re: think! arf A The Research Authority Cross-Border Data Comparison -Try Managing Without It Andrea Dinning Global TGI KMR kmr Global vs. Local "Think globally, act locally" arf The Research Authority A kmr Global vs. Local " Think globally, act locally" 2 views: • Markets have become so homogenized that the same marketing approach can be adopted everywhere • Variation exists between countries and cultures, highlighting the need for tailored approaches arf The Research Authority A mix of both views is necessary A kmr Global vs. Local "Think globally, act locally" 1. Analyze each individual country 2. Build regional pictures 3. Build the global picture arfA The Research Authority 25,8441 12,6841 13,160 1,3401 7,0901 7,5371 4,9301 3,8631 2,6391 6,6111 6,1251 4,5681 5,9011 45,435| 22,168| 23,26S| 1,989| 12,934| 12,138| 9668 8,707 10,7. 98k 98k 98k 99k 1,255 98k 98k 98k 99k 33k 99k 37k 33k 37k 97k 97k 97k 94k 97k 98k 96k 96k % 23,303 11,549 11,754 6,709 7,022 4,264 3,065 2,539 6,271 5,686 3,357 4,843 38,613 19,327 19,286 1,788 11,399 10,545 8074 6,807 9j 89k 89k ssk 92k 92k 92k 85k 78k 35k 94k 30k 86k 80k 82k 84K 80k 85k 86k 85k 81k 75k tfl 22,700 10,782 11,956 1,203 2,033 19K 6,479 6,752 4,373 3,132 2,461 6,137 5,223 3,331 4,316 38,905 18,233 20,672 1,970 11,635 10,492 7920 6,888 M 2,412 2,448 2,376 2,294 2,358 2,515 2,580 2,270 2,296 2,580 2,536 2,570 83K 80K 86K 93K 87K 84K 79K 76K 1 23K 23K 22k 22K 23K 23K 22K 24K 22K 23K 22K 20K 37,031 18,517 18,513 1,432 10,023 9,782 8270 7,523 9l 492 429 553 641 554 488 452 422 532 512 493 431 435 79K 81K 77K 68K 75K 79K 83K 83K J 5k 4k 5K %y. 1,088 5K 5K 4K 4K 6K 5K 4K 4K 3K 22,536 12,161 10,375 1,644 8,607 7,447 3753 1,084 m 1,013 1,140 893 1,007 967 1,057 1,019 1,064 989 1,083 971 865 48K 53K 43K 78K 65K 60K 37K 12K jj 9K 11K 8k 10K 10K 10K 10K 9K 11K 10K 10K 8K 7K 46,965 22,928 24,037 2,107 13,321 12,434 10019 9,085 ňq 315 402 232 546 456 391 225 57 484 382 231 162 113 6,534 6,241 6,815 6,572 6,197 5,327 6,696 7,643 3k 4k ly. 5k 4k 4k 2k OK 5k 4k 2k IK 1k 61k 59k 64k 61k 59k 59k 61k 66k J 10,676 10,647 10,714 10,878 10,520 10,109 10,817 11,476 3,439 10,210 11,113 11,606 12,791 2,486 2,626 2,352 2,136 2,513 2,404 2,606 2,514 2,2; 15,792 7,482 3,310 2,179 5,623 3,843 2,772 N/A 1,780 1,657 3,835 4,316 4,205 23k 25k 22k 20k 24K 24k 24k 22k Ů 96k 96k 97k 97k| 96kI 96x1 97k 1,8731 4,9591 3,2581 2,420 N/A 97k 38k 36k 36k 96k 448 386 508 583 493 458 419 367 51 13,686 6,708 6,978 N/A 1,454 1,418 3,376 3,737 3,700 4k 4k 5k 5k 5k 5k 4K 3k 83K 86K 81K 83K 84K 82K 85K N/A 79K 84K 85K 83K 84K 996 1,118 881 988 954 1,011 1,071 961 1,0! 