LMFAO Leveraging Machines for Awesome Outreach
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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…
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LMFAO Leveraging Machines for Awesome Outreach
- 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
- 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
- 26. 26 Email text extraction • HARO & Talkwalker • Auto-output to sheet • Great for notification emails!
- 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
- 33. Thank you