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Advertising Analytics and BJP 2014.

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

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Advertising Analytics and BJP 2014.

  1. 1. That historical 2014 Indian Election BJP’s win..
  2. 2. What was the real secret behind this achievement? BJP = 282/543 282
  3. 3. What it just the effective speeches that made BJP win?
  4. 4. Or the rallies and “MODI LEHER”…..??
  5. 5. It was a lot more….
  6. 6. Effective target Advertising played a key role in this win…
  7. 7. But how did BJP targeted people so effectively in a country like India with huge diversity?
  8. 8. How did they analysed how much resources they need to allocate to different areas and segments of advertising?
  9. 9. The insight in HBR article could provide the answers to our questions.. By Wes Nichols
  10. 10. What is meant by
  11. 11. It is the art of evaluating advertising effectiveness and performance. ADVERTISING ANALYTICS
  12. 12. It quantifies impressions, clicks, conversions and buying behaviour that different ads generate. ADVERTISING ANALYTICS
  13. 13. What are the of this article?
  14. 14. OBJECTIVES
  15. 15. What are the of allocating resources in different areas of advertising ?
  16. 16. Traditional ways It helped marketers link scanner data with advertising and decide how to allocate marketing resources.
  17. 17. Traditional ways Marketers started tracking consumers most recent online action- say a click on a banner ad and attributed a purchases behaviour to it
  18. 18. Traditional ways Different teams and marketers measure the performance of each of their marketing activities as if they work independently of one another
  19. 19. What are the of using such traditional methods ?
  20. 20. 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.
  21. 21. Suppose, a user might see a T.V. ad & newspaper ad inspired and search Google , sees it banner later, click and make purchase.
  22. 22. Disadvantages Here, T.V. , newspaper, google search all have assisted in buying behaviour which these traditional methods could ignore and measure their effects independently.
  23. 23. Disadvantages This could result in over attribution or under attribution of advertising revenues
  24. 24. What is the other big problem marketers could face?
  25. 25. Virtually infinite record of data
  26. 26. LACK OF CAPABILITY OF STORAGE LACK OF CAPABILITY OF ANALYSIS
  27. 27. How should the company know that how ads assist each other and which combination is the best?
  28. 28. What should be the right amount to be invested at right points in the customer-decision journey?
  29. 29. The companies need to prepare segment of people by proper analysis and then targeting them by allocating appropriate resources.
  30. 30. are involved 3. ALLOCATION 1. ATTRIBUTION 2. OPTIMISATION Analytics 2.0
  31. 31. It is the process of quantifying the contribution of each element of advertising using analytics engine storing huge data. Analytics 2.0 - ATTRIBUTION
  32. 32. market conditions consumer response competitive activities, marketing actions business outcomes. Data is collected across categories.. Analytics 2.0 - ATTRIBUTION
  33. 33. 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
  34. 34. 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
  35. 35. 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
  36. 36. Uses such test to place its ads to see, which has the maximum response.
  37. 37. 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
  38. 38. It redistributes the resources across marketing activities according to optimisation scenarios. Analytics 2.0 – ALLOCATION
  39. 39. 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.
  40. 40. are involved 3. ALLOCATION 1. ATTRIBUTION 2. OPTIMISATION
  41. 41. 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”
  42. 42. 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.
  43. 43. What are the
  44. 44. 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.
  45. 45. - Traditional methods of advertising - Disadvantages - Move to analytics 2.0 - Attribution, optimisation, allocation - Ways of implementation
  46. 46. - 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.
  47. 47. Coming back to the BJP case…
  48. 48. 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
  49. 49. Many companies like SAP, Oracle, InMobi, Modak Analytics were tasked with analysing data for electoral campaign and developing customised tools for the elections.
  50. 50. 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.
  51. 51. 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.
  52. 52. 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.
  53. 53. They even planted cookies in computers of people who visited their website to track their activities online and target them at the appropriate time.
  54. 54. Such twitter analysis were also made to see what are people response.
  55. 55.  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 .
  56. 56. - 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
  57. 57. Created by- Deepali jain HBTI, KANPUR during an internship by Prof. Sameer Mathur, IIM Lucknow www.iiminternship.com

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