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Housekeeping
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Overview
1. What is Predictive Content?
2. Content Discovery and Mapping
3. Predictive Content Recommendations Across all Channels
4. Using AI and Predictive Analytics to Increase Content Engagement
5. Case Studies and Success Stories
4. ERIC JOHNSON
Chief Sales Officer, MARKETO
WELCOME!
Digital Transformation
has changed everything
Buyer
expectations have
CHANGED THE GAME.
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Intro
Using AI and predictive analytics to automatically recommend the right content
to the right prospect based on visitor and behavior profiles.
• Increase content consumption
• Improve engagement rates
• Accelerate sales cycles
7. 89%of marketers have predictive
analytics in 2016 roadmap
+290average number of content
assets for an SMB
74%of consumers frustrated with
non-relevant content
The Predictive Trend
1.8XMarketers using predictive
tech are 1.8X more likely to
exceed organizational goals
2.9XMarketers using predictive
tech are 2.9X more likely to
report revenue growth
higher than industry average
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Background
• Marketers drive thousands of visitors to websites
• Marketers have thousands of leads in their databases
• SMB’s: 10 - 50K visitors per month / Enterprises: 100 - 500K visitors per month
• Leads: 50K Leads
• Marketers create hundreds of content assets
• Average SMB has +290 content assets
• Average Enterprise has thousands
• White papers, videos, case studies, blog posts, special offers
Rule-based campaigns are simply not enough…
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Predictive Content
• Enables marketers to optimize engagements and conversions in
every digital interaction
• Automatically presents the most relevant content to each
individual person
• Based on machine learning algorithms and predictive analytics
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Predictive Content
Unlike some predictive technologies, Predictive Content is:
Goal-driven
Proactive
Automated
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Predictive Content – How it Works
1. Auto-Discovers and maps all your content assets
2. Learns which content works best and for who
3. Recommends relevant content to web visitors & leads
Increases content consumption and engagement
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• Auto-discover your content
• Analyze and track behavior to discover assets
DISCOVER LEARN RECOMMEND
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• Record actual content consumption
• Analyze performance for different audiences
• Run algorithms that learn what works best for who
DISCOVER LEARN RECOMMEND
Machine Learning
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Algorithm Objectives
1. Use machine learning to increase content engagement and ROI
2. For each impression served, recommend the most relevant
content with the highest chance of a generating engagement
3. Continuously optimize recommendations
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Algorithm Models - How
Steps for Recommendation
1. Detect successful and unsuccessful content engagements
2. Identify visitor/lead attributes in real-time
3. Discover positive correlations that could predict future
engagements
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Algorithm Types
• Classification Algorithms (Logistic regression, Boosted decision Trees)
• Markov/Item-Based (Last content)
• Collaborative Filtering (Content-based)
• New/Popular/Trending
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• Identify web visitor attributes
• Recommend most relevant content across all channels
• Optimize engagements
DISCOVER LEARN RECOMMEND
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Predictive Content Results
315%
Increase in site engagements
(pages per visit, time on site) for
targeted visitors
201%
Increase in content specific lead
conversion
3x
More views on recommended content
compared to generic views
18%
Increase in content consumption
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Success with Predictive Content
The Story of Hatch
Early Learning
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Predictive Content Results
• 75% direct lead conversion rate
from predictive content
recommendation engine.
• Generated $100K in
opportunities
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How Predictive Content for Email helps
Marketers?
• Nurture customers with content that is personalized
• Rely on intelligent algorithms to do the matching without
losing the opportunity to add the human touch
• Leverage existing content in a relevant way across channels
• Improve Results! More content consumption, better
engagement, and more conversion. AT SCALE!
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Predictive Content - Summary
• Enables marketers to optimize engagements and conversions
• Automatically recommends the most relevant content for each
individual visitor across all channels
• Based on AI algorithms and predictive analytics