1 00:00:01,251 --> 00:00:03,533 Like a lot of people around the world, 2 00:00:03,533 --> 00:00:06,395 earlier this summer my friends and I were obsessed 3 00:00:06,395 --> 00:00:08,762 with the Women's World Cup held in France. 4 00:00:08,762 --> 00:00:11,647 Here we are, watching these incredible athletes, 5 00:00:11,647 --> 00:00:15,408 the goals were amazing, the games were clean and engaging, 6 00:00:15,408 --> 00:00:17,834 and at the same time, outside the field, 7 00:00:17,834 --> 00:00:20,379 these women are talking about equal pay, 8 00:00:20,379 --> 00:00:23,754 and in the case of some countries, any pay at all for their sport. 9 00:00:23,754 --> 00:00:27,311 So because we were mildly obsessed, we wanted to watch the games live, 10 00:00:27,311 --> 00:00:31,015 and we decided that one of the Spanish-speaking networks in the US 11 00:00:31,015 --> 00:00:32,807 was the best place for us to start, 12 00:00:32,807 --> 00:00:36,058 and it wasn't until a few games into the tournament 13 00:00:36,058 --> 00:00:38,702 that a friend of mine talks to me and says, 14 00:00:38,702 --> 00:00:41,020 "Why does it feel like everything I'm seeing 15 00:00:41,020 --> 00:00:45,021 is commercials for makeup and household cleaning products and diets?" 16 00:00:45,021 --> 00:00:46,966 It did feel a little bit too obvious, 17 00:00:46,966 --> 00:00:49,211 and I don't know if we were sensitive about it 18 00:00:49,211 --> 00:00:52,358 or the fact that we were watching with men and boys in our lives, 19 00:00:52,358 --> 00:00:55,812 but it did feel a little bit too obvious 20 00:00:55,812 --> 00:00:57,722 that we're being targeted for being women. 21 00:00:57,722 --> 00:01:01,016 And to be honest there's nothing necessarily wrong with that. 22 00:01:01,016 --> 00:01:04,825 Someone sat down and looked at the tournament and said, 23 00:01:04,825 --> 00:01:07,534 "Well, this thing is likely to be seen by more women, 24 00:01:07,534 --> 00:01:10,611 these women are Hispanic because they're watching in Spanish, 25 00:01:10,611 --> 00:01:11,841 and this is women content. 26 00:01:11,841 --> 00:01:15,366 Therefore, this is a great place for me to place all these commercials 27 00:01:15,366 --> 00:01:18,606 that are female-centric and maybe not other things." 28 00:01:18,606 --> 00:01:20,204 If I think about it as a marketer, 29 00:01:20,204 --> 00:01:23,082 I know that I absolutely should not be annoyed about it, 30 00:01:23,082 --> 00:01:26,149 because this is what marketers are tasked with doing. 31 00:01:26,149 --> 00:01:29,898 Marketers are tasked with building brands with very limited budget, 32 00:01:29,898 --> 00:01:32,226 so there's a little bit of an incentive 33 00:01:32,226 --> 00:01:34,475 to categorize people in buckets 34 00:01:34,475 --> 00:01:36,230 so they can reach their target faster. 35 00:01:36,230 --> 00:01:37,725 So if you think about this, 36 00:01:37,725 --> 00:01:39,294 it's kind of like a shortcut. 37 00:01:39,294 --> 00:01:43,394 They're using gender as a shortcut to get to their target consumer. 38 00:01:43,622 --> 00:01:47,623 The issue is that as logical as that argument seems, 39 00:01:47,623 --> 00:01:50,392 gender as a shortcut is actually not great. 40 00:01:50,392 --> 00:01:53,972 In this day and age, if you still blindly use a gender view 41 00:01:53,972 --> 00:01:55,685 for your marketing activities, 42 00:01:55,685 --> 00:01:57,806 actually it's just plain bad business. 43 00:01:57,806 --> 00:02:02,226 I'm not talking even about the backlash on stereotypes in advertising, 44 00:02:02,226 --> 00:02:04,823 which is a very real thing that has to be addressed. 45 00:02:04,823 --> 00:02:08,286 I'm saying it's bad business because you're leaving money on the table 46 00:02:08,286 --> 00:02:10,139 for your brands and your products. 47 00:02:10,139 --> 00:02:13,828 Because gender is such an easy thing to find in the market 48 00:02:13,828 --> 00:02:15,468 and to target and to talk about, 49 00:02:15,468 --> 00:02:17,852 it actually distracts you from the fun things 50 00:02:17,852 --> 00:02:19,877 that could be driving growth from your brands, 51 00:02:19,877 --> 00:02:22,001 and, at the same time, 52 00:02:22,001 --> 00:02:24,602 it continues to create separation around genders 53 00:02:24,602 --> 00:02:25,899 and perpetuating stereotypes. 