Marketo Predictive content
Transcript of Marketo Predictive content
Predictive ContentIntroduction Sept, 2016
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Overview
1. What is Predictive Content
2. How it works
3. Recommendation Algorithms
4. Case studies and real-life examples
5. How Marketo uses Predictive Content
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Intro• Listening everywhere and communicating anywhere powered by
machine learning empowers marketers to maximize bottom line results leveraging marketing assets and audience data
• Automatically recommend the right content to the right prospect in a scalable way driven by data
• Self learning engine designed for rocket scientists marketers
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Background• Marketers drive thousands of customers to their web site
• SMB’s: 10 - 50K visitors per month
• Enterprises: 100 - 500K visitors per month
• B2C’s: 100K - over 1 million visitors per month
• They also create hundreds of content assets to engage more effectively • Average SMB has +290 content assets
• Average Enterprise has thousands
• White papers, videos, case studies, blog posts, special offers
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Engagement (R)evolution• Rule based targeting
• Great for small # of events, content products and verticals
• A/B testing• Further optimizing multiple engagement options for
segmented audience
• Predictive• A scalable way to auto-engage with the most relevant content
to each individual customer driven by data, powered by machine learning
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Predictive Content
• Enables marketers to optimize engagements and conversions in
every online interaction
• Automatically presents the most relevant content for each
individual visitor
• Based on machine learning algorithms and predictive analytics
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How it Works1. Auto-Discovers and maps all of your content assets
2. Learns which content works best and for who (training/testing)
3. Recommends relevant content to web visitors & leads
Increases content consumption and onsite engagement
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• Auto-discover your content
• Analyze and track behavior to discover assets
DISCOVER LEARN RECOMMEND
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Content Discovery
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Content Discovery
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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 Types• Classification Algorithms (Logistic regression, Boosted decision Trees)
• Probabilistic algorithms Markov-chain
• Interest graphs
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The models are tuned to..1. Detect successful and unsuccessful content engagements
2. Identify visitor/lead attributes in real-time
3. Discover positive correlations that could predict future engagements
4. Continuously optimize recommendations (every 8 – 24h)
5. Expose NEW content, detect trending and tune down old content
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• Identify web visitor attributes
• Recommend most relevant content
• Optimize engagements
DISCOVER LEARN RECOMMEND
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Example – Web Recommendation Bar
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Example – Web Recommendation Bar
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Example – Web Rich Media
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Customer Example (Web Rich Media)
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Customer Example• 75% direct lead conversion rate
from predictive content recommendation engine.
• Generated $100K in opportunities
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Customer Example (Web Rich Media)
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Customer Example (Web Rich Media)
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Cross ChannelsWeb Mobile Email
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Content Recommendation Results
315%Increase in site engagements (pages per visit, time on site) for targeted visitors
201%Increase in content specific lead conversion
3xMore views on recommended content compared to generic views
18%Increase in content consumption
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1. Enable Content Discovery
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2. Select Your Content
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2. Select Your Content
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2. Select Your Content
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2. Select Your Content
Advanced: use categories
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3. Add Some Code
rtp('send','view');rtp('get','rcmd', 'richmedia');
<div class="RTP_RCMD2" data-rtp-template-id="template1"></div>
Add 2 lines to your standard RTP snippet:
Add a line of code where you want the recommendations to appear on your page:
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A Measurable Lift in Downloads
New website launch
Recommendations added
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Q4 – Predictive Content in Email
*not final
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Q4 – Predictive Content in Email
*not final
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Q4 – Predictive Content in Email
*not final
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Q4 – Predictive Content in Email
*not final
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Q4 – Predictive Content in Email
*not final
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Predictive Content - Summary
• Enables marketers to optimize engagements and conversions
• Automatically recommends the most relevant content
for each individual visitor
• Based on machine learning algorithms and predictive
analytics
Thank you.