45,876 22,365 23,511 1,964 12,683 12,336 10,106 8,788 11,498 12,998 9,599 7,616 4,166 9K 10K 8K 9K 9K 10K 10K 8K 97K 97K 97k 96k 97K 98K 98K 96K 97K 97K 98K 96K 95K 220 287 156 413 317 270 152 30 3' 39,411 13,738 19,673 1,793 11,336 10,877 8333 6,346 10,628 11,364 8,150 6,033 3,176 2K 3K V. 4K 3K 3K IK OK 83k 86K 81k 88K 87K 86K 81K 76K 90K 85K 83K 77K 72K 10,684 10,657 10,711 10,692 10,474 10,070 10,945 11,520 9,4i 39,619 18,664 20,955 1,8941 11,856 93kI 90k 10,603 8180 7,079 10,478 11,535 8,119 6,160 3,327 45,461 22,176 23,285 2,188 13,063 11,763 9,755 8,693 10,8 84k 8lK 87^ 84k 73k 77k 89k 86k 83k 78k 76k 97k 97k 37k 97k 38k 38k 98k 96k 3 35,222 17,752 17,470 1,372 9,266 9,020 8200 7,364 9,196 9,809 7,536 5,722 2,960 39,071 19,417 19,654 1,953 11,490 10,492 8,127 7,034 9,7; 75k 77k 11'/. 67k 1,679 71k 72k 80k 81k 78k 73k 77k 73k 68k 84k 85k 82k 86k 86k 87k 82k 78k 8 25,663 13,663 12,000 9,251 8,409 4821 1,502 8,731 8,561 4,531 2,688 1,092 38,001 17,801 20,200 2,114 11,675 10,012 7,729 6,472 9,6 54k 59k 50k 82k 70k 67k 47k 16k 74k 64k 47k 34k 25k 81k 78k 84k 93k 87k 83k 78k 71k 8 47,253 23,034 24,219 2,039 13,143 12,614 10,313 9,144 11,733 13,355 3,831 7,832 4,381 38,493 19,138 19,355 1,703 10,207 10,024 * 6,240 5,460 7,020 4,050 5,430 6,210 7,574 9,240 5,430 5,970 6,510 7,020 6,540 82K 84K 81K 75K 76K 83K l\y. 63K 78k 84K 70K 69K 70K 76K 66K 69K 72K 73K 71K 17,909 9,864 8,044 1,438 6,855 5,823 _-r 1,170 1,530 780 210 990 1,260 1,676 1,050 960 1,260 1,260 1,260 1,380 38K 43K 34K 64K 51K 48K 6,552 6,282 6,822 6,930 6,228 6,030 6,570 7,722 5,148 6,066 6,768 7,632 8,382 46,685 22,753 23,332 2,261 13,356 12,045 6,098 5,832 61K 58K 64K 61K 59K 59K 61K 67K 54K 59K 61K 66K 71K 6,429 6,140 6,705 6,466 2,419 2,623 2,223 2,422 2,354 2,398 2,578 2,367 2,296 2,377 2,639 2,441 2,322 59K 57K 62K 59K 59K 57K 22k 24k l\y. 21k 22k 23k 24k 21k 24k 23k 24k 21k 18k 2,762 2,909 2,622 2,674 2,613 2,647 462 400 521 574 522 471 417 383 517 485 454 405 390 26k 27k 24k 24k 25k 26k 4k 4k 5k 5k 5k 5k 4k 3k 5k 5k 4k 3k 3k 444 387 498 526 468 452 1,010 1,133 893 953 998 1,001 1,075 382 1,080 338 1,033 330 861 4k 4k 5k 5k 5k 4k 9k 10k 8k 8k 486 9k 10k 10k 3k 11k 10k 9k 3k 7k 1,040 1,157 929 1,150 983 1,038 311 399 226 477 381 203 45 499 388 237 145 104 10k 11k 9k 10k 9k 10k 3K 4K ly. 4K 5K 4K 2K OK 5K 4K 2K IK 1K 137 196 81 204 203 175 arfA The Research Authority Chernoffs Faces • Developed by Herman Chernoff (1973) • Method of pictorially representing multi-dimensional data, using the characteristics of the human face arfA Chernoffs Faces • Each facial feature represents a variable, i.e. age 25-34 • Up to ten facial features can be used - allowing representation of up to 10 variables arfA Chernofťs Faces Eye spacing Head eccentricity Eye eccentricity Pupil size Eyebrow slant Eye size Nose size Mouth shape Mouth length Degree of mouth opening arf The Research Authority A km r Chernoffs Faces The index for each variable is assigned a value between 0 and 1 0 represents the lowest index; 1 represents the highest index Value of 0 arf The Research Authority A Value of 1 kmr Applications •Apply Chernoff's