54 00:02:25,899 --> 00:02:29,059 So at the same time this activity is bad for your business 55 00:02:29,059 --> 00:02:32,079 and bad for society, so double whammy. 56 00:02:32,079 --> 00:02:35,810 And gender is one of those things like other demographics 57 00:02:35,810 --> 00:02:38,436 that have historically been good marketing shortcuts. 58 00:02:38,560 --> 00:02:39,969 At some point, however, 59 00:02:39,969 --> 00:02:42,414 we forgot that at the core we were targeting needs 60 00:02:42,414 --> 00:02:47,070 around cooking and cleaning and personal care and driving and sports 61 00:02:47,070 --> 00:02:51,002 and we just made it all a bucket and we said, "Men and women are different." 62 00:02:51,002 --> 00:02:53,670 We got used to it and we never challenged it again, 63 00:02:53,670 --> 00:02:55,332 and it's fascinating to me 64 00:02:55,332 --> 00:02:58,092 and by fascinating I mean a little bit insane 65 00:02:58,092 --> 00:03:00,537 that we still talk about this as a segment 66 00:03:00,537 --> 00:03:02,866 when it's most likely carryover bias. 67 00:03:03,615 --> 00:03:06,262 In fact, I don't come to this conclusion lightly. 68 00:03:06,262 --> 00:03:10,314 We have enough data to suggest that gender is not the best place 69 00:03:10,314 --> 00:03:13,771 to start for you to design and target your brands. 70 00:03:13,771 --> 00:03:15,791 And I would even go one step further: 71 00:03:15,791 --> 00:03:20,325 unless you are working in a very gender-specific product category, 72 00:03:20,325 --> 00:03:21,807 probably anything else 73 00:03:21,807 --> 00:03:24,314 you're hypothesizing about your consumer right now 74 00:03:24,314 --> 00:03:26,743 is going to be more useful than gender. 75 00:03:27,646 --> 00:03:31,083 We did not set up to draw this conclusion specifically. 76 00:03:31,083 --> 00:03:32,344 We found it. 77 00:03:32,344 --> 00:03:34,870 As consultants, our job is to go with our clients 78 00:03:34,870 --> 00:03:36,273 and understand their business 79 00:03:36,273 --> 00:03:40,011 and try to help them find spaces for their brands to grow, 80 00:03:40,011 --> 00:03:44,253 and it is our belief that if you want to find disruptive growth in the market, 81 00:03:44,253 --> 00:03:45,963 you have to go to the consumer 82 00:03:45,963 --> 00:03:48,384 and take a very agnostic view of the consumer. 83 00:03:48,384 --> 00:03:50,653 You have to go and look at them from scratch, 84 00:03:50,653 --> 00:03:54,940 remove yourself from biases and segments that you thought were important, 85 00:03:54,940 --> 00:03:57,719 just take a look to see where the growth is. 86 00:03:57,719 --> 00:04:01,002 And we built ourselves an algorithm precisely for that. 87 00:04:01,002 --> 00:04:04,855 So imagine that we have a person and we know a person 88 00:04:04,855 --> 00:04:07,609 is making a choice about a product or service, 89 00:04:07,609 --> 00:04:10,776 and from this person, I can know their gender, of course, 90 00:04:10,776 --> 00:04:13,795 other demographics, where they live, their income, other things. 91 00:04:13,795 --> 00:04:17,154 I know the context where this person is making a decision, 92 00:04:17,154 --> 00:04:19,351 where they are, who they're with, 93 00:04:19,351 --> 00:04:21,002 the energy, anything, 94 00:04:21,002 --> 00:04:23,169 and I can also put other things in the mix. 95 00:04:23,169 --> 00:04:24,671 I can know their attitudes, 96 00:04:24,671 --> 00:04:26,222 how they feel about the category, 97 00:04:26,222 --> 00:04:27,726 their behaviors. 98 00:04:27,726 --> 00:04:31,838 So if you imagine this kind of blob of big data about a person, 99 00:04:31,838 --> 00:04:34,731 I'm going to oversimplify the science here 100 00:04:34,731 --> 00:04:37,942 but we basically built an algorithm for statistical tournaments. 