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Getting the best out of AMPS A user’s perspective

Getting the best out of AMPS A user’s perspective. Amit Kishore General Manager - Operations mediaReach OMD Lagos. 10th ALL AFRICA MEDIA RESEARCH CONFERENCE Pan Africa Media Research Organisation (PAMRO) Tuesday, 19 August, 2008 Lagos. In this session….

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Getting the best out of AMPS A user’s perspective

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  1. Getting the best out of AMPSA user’s perspective Amit Kishore General Manager - Operations mediaReach OMD Lagos 10th ALL AFRICA MEDIA RESEARCH CONFERENCE Pan Africa Media Research Organisation (PAMRO) Tuesday, 19 August, 2008 Lagos

  2. In this session… • Nigeria’s All Media Product Survey, 2008 (AMPS) • Psychographic Profiling • Demographic segmentation • Market Planning – Market Prioritisation • Market Planning – Cross Promotion • Conclusion

  3. Nigeria’s AMPS

  4. All Media Product Survey • Survey commissioned by Media Planning Services • Research conducted by Media Research Consultancy • Over 14,000 respondents pan-Nigeria • Covers Urban & Semi-urban locations • Readership of all major print titles covered • Consumption of over 60 product categories covered • All respondents above the age of 7 years • Single source database combining demographics, psychographics, media habits & product consumption

  5. Psychographic Profiling

  6. Psychographic Profiling • Using the battery of lifestyle statements in AMPS • Two leading brands in the toothpaste category • ‘Strongly Agree’ to the statements for each brand • Normalise to category score i.e. all toothpaste users • Difference in scores demonstrate the ‘distance’ between the two brands’ target group on each statement

  7. Example ToothpasteBrand ABrand B • ‘Rather care for family than work at job’ 27% 19% 29% • Normalised to Category scores 100 69 106 • Final scores -31 +6 Brand A Category Brand B 106 100 69

  8. Family Values Close Up consumers are more family oriented with a strong bond Close-Up Users Macleans Users Source: AMPS 2008 TG: Users of respective brands Market: Pan-Nigeria Figures are difference of Brand Users Strongly Agree and Total Toothpaste Users Strongly Agree

  9. Attitude to Work Close Up consumers are career minded and achievers Macleans Users Close-Up Users Source: AMPS 2008 TG: Users of respective brands Market: Pan-Nigeria Figures are difference of Brand Users Strongly Agree and Total Toothpaste Users Strongly Agree

  10. Self Perception Close Up consumers are introverted but have quiet confidence Close-Up Users Macleans Users Source: AMPS 2008 TG: Users of respective brands Market: Pan-Nigeria Figures are difference of Brand Users Strongly Agree and Total Toothpaste Users Strongly Agree

  11. Attitude to Money Macleans consumers are conservative with money & cost conscious Close-Up Users Macleans Users Source: AMPS 2008 TG: Users of respective brands Market: Pan-Nigeria Figures are difference of Brand Users Strongly Agree and Total Toothpaste Users Strongly Agree

  12. Very different but are they different in Media habits too?

  13. Radio Habits Radio Stations Programming Genres Source: AMPS 2008 TG: Users of respective brands Market: Lagos Figures are difference of Brand Users Strongly Agree and Total Toothpaste Users Strongly Agree

  14. Television Habits Television Stations Programming Genres Source: AMPS 2008 TG: Users of respective brands Market: Lagos Figures are difference of Brand Users Strongly Agree and Total Toothpaste Users Strongly Agree

  15. Magazine Habits Magazine Titles Source: AMPS 2008 TG: Users of respective brands Market: Lagos Figures are difference of Brand Users Strongly Agree and Total Toothpaste Users Strongly Agree

  16. Conclusion • Possible to differentiate in media between 2 ‘similar’ brands • Important to gain strategic advantage in media • Specifically useful in situation where a client’s portfolio contains more than one brand in the same category • “I have two different brands, but the media plans looks the same”

