This document discusses how machine learning technologies can be used in various business applications such as marketing, customer analytics, and image recognition. It provides examples of companies like Netflix and Amazon that are successfully using machine learning. It also outlines a layered approach for implementing these technologies and discusses some limitations and cautions when adopting machine learning.
9. How we use text mining
First-party data
like internal search
queries, help desk
questions, chat
logs, phone logs
Social media data,
reviews and online
coverage/links
Search queries
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10. What can we do with this data?
PredictVisualise
trends
Connect
data to
customer
records
Compile text
data in one
place
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11. Possible use cases
Which keywords
generate the most
profitable
customers?
Does survey data
match up to sales?
Which customer
complaints lead to
churn?
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15. A quick history
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Early
1900s
1970s
1990s
Now
Intuition Statistical
programming languages
Automated
machine learning
Manual analysis Visual statistical software
Using experience
and judgement to
predict outcomes
Writing code to construct
statistical models
The software knows how to analyze
your data and does it for you
Manual
calculations to
predict
outcomes
Drag and drop workflows with
menu driven commands to set
up and statistical analysis
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18. Confidential
Layered approach to implementation
4. One-to-one communications
3. Life style centred
2. Customer life cycle
centred
1. Basic
Increasingsophistication
(Data,Audienceinsight,
Technology)
Example types of data:Example communications:
Customer recognition
Personalisation to drive
relevancy and CTA
Segmentation
centred on lifestyle
approach
Fully
personalised
Basic information at purchase
Customer life cycle position,
purchase history
Demographic: Age,
affluence, geo-
location, motivation
Full benefits of
Single
Customer View
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21. “Machine learning is a
core, transformative
way by which we’re
re-thinking how we’re
doing everything.”
22. Machine learning in the Google stack
In-Market
Audiences
Smart Display
Campaigns
Smart Bidding
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23. Limitations
You can create
more customised
audiences
manually
Brand safety has
made marketers
want more control
over creative
Seasonal
campaigns are still
manual
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28. Analytics maturity
Market sizing
Market sensing
NBA modelling Proposition development
Price modellingCampaign evaluation
Data visualisation
A/B testing PREDICTIVE
Profiling / segmentation Customer lifetime value PRESCRIPTIVE
Web analytics Propensity modelling Machine learning PRE-EMPTIVE
KPI reporting
Research analysis Upsell modelling
DIAGNOSTIC
DESCRIPTIVE
COMMERCIAL
VALUE
COMPLEXITY
Attribution / marketing mix
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29. Developing personas
Demographics
• The youngest group
• x% are families
• Long distance travel
Behaviour
• Summer time travel
• Book 4-6 months in
advance
• Less likely to visit in 12
months
Lifestyle
• Into music
• Follow current affairs
• Shop online
• Lower income
% of Base – a%
% Value – b%
Spend per visit - £c
HH 3 year value - £d
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42. Confidential
The Great Hall was filled with incredible
moaning chandeliers and a large librarian
had decorated the sinks with books about
masonry. Mountains of mice exploded.
Several long pumpkins fell out of
McGonagall. Dumbledore’s hair scooted
next to Hermione as Dumbledore arrived at
School.
The pig of Hufflepuff pulsed like a large
bullfrog. Dumbledore smiled at it, and
placed his hand on its head:
“You are Hagrid now.”
http://botnik.org/content/harry-potter.html