If you are an eCommerce store and have the basic Enhanced Ecommerce GA implemented, you shall get insights into what business decisions you can take from your GA reports without bringing in more of predictive analysis and data science. Basic understanding of Enhanced Ecommerce Reports 1) The Shopping Behavior and Checkout Funnel 2) Product Performance Reports 3) Product List Performance Reports 4) Internal Promotion Reports Using CLV model on your GA data to know your best customers Minimizing the conversion path for your customers using feature attribution and hence driving in more conversions.

1. Basics, Use Cases & its Applications
How eCommerce Businesses can make
best use of Google Analytics data

2. SpeakersModerator
Aditi Buch
Marketing Manager, Tatvic
2
Who’s Who
Anshul Bhatt
Customer Success
Manager, Tatvic
Bismayy Mohapatra
Data Science Lead, Tatvic

3. 3
How to Interact
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Control Panel

4. 4
How to Interact
Take a Poll Ask a Question

5. OUTLINE
5
1 Enhanced Ecommerce – An
Introduction 2 The six utilitarian EE
Reports
3
Knowing Your Best
Customers using LTV
models
4
Optimize the conversion path
using Markov Chain Feature
Attribution

6. Introduction
6
3 Questions to answer:
What do the several Ecommerce reports under
“Conversion” section mean for my business?
Can Google Analytics help me identify my high
value customers?
Can Google Analytics dissect my user journeys
to help me optimize their online experience?

7. OUTLINE
7
1 2 The six utilitarian EE
Reports
3 Knowing Your Best
Customers using LTV
models
4 Optimize the conversion path using
Markov Chain Feature Attribution
Enhanced Ecommerce – An
Introduction

8. Shopping Behavior
8
Know where users drop off from ecommerce
funnel
Analyze Abandonments using Dimension Filters
(Device, Region, Source..)
Remarket to dropped off users (Shopping Cart
Abandoners)
Transactions
Conversion Rate
Drop-offs

9. Checkout Funnel
9
Checkout
Page
Optimizations
Increased
Sales and
CVR
Investigate the steps disrupting checkout phase
of the funnel
Shipping Address -> Check Pincode Delivery ->
Review Order -> Payment
Remarket on several checkout steps (Shipping
Drop off Users)

10. Product Performance Report
10
Measure performance
of individual Products,
Product categories,
Product SKUs, Brands
Take marketing
decisions to promote
aggressively or to stop
certain products/brands/
SKUs
Measure
Performance of
Products, Brands,
Categories
Drill Down into
Purchase funnel
(Shopping
Behavior Tab)
Decide to
promote or stop
selling certain
Products, Brands

11. Sales Performance
1111
Check accuracy of
reported transactions in
GA vs CRM via unique
transaction identifier
Apply custom reports on
transaction id to know
which product category
and brands are incurring
major shipping charges
Analyze #quantity,
#revenue, #taxes,
#shipping charges for
each transaction over
different dates
Compare
accuracy of
transactional data
in GA vs CRM
Check revenue,
quantity, shipping
charges and
taxes
Report products,
brands incurring
major shipping
charges

12. Market Basket Analysis (Product Performance and Sales Performance
Reports)
12
Form clusters of products
categories that users usually bought
together
Cross sell to users who have
bought one or more of the related
products

13. Product List Performance
13
Analyze which product feed and features lead to
conversion
Track list names – Catalog, Search, Wishlist,
Related Products, Recently Viewed
Maximize CTR and conversions from cross
selling and upselling
Product
Positioning

14. Internal Promotion Report
14
Measure
CTR of Banners on website/ app
Last Click Transactions and Sales
Analyze
Reports for banner names, id and
creative
Position of banner on website/app
Act
Restructure position of banner
based on #clicks
Remove banners with low CTR and
promote with high CTR

15. OUTLINE
15
1 2 The six utilitarian EE
Reports
3 Knowing Your Best Customers
using LTV models 4 Optimize the conversion path using
Markov Chain Feature Attribution
Enhanced Ecommerce – An
Introduction

16. Customer LTV
16
Buys in volumes
Buys frequently
High Order Value
High Profits
Low Retention Cost
Low Acquisition Cost

17. Customer LTV in GA via User Explorer
17

18. Predictive LTV Process
Channel Product
Category
CampaignAcquisition
Period
Custom
Attribute
Data
Extraction
Extract transactional and
behavioral variables for all
users acquired during a
cohort of N days
LTV
Prediction
Run LTV Model to predict the
future revenue, expected
number of transactions and
probability of staying alive
Rank your users,
acquisition channels,
campaigns and more
Analysis &
Reporting
18

