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Q-LSMs A PROUDLY South African Solution to an uniquely South African Market Research sin Developed by: Millward Brown: Erik du Plessis Kamendra TNS: Neil.

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Presentation on theme: "Q-LSMs A PROUDLY South African Solution to an uniquely South African Market Research sin Developed by: Millward Brown: Erik du Plessis Kamendra TNS: Neil."— Presentation transcript:

1 Q-LSMs A PROUDLY South African Solution to an uniquely South African Market Research sin Developed by: Millward Brown: Erik du Plessis Kamendra TNS: Neil Higgs

2 A Definition of Market Research “Purveyors of Consumers’ Valuable Time to Marketers” We Persuade Well Selected Respondents to give some of their time to answer questions, Which we analyse, interpret, present to marketers I.e. We add value to the time we got from consumers IT IS A RESEARCH SIN TO WASTE THIS VALUABLE TIME OF THE RESPONDENTS

3 AMPS is proudly unique to South Africa Nowhere in world does marketers and media owners combine to have an industry respected single source database, Unilever (Eddie Schulze) and SAARF developed LSM’s as a shorthand to differentiate between the haves and have-nots based on their living standard Prof. Jackie Galpin did the statistical analysis Has become a South African industry norm BUT:

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6 This can take minutes.

7 Sufficiency versus Redundancy Determining the LSM algorithm – When Prof Galpin does the analysis the data has been collected. There is no need to use as few as possible variables in the classification algorithm, – Whether the algorithm uses 28 variables, or all the variables in the AMPS survey does not matter, – Much rather err on the side of redundancy. Using LSM inside the AMPS database – When using LSM for analyses inside the AMPS database the LSM is coded per respondent. – It does not matter how many variables are used in the algorithm Using LSMs to collect primary data from respondents – It really matters how much time you are wasting – SUFFICIENCY is a real priority

8 This ‘wasting my time’ is coming back to bite us Respondents do not feel it is impolite to end a cell phone or on-line interview that wastes time. JUST CLICK THE END BUTTON!

9 Is Personal Income a good surrogate?

10 Is Personal Income a good surrogate? Hardly 11 million out of 37 million said ‘No Income’, but we know they live and consume stuff and have things (Living Standard), 5 th largest income category, nearly 1m., refused to answer the question Not much correlation with LSM

11 Is it better when we look at Metropolitan?

12 4.4m. out of 15m. has no income – LSM 6 : 1.4m. – LSM 7 : – LSM 10: out of 1.6m! refused to answer the question: – Mostly LSM 9&10,

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14 Introducing Q-LSMs Toward Better Market Research practices via good science

15 Research Question: I want to split my sample into: LSM 8-10, LSM 5-7 LSM 1-4 OR: I want to interview LSM 7-10 How do I not waste 20 minutes of interviewing time just to do this? 23,9 m is the number of people in non-rural South Africa, Solve this problem for a non-rural sample

16 Research Question: I want to split my sample into: LSM 8-10, LSM 5-7 LSM 1-4 OR: I want to interview LSM 7-10 How do I not waste 20 minutes of interviewing time just to do this? 23,9 m is the number of people in non-rural South Africa, Solve this problem for a non-rural sample

17 Splitting Sample into 3 Supergroups (Non-Rural Sample) C: 1,3m B:14.1m A: 8.5m

18 (sulsmr1101) Tap water in house/on plot (sulsmr1102) Hot running water from a geyser (sulsmr1103) Flush toilet in/outside house (sulsmr1104) Built-in kitchen sink (sulsmr1105) Home telephone (excl. cellphone) (sulsmr1106) 3 or more cellphones in household (sulsmr1107) 2 cellphones in household (sulsmr1108) 0 or 1 radio set in household (sulsmr1109) Swimming pool (sulsmr1110) TV set (sulsmr1111) M-Net/DStv subscription (sulsmr1112) Air conditioner (sulsmr1113) DVD/Blu Ray player (sulsmr1114) Home theatre system (sulsmr1115) Computer - Desktop/Laptop (sulsmr1116) Refrigerator of combined fridge/freezer (sulsmr1117) Deep freezer - free standing (sulsmr1118) Electric Stove (sulsmr1119) Microwave oven (sulsmr1120) Vacuum cleaner/floor polisher (sulsmr1121) Washing machine (sulsmr1122) Tumble dryer (sulsmr1123) Dishwashing machine (sulsmr1124) Motor vehicle in household (sulsmr1125) No domestic workers or household helpers in household (sulsmr1126) Home security service (sulsmr1127) House/cluster house/town house (sulsmr1128) Metropolitan dweller (sulsmr1129) Rural rest (excl. W. Cape & Gauteng rural) 2. COMMUNITY SIZE (commun1 ) ( ) - Part of Large Urban (commun2) ( ) - Part of Large Urban (commun3) ( ) - Part of Small Urban+Rural (commun4) (Less than 500/Rural) - Part of Small Urban+Rural (commun5) ( ) - Large Urban (commun6) (Less than Non-Urban) - Small Urban+Rural 3. METRO AREA (armetro01) Cape Town (armetro02) Cape Town Fringe Area (armetro03) Port Elizabeth/Uitenhage (armetro04) East London (armetro05) Durban (armetro06) Bloemfontein (armetro07) Greater Johannesburg (Alex, JHB, Sandton, Soweto, Randburg) (armetro08) Reef (Urban Gauteng, excl. JHB, PTA, Vaal) (armetro09) Pretoria (armetro10) Kimberley (armetro11) PMB (armetro12) Soweto (armetro13) Vaal (De Deur, Meyerton, Sasolburg, Vanderbijlpark, Vereeniging) (armetro14) eMalahleni / Witbank (armetro15) East Rand (armetro16) West Rand 4. PROVINCE (prov) Province 5. GENDER (sex) Gender

