This document outlines a data-driven approach for enhancing user engagement and sales through analytics and tracking methodologies. It covers various use cases for implementing analytics to measure key performance indicators, drive insights, and improve conversion rates across different digital marketing channels. Additionally, it presents case studies demonstrating successful strategies and the impact of segmentation, remarketing, and custom tracking on business outcomes.

2. A Data Driven Digital Agency
We let Numbers and Science speak
Our solutions are always backed by data.
They’re not just personal opinions.
“They brought value into each stage of the project lifecycle…to
drive user engagement and sales.”
Tania Aidrus
Country Manager – Emerging Markets Google

3. A few Key Clients

4. Todays Speakers
Niroshan Samuel
Manager – Web Analytics
& Optimization
Malinda Senanayake
Senior Associate –
Digital Marketing
Duminda Sanjeewa
Senior Web Analyst

5. Implementation Framework from Digital Measurement Model
Goals Objectives KPI’s Implementation
1. Increase Sales Improve Average Order Value − Average product
price
− Ecommerce
− Products per basket − Ecommerce
Improve Conversion Rate − Conversion rate − Goals for Conversions
2. Increase Leads Increase contact form fills − Form fills conversion
rate
− Form fill track
3. Improve
Engagement
Increase video engagement − Video view rate
− Video viewed
percentage
− Track video views
Increase social shares − Social shares per
session
− Track social shares

6. Basic Tracking Using Tag Manager
Tag Manager can be used to track basic interactions that a visitor does in a page
Tracking ‘Add to Bag’ clicks
Capturing dropdown values
Capturing the selected color
Tracking ‘size guide’ views

7. Business Problem
 Identify the visitors who download the products
catalog in a site which sells wholesale items
Analytics Implementation
 An event is triggered whenever a visitor click on the
products catalog link
Result
 Find a segment of users who are showing interest
Link Tracking – Use Case 1

8. Motive
 Identify the segment of visitors who ‘add products
to cart’ in an ecommerce site but not converting
Analytics Implementation
 Triggering an event whenever a visitor click on the
‘Add to Bag’ button
Result
 Identifying a narrow and conversion ready audience
to reach out through channels such as remarketing,
email etc.
Button Clicks – Use Case 2

9. Motive
 Segment the audience based on their preference in
selecting the type of tour
Analytics Implementation
 Capture the ‘type of tour’ dropdown input
Result
 Target dropped out users with personalized
offerings in a remarketing campaign depending on
their choice
Dropdown Inputs – Use Case 3

10. Advanced Tracking Using Tag Manager
Tag Manager can be used to track advanced interactions such as form submissions, video
stats etc.
Form related data and actionsVideo engagement stats

11. Business Problem
 Find out the number of leads generated
Analytics Implementation
 Track form submission as an event and create a
goal based on that
Result
 Optimizing the marketing campaigns’ ROIs based
on the leads
Form Submission – Use Case 4

12. Business Problem
 Find the effectiveness of the form
Analytics Implementation
 Capture activities (filled or not) related to each form
field
Result
 Identify the fields which are skipped most and
optimize the form fields accordingly
Form Field Activity Tracking – Use Case 5

13. Business Problem
 Identify the sales which are coming through the
sales agents
Analytics Implementation
 Capture the ‘booking code’ as a custom dimension
Result
 By filtering out the sales agents conversions, we
were able to perform analyses based on the sales
coming through actual customers
Form Field Data Capturing – Use Case 6

14. Motive
 Capture marketable information such as
‘designation’
Analytics Implementation
 Capture the ‘designation’ as a custom dimension
Result
 Personalized remarketing campaigns based on
designation bring more conversions at a lower cost
Form Field Data Capturing – Use Case 7

15. Case Study 1 – Designation Based Remarketing
Decreased CPA by 82%
Designation Based
Remarketing $150
Generic Remarketing $850
CPA

16. Case Study 2 – Reducing Form Fields
Increased Conversion Rate by 13%
Regular Form with
‘Country’ field
1.5%
Conversion Rate
Captured the IP address and identified the GEO location also feed that info in to Google
Analytics as a custom dimension
Difference between IP location and Customer filled data had only 9% difference

17. Motive
 How visitors are interacting with video content
Analytics Implementation
 Track ‘play/pause’ state and ‘% time played’ as
events
Results
 Finding the videos which have high engagement
 Remarketing to the visitors who engage more
Video Engagement Tracking – Use Case 8
Capturing play/pause state
% Time played