19. Predict LTV of User
Channel
City/ Region
Product
Category
CampaignHigh LTV Cities / Regionsuisition
Period
1
2
3
4High LTV Sources / ChannelsPeriod
High LTV Campaignsuisition Period
High LTV Products / Brandsuisition
Period
Rank LTV of Users and Find out:
17

20. OUTLINE
20
1 2 The six utilitarian EE
Reports
Knowing Your Best
Customers using LTV models 4
Optimize the conversion path using
Markov Chain Feature Attribution3
Enhanced Ecommerce – An
Introduction

21. Feature Attribution Importance
21
Which Features are
helping your users
convert on your
website/app
Which Features are
leading to users exiting
from your website/app
Which Features of your
product need
optimization in The next
release

22. Basics on Markov Model first
22
Navigation
Filter
Search
States of system
Transition
between states
Rate of transition

23. User Journeys
23
Mac’s Journey
Navigation Search Filter Conversion

24. Mac’s Journey
24
Navigation
Search
Conversion
Filter
Start

25. User Journeys
25
Mac’s Journey
Navigation Search Filter Conversion
Maya’s Journey
Navigation Unsuccessful Conversion

26. Maya’s Journey
26
Navigation
Search
Conversion
Filter
StartStart
ExitNavigation

27. User Journeys
27
Mac’s Journey
Navigation Search Filter Conversion
Maya’s Journey
Navigation Unsuccessful Conversion
Jack’s Journey
Search Filter Unsuccessful Conversion

28. Jack’s Journey
28
Navigation
Search
Conversion
Filter
Start
Exit
Start
Search Filter

29. User Journeys
29
Mac’s Journey
Navigation Search Filter Conversion
Maya’s Journey
Navigation Unsuccessful Conversion
Jack’s Journey
Search Filter Unsuccessful Conversion

30. Probabilities of Conversion
30
Navigation
Search
Conversion
Filter
Start
Exit
33.3%
66.7%
50%
50%
50%
50%
100%
Total Probability
of Conversion
32.75%

31. User Journey in GA via Custom Reports
31

32. Removal Effect
32
 Pin Code Checked Feature
carries the highest
contribution (35%)
Feature
Prob. After
Removal
Weighted
Conversion Cont.
Coupon Applied by User 0.76% 11%
Filter 0.84% 5%
Image Change 0.78% 9%
Internal banner 0.84% 5%
Pin Code Checked 0.43% 35%
Recommendations 0.81% 7%
Search 0.79% 9%
Size Guide Clicked 0.76% 11%
Top Nav Clicked 0.80% 8%
Try and Buy 0.89% 1%
 Coupon Applied and Size
Guide carries same
contribution to conversion
(11%)
 Try and Buy carries the
least attribution to
conversion (1%)

33. Transition Matrix
33

34. Insights on Transition Matrix
34
Internal Search
Optimization?• Exit from Search feature58%

35. Transition Matrix
35

36. Insights on Transition Matrix
36
Internal Search
Optimization?• Exit from Search feature58%
• Conversion from Coupon Applied17%
Discount Coupons
Usage?

37. Transition Matrix
37

38. Insights on Transition Matrix
38
Internal Search
Optimization?• Exit from Search feature58%
• Conversion from Coupon Applied17%
Discount Coupons
Usage?
• Exit from Internal Banner45%
Internal Banners
Creative design?

39. Further Use Cases of Markov Model
39
Multi Channel Attribution Model
Ref: Semrush

40. Feature Attribution Process
40
Extract data from
BigQuery/ Google
Analytics
Select Features
and Perform
Markov Modeling
Use Removal Key
Effect and Build
Transition Matrix
Analyze Insights
and Incorporate in
Product Dev.

41. Any Questions?
41
Ask away!

42. A Token of Gratitude
42
Use the following coupon code & get a free Google Analytics audit
WEB-J1118-GAAUDIT

43. Upcoming Webinar
43
Topic: How eCommerce Businesses Can Multiply Sales with
Advanced Analytics on Google Analytics 360 Data
Speakers: Anshul & Bismayy
When: Thursday - Feb 08, 2018
Time: 8:30 PM IST

44. THANKS!
44
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