Use machine learning for data heavy lifting in order to increase efficiency at multiple stages of the outreach lifecycle. Gareth will share how his agency is scaling their outreach activities, allowing them to have more conversations with the right people, and ultimately win more links. All of this is already possible with readily-available technologies: most of these tactics aren’t as far-fetched as you might think…

1. HTTPS://WWW.LINKEDIN.COM/IN/SIMPSONGARETH/
LMFAO: LEVERAGING MACHINES
FOR AWESOME OUTREACH
GARETH SIMPSON // SEEKER DIGITAL //
@SEEKERDIGITAL

2. AI Powered Outreach
E N A B L I N G B E T T E R C O M M U N I C A T I O N S

3. 3
Takeaways
INTRODUCTION TO MACHINE LEARNING
HOW TO APPLY ML TO OUTREACH

4. 4

5. 5
ML foundations
Supervised
• Apply previous actions to new situations
• Scale repetition
Unsupervised
• Discover patterns in data
• Unlock hidden insights
Semi-Supervised
• Hybrid / Combination
• Best of both

9. 9
Only 27% of global consumers
say AI can deliver the same or
better service than humans.
However, 38% say AI will help
to improve service.
SOURCE: GARTNER

10. Outreach is about relationships

11. 11
OBJECTIVE
Set page & targets
Outreach Process
IDEATION
Ideation & tactics
PROSPECT
Identify opportunities
PITCH
Collaboration proposal
NEGOTIATE
Respond to queries
DELIVER
Secure placement
QUALITY SPEED UNFULFILLING WORKISSUES LIMITED INSIGHT

12. 12.5% of staff time is lost in data
collection. That’s five hours a
week in a 40-hour work week.SOURCE: GARTNER

13. AI helps us to
reach new
heights

14. 14
AI solution
Automate repetitive tasks
Emulate human decisions
Content classification & production
Benefits
InsightSpeed Cost Competition

15. 15
ML for outreach
1. Keyword research
2. Ranking projections
3. Intelligent prospecting
4. Auto-routing
5. Pitch optimisation
6. Conversation assistance

16. Machine learning helps
us concentrate on more
meaningful work

17. 17
App stack
DATA
ML ENGINES

18. 18
21 3 4 5
Supervised ML process
Gather data
from multiple
sources
Auto-cleanse
data for
consistency
Gain insights
from the
model’s results
Output to
platform or
process
Select an
algorithm and
build a model

19. 19
Data pipeline
Data
• Emails
• SEO data
• Social media
Processor
• Models
• Text classifier
• Semantics
Output
• Action
• Routing
• Improvements

20. 20
Contact filtering &
enrichment
• Manual contact research & personalisation
• Identify appropriate person
• Fatigue & human errors

21. 21
1
2
3
4
Human processed data
Features & tags
Good data in > good data out
Refine over time
Training data

22. 22
Reach the right person
Opportunity
Prospecting
Website data
from prospecting
tools
Send To
MonkeyLearn
Pull in contact
data from
Pitchbox
Update
Pitchbox
Classify job title,
industry and
seniority
Contact
Research
Filter contacts
and send to
Pitchbox
Pitch!
To the most
relevant
decision maker

24. 24
Cut through the noise
• Train model using email
conversations
• Feedback on model to improve
accuracy
• Classify inbound email and
route to team member

25. 25
#journorequest
FILTERCLASSIFY NOTIFICATIONSHASHTAGS

26. 26
Email text extraction
• HARO & Talkwalker • Auto-output to sheet • Great for notification emails!

27. 27
Pitch
optimisation
(Phrasee)

28. 28
By 2020, 30% of all B2B
companies will employ
AI to augment at least
one of their primary
sales processes.
SOURCE: GARTNER

29. • Psychology techniques in AI
• Auto-complete sentences
• Suggest responses
Conversational

30. “With Bing Autosuggest API v7, help users complete queries
faster by adding intelligent type-ahead capabilities to your app
or website. Empower users to type less and do more with
automated and complete search suggestions.”
Conversational

31. Awesome autocomplete

32. Future
EXPONENTIAL
GROWTH
GRASSROOTS
APPLICATIONS
COMPETITIVE
EDGE

33. Thank you