18. Case Study 3 – Campaign Based on % Time Played
50% Played
75% Played
1.2%
1.4%
Conversion Rate
Increased Conversion Rate by 17%

19. Custom Dimensions and Metrics
We can capture any additional information
available in the website as variables and then
store them in Analytics as ‘custom
dimensions’ or ‘custom metrics’

20. Calculated Metrics
Analytics allow us to create our own ‘calculated metrics’
by performing basic mathematical operations on default
metrics.
E.g. –
• Revenue per user
• Goal completions per user

21. Custom Dashboards
Custom dashboards allow us to view
key metrics at a glance and tailor the
view to our own needs.
Examples
• Ecommerce dashboard
• Content marketing metrics
• PPC dashboard
SEO Dashboard

22. Custom Report
Hourly Data
Analytics users can generate custom reports using dimensions and metrics as they want.

23. Motive
 Identify the influence documentation in capturing
leads
Analytics Implementation
 Create a sequential advanced segment in Analytics
to identify the funnel
Results
 Visitors who go to documentation after visiting the
product pages show higher probability in converting
to a lead
Advanced Segments – Use Case 9
Product Pages
Documentations
Contact
Lead

24. Motive
 Track the number of times ‘out of stock’ message
pops up
Analytics Implementation
 Trigger an event each time when the selected item
is ‘out of stock’
Results
 Identify the demand for each type of product & the
sizes
 Optimize the product sizes mix based on the
demand
Report Automation – Out of Stock Products

25. Motive
 Identify the pages which give ‘404’ error
Analytics Implementation
 Send the URLs of the pages which give 404 errors
when someone visit
Results
 Take corrective actions to minimize the error pages
easily as the URLs get recorded in Analytics
Alerts – Tracking 404 Error Pages

26. Google Analytics Stats Across 20 Shopping Websites
(Last 30 Days – Sri Lanka)
1.5M sessions
9M page views

27. Data Is Useless Without the Skills to
Analyze It - HBR

28. Two Successful Methods to Find
Insights
1. Segmentation
2. Find Commonalties

29. 1. Segmentation
Find profitable segments Find unprofitable segments

30. 30
Device Level Segmentation
Profitable
Un-profitable

31. 31
Profitable
Un-profitable
Un-profitable
Profitable
Insight and Actions using Segmentation
Profitable
Segments Dedicated Campaigns, Expand, Replicate
Unprofitable
Segments Segment more, Test Variation or STOP

32. 32
Case Study 4 - Content Influence on Contact Us Form Fills
0.45%
1.57%
2.08%
3.58%
0.46%
0%
1%
1%
2%
2%
3%
3%
4%
4%
Site Average GNN Infographic PDF Video
Insight
 Visitors who read PDF have higher probability to fill the
contact us form.
Actions
 Plan remarketing campaign with PDFs
 Auto recommend PDFs to users who dropped from
contact page

33. 33
Case Study 5 - Device Level Conversion Rate
Apple Devices
Conversion rate is high with high value products
Samsung Devices
Conversion rate is high with mobile phones and mobile
accessories
Launched separate device level campaign using Facebook Platform.
And achieved 20% to 40% improvement in Conversion Rate.

34. Two Successful Methods to Find
Insights
1. Segmentation
2. Find Commonalties

35. 35
We have weight and food consumption data for
each GoT Character
We need to find insight to lose our weight using commonalties

36. 36
Ideal Weight Over Weight
Common Characteristic?

37. 37

38. 38
Landing Pages and Conversion Rate
Converted landing pages Worst converted landing pagesVs

39. 39
Case Study 6 - Landing Pages – Category Page Analysis
Category Pages with Few Products Category Pages with Many Products
Higher Conversion Rate
Add products to categories with few products or don’t drive visitors
to those pages

40. “Luxury Apartments” Related
Keywords performed
“Retirement”
Related Keywords performed
Case Study 7 – Keyword Analysis

41. Case Study 8 - Visitor Behavior – Mobile & Tablet Category
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
18-24 25-34 35-44
Sub Categories
Brand Terms
Apple, Samsung, Windows
Smart Phones, Feature Phones

42. 42
Improve room nights per customer
Vs
Properties with low room nights per customer Properties with high room nights per customer

43. 43
Properties with high no of Room nights per customer had Quality
Images

44. 44

45. With Google Analytics

46. 46
Case Study 9 - Content Influence on Conversion Rates
8%
10%
11%
0%
2%
4%
6%
8%
10%
12%
ConversionRate
Blog+MarketingInsights
MarketingInsights+Funds
Blog+Funds
3 Content Section – Blog, Marketing Insights, Funds Section
If a visitor goes to the blog and to the fund
section his conversion probability is high.