  17. Demographic Segmentation Identifying the Potential Segment of Small Cars Buyers within All Potential Car Buyers

  18. Which variables discriminate car Buyers? If car purchasers are uniform across a variable, e.g. SEC, then it is NOT a good discriminator

  19. Targeting the potential segment If car purchasers vary highly across a variable, then that variable is a good discriminator of car purchasers Calculating a Discrimination Index (DI), to identify the variable which discriminates the most Penetration of Cars in the Top 5% DI (T5/B95) = ------------------------------------------------------- X 100 Penetration of Cars in the Bottom 95%

  20. Target = new small car buyers • % Households purchasing Small Cars in the last XXX years = 3.4% • % Households owning Any Cars = 7.5% a x XXX – your best estimate of how far back is a good representation of a future buyer In a scenario like this a Top5%/Bottom95% will do. Higher figures demand higher dissection, say Top10%/Bottom90%

  21. Data from AMPS

  22. Work on data from AMPS Calculate the Index Sort the data on Index

  23. Breaking the population into T5/B95 5% 95% SEC A (=9%) gets broken in to T5/B10 (5:4) SEC A Small Car Owners (=45%) is also broken in the same proportion [5:4] And index calculated for A’ & A” again The next step is to calculate the penetration for T5 and B95

  24. Calculating the Discrimination Index Calculating penetration for the Top 5% and bottom 95% by adding up the no. of Small Car Owners & Total population Calculating the final Discrimination Index for SEC (17/3*100=633)

  25. Discrimination Index of other variables • The same method is used to calculate the DI for as many variable as possible (a good checklist would be ones that we think will discriminate for the given product category) • Include demographics as well product ownership variables viz. household goods as well as personal products • Sort the DI of all the variables and select the top few (lets say top 5)

  26. Powerful Discriminating variables a MHI, Television, Ownership of Washing Machine, Air conditioner and Refrigerator SEC, ownership of 2 wheelers, family structure, Head of Household’s Age, and No. of working members x

  27. Discrimination power by combining powerful variables [Washing Machine AND Refrigerator AND AC] has the highest index Combining high DI variables to arrive at an even better discriminator

  28. Ideal Discrimination Variable • If Washing Machine ownership is the best discriminator, then • Amongst Car Owners • Washing Machine ownership should be 100% • Washing Machine non-ownership should be 0%

  29. Best discriminator defining the segment a Individuals owning [TV & Refrigerator & AC]

  30. Better TG definition • Traditional TG definition • SEC AB, Male, Age 25-35yrs • New TG definition • Individuals owning [TV & Refrigerator & AC] Focused TG definition leads to focused media plan

  31. Market Planning Market Prioritisation

  32. Sausage Category market

  33. Learnings • Sausage as a category is more popular in the South • North constitutes 47% of the population • but contributes only 17% of sausage consumption • But don’t northerners eat sausage rolls at all? • In which case it’s a case of developing the habit • Sampling, hence a marketing task • But could it be related to their religious sentiments? • ‘Sausage rolls contain pork’ – taboo • In which case it’s a matter of informing them hence a communication task

  34. Market Planning Cross Promotion

  35. Cross Promotion • A coffee brand wants to do a cross promotion with another brand • Milk among many categories may be the easy choice • But which brand among the milk category is the best to go with? • Ultimately the success of the promotion depends on what is most attractive to the consumer • And it need not just be the price-off

  36. Nescafe Promotion 46% of Nescafe buyers also buy Peak Evap Milk

  37. Hollandia Promotion 50% of Hollandia buyers also buy Enrista Coffee

  38. Conclusions

  39. Conclusions • AMPS is a very powerful single source database • Data from AMPS needs to be put to rigorous analysis • When put to rigorous analysis it can result in very useful metrics • Usefulness of AMPS goes beyond media planning • Large scale quantitative database for market planning

  40. Thank you.

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