This internship report discusses the evolution of web analytics from version 1.0 to 2.0. It describes how traditional analytics focused on descriptive reporting and internal decision making, while analytics 2.0 enables attribution, optimization, and allocation of marketing activities using predictive modeling. The report also discusses how one gaming company implemented analytics 2.0 to increase sales and profits by quantifying campaign contributions and optimizing resource allocation in real-time. Finally, it outlines Flipkart's use of an A/B testing framework to conduct live experiments, drive innovation, and improve performance metrics.

1. SUMMER INTERNSHIP REPORT

2. Web Analytics 2.0

3. Web Analytics 2.0
Analytics 1.0
Traditional Analysis
• Primarily descriptive analytics and reporting
• Internally sourced, relatively small
structured data
• “Back rom” teams of analysis
• Internal Decision support focus
• Slowly-developed models

4. Web Analytics 2.0

5. Web Analytics 2.0

6. Web Analytics 2.0
SWIM-LANE MEASUREMENT
Marketers commonly measure the performance of each of their
marketing activities as if they are independently of one another –
so called swim-lane measurement
Swim-lane measurement grossly underestimated the revenues
attributable to social-media marketing and display advertising
while overestimating PR and paid-search revenue

7. Web Analytics 2.0
SWIM-LANE MEASUREMENT

8. Web Analytics 2.0
DATA DELUGE

9. Web Analytics 2.0

10. Web Analytics 2.0

11. Web Analytics 2.0
THE MOVE TO 2.0

12. Web Analytics 2.0
Analytics 2.0 involves three broad activities :
ATTRIBUTION
OPTIMIZATION
ALLOCATION

13. Web Analytics 2.0
ATTRIBUTION
The process of
quantifying
the contribution of each
element of advertising

14. Web Analytics 2.0
OPTIMIZING
Also called WAR GAMING
Optimization by using predictive analytics
tools to run scenarios for business
planning

15. Web Analytics 2.0
ALLOCATION
The real-time redistribution of resources
across marketing activities according to
optimization scenarios

16. Web Analytics 2.0
One-of the world’s largest
software gaming
companies
Successfully Attributed,
Optimized, and
Allocated to increase its
selling and profit

17. Web Analytics 2.0

18. Web Analytics 2.0
Five steps to Implementation (1/5)
Embrace analytics 2.0 as an
organization-wide effort that
must be championed by a C-
level executive sponsor

19. Web Analytics 2.0
Five steps to Implementation (2/5)
Assign an analytics-minded
director or manager to
become the point person for the
effort. It should be someone
with strong analytical skills and
a reputation for objectivity

20. Web Analytics 2.0
Five steps to Implementation (3/5)
Armed with a prioritized list
of questions you seek to
answer, conduct an inventory
of data throughout the
organization

21. Web Analytics 2.0
Five steps to Implementation (4/5)
Start small with proof of
concepts involving a particular
line of business, geography, or
product group. Build limited-
scope models that aim to
achieve early wins

22. Web Analytics 2.0
Five steps to Implementation (5/5)
Test aggressively and
feed the results back
into the model

23. CONCLUSION

24. Web Analytics 2.0
Marketing is rapidly becoming a war of knowledge,
insight, and asymmetric advantage gained through
analytics 2.0
Companies that don’t adopt next-generation analytics
will be overtaken by those that do.

25. Marketing & Advertisement in Flipkart
.Sachin Bansal
Binny Bansal

26. Marketing & Advertisement in Flipkart
.Sachin Bansal
Binny Bansal
Despite a slow start, e-commerce in India has grown by
70 % year-on-year, according to Internet and Mobile
Association of India.
The brain child of ex-Amazon employees Flipkart has
today become of the most trusted avenues for e-
commerce in India.

27. Marketing & Advertisement in Flipkart
.Sachin Bansal
Binny Bansal

28. Marketing & Advertisement in Flipkart
.Sachin Bansal
Binny Bansal
Flipkart did its advertisement on TV Channels,
Newspapers, Radio Channels, and on Social Media like
Facebook
But owing to its strategy in distributing funds for
advertisement especially on Social Media helped Flipcart
to gain its control over e-market.

29. Marketing & Advertisement in Flipkart
.Sachin Bansal
Binny Bansal
It introduced Mobile App and
compelled mobile users to use it.
The company had the vision that
many users browse these sites
while travelling or they are free.
They end up buying if they like
something

30. Marketing & Advertisement in Flipkart
.Sachin Bansal
Binny Bansal
It became easier to uniquely identify customer and their
behavior patterns in greater details
Targeted Marketing and offers can be done
Finally, this app helped a lot in fighting stiff competition from
other companies like Amazon, Snapdeal

31. TARGETED MARKETING

32. Flipkart drives Innovation
through Intelligent use of IT

33. The project (1/2)
With the goal in mind, Flipkart implemented a
framework. The framework has two components -
One measures the performance of the website
through various defined metrics

34. The project (2/2)
The second, more interesting one, uses an A/B
framework (used to test the success of web marketing
campaigns), allows multiple versions of the site to be live
simultaneously.
This helps the company conduct live experiments by
siphoning off a small portion of traffic and studying the
results

35. EXAMPLE

36. Example (1/2)
.Sachin Bansal
Binny Bansal
Whenever an engineer wants to change the design of
the homepage, the A/B framework redirects 10% of
Flipcart’s traffic to the new design
They can evaluate the impact of the change with the
metrics collection tool

37. Example (2/2)
.Sachin Bansal
Binny Bansal
If it leads to dip in sales, they can immediately roll it
back, and less 10% of traffic is impacted
This has been a huge hit with business users who can
now float new products and toy with business ideas.

38. THE BENEFITS

39. Since the introduction of the tool in mid-2010, the
company today sells more than 20 categories of products,
clocking in sales of INR. 75 Crore a month
In fact, in the last eight quarters, the company has
doubled its revenue every quarter

40. Recap :
• Introduction to Advertising Analytics 2.0
• Getting out of Swim Lanes
• Data Deluge
• Moving towards 2.0
• Three broad activities in 2.0 : attribution,
optimization and allocation
• Five Steps to Implementation

42. Summer Internship Report
CREDITS
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