This presentation is based on a HBR article ADVERTISING ANALYTICS 2.0 and has been prepared during the internship under Prof. Sameer Mathur, IIML.

2. That historical
2014 Indian
Election
BJP’s
win..

3. What was the
real secret
behind this
achievement?
BJP = 282/543
282

4. What it just the
effective
speeches that
made BJP win?

5. Or the rallies and
“MODI LEHER”…..??

6. It was a lot more….

7. Effective target
Advertising played
a key role in this
win…

8. But how did BJP targeted
people so effectively in a
country like India with
huge diversity?

9. How did they analysed
how much resources
they need to allocate to
different areas and
segments of advertising?

10. The insight in HBR article
could provide the answers to our questions..
By Wes Nichols

11. What is meant by

12. It is the art of
evaluating advertising
effectiveness and
performance.
ADVERTISING ANALYTICS

13. It quantifies impressions,
clicks, conversions and
buying behaviour that
different ads generate.
ADVERTISING ANALYTICS

14. What are the of
this article?

15. OBJECTIVES

16. What are the
of allocating resources in
different areas of advertising ?

17. Traditional ways
It helped marketers
link scanner data
with advertising and
decide how to allocate
marketing resources.

18. Traditional ways
Marketers started
tracking consumers most
recent online action- say
a click on a banner ad and
attributed a purchases
behaviour to it

19. Traditional ways
Different teams and
marketers measure the
performance of each of
their marketing activities as
if they work independently
of one another

20. What are the
of using such traditional
methods ?

21. The traditional ad techniques ignores the assisted effects of
marketing.
Disadvantages
The change in an
advertisement viewing
behaviour due to
influence of another ad
of the same company.

22. Suppose, a user might see a T.V.
ad & newspaper ad inspired and
search Google , sees it banner
later, click and make purchase.

23. Disadvantages
Here, T.V. , newspaper,
google search all have assisted
in buying behaviour which
these traditional methods
could ignore and measure
their effects independently.

24. Disadvantages
This could result in
over attribution or
under attribution of
advertising
revenues

25. What is the other big
problem marketers could
face?

26. Virtually infinite record of data

27. LACK OF
CAPABILITY
OF STORAGE
LACK OF
CAPABILITY
OF ANALYSIS

28. How should the company
know that how ads assist
each other and which
combination is the best?

29. What should be the right
amount to be invested at
right points in the
customer-decision journey?

30. The companies need to prepare
segment of people by proper
analysis and then targeting them
by allocating appropriate
resources.

32. are involved
3. ALLOCATION
1. ATTRIBUTION 2. OPTIMISATION
Analytics 2.0

33. It is the process of
quantifying the
contribution of each
element of advertising
using analytics engine
storing huge data.
Analytics 2.0 - ATTRIBUTION

34. market
conditions
consumer
response
competitive
activities, marketing
actions
business
outcomes.
Data is collected across categories..
Analytics 2.0 - ATTRIBUTION

35. The huge data is stored using
and
where, record of every
activities and their inter-related
effects could be analysed to
allocate the marketing resources.
Analytics 2.0 - ATTRIBUTION

36. It runs various
testing analytic tools
to run scenarios for
business planning on
a small scale.
Switching the shade of blue used on advertising links
in Gmail and Google search earned the company an
extra $200m a year in revenue.
Analytics 2.0 - OPTIMISATION

37. In a war-gaming process , team
members define marketing
goals and software generates a
set market scenarios and
recommendation to achieve
them.
Elasticity points are given in respect that by how
much percentage sales in changes by changing
any factor.
Analytics 2.0 - OPTIMISATION

38. Uses such test to place its ads to see,
which has the maximum response.

39. 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

40. It redistributes the
resources across
marketing activities
according to
optimisation scenarios.
Analytics 2.0 – ALLOCATION

41. The Obama team in 2008 U.S.
elections looked at 24 different
variations of the splash page using
a mix of images and CTA buttons
to determine which combination
produced the greatest results. Each
variation was seen by more than
13,000 people
The winning combination saw a
40% increase in sign-up rates and
an additional 2.8 million email
addresses which ultimately led to
$60 million in donations.

42. are involved
3. ALLOCATION
1. ATTRIBUTION 2. OPTIMISATION

44. One-of the world’s largest software gaming
companies Successfully Attributed, Optimized,
and Allocated to increase its selling and profit by
increasing its revenue by 23% in a very popular
game “BATTLE FIELD”

45. Analytics 2.0 allowed Samsung Mobile to
cost-effectively determine which markets
would shift the overall brand preference
score in their favor and the right
advertising techniques to target people.

46. What are the

47. 1. Embrace analytics as an organization
2. Appoint an analytical minded person to tasks.
3. Conduct an inventory of data through
organization
4.Build limited scope models that aim to achieve
early wins
5. Aggressive testing and feeding of results.

48. - Traditional methods of advertising
- Disadvantages
- Move to analytics 2.0
- Attribution, optimisation, allocation
- Ways of implementation

49. - Analytics can be used to find out the appropriate
resources needed to allocate.
- The revenue of the company could take a high growth
by applying analytics.
- 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.

50. Coming back to the
BJP case…

51. India consisted of :
-543 parliamentary and 4120 assembly
constitution
-930,000 polling booths
-Voter rolls in PDF’s in 12 languages
-900,000 PFD’s
- Diverse range of voter names and
information

52. Many companies like SAP, Oracle, InMobi, Modak
Analytics were tasked with analysing data for electoral
campaign and developing customised tools for the
elections.

53. A large professional data analytics team was appointed
to process and filter data from various sources like
debates on mygov.in, social network, economic
conditions of people etc.

54. The companies developed their own
customised digital tools based on
commisioned and open source data
including a 64 node Hadoop, PostgreSQL,
a d ser ers that process a aster file
containing over 8 Terabytes of data.
Machine learning algorithms were
also developed to help categorize
people based on name, geography,
religion, caste and ethnicity.

55. They analysed historic voter pattern and newly
available voter rolls to identify the pocket boroughs
of their party, the booths unlikely to elect them and
the ones that could go either way, or the swing
booths.
It helped them know were to
allocate resources and how
much.

56. They even planted cookies in computers
of people who visited their website to track
their activities online and target them at the
appropriate time.

57. Such twitter analysis
were also made to see
what are people
response.

58.  BJP was able to categorise people on the basis of gender, age, race, religion and
interests through the digital tools.
 Also, by proper analysis, the party segmented people on the basis of who are
likely to vote.
 The party did ’t spe d o ey i those areas ore here the people ere sure
of not voting BJP for any reason.
 The party effectively targeted people at various places of their interest
 The party was able to interact with people ith ariety of its progra s like Chai
pe Charcha .

60. - HBR Article : Advertising Analytics 2.0
- http://dataconomy.com/narendra-modi-first-prime-minister-use-big-
data-analytics/
- http://articles.economictimes.indiatimes.com/2013-11-
26/news/44487290_1_data-analytics-bjp-and-congress-delhi-
bungalow
- http://www.cnbc.com/id/101571567
- http://www.firstpost.com/india/big-data-analysis-how-it-firms-like-
sap-oracle-helped-modi-win-1576355.html
- https://en.wikipedia.org/wiki/Big_data

61. Created by-
Deepali jain
HBTI, KANPUR
during an internship by Prof.
Sameer Mathur, IIM
Lucknow
www.iiminternship.com