Faces to the analysis of brand profiles • This method can be used to: • compare the same brands in 2+ markets • compare a brand with its competitors • track trends over time • Simple representation that provides global themes and local differences at a glance arfA The Research Authority Case Study Levi's Jeans "As US (and to some extent, Western European) marketers continue to export Western popular culture to a globe full of increasingly affluent consumers, many are eagerly waiting to replace their traditional products and practices with the likes of McDonalds, Levis and MTV" Michael Solomon Consumer Behavior: Buying, Having and Being • Established global market • Is there a global consumer? • They have a brand of jeans in common but what else? arfA The Research Authority Countries TGI data from: Argentina Ecuador Mexico Brazil France Peru Bulgaria Germany Poland Chile Great Britain Puerto Rico China Greece Romania Colombia India Russia Croatia Ireland Spain Czech Republic Israel Venezuela arf The Research Authority A kmr Variables Age 25-34 Head eccentricity Female Eye eccentricity & Pupil size SELAB Eyebrow slant "I like to stand out in a crowd" Nose size "I like to keep up with the latest fashions" Mouth shape "A designer label improves a person's image" Eye spacing "I always look for special offers when I shop" Eye size "It's worth paying more for quality goods" Mouth length & degree of opening Source: Global TGI 2003 arfA The Research Authority Levi's Jeans Age 25-34 Levi's jeans wearers are more likely to be aged 25-34 in Chile • Head Eccentricity = Age 25-34 • a very elongated head represents a value of 1 Chile • a value of 1 represents a high index • the head shape for India represents a value of 0 • a value of 0 represents a low index India arfA The Research Authority Source: Global TGI 2003 Gender Levi's jeans wearers are more likely to be female in the Czech Republic Czech Republic • a value of 1 represents a high index Eye Eccentricity = Female very elongated eyes represent a value of 1 the eye shape for Israel represents a value of 0 a value of 0 represents a low index arf The Research Authority A Source: Global TGI 2003 kmr Socio-Economic Level Levi's jeans wearers are more up-market in China, compared to the USA • Eyebrow Slant = High Socio-Economic Level China Venezuela USA Value 1 Value 0.5 Value 0 arfA The Research Authority Source: Global TGI 2003 "I like to stand out in a crowd" Generally, Levi's jeans wearers around the world agree with this statement • Nose Size = agreement with "I like to stand out in a crowd" Croatia France Poland Argentina Serbia Despite differences in other facial features, the nose size is fairly consistent across countries arfA The Research Authority Source: Global TGI 2003 "It's worth paying more for quality goods" Levi's jeans wearers around the world tend to disagree