alison wagonfeld: marketing meets science

Marketing Meets Science

Alison WagonfeldVP Marketing, Google Cloud

greenlight.com

California Research Center

My journey

Maps Chrome Android

Marketing demands constant innovation

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

Big Data Analytics Machine Learning

Gives computers the ability to learn without being explicitly programmed. Machine Learning

Machine Learning Opportunities in Marketing

IDENTIFYCustomers

TARGET & APPROACH

Customers

GROWCustomers

OPTIMIZE SPEND

1. Acquisition

2. Customer growth

Examples of Machine Learning in marketing at Google Cloud

Which campaigns are driving the most paid G Suite users?

Business Challenge #1

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

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

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

What is the optimal “next” product for each Google Cloud Platform (GCP) customer?

Business Challenge #2

Channels

Example 2:

Customer account growth for GCPTraditional product correlation model

A

B

C

B

D

X

Recommendation engine

ACCOUNT BEHAVIOR

Example 2:

Customer growth for GCP

RECOMMENDATION ENGINE

A

LEARNING MODEL

A X

COMMUNICATIONCHANNELS

MOST RELEVANT

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

ML Model suggests optimal outreach for each account

In-console message

Direct mailEmail

How do I try this at home?

Where to start

1. Team: marketer + engineer + data analyst

2. Products:a. Data & Analysis: BigQuery,

Data Studiob. Machine Learning: Cloud ML

Engine, TensorFlow

1. Data integrity

2. Experimental mindset

3. Select business problem to address

Setting your team up for success

Common pitfalls

1. Incomplete Data

2. Privacy & Legal

3. Bias

4. Resources

5. Creative

Is Machine Learning the “Holy Grail” for Marketing?

Alas, no such thing.

We still need the “Marketing” to go with the “Science.”It’s all about the combo.

Q&A