47. 47
Content Map & Possible Visitor Paths
Based Equation: nPr = n! / (n - r)!
1956 possible Visitor
PathsBlog
Market InsightsAbout Us Page
Contact Page
Funds
Funds

48. 48
You Need Help From…

49. 49
Case Study 10 - Machine Learning with Google Analytics
Count of Session < 5
Product Page View
Video View
=Yes
Device
=Mobile
Site Search Used
0.8% (High)
Conversion Rate
0.7% (high)
Conversion Rate
1.35% (Very high)
Conversion Rate
0.21% (Low)
Conversion Rate
0.55% (medium)
Conversion Rate
2% (high)
Conversion Rate
YES NO
Count of Session < 5, Video Viewed, Device mobile
Count of Session > 5, Site Search Used
Decision Tree for Ecommerce Business
Conversion Rate
Improved by 10X

50. 50
Cluster 3
Mobile Users
Case Study 11 - Predict Bounce Rate Based on Device, OS and
Browser
Site Bounce Rate ( 50%)
Low
Medium
High
Very High
Cluster 4
Browser: Android, Chrome, Safari
Device Category: desktop, tablet
Operating System: Android, IOS, Macintosh,
Windows
Cluster 5
Browser is not: Android, Chrome, Safari
Device Category: desktop
Operating System: Windows
Cluster 1
Browser: Android, Chrome, Safari
Device Category: desktop, tablet
Operating System is not: Android, IOS, Macintosh, Windows
Cluster 2
Browser is not: Android, Chrome, Safari
Operating System is not: Linux, Macintosh, Windows
Cluster 6
Browser is not: Android, Chrome, Safari
:Operating System: Linux, Macintosh
Cluster 7
Browser is not: Android, Chrome, Safari
Operating System: windows
Device Category: mobile, tablet

51. Control Experiment
Control Experiments

52. Control Experiments
One of the most serious challenges in marketing is identifying
the true impact of a given marketing spend change. - Google

53. Adwords shows tons of View-Though conversions. Does that
matter?

54. Experiment Results so far
Directed to Non related Indian blog
By investing $1 in Adwords remarketing Customer can increase conversions by +2
Case Study 12 - Control Experiment to Identify Impact of View-
Through Conversions
Directed to Brand Site

55. Action in the Insights

56. Testing
Process
Insights
Hypothesis
Test
Analyze

57. Case Study 13 – Site Search Insight
Highest searches for Frocks
not for Dresses.

58. 12%
Hypothesis: By using the word frocks will improve conversions

59. Sub Category - Men's Fashion
Case Study 14 – Site Navigation Insight
Visitors who landed to category page visited the sub categories
without purchasing Products

60. Control & Variations
Control Variation
Hypothesis :
Displaying most visited sub-categories at the top of the category page will assist users’
navigation and hence reduce the bounce rate as well as improving conversions ultimately.
12%

61. Test

62. A/B Test

63. Multi Variate Test

64. Redirect Test

65. Capabilities
01
02
03
04
Integrate Google Analytics
Reporting
Visual Editor
Run all 3 types of tests

66. The most value metric you are not measuring
Retention

67. 2.1
2.3
3.2
6
0
1
2
3
4
5
6
7
1 2 3 4
No. of orders within first 3 months
Case Study 15 – Orders within First MonthOrdersmonth4to12
Push 4 orders within 3 months to
achieve 6 orders within next 9
months

68. Case Study 16 - First Purchase “Size“ & ”Color” – Purchases within
12 months
2.9
3.1
3.3
3.9
4
4.7
4.9
5.5
5.8
6.4
0 1 2 3 4 5 6 7
Size - 8 (Black)
Size - 10 (White)
Size - 12 (Red)
Size - 14 (Red)
Size - 10 (Blue)
Size - 8 (Green)
Size - 10 (Black)
Size - 12 (White)
Size - 14 (Black)
Size - 12 (Blue)
Size&Color
Orders per User
Orders per User
How the combination of first purchase’s color and
size affect the LTV of the customer.
This helps to identify more granular segment of
visitors.

69. Conclusion

70. Start Data Driven Business Today…
Analytics
Setup
Identify Right
Attribution Model
Measurement
Framework
Insights