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
Leon Zemel
Achieving big marketing goals through Big Data
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
What has small data driven?
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
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
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
Client Journey
Business Growth
User InteractionsUser Interactions
Client Journey
Audience ImpactAudience
Audience
AudienceProduct Relevancy and User Need
College gradUrban dweller Family guy
Audience Behavior
Audience BehaviorIn Market Readiness
Clicked bannerSearched keywords
Visited website
5 billion monthly marketing interactions
Audience Impact
Performance campaigns
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
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
Au
die
nc
e Q
ua
lity
Audience + ImpactOptimizing Negotiated Media
Au
die
nc
e Q
ua
lity
Audience Impact
Au
die
nc
e Q
ua
lity
Audience + ImpactOptimizing Negotiated Media
Au
die
nc
e Q
ua
lity
Audience Impact
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
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
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
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
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
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
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?