with this statement • Mouth length and degree of opening = agreement with "It's worth paying more for quality goods" "A Designer label improves a person's image" Generally, Levi's jeans wearers around the world agree with this statement • Eye Spacing = agreement with "A Designer label improves a person's image" Global vs. Local Levis ® Puerto Rico Germany GB Colombia Spain Bulgaria Global vs. Local Levis ® Puerto Rico Germany GB Colombia Spain Bulgaria Global vs. Local 1 Levis ® Puerto Rico Germany GB Colombia Spain Bulgaria Global vs. Local Levis ® Puerto Rico Germany GB Colombia Spain Bulgaria Global vs. Local Levis ® Puerto Rico Germany GB Colombia Spain Bulgaria Summary • Through the analysis of Chernoff's Faces, common global themes have easily been identified, as well as local differences Global themes: • Image of Designer labels • Price vs. Quality Regional/Local differences: • Demographic profiles • Attitudes towards fashion They wear the same jeans but they are not the same people • The first step in understanding how to approach and communicate with Levi's Jeans wearers around the world arfA The Research Authority 25,8441 12,6841 13,160 1,3401 7,0901 7,5371 4,9301 3,8631 2,6391 6,6111 6,1251 4,5681 5,9011 45,435| 22,168| 23,26S| 1,989| 12,934| 12,138| 9668 8,707 10,7. 98K 98K 98K 99K 1,255 98K 98K 98K 99K 33K 99K 37K 33K 37K 97K 97K 97K 94K 97K 98K 96K 96K % 23,303 11,549 11,754 6,709 7,022 4,264 3,065 2,539 6,271 5,686 3,357 4,843 38,613 19,327 19,286 1,788 11,399 10,545 8074 6,807 9j 89K 89K 88K 92k 92K 92K 85K 78K 35K 94K 30K 86K 80K 82K 84K 80K 85K 86K 85K 81K 75K tfl 22,700 10,782 11,956 1,203 2,033 19K 6,479 6,752 4,373 3,132 2,461 6,137 5,223 3,331 4,316 38,905 18,233 20,672 1,970 11,635 10,492 7920 6,888 M 2,412 2,448 2,376 2,294 2,358 2,515 2,580 2,270 2,296 2,580 2,536 2,570 83K 80K 86K 93K 87K 84K 79K 76K 1 23K 23K 22k 22K 23K 23K 22K 24K 22K 23K 22K 20K 37,031 18,517 18,513 1,432 10,023 9,782 8270 7,523 9l 492 429 553 641 554 488 452 422 532 512 493 431 435 79K 81K 77K 68K 75K 79K 83K 83K J 5k 4k 5K %y. 1,088 5K 5K 4K 4K 6K 5K 4K 4K 3K 22,536 12,161 10,375 1,644 8,607 7,447 3753 1,084 m 1,013 1,140 893 1,007 967 1,057 1,019 1,064 989 1,083 971 865 48K 53K 43K 78K 65K 60K 37K 12K jj 9K 11K 8k 10K 10K 10K 10K 9K 11K 10K 10K 8K 7K 46,965 22,928 24,037 2,107 13,321 12,434 10019 9,085 ňq 315 402 232 546 456 391 225 57 484 382 231 162 113 6,534 6,241 6,815 6,572 6,197 5,327 6,636 7,643 3K 4K ly. 5K 4K 4K 2k OK 5K 4K 2k IK 1k 61K 59K 64K 61K 59K 59K 61K 66K J 10,676 10,647 10,714 10,878 10,520 10,109 10,817 11,476 3,439 10,210 11,113 11,606 12,791 2,486 2,626 2,352 2,136 2,513 2,404 2,606 2,514 2,2; 15,792 7,482 3,310 2,179 5,623 3,843 2,772 N/A 1,780 1,657 3,835 4,316 4,205 23K 25K 22k 20k 24K 24K 24K 22k Ů 96K 96K 97K 97k| 96KI 96x1 97K 1,8731 4,9591 3,2581 2,420 N/A 97K 38K 36K 36K 96K 448 386 508 583 493 458 419 367 51 13,686 6,708 6,978 N/A 1,454 1,418 3,376 3,737 3,700 4K 4K 5K 5K 5K 5K 4K 3K 83K 86K 81K 83K 84K 82K 85K N/A 79K 84K 85K 83K 84K 996 1,118 881 988 954 1,011 1,071 961 1,0! 