leon zemel: achieving big marketing goals through big data
Post on 19-Oct-2014
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Leon Zemel, Chief Analytics Officer of [x+1]: Client case study on leveraging big data.TRANSCRIPT
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Leon Zemel
Achieving big marketing goals through Big Data
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What has small data driven?
Too broad &Too broad &Too broad &Too broad &
Too low frequencyToo low frequencyToo low frequencyToo low frequency
Too broad &Too broad &Too broad &Too broad &
Too low frequencyToo low frequencyToo low frequencyToo low frequency
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What has small data driven?
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What has small data driven?
Too broad &Too broad &Too broad &Too broad &
Too low impactToo low impactToo low impactToo low impact
Too broad &Too broad &Too broad &Too broad &
Too low impactToo low impactToo low impactToo low impact
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The Digital Marketer Challenge
1,000
1,200
1,400
1,600
Display conversions top out
Display attributed conversions
-
200
400
600
800
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11
Display attributed conversions
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The Digital Marketer Challenge
1,000
1,200
1,400
1,600
2,500
3,000
3,500
4,000
Display not growing overall business objectives
Digital Sales
-
200
400
600
800
-
500
1,000
1,500
2,000
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11
Display attributed conversions
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Client Journey
Business Growth
User InteractionsUser Interactions
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Client Journey
Audience ImpactAudience
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Audience
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AudienceProduct Relevancy and User Need
College gradUrban dweller Family guy
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Audience Behavior
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Audience BehaviorIn Market Readiness
Clicked bannerSearched keywords
Visited website
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5 billion monthly marketing interactions
Audience Impact
Performance campaigns
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15%
20%
25%
20,000
25,000
30,000
Audience ImpactFinding the OFR by Audience Segment
Performance
0%
5%
10%
-
5,000
10,000
15,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Frequency
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15%
20%
25%
80,000
100,000
120,000
Audience ImpactFinding the OFR by Audience Segment
Performance
0%
5%
10%
-
20,000
40,000
60,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Frequency
Not in target
In target
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Au
die
nc
e Q
ua
lity
Audience + ImpactOptimizing Negotiated Media
Au
die
nc
e Q
ua
lity
Audience Impact
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Au
die
nc
e Q
ua
lity
Audience + ImpactOptimizing Negotiated Media
Au
die
nc
e Q
ua
lity
Audience Impact
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15%
20%
25%
80,000
100,000
120,000
Audience + ImpactOptimizing in RTB
Performance
0%
5%
10%
-
20,000
40,000
60,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Frequency
Not in target
In target
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15%
20%
25%
80,000
100,000
120,000
Audience + ImpactOptimizing in RTB
Performance
0%
5%
10%
-
20,000
40,000
60,000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Not in target
In target
Frequency
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3,000
3,500
4,000
4,500
5,000
5,500
50,000,000
60,000,000
70,000,000
80,000,000
90,000,000
100,000,000
And the Results…
Digital Sales
CalibrationPeriod
The old way
1,000
1,500
2,000
2,500
3,000
-
10,000,000
20,000,000
30,000,000
40,000,000
Marketing Investment
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3,000
3,500
4,000
4,500
5,000
5,500
50,000,000
60,000,000
70,000,000
80,000,000
90,000,000
100,000,000
And the Results…
CalibrationPeriod
The old way Optimized
And the Results…
Digital Sales
1,000
1,500
2,000
2,500
3,000
-
10,000,000
20,000,000
30,000,000
40,000,000
+20% growthin total digital channel sales
Marketing Investment
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Key Takeaways
Unlock value from marketing data:
Identify key consumer states at the
user level
Balance marketing strategy and
investment to each user segment, in
real-time when possible
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Key Takeaways
Advertisers and agencies have
unprecedented control over optimizing frequency by audience
segment segment
Companies manage business impact by user segment have driven
significant growth in their digital
channel
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Customer Insight Through Deeper Analysis(The Fastest Climbers Win)
BI Reporting and
Ad-Hoc Analysis
Predictive
Analytics
Optimization
• What happened?
• When and where?
• How much?
• What will happen?
• What will the impact be?
• What is the
best choice?
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