The document provides an overview of web analytics, emphasizing its importance for understanding and optimizing web usage, featuring key metrics such as visitors, conversions, and page views. It discusses A/B testing as a method for validating changes to web design and predictive analytics for forecasting customer behavior and market trends. Best practices for effective analytics include tracking data, continuous optimization, and thorough documentation.

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.”

4. Clothing Store

6. Analytics

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

9. Adobe Analytics (Omniture)

10. Adobe Analytics – User IDs

11. Adobe Analytics – Orders

12. Segments
Inbuilt segments
Custom Segments

13. Funnel

14. User profiles
Data captured helps us identify their
personas by assessing their behaviors
and patterns.

15. Analytics Tools

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

19. Results

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