master data science for marketing...customer insights are the gold buried within your data. 4/3/2018...

4/3/2018

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Consumer Marketing 2018

Brandon Purcell

Master Data Science For Marketing

2© 2017 FORRESTER. REPRODUCTION PROHIBITED.

Customer insights are the gold buried

within your data

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“We are drowning in data and starving for insight.”

— Global Bank

4© 2017 Forrester Research, Inc. Reproduction Prohibited

Key phases of the insights lifecycle

Insights

Action

Data

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Key phases of the insights lifecycle

Insights

Action

Data

Data-to-insights gap

Insights-to-action gapContinuous

improvement gap


The data-to-insights gap is most prevalent

24%

26%

28%

40%

43%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Hiring talent to manage measurement and analytics

Providing real-time insights to the business

Getting buy-in from business stakeholders on thevalue of measurement and analytics

Accessing data from a variety of sources

Ensuring data quality from a variety of sources

Please rank the top three challenges that prevent your organization from making use of measurement and analytics.

Data-to-insights gap

Insights-to-action gap

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Customer analytic techniques bridge the data-to-insights gap


Now let’s explore the menu

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Methods that drive acquisition…

Source: February 2014 “TechRadar™: Customer Analytics Methods, Q1 2014”

Acquisition analytics

methods:

• Behavioral customer

segmentation

• Customer lifetime

value analysis

• Customer lookalike

targeting


Methods that increase retention and loyalty…


Retention and loyalty

analytics methods:

• Customer propensity

analysis

• Churn and attrition

analysis

• Social network

analysis

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Methods that drive personalization…


Personalization

analytics methods:

• Next best action

• Recommendation

analysis

• Cross-sell and upsell

analysis


Identify dependencies between methods


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Some methods enjoy more success than others


Most customer analytics techniques rely on

machine learning

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© 2017 Forrester Research, Inc. Reproduction Prohibited 15

Supervised vs unsupervised machine learning

Supervised learning Unsupervised learning

Purpose To predict / classify To explore / understand

Training data Labelled (knows the

“answer”)

Not labelled (no “right

answer”)

Accuracy Measurable Qualitatively evaluated

Use cases for

marketing

Predict which customers

are likely to respond /

churn / buy

Behavioral customer

segmentation


An algorithm detects patterns in data to create a model

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Supervised learning – Will I play tennis?

Day Outlook Temp. Humidity Wind Play Tennis?

D1 Sunny Hot High Weak No

D2 Sunny Hot High Strong No

D3 Overcast Hot High Weak Yes

D4 Rain Mild High Weak Yes

D5 Rain Cool Normal Weak Yes

D6 Rain Cool Normal Strong No

D7 Overcast Cool Normal Weak Yes

D8 Sunny Mild High Weak No

D9 Sunny Cold Normal Weak Yes

D10 Rain Mild Normal Strong Yes

D11 Sunny Mild Normal Strong Yes

D12 Overcast Mild High Strong Yes

D13 Overcast Hot Normal Weak Yes

D14 Rain Mild High Strong No


Decision tree for “Play tennis?”

Outlook

Sunny Overcast Rain

Humidity

High Normal

Wind

Strong Weak

No Yes

Yes

YesNo

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Data type Definition Examples

Nu

meri

cal Continuous All numerical values, including

fractions

Purchase amount

Discrete Only integer values Website visits

Cate

gori

cal

Nominal Qualitative data without order Region

Ordinal Ordered qualitative data Level of education

Binary Qualitative data with 2 values Offer click

Time and date Time and date data Timestamp

String Text data Email content

From an algorithmic perspective, there are only a few types of data


Meet the algorithms!

Use case:

Segmentation

Use case:

Product

recommendation

Use case:

Forecasting

Use case:

Churn / propensity

analysis

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Don’t be confused by the confusion matrix


A cumulative gains curve shows a model’s expected lift

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Machine learning results in new outputs requiring different operational processes


➢ What is the business objective of the project?

➢ Who is the project owner? Who are the relevant

stakeholders?

➢ Are there risks / constraints we need to take into

account?

➢ What would an ideal solution look like in action?

➢ How will we measure the success of this project?

Data science starts with answering key business questions

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Thank youBrandon Purcell

+1 510.926.2694

[email protected]


Meet the Panel

Richard P. WatsonDivision Vice President Client Experience, ADP

Newcombe ClarkGlobal Director, Rapid Learning, AIG

master data science for marketing...customer insights are the gold buried within your data. 4/3/2018...

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