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Digital Marketing Webinar
Go Mobile Or Go Web?
May 8, 2015 | Kunal Mehta (Lead Trainer at Digital Vidya)
www.digitalvidya.com
Web Analytics 2.0
An Introduction
“Web Analytics is the measurement, collection, analysis
and reporting of Internet data for the purposes of
understanding and optimizing Web usage.”
Clothing Store
Analytics
Web Analytics
• Data accumulation
• Get user profile
• Get customer behavior
• Get site vitals
• Provide actionable
inferences
• Provide data to drive
decisions
No Analytics
Common Metrics
It could be a count of number of times a milestone has
occurred on site.
•Visitors
•Visits
•Conversions
•Page Views
•Page Views per Visit
•Time on Site
•Returning Customers
•Bounce Rate
•Newsletter signup
•App Launch
Adobe Analytics (Omniture)
Adobe Analytics – User IDs
Adobe Analytics – Orders
Segments
Inbuilt segments
Custom Segments
Funnel
User profiles
Data captured helps us identify their
personas by assessing their behaviors
and patterns.
Analytics Tools
A/B Testing
A/B testing is a simple way to test changes to your page against the current
design and determine which ones produce positive results. It is a method to
validate that any new design or change to an element on your webpage is
improving your conversion rate before you make that change to your site code.
It offers real-time personalization of all content for each individual visitor as
they are going through the site and in context.
1:1 Targeting
Predictive Analytics
Predictive analytics is the practice of extracting information from existing data
sets in order to determine patterns and predict future outcomes and trends.
Applications:
•Which marketing effort would a customer respond to?
•Which customers are more likely to buy?
•Which customers are more likely to miss their mortgage payments?
•Credit Scores
•Who is more likely to get into accidents?
•Predicting market ups and downs by reading sentiments through blogs.
•Recommendations engine
• Completely Based on PA
• Customer Lifetime Value
• Customer Profiles
Results
Best Practices
• Track everything and get solid data
• Update and optimize consistently
• Good understanding of business requirements
• Good documentation
• Trust not only Lift, but also Confidence
• Always test changes before they go live.
• Early involvement
• Fire code towards the end of the page
• Do not capture PII
fin
Join upcoming
Digital Marketing Webinar live!
http://www.digitalvidya.com/
www.digitalvidya.com
Join upcoming
Digital Marketing Webinar live!
http://www.digitalvidya.com/
www.digitalvidya.com

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Go Mobile Or Go Web?

  • 1. Digital Marketing Webinar Go Mobile Or Go Web? May 8, 2015 | Kunal Mehta (Lead Trainer at Digital Vidya) www.digitalvidya.com
  • 2. Web Analytics 2.0 An Introduction
  • 3. “Web Analytics is the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage.”
  • 5.
  • 7. Web Analytics • Data accumulation • Get user profile • Get customer behavior • Get site vitals • Provide actionable inferences • Provide data to drive decisions No Analytics
  • 8. Common Metrics It could be a count of number of times a milestone has occurred on site. •Visitors •Visits •Conversions •Page Views •Page Views per Visit •Time on Site •Returning Customers •Bounce Rate •Newsletter signup •App Launch
  • 14. User profiles Data captured helps us identify their personas by assessing their behaviors and patterns.
  • 16. A/B Testing A/B testing is a simple way to test changes to your page against the current design and determine which ones produce positive results. It is a method to validate that any new design or change to an element on your webpage is improving your conversion rate before you make that change to your site code.
  • 17. It offers real-time personalization of all content for each individual visitor as they are going through the site and in context. 1:1 Targeting
  • 18. Predictive Analytics Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Applications: •Which marketing effort would a customer respond to? •Which customers are more likely to buy? •Which customers are more likely to miss their mortgage payments? •Credit Scores •Who is more likely to get into accidents? •Predicting market ups and downs by reading sentiments through blogs. •Recommendations engine • Completely Based on PA • Customer Lifetime Value • Customer Profiles
  • 20. Best Practices • Track everything and get solid data • Update and optimize consistently • Good understanding of business requirements • Good documentation • Trust not only Lift, but also Confidence • Always test changes before they go live. • Early involvement • Fire code towards the end of the page • Do not capture PII
  • 21. fin
  • 22. Join upcoming Digital Marketing Webinar live! http://www.digitalvidya.com/ www.digitalvidya.com
  • 23. Join upcoming Digital Marketing Webinar live! http://www.digitalvidya.com/ www.digitalvidya.com