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…
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
6.
7.
8.
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
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
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
23.
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
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