101 00:04:37,942 --> 00:04:42,182 So a statistical tournament is like asking this big thing of data, 102 00:04:42,182 --> 00:04:46,705 "So, data, from everything you know about consumers at this point, 103 00:04:46,705 --> 00:04:50,815 what is the most useful thing I need to know 104 00:04:50,815 --> 00:04:52,773 that tells me more about what consumers need? 105 00:04:52,773 --> 00:04:54,834 So the tournament is going to have winners and losers. 106 00:04:54,834 --> 00:04:57,810 The winners are those variables, those dimensions, 107 00:04:57,810 --> 00:04:59,673 that actually teach you a lot about your consumer, 108 00:04:59,673 --> 00:05:02,367 that if you know that, you know what they need, 109 00:05:02,367 --> 00:05:05,210 and there's losing variables that are just not that practical, 110 00:05:05,210 --> 00:05:08,647 and this matters because in a world of limited resources, 111 00:05:08,647 --> 00:05:12,111 you don't want to waste it on people that actually have the same needs. 112 00:05:12,111 --> 00:05:14,022 So why treat them differently? 113 00:05:14,022 --> 00:05:16,860 So at this point, I know, suspense is not killing you, 114 00:05:16,860 --> 00:05:18,451 because I told you what the output is, 115 00:05:18,451 --> 00:05:22,055 but what we found over time 116 00:05:22,055 --> 00:05:25,720 is, after 200 projects around the world, this is covering 20 countries or more, 117 00:05:25,720 --> 00:05:29,118 in essence we ran about a hundred thousand of these tournaments, 118 00:05:29,118 --> 00:05:36,069 and, no surprise, gender was very rarely the most predictive thing 119 00:05:36,069 --> 00:05:37,402 to understand consumer needs. 120 00:05:37,402 --> 00:05:39,608 From a hundred thousand tournaments, 121 00:05:39,608 --> 00:05:42,293 gender only came out as the winning variable 122 00:05:42,293 --> 00:05:44,117 in about five percent of them. 123 00:05:44,117 --> 00:05:47,002 This is true around the world, by the way. 124 00:05:47,002 --> 00:05:49,834 We did this in places where traditional gender roles 125 00:05:49,834 --> 00:05:52,252 are little more pronounced, 126 00:05:52,252 --> 00:05:53,195 and the conclusions were exactly the same. 127 00:05:53,195 --> 00:05:55,840 It was a little bit more important, gender, than five percent, 128 00:05:55,840 --> 00:05:57,287 but not material. 129 00:05:57,287 --> 00:05:59,349 So let's let that sink in for a second. 130 00:05:59,349 --> 00:06:01,630 No matter how you're looking at a consumer, 131 00:06:01,630 --> 00:06:06,648 most likely anything else is going to be more interesting to you than gender. 132 00:06:06,648 --> 00:06:09,793 There's probably something very important you need to know about them, 133 00:06:09,793 --> 00:06:13,566 and you're getting distracted because you're doing everything based on gender. 134 00:06:13,566 --> 00:06:15,791 And that's why I say you're leaving money on the table. 135 00:06:15,791 --> 00:06:16,836 Gender is easy. 136 00:06:16,836 --> 00:06:19,065 It's easy to design advertising based on gender, 137 00:06:19,065 --> 00:06:21,252 it's easy to target people online and on TV based on gender. 138 00:06:21,252 --> 00:06:21,722 But at the end, that's not where the exciting growth will come from. 139 00:06:21,722 --> 00:06:28,428 If you're a food company, for example, 140 00:06:28,428 --> 00:06:31,358 it's actually much more interesting to you 141 00:06:31,358 --> 00:06:33,618 to know where people are eating, 142 00:06:33,618 --> 00:06:35,148 who they are eating with, 143 00:06:35,148 --> 00:06:38,473 are they very nutritionally oriented. 144 00:06:38,473 --> 00:06:41,148 All of those things are actually significantly more powerful and useful 145 00:06:41,148 --> 00:06:43,427 than knowing if a person is a man or a woman. 146 00:06:43,427 --> 00:06:45,422 And that matters, of course, 147 00:06:45,422 --> 00:06:48,300 because then if you're putting your limited budget into action, 148 00:06:48,300 --> 00:06:50,912 then you're better off creating solutions for different occasions 149 00:06:50,912 --> 00:06:54,619 than trying to target women versus young men. 150 00:06:54,619 --> 00:06:57,129 Another example if alcoholic beverages. 