19 Desktop/laptop? NY Splitting Sample into 3 Supergroups (Non-Rural Sample) C: 1,3m B:14.1m A: 8.5m C: 0 B: 1,3 A: 6.3 C: 1,3 B: 12.8 A: 2,2

20 Desktop/laptop? N Motor Veh. Vacuum Geyser Y N 5-7 Y 8-10 N Y N Y 5-7 Splitting Sample into 3 Supergroups (Non-Rural Sample) C: 1,3m B:14.1m A: 8.5m C: 0 B: 1,3 A: 6.3 C: 0 B:,4 A: 5.7 C: 0 B:,2 A:,1 C: 0 B:,2 A: 5.6 C: 0 B:,9 A:,6 C: 0 B: 0 A:.3 C: 0 B:,8 A:,3 C: 1,3 B: 12.8 A: 2,2

21 Desktop/laptop? TV Set? Vacuum Stove N Y N 5-7 Y 8-10 N 5-7 Y 1-4 N Motor Veh. Vacuum Geyser Y N 5-7 Y 8-10 N Y N Y 5-7 Splitting Sample into 3 Supergroups (Non-Rural Sample) C: 1,3m B:14.1m A: 8.5m C: 0 B: 1,3 A: 6.3 C: 0 B:,4 A: 5.7 C: 0 B:,2 A:,1 C: 0 B:,2 A: 5.6 C: 0 B:,9 A:,6 C: 0 B: 0 A:.3 C: 0 B:,8 A:,3 C: 1,3 B: 12.8 A: 2,2 C:,7 B:12,4 A: 2,2 C:,7 B:,3 A: 0 C:,7 B: 11,5 A:,8 C: 0 B:,9 A: 1,3 C: 0 B:,2 A: 0 C:,6 B:,1 A: 0

22 Accuracy of the rule: 23.9 million people in the non-rural universe. 8.5 m. (36%) are LSM 7-10, this decision rule will: – Correctly identify 7.4 million (87%) of these as LSM 8- 10, – Classify.78 m. LSMs 5-7 incorrectly into this category (i.e. 9.6% of the sample) – Clasify ZERO LSM 1-4 as LSM 7-10! 14m. are LSM 5-7, this decision rule will: – Correctly identify 13.2m. (94%) of these as LSM 5-7, – Classify 1.1m. LSM 8-10 incorrectly in this category (7% of sample)

23 Can the accuracy be improved? YES, Just ask a few more questions!! (I.e. Run the analysis for another layer) Why don’t we show this? Limitation of a PowerPoint slide!

24 Desktop/laptop? Domestic Worker Vacuum N YN Y Rest N Vacuum Domestic Motor Veh. Y N Y 9-10 N YN Y Splitting LSM 9-10 from Rest (Non-Rural Sample) Rest 9-10Rest 9-10

25 Finding LSM C versus Not

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27 Isolating LSM C (or Not)

28 The Q-LSM ‘law of diminishing returns’ Example: if, to identify LSM 7-10, we just interview everyone at random, 36% will e correct, Every ‘decision rule’ will increase this percentage correct, With our three question decision rule we get 87% correct, To get 100% correct we need to ask all 28 LSM question and do the weighting,

29 Does it Matter? LSMs is a continuum! – Misclassification happens close to the borders of the categories. The whole SA population is continuously migrating up the LSM scales!

30 My Suggestions:

31 Timing: Implement every time a new study is done, Especially when migrating from F2F interviews.

32 Plans: Neil Higgs: – SAMRA – PAMRO – ESOMAR Africa Other Emerging Unilever (Source: Neil) – Own LSM questions: tried once in two countries – Big Segmentation – Interest in Rural increased

33 Q-LSMs A PROUDLY South African Solution to an uniquely South African Market Research sin Developed by: Millward Brown: Erik du Plessis Kamendra TNS: Neil Higgs


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