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1
Media vs. Data:
Why the Double
Standard?
Jake Moskowitz
Head of the Emodo Institute
2
Let’s Get to Know Each
Other
The Emodo Institute
• Research, education & resolution of data
concerns that challenge mobi...
3
VS
WHERE
− Segment designations
− Bid request metadata that
triggers bidder decisions
− Attribution studies that lead to...
4
Data:
$1 - $2 CPM
Media:
$1 - $2 CPM
Balancing Out Our Focus
5
We’ve Done So Much Work to Eliminate Media Waste
6
Case Study: Demographic Data
• Nielsen DAR benchmarks
show 50-60% of data is
wrong for even medium-
level specificity of...
7
This Lat/Long location data is
extremely precise, right?
40.759463° N, 73.984597° W
8
Right.
But it points (very precisely) to the wrong
place.
Not just 10 feet to the left or right.
Often, the data is 10s,...
9
Only 39% of device location
data points were within one
mile of where they were
claimed to be.
Case Study: Location Data
10
Across all raw data tested,
vendors only eliminate
28.4% of data inaccuracies
Pattern recognition techniques used
by ve...
11
CASE STUDY: QUESTION ASSUMPTIONS
• SDK scores are extremely consistent
• All accuracy scores are between 60
and 75%
Emo...
12
Audience scores for a single
vendor:
• For the same use case,
• For two separate campaigns,
• From two separate brands,...
We care about accuracy. So why do we prioritize other factors?
Source: Factual Survey, May 2019
14
So, what do we end up with?
67M devices that have
visited a Hyundai dealership
in the last 30 days?
15
More segment stuffing...
Step 2: Understand Why is the Data Inaccurate?
102M devices that are
active members of the BP
...
16
And another...
128M likely Millstone
coffee drinkers?
17
Determinisitic
18
Transparency
19
Accurate
20
Scale and
Quality
21
A Day in the Life of
a Data Point
Occurrenc
e
Categorize Define
ExpandMatch
Cross-
platform
Use
A Day in the Life of a ...
22
Introducing the “aCPM”
Inaccurate data is a primary cause of wasted impressions.
• The aCPM adjusts cost for lost value...
23
SEGMENT BMW
INTENDER
PRICE $0.80
DEFINITION
VISITED THE BMW
PART OF A 3RD
PARTY AUTO SITE
IN THE LAST 90
DAYS
LOOK-A-LI...
24
What Could Go
Wrong?
1. Tech problems: Data captured isn’t
correct because technology failed, such
as “last known locat...
25
What Could Go
Wrong?
1. Optimizing scale at the cost of
accuracy: Lookalike model extrapolation
ratio is high
Step 3: A...
26
1. Prioritize: Establish data accuracy as a
top priority – equal to media quality
2. Recognize: Remember that price and...
27
Q & A
Q&A
29
Any follow-up questions, feel free to reach
out:
THANK YOU
Jake Moskowitz
Jake.moskowitz@emodoinc.com
Emodo Institute b...
30
Why Data Science is Veiled Science
1 Competition
2 Sales Cycle
3 Accountability
4 Evolution
5 Weakness
6 Perception
Media vs Data: Why the Double Standard?
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Media vs Data: Why the Double Standard?

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Over the last few years, the ad industry has been decisive and diligent about demanding better media quality. As a result, we've seen dramatic reductions in waste and made huge improvements in the areas of Viewability, Fraud and Brand Safety. Now it's time for the industry to set its sights on the new leading cause of waste. Join Jake Moskowitz, head of the Emodo Institute, for a glimpse into the pervasive problem of data inaccuracy. In this session, Jake will outline the causes, scope and magnitude of today's data quality issues, and discuss tactical ways to ensure advertisers get what they pay for.

Emodo is the data arm of Ericsson, the telecommunications company that powers roughly 80% of US mobile traffic.

Published in: Marketing
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Media vs Data: Why the Double Standard?

