ePerformance 2017 - Predictive Retargeting, Real-time koopintenteis voorspellen - Job Deibel - Dept...
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Dept Agency 16 November 2017 Build real-time Audiences based on Machine Learning
Transcript of ePerformance 2017 - Predictive Retargeting, Real-time koopintenteis voorspellen - Job Deibel - Dept...
Dept Agency16 November 2017
Build real-time Audiences based on Machine Learning
Thanks for having us!
Mustafa Himdi
Digital Strategist
Job Deibel
Data Scientist
Predictive Retargeting
Current retargeting practices
Standard Retargeting Audiences
All visitors Standard retargeting
Assumption
Assumption
“A site visitor is interested in my product/service”
Observation
“Job is a site visitor”
Conclusion
“Job is interested”
[PLAATJE WINKEL]
Custom Retargeting Audiences
Business knowledge
Analysis
Gut feeling
This hopefully results in higher conversion rateaverage
Analysis
Standard retargeting
Custom retargeting
1% conversion rate
5% conversion rate
5%3%8%6%2%10%40%30%10%15%25%3%1%1%
Analysis
average
Standard retargeting
Custom retargeting
1% conversion rate
5% conversion rate
Analysis
average
5%
3%
8%
6%
2%3%
1% 1%Standard retargeting
Custom retargeting
1%
5%80%
60%
55%
75%
95%
50%
Analysis
5%
3%
8%
6%
2%
80%
60%
55%
75%
95%
50%
3%
1% 1%
But we only want to target "high potentials”
Standard retargeting
Custom retargeting
1%
5%
"Half the money I spend on advertising is wasted, the trouble is I don't know which half"- John Wanamaker, 1908
How do we predict conversion intent?
Machine Learning
Algoritms Applied to
big data sets
Predictive
model
70%
Predict
purchase
probability
OK,But how does this work?
Data collection
Data source -User level
clickstream data
Data collection
Data source -User level
clickstream data
R-programming
Data collection
Data source -User level
clickstream data
Raw dataset -Training data
R-programming
Data collection Algorithm
Raw dataset -Training data
Machine learning
algorithms Neural Network
Naive Bayes Random Forest
Gradient Boosting Support Vector Machine
Classification Trees
Data collection Algorithm
Raw dataset -Training data
Machine learning
algorithms Neural Network
Naive Bayes Random Forest
Gradient Boosting Support Vector Machine
Classification Trees
Data collection Algorithm
Predictive
Model
Predictive Model
Raw dataset -Training data
Deployment
Data collection Algorithm
Predictive
Model
Predictive ModelData collection
Deployment
Data collection Algorithm
Predictive
Model
Predictive ModelData collection
JavaScript
in GTM
Deployment
Data collection Algorithm Predictive Model PredictingData collection
35%
75%
JavaScript
in GTM
Real-time
predictions
Deployment
Data collection Algorithm Predictive Model PredictingData collection
35%
75%
Real-time
predictions
Audience building & bid-strategy
Campaigning
Marketing Automation
First results
Facebook Retargeting1.
Facebook Retargeting - Audience1.
Audience Size 18640185Total Conversions (last-click)
Facebook Retargeting - Audience1.
High Conversion IntentLow Conversion Intent
Facebook Retargeting - Audience1.
Audience Size 1258371
Conversion Rate 0.56%Total Conversions (last-click)
Audience Size 6057114
Conversion Rate 1.88%Total Conversions (last-click)
High Conversion IntentLow Conversion Intent
48%
Audience Size 1258371
Conversion Rate 0.56%
Audience Size 6057114
Conversion Rate 1.88%Total Conversions (last-click) Total Conversions (last-click)
High Conversion IntentLow Conversion Intent
161%
Audience Size 1258371
Conversion Rate 0.56%
Audience Size 6057114
Conversion Rate 1.88%Total Conversions (last-click) Total Conversions (last-click)
High Conversion IntentLow Conversion Intent
Uplift Conversion Rate
236%
Audience Size 1258371
Conversion Rate 0.56%
Audience Size 6057114
Conversion Rate 1.88%Total Conversions (last-click) Total Conversions (last-click)
High Conversion IntentLow Conversion Intent
Continuous learning
What if…
20,000 website visitors
Strategy
High
Medium
Low
Testing
High
Follow up (50%)
No follow up (50%)
Testing
High
High frequency cap (50%)
Low frequency cap (50%)
What else?Hard CTA
(50%)
No discount (50%)
Low bid (50%)
High bid (50%)
Soft CTA (50%)
Discount (50%)
High
Alternative Applications
Alternative Applications
Activate dynamic content
Predict which channel to use for retargeting
Predict expected revenue when retargeted
Predict the ideal frequency cap
Make predictions on product category level
Send predictions to external datasource
Conclusion
All business problems have a data solution!
Any questions? Fire away.
Mustafa Himdi
Job Deibel
The heart and mind of your digital business
