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Alison Wagonfeld: Marketing Meets Science
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Transcript of Alison Wagonfeld: Marketing Meets Science
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Marketing Meets Science
Alison WagonfeldVP Marketing, Google Cloud
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greenlight.com
California Research Center
My journey
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Maps Chrome Android
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Marketing demands constant innovation
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The marketer’s role is changing
Traditional campaign execution
Hyper-relevant, Real-time engagement
Data capture
Retroactive performance analysis
Data-backed customer insights
Performance-led strategy
Thoughtful, targeted, scalable
Proactive
FROM TO
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Big Data Analytics Machine Learning
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Gives computers the ability to learn without being explicitly programmed. Machine Learning
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Machine Learning Opportunities in Marketing
IDENTIFYCustomers
TARGET & APPROACH
Customers
GROWCustomers
OPTIMIZE SPEND
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1. Acquisition
2. Customer growth
Examples of Machine Learning in marketing at Google Cloud
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Which campaigns are driving the most paid G Suite users?
Business Challenge #1
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Example 1:
Customer Acquisition for G-Suite
FREE TRIALSCAMPAIGN CONVERSIONS
ANALYSIS
Traditional marketing model requires 45+ days to optimize
45 DAYS
OPTIMIZE
START END PAID SEATS
Channels
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Example 1:
Customer Acquisition for G-Suite
COURSE CORRECT
2-day marketing optimization model
PREDICT
2 DAYS
PAID SEATS
UPDATE MODEL
FREE TRIALSCAMPAIGN CONVERSIONS
ANALYSIS
Channels
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Example 2:
Customer acquisition for G-Suite with ML
3
Looking under the hood
Data is collected.
Data is analyzed & visualized.
New free trial accounts get scored.
1 2 4 5 6
ML model is created and trained.
Historical data
Paid media campaigns begin.
ML makes automated paid media campaign adjustments.
Real-time data
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What is the optimal “next” product for each Google Cloud Platform (GCP) customer?
Business Challenge #2
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Channels
Example 2:
Customer account growth for GCPTraditional product correlation model
A
B
C
B
D
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X
Recommendation engine
ACCOUNT BEHAVIOR
Example 2:
Customer growth for GCP
RECOMMENDATION ENGINE
A
LEARNING MODEL
A X
COMMUNICATIONCHANNELS
MOST RELEVANT
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3
Example 2:
Customer Growth for GCP with MLLooking under the hood
Data is collected.
Data is analyzed & visualized.
Recom-mendation campaigns begin across channels.
1 2 4 5 6
ML model is created and trained.
Historical data
ML scores accounts, suggesting products and offers.
Consumer takes action, informing the ML model.
Real-time data
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ML Model suggests optimal outreach for each account
In-console message
Direct mailEmail
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How do I try this at home?
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Where to start
1. Team: marketer + engineer + data analyst
2. Products:a. Data & Analysis: BigQuery,
Data Studiob. Machine Learning: Cloud ML
Engine, TensorFlow
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1. Data integrity
2. Experimental mindset
3. Select business problem to address
Setting your team up for success
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Common pitfalls
1. Incomplete Data
2. Privacy & Legal
3. Bias
4. Resources
5. Creative
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Is Machine Learning the “Holy Grail” for Marketing?
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Alas, no such thing.
We still need the “Marketing” to go with the “Science.”It’s all about the combo.
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Q&A