Edit's Marketing Director, Stephen Kenwright, takes you through a list of machine learning tech you could and should use during his #HeroConf London talk on 22/10/18.

1. Machine Learning Tech
you could and should use tomorrow
Stephen Kenwright
Marketing Director

2. Sheffield Hallam University Confidential

3. Confidential
I studied this guy

4. This guy was one of my lecturers
Ray was using machine learning to identify authorship
@stekenwright

5. Devonshire Manuscript.
Confidential

6. Confidential
One potential author.

7. ConfidentialConfidentialConfidential
Text mining.

8. Luminance.
Confidential

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
Confidential@stekenwright

10. What can we do with this data?
PredictVisualise
trends
Connect
data to
customer
records
Compile text
data in one
place
Confidential@stekenwright

11. Possible use cases
Which keywords
generate the most
profitable
customers?
Does survey data
match up to sales?
Which customer
complaints lead to
churn?
Confidential@stekenwright

12. ConfidentialConfidentialConfidential
Identify pain points
through call log
mining.

13. Data Robot.

14. ConfidentialConfidentialConfidential
We’re getting
ahead of ourselves.

15. A quick history
Confidential
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
@stekenwright

16. Today
Confidential@stekenwright

17. ConfidentialConfidentialConfidential
By 2020, 85% of
interactions will be
handled without
a human.
Gartner

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
@stekenwright

19. Netflix
In the wild: Netflix.

20. Confidential
Or Amazon.
@stekenwright

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
Confidential@stekenwright

23. Limitations
You can create
more customised
audiences
manually
Brand safety has
made marketers
want more control
over creative
Seasonal
campaigns are still
manual
Confidential@stekenwright

24. Choosing tech, according to Forrester
Confidential@stekenwright

25. Everyone’s machine learning is, err…learning
Pre 2010 2015 2017+
Confidential@stekenwright

26. Where we are now.

27. Confidential

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
Confidential@stekenwright

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
@stekenwright

30. Marketing mix modelling
saves £2.4m PPC spend.
Confidential

31. ConfidentialConfidentialConfidential
Use email open rates
to inform ad copy.

32. Phrasee personalises email
using machine learning.
Confidential

33. Self-serve data cleanse – puradata.co.uk
3. Dedupe

34. Olduvai gorge, Tanzania Confidential

35. Reducing response times
for humanitarian aid.
Confidential

36. Mara Tanzania project.

37. Basic image…
with highlights.

38. Edit Classifier.
• EditBuildings_1406043538
• "custom_classes": 5, “
• "class": "HomeComplex",
• "class": "HomeSmall",
• "class": "TinComplex“,
• "class": "TinLarge",
• "class": "TinSmall",
Uses reinforcement learning to train the Watson image recognition and classification algorithm
Confidential@stekenwright

39. Confidential
Watson.

40. ConfidentialConfidentialConfidential
74% of organisations
implementing AI who
believe it’s making them
more creative.
Capgemini 2017

41. ConfidentialConfidentialConfidential
A cautionary
tale.

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

43. Let’s get started.