Basic introductory session to online data.

1. DATA
Sizmek Honours
March 2015

2. DECISIONS

3. 3

4. DATA

5. What is data?
Facts and statistics
Collected together for reference or analysis
For reference or analysis
Data by itself is useless without action

6. COLLECT
ANALYSEINSIGHTS
ACTION

7. 7
Imagine you run a café….

8. 8
Period
Customers
jan
1,000
feb
2,500
mar
1,300
apr
1,000
may
2,200
jun
2,000
jul
1,400
aug
3,500
sep
2,000
oct
1,000
nov
1,500
dec
4,000
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0 0 0 0 0 0 0 0 0 0 0
#REF!

9. 9
Analysis
Insight
Idea
Sales peak during festive & holiday
season
No Atmosphere when non-festive,
customers aren’t attracted
Create an event calendar to increase
footfall during quieter periods

12. We hate math, say 4 in 10
– a majority of Americans

15. but people do.
,

16. COLLECT & ANALYSE
ACTION

17. Digital Marketing and Measurement
John Wanamaker: father of modern advertising
“Half the money I spend on
advertising is wasted; the trouble is
I don’t know which half.”

19. 19
COLLECT

20. Search Display Affiliate Video Mobile Social E-mail
Paid Media Channels

21. BasicDelivery
Monitor cost
§ Planned vs. Actual
§ Over and Under Delivery
§ Pacing
Calculate extended metrics
§ Impressions: CPM
§ Clicks: CPC
§ Video Views: CPV
§ Effective Cost Performace
§ eCPM
Verification
Brand Safety
§ Negative Content
§ Content Categories
Geo-Delivery
§ Country level
§ Region/City level
Viewability
§ Recordable Rate
§ Viewability Rate
§ Avg. time on screen
§ Avg. ad % on screen
Audience
§ Age
§ Gender
§ Income

22. RichMedia
Direct
§ Expand / Collapse
§ Play / pause
§ Sound on / off
§ Replay
§ Custom Interactions
§ Automatic Events
Indirect
§ Frequency
§ Ad Duration
§ Dwell (Engagement)
§ Unique Viewers
Video
§ Video Starts
§ % of video viewed
§ Sound mute on / off
§ Replays
§ Full screen
§ Unique viewers

24. ANALYSE

27. Not All Sales Are Equal

29. Attribution Models
But how do we decide
how credit should be
rewarded?

30. Fractional Attribution
Linear Bathtub Customised Time Decay

31. 31

32. Fractional Attribution
Linear Bathtub Customised Time Decay

33. Big Data Attribution
Leverages statistical or machine learning
techniques to distribute credit
Looks at data points that rules-based attribution
doesn’t typically look at
Can sometimes be black-box

34. 34
Advanced Attribution Pain PointsThe Challenge

35. ACTION

37. Adaptive Planning

39. 39

40. The Rise of
RTB

43. 43

45. THANK YOU