45,876 22,365 23,511 1,964 12,683 12,336 10,106 8,788 11,498 12,998 9,599 7,616 4,166 9K 10K 8K 9K 9K 10K 10K 8K 97K 97K 97k 96k 97K 98K 98K 96K 97K 97K 98K 96K 95K 220 287 156 413 317 270 152 30 3' 39,411 13,738 19,673 1,793 11,336 10,877 8333 6,346 10,628 11,364 8,150 6,033 3,176 2K 3K V. 4K 3K 3K IK OK 83K 86K 81k 88K 87K 86K 81K 76K 90K 85K 83K 77K 72K 10,684 10,657 10,711 10,692 10,474 10,070 10,945 11,520 9,4i 39,619 18,664 20,955 1,8941 11,856 93KI 90K 10,603 8180 7,079 10,478 11,535 8,119 6,160 3,327 45,461 22,176 23,285 2,188 13,063 11,763 9,755 8,693 10,8 84K 8lK 87^ 84K 79K 77K 89K 86K 83K 78K 76K 97K 97K 37K 97K 38K 38K 98K 36K 3 35,222 17,752 17,470 1,372 9,266 9,020 8200 7,364 9,196 9,809 7,536 5,722 2,960 39,071 19,417 19,654 1,953 11,490 10,492 8,127 7,034 9,7; 75K 77K 11'/. 67K 1,679 71K 72K 80K 81K 78K 73K 77K 73K 68K 84K 85K 82K 86K 86K 87K 82K 78K 8 25,663 13,663 12,000 9,251 8,409 4821 1,502 8,731 8,561 4,531 2,688 1,092 38,001 17,801 20,200 2,114 11,675 10,012 7,729 6,472 9,6 54K 59K 50K 82K 70K 67K 47K 16K 74K 64K 47K 34K 25K 81K 78K 84K 93K 87K 83K 78K 71k 8 47,253 23,034 24,219 2,039 13,143 12,614 10,313 9,144 11,733 13,355 3,831 7,832 4,381 38,493 19,138 19,355 1,703 10,207 10,024 * 6,240 5,460 7,020 4,050 5,430 6,210 7,574 9,240 5,430 5,970 6,510 7,020 6,540 82K 84K 81K 75K 76K 83K l\y. 63K 78k 84K 70K 69K 70K 76K 66K 69K 72K 73K 71K 17,909 9,864 8,044 1,438 6,855 5,823 _-r 1,170 1,530 780 210 990 1,260 1,676 1,050 960 1,260 1,260 1,260 1,380 38K 43K 34K 64K 51K 48K 6,552 6,282 6,822 6,930 6,228 6,030 6,570 7,722 5,148 6,066 6,768 7,632 8,382 46,685 22,753 23,332 2,261 13,356 12,045 6,098 5,832 61K 58K 64K 61K 59K 59K 61K 67K 54K 59K 61K 66K 71K 6,429 6,140 6,705 6,466 2,419 2,623 2,223 2,422 2,354 2,398 2,578 2,367 2,296 2,377 2,639 2,441 2,322 59K 57K 62K 59K 59K 57K 22k 24K l\y. 21k 22k 23K 24K 21k 24K 23K 24K 21k 18K 2,762 2,909 2,622 2,674 2,613 2,647 462 400 521 574 522 471 417 383 517 485 454 405 390 26K 27K 24K 24K 25K 26K 4k 4K 5K 5K 5K 5K 4K 3K 5K 5K 4K 3K 3K 444 387 498 526 468 452 1,010 1,133 893 953 998 1,001 1,075 382 1,080 338 1,033 330 861 4K 4K 5K 5K 5K 4K 9K 10k 8k 8K 486 9K 10k 10k 3K 11k 10k 9K 3K 7K 1,040 1,157 929 1,150 983 1,038 311 399 226 477 381 203 45 499 388 237 145 104 10k 11k 9K 10k 9K 10k 3K 4K ly. 4K 5K 4K 2K OK 5K 4K 2K IK 1K 137 196 81 204 203 175 Bulgaria Czech Republic Puerto Rico Colombia USA Croatia Greece Romania Serbia Germany China Russia Mexico Turkey Spain Israel Argentina Peru Slovak Republic France Ireland Brazil Venezuela GB Poland Chile India rp- think I arfA I J The Research authority Cross-Border Data Comparison -Try Managing Without It Thank you kmr