
Creation of control group is computationally heavy and expensive
Can we meet our objective by just showing that the exposed audience is spending
more than other audiences?
I.e. Can we show spend uplift by proving that the NY Mets audience is spending
more than audiences from the NY Yankees / Giants / Knicks / Nets / etc…??
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Statistical Confidence
3131

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Why is statistical confidence important?
A reported "Spend Lift %" may be due to random luck (sampling variation)
OR
the sponsorship was TRULY effective and contributed to "Spend Lift %".
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Confidence scoring is key ...
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Statistical Confidence Definition
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t = statistical confidence
X1
= mean spend of exposed group
X2
= mean spend of control group
N1
= sample size of exposed group
N2
= sample size of control group
s1
= standard deviation of spend in exposed group
s2
= standard deviation of spend in control group
se1
= standard error of exposed group
se2
= standard error of control group
Spend Uplift %:
(X1
-X2
)/X2
* 100%
Tech: scipy.stats

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What is acceptable confidence?
95%??? … depends on context
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Sample Size Minimum Spend Lift % to Ensure 95% Confidence
500 5.578
1,000 3.944
2,000 2.789
5,000 1.764
10,000 1.247
50,000 .558
100,000 .394
200,000 .279
500,000 .176

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Data Privacy Considerations
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GDPR & CCPA
FanAI is data processor under GDPR and service provider under CCPA
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Most important obligations:
1. Delete / forget data upon request
2. Notify data controllers / businesses who collect data of any
breach to our systems

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Final Analysis
3838

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Spend Attribution by Channel
Spend Attribution by Region

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Spend Attribution by Customer Recency
Spend Attribution by Day of Week

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Spend Attribution by Time of Day

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In Summary ...
Spend attribution is difficult, but we solved it by:
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1. Gathering rightsholder audience data
2. Gathering TV audience data
3. Gathering location data
4. Segmenting data demographically
5. Matching w/ transaction data providers
6. Enriching with transaction data
7. Anonymizing & aggregating enriched data

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Karim Varela, CTO & CISO
karim@fan.ai | @KarimVarela
THANK YOU

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Appendix
4646

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What is FanAI?
FanAI is a big data platform that enables
brands to measure and maximize the
impact of their marketing investments
(sponsorships)

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Statistical Confidence Example
● Mean spends = ($1025, $1000) => 2.5% spend lift
● Sample sizes = (100, 100)
● Standard Deviations = ($400, $360)
t = 65%
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Market Opportunity
Many Brands are unable to measure
the sales impact of billions of dollars
in marketing and limited in their
ability to do targeting

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The Problem
Brands struggle to measure and
optimize their sponsorship portfolios
against
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Brand Awareness
Purchase Consideration
Purchase Intent
Store Visits
Share of Voice
Click-Through Rate
Social Buzz
Followers
Unique Reach
Media Value
Brand Perception
Brand Preference
Sales Transactions Uplift Vs. Avg CustomerLTV

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“Real-World Outcomes”?
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1. Mindshare Uplift
The percentage change in shared
social media following between a
brand and a property.
2. Spend Uplift
The percentage change in
spend for fans exposed to a
sponsorship vs. a control group.

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Big Data on Hand
6TB
Twitch Data
62TB
Twitter Data
65MM+
First Party Records
(PII)
1,000s
Brands’ Spend Data

53. Social / Streaming Data Providers

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Statistical Confidence Example
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We need at least one of the following steps to boost "65%
Statistical Confidence" to "85%":
1. Gather more data from (100, 100) to (450, 450), assuming other
metrics stay the same.
2. If we observed 5.5% Spend Lift instead of 2.5%, we would have
gotten "85% Statistical Confidence".

55. Details:
● We use free and public API (“new” API)
○ /clips
○ /games/top
○ /streams
○ /users
○ /users/follows
○ /chatters (not documented, will be deprecated)
● Would like to do attribution for Twitch advertisers as well
○ Twitch would give us krux IDs of viewers watching streams with ads
○ We send krux IDs through our attribution pipeline
○ Still in negotiations

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Brands struggles Connecting
Real-World Outcomes to Marketing Efforts
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$656B Global Ad Spend $89B Sponsorship Spend
Unaided
Awareness
Purchase ConsiderationPurchase Intent
Store Visits
Click-Through Rate
Social Buzz
Followers
Cost Per Lead
Unique Reach Media Value
Brand Perception
Brand Preference
Share of Voice
Impressions
Viewable
Impression
Verification
*WARC 2020 Forecast **Two Circles 2024 Forecast
3rd Party CookiesContextual Intelligence
Sales
✔
Transactions
✔
Mindshare ✔LTV
✔
But what’s the impact???
Aided Awareness

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Sponsor Portfolio Performance Summary- MVP
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Consented

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Fan
Matches
X.XM
Customer
Matches
XXXK
Assisted
Sales
$XX.XM
Assisted Sales
Lift
X.X%
QSR Assisted
Sales Lift
X.X%
Note: Purchase outcome data represents insights for a subset of each property’s fans
Data from Ticketing,
Onsite, or TV data
sets that can be
matched against a
transaction database
using a common
identifier (e.g.
LiveRampID)
Unique ID’s that have
been matched to
Brand A’s credit card
transactions,
representing fans of
tracked properties
who are also
customers of Brand A
Total spending from
“Customer Matches”
with brand x’s.
“Assisted” indicates
that the sponsorship
was one of the
marketing
touchpoints that
contributed to a
purchase.
Total spending from
“Customer Matches”
compared to
consumers with
similar attributes who
were are not fans of
said property (control
group) to isolate
being a fan’s impact
vs. other variables
like seasonality
Total spending of
“Fan Matches” at a
group of 5 QSR
competitors
compared to
consumers with
similar attributes who
were not exposed to
the sponsorships
(control group)
Methodology- Terminology

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