  1. 1. 1 Media vs. Data: Why the Double Standard? Jake Moskowitz Head of the Emodo Institute
  2. 2. 2 Let’s Get to Know Each Other The Emodo Institute • Research, education & resolution of data concerns that challenge mobile advertising. • Helps media planners, buyers & service providers sharpen the efficacy of mobile data, so campaigns have a greater impact.
  3. 3. 3 VS WHERE − Segment designations − Bid request metadata that triggers bidder decisions − Attribution studies that lead to reallocation Media Data We focussomuchon verifyingwhere our adsrun…. − Whitelists/blacklists − Brand Safety − Viewability − Fraud HOW … andsolittleon verifying howwe decidewhat to buy
  4. 4. 4 Data: $1 - $2 CPM Media: $1 - $2 CPM Balancing Out Our Focus
  5. 5. 5 We’ve Done So Much Work to Eliminate Media Waste
  6. 6. 6 Case Study: Demographic Data • Nielsen DAR benchmarks show 50-60% of data is wrong for even medium- level specificity of demo targeting • And it’s not improving in any substantive way Demographic, Age Span Nielsen DAR Mobile Demo Benchmarks – Q1’18
  7. 7. 7 This Lat/Long location data is extremely precise, right? 40.759463° N, 73.984597° W
  8. 8. 8 Right. But it points (very precisely) to the wrong place. Not just 10 feet to the left or right. Often, the data is 10s, 100s, 1000s of miles off.
  9. 9. 9 Only 39% of device location data points were within one mile of where they were claimed to be. Case Study: Location Data
  10. 10. 10 Across all raw data tested, vendors only eliminate 28.4% of data inaccuracies Pattern recognition techniques used by vendors don’t seem to work. Case Study: Pattern Recognition
  11. 11. 11 CASE STUDY: QUESTION ASSUMPTIONS • SDK scores are extremely consistent • All accuracy scores are between 60 and 75% Emodo has tested a wide range of 3rd party SDKs Case Study: SDK Data
  12. 12. 12 Audience scores for a single vendor: • For the same use case, • For two separate campaigns, • From two separate brands, • Via two separate agencies, • Less than 60 days apart. Campaign #1 88.78% accurate Campaign #2 11.65% accurate (audience A) 25.44% accurate (audience B) Case Study: Vendor Consistency
  13. 13. We care about accuracy. So why do we prioritize other factors? Source: Factual Survey, May 2019
  14. 14. 14 So, what do we end up with? 67M devices that have visited a Hyundai dealership in the last 30 days?
  15. 15. 15 More segment stuffing... Step 2: Understand Why is the Data Inaccurate? 102M devices that are active members of the BP Motor Club?
  16. 16. 16 And another... 128M likely Millstone coffee drinkers?
  17. 17. 17 Determinisitic
  18. 18. 18 Transparency
  19. 19. 19 Accurate
  20. 20. 20 Scale and Quality
  21. 21. 21 A Day in the Life of a Data Point Occurrenc e Categorize Define ExpandMatch Cross- platform Use A Day in the Life of a Data Point
  22. 22. 22 Introducing the “aCPM” Inaccurate data is a primary cause of wasted impressions. • The aCPM adjusts cost for lost value from data inaccuracy • Applies the accuracy rate to the original CPM to calculate cost of only the accurate impressions. • Example: If CPM is $3.00 and data is 50% accurate, the aCPM would actually be $6.00 • Taking steps to improve accuracy can significantly reduce aCPM
  23. 23. 23 SEGMENT BMW INTENDER PRICE $0.80 DEFINITION VISITED THE BMW PART OF A 3RD PARTY AUTO SITE IN THE LAST 90 DAYS LOOK-A-LIKE MODELING YES (70% modeled) DETERMINISTIC DATA USED YES Calculate an aCPM x 2 x4 ________ = $ 6.40 aCPM x 2 x1.5 ________ = $ 4.50 aCPM SUPPLY QSR LUNCH PRICE $1.50 GEOFENCE CRITERIA WITHIN 1 MILE OF A STORE POI DATABASE UPDATED QUARTERLY LAT/LONG FILTERING NO
  24. 24. 24 What Could Go Wrong? 1. Tech problems: Data captured isn’t correct because technology failed, such as “last known location” Step 3: Ask Your Data Vendors Revealing Questions Bad Data Sources: 2. Low quality data: Data that isn’t persistently collected, honestly provided, adequately scalable, etc. 3. Categorizations are wrong: store definitions are wrong or too liberal; irresponsible assumptions about meaning Questions to Ask Your Data Vendor: What % of your data do you throw out? How do you verify accuracy? How do you verify your POI? What restrictions on use of deterministic? 4. Privacy restrictions: no use of deterministic data due to privacy concerns (only modeled data used)
  25. 25. 25 What Could Go Wrong? 1. Optimizing scale at the cost of accuracy: Lookalike model extrapolation ratio is high Step 3: Ask Your Data Vendors Revealing Questions Data Trade-Offs: 2. Data loss due to low match rates 3. Inaccurate cross-device due to probabilistic methods, or incorrect assignment of a person within a household Questions to Ask Your Data Vendor: What % are modeled? What’s your match rate to x? What % are running on source platform? 4. Use of wrong segments (accidental or purposeful) such as to increase scale or deliver in full Exactly which segments were used?
  26. 26. 26 1. Prioritize: Establish data accuracy as a top priority – equal to media quality 2. Recognize: Remember that price and scale are negatively correlated with quality 3. Calculate: Assess the value of data options by doing a simple aCPM calculation 4. Ask: Seek deeper answers to revealing vendor questions Crucial Steps to Better Data Summary
  27. 27. 27 Q & A Q&A
  28. 28. 29 Any follow-up questions, feel free to reach out: THANK YOU Jake Moskowitz Jake.moskowitz@emodoinc.com Emodo Institute blurb here…
  29. 29. 30 Why Data Science is Veiled Science 1 Competition 2 Sales Cycle 3 Accountability 4 Evolution 5 Weakness 6 Perception

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