Florian Gramshammer, MD, Impact The quality of (big) data has for a long time been a crux for CMOs. Instead of creating a true view that positions their brand to make sound decisions, CMOs are creating data Frankenstein monsters by stitching data together from different warehouses, assembling disparate findings, and failing to optimize decisions around the customer journey. In this session, Florian Gramshammer, MD of Impact EMEA, will discuss the importance of examining, cleansing, and accounting for your data carefully in a way that will inform sound decisions about your paid marketing and media spend; how CMOs can become data stewards within their own organizations and lead their teams with confidence; and how to establish whole, pure entities that will promote industry-wide innovation

1. FIX, DON’T STITCH
Be a steward of your marketing data
How to Create Pure, Actionable Data and Avoid a Data Frankenstein Monster
With Impact CEO, David A. Yovanno

2. What was Dr. Frankenstein
inspired to create?

3. 3
LIFE

4. 4
What are CMOs inspired
to create?

5. 5
GROWTH

6. 6
So how have CMOs gone
about their creation?

7. TV | Radio | Print

8. 8

9. Addressability

12. 26
%
of organizations have
a solid data-driven
marketing strategy
Only
Source: Econsultancy

13. of advertisers consider
their measurement data
completely trustworthy
Source: Advertiser Perception
Only
29
%

14. $1T spent on Marketing globally
Source: GroupM

15. 15
What’s going on?

16. Super-stack

17. Franken-stack

18. Channels
TVSocial
Paid
search
SEOEmailDisplayDisplay
Perfor
mance

19. Channels
TVSocial
Paid
search
SEOEmailDisplay
Perfor
mance

20. Channels
TVSocial
Paid
search
SEOEmailDisplay
Perfor
mance

21. Channels
CMO
What’s
going on?
TVSocial
Paid
search
SEOEmailDisplay
Perfor
mance

22. Tools
Channels
CMO
What’s
going on?
TVSocial
Paid
search
SEOEmailDisplay
Perfor
mance

23. Tools
Channels
Spread
sheets
Data
Warehouse
Attribution Visualization
CMO
What’s
going on?
TVSocial
Paid
search
SEOEmailDisplay
Perfor
mance

24. Tools
Channels
Spread
sheets
Data
Warehouse
Attribution Visualization
CMO
What’s
going on?
TVSocial
Paid
search
SEOEmailDisplay
Perfor
mance

25. A Franken-stack has:
Lack of data transparency
Aggregate level data only
Non-scalable tools and systems
No de-duping or normalization
No cross-device consumer journey

26. A Franken-stack has:
Lack of data transparency
Aggregate level data only
Non-scalable tools and systems
No de-duping or normalization
No cross-device consumer journey

27. A Franken-stack has:
Lack of data transparency
Aggregate level data only
Non-scalable tools and systems
No de-duping or normalization
No cross-device consumer journey

28. A Franken-stack has:
Lack of data transparency
Aggregate level data only
Non-scalable tools and systems
No de-duping or normalization
No cross-device consumer journey

29. A Franken-stack has:
Lack of data transparency
Aggregate level data only
Non-scalable tools and systems
No de-duping or normalization
No cross-device consumer journey

30. Super-stack

31. Person-Oriented
Data Lake

32. OMNI-CHANNEL MEDIA MIX + ADTECH / MARTECH
Person-Oriented
Data Lake
Tracking ⬝ Verification ⬝ Identity ⬝ Pathing ⬝ Normalization

33. Tracking ⬝ Verification ⬝ Identity ⬝ Pathing ⬝ Normalization
OMNI-CHANNEL MEDIA MIX + ADTECH / MARTECH
Real-Time Marketing Intelligence
Person-Oriented
Data Lake
ATTRIBUTION
MODELING
REPORTING/
VISUALIZATIONS
ANALYTICS APIs
Data In
Data Out

34. To build a Super-stack, you need:
● Complete data ingestion

35. To build a Super-stack, you need:
● Complete data ingestion
● Event-level tracking

36. To build a Super-stack, you need:
● Complete data ingestion
● Event-level tracking
● Fraud verification

37. To build a Super-stack, you need:
● Complete data ingestion
● Event-level tracking
● Fraud verification
● Person-oriented view

38. To build a Super-stack, you need:
● Complete data ingestion
● Event-level tracking
● Fraud verification
● Person-oriented view
● Forecasting and scenario planning

39. 3
1
2
Digital media mix
Point of sale
Offline media mix
Other vendorseCommerce systems
Reporting/
Visualization
Attribution models
Predictive analyticsDynamic reporting / dashboards Machine learning algorithms
APIs
Data Lake
Event level tracking and ingestion
Verification ⬝ Identity ⬝ Pathing ⬝ Normalization

40. Kryptonite

41. Krypto-data

42. https://www.acmeinc.com/anniversary?utm_campaign=campaig
n_A+Holiday&addisttype=s&utm_medium=cpc&kw=gifts+for+w
omen&utm_source=bing&xx_mmc=tagged-_-google-_-na-_-na
&gaclkid=2a0aeabc3a7811e2bc5fb1e7e4d1560c&adgroupid=1
225&storeId=251&adtype=text&adid=6943452253562&matchty
pe=p

43. Discrepancies
Unavoidable Avoidable

44. Some UNAVOIDABLE discrepancies:
● Event counting methods

45. Some UNAVOIDABLE discrepancies:
● Event counting methods
● Internet latency

46. Some UNAVOIDABLE discrepancies:
● Event counting methods
● Internet latency
● Site/browser performance

47. Some AVOIDABLE discrepancies:
● Inconsistent tagging methods

48. Some AVOIDABLE discrepancies:
● Inconsistent tagging methods
● Limited data ownership

49. Some AVOIDABLE discrepancies:
● Inconsistent tagging methods
● Limited data ownership
● Inconsistent legacy setup

50. Data governance

51. 51

52. 52
Pick a data
governance team
1

53. 53
Audit your tags
2

54. 54
Embrace discrepancies
3

55. Build your Super-stack
4
4

56. Thank You
How to Create Pure, Actionable Data and Avoid a Data Frankenstein Monster
With Impact CEO, David A. Yovanno

57. 57

58. 58
A more compelling narrative is to tell a story on an actual use case of a
how marketer goes through the process to make an ad spend decision.
These are major bets and most marketers are using inaccurate data to
place these bets. Attribution was supposed to be the silver bullet - but for
the most part has gotten limited adoption. Why? There is a lack of trust in
the results. Why? There is no consensus on the accuracy of the data
being fed into the attribution model. Why? As example GA says you had
100,000 google display clicks, DCM says you have 110,000 clicks, Google
Ads says you have 90,000 clicks, and your attribution vendor tracking
80,000 clicks. Who is right? Maybe they all are - it all depends how you
you define a “click”. However, this assume all of the display ads are
tagged correctly - what happens if they are not, what happens is tag values
are being stripped, what happens if certain landing page URLs do track the
clicks. In these cases the google displays clicks may get attributed to a
different channel - thereby making your attribution results inaccurate. The
scary part is that most marketers are not even aware that the results they
are using to make ad spend decisions are based on inaccurate data.

62. “The abundance of data from marketing
tools seduces marketers with a false sense of
precision, while marketers lack the tools,
skills, and processes to produce a holistic
analysis.” – Forrester

63. 4
Identity ⬝ Verification ⬝ Pathing ⬝ Normalization
OMNI-CHANNEL MEDIA MIX + ADTECH / MARTECH
Real-Time Marketing Intelligence
Person-Oriented
Data Lake
REPORTING ANALYTICS VISUALIZATIONS
ATTRIBUTION
MODELING