151 00:06:57,129 --> 00:06:59,890 Thirty-five percent to 40 percent of consumption in alcoholic beverages 152 00:06:59,890 --> 00:07:02,651 around the world actually happens with women, 153 00:07:02,651 --> 00:07:04,991 but, you know, "women don't drink beer." 154 00:07:04,991 --> 00:07:07,645 Those are the things that we typically hear. 155 00:07:07,645 --> 00:07:12,909 But actually, when a man and a woman are, for the most part, in the same location, 156 00:07:12,909 --> 00:07:15,788 the emotional and functional needs they have at that moment 157 00:07:15,788 --> 00:07:17,101 are very similar. 158 00:07:17,101 --> 00:07:19,395 There's only one exception, by the way, 159 00:07:19,395 --> 00:07:21,287 and the exceptions exist, 160 00:07:21,287 --> 00:07:23,364 where if you have a man and a woman 161 00:07:23,364 --> 00:07:24,245 on a date, 162 00:07:24,245 --> 00:07:26,877 the man is trying to impress the woman 163 00:07:26,877 --> 00:07:29,189 and the woman is trying to connect with the man, 164 00:07:29,189 --> 00:07:30,851 so there's going to be a little bit of tension, 165 00:07:30,851 --> 00:07:33,840 but that's important to know. 166 00:07:33,840 --> 00:07:36,300 We'll take a few dates. 167 00:07:36,300 --> 00:07:36,918 Financial institutions: 168 00:07:36,918 --> 00:07:38,952 that's something where we've heard 169 00:07:38,952 --> 00:07:39,312 a lot about the difference between men and women, 170 00:07:39,312 --> 00:07:41,485 but actually talking about men and women as different 171 00:07:41,485 --> 00:07:44,398 actually is distracting you from the thing that is underneath. 172 00:07:44,398 --> 00:07:47,645 We made it so simple as "women don't like to invest," 173 00:07:47,645 --> 00:07:49,467 "women hate managing their money," 174 00:07:49,467 --> 00:07:51,736 "men are great and aggressive and risk-takers," 175 00:07:51,736 --> 00:07:53,727 but at the end it's not about men and women. 176 00:07:53,727 --> 00:07:55,586 It is actually a different narrative. 177 00:07:55,586 --> 00:07:57,723 It is about, there are people that are excited and energized 178 00:07:57,723 --> 00:08:01,644 and educated to manage their finances 179 00:08:01,644 --> 00:08:03,141 versus people that are not. 180 00:08:03,141 --> 00:08:04,888 So if you change the conversation 181 00:08:04,888 --> 00:08:07,637 from men and women to actually what's underneath 182 00:08:07,637 --> 00:08:11,040 then probably you'll stop being so condescending to women 183 00:08:11,040 --> 00:08:12,919 and you may start serving some men 184 00:08:12,919 --> 00:08:15,580 that are actually shy about managing their finances. 185 00:08:15,580 --> 00:08:17,836 I'll leave one more example. 186 00:08:17,836 --> 00:08:20,501 If I go back to the women that were playing sport at the beginning, 187 00:08:20,501 --> 00:08:22,395 one of the fascinating things we found 188 00:08:22,395 --> 00:08:24,325 over different countries 189 00:08:24,325 --> 00:08:26,364 exploring sportswear 190 00:08:26,364 --> 00:08:28,824 that if a person is a competitive person 191 00:08:28,824 --> 00:08:30,708 and they are in the moment of action, 192 00:08:30,708 --> 00:08:33,587 the needs are not different between a man and a woman. 193 00:08:33,587 --> 00:08:35,446 An athlete is an athlete. 194 00:08:35,446 --> 00:08:37,565 It doesn't matter for men and women, it doesn't matter for old and young, 195 00:08:37,565 --> 00:08:39,189 you are an athlete, 196 00:08:39,189 --> 00:08:41,819 and in the moment of action and extreme competition, 197 00:08:41,819 --> 00:08:43,759 you need this gear to work for you. 198 00:08:43,759 --> 00:08:47,285 So these soccer-playing women have a lot in common with their counterparts. 199 00:08:47,285 --> 00:08:49,417 Out of the field, it doesn't matter. 200 00:08:49,417 --> 00:08:51,256 Out of the field, they may be into fashion, into other things, 201 00:08:51,256 --> 00:08:52,787 but on the field, 202 00:08:52,787 --> 00:08:54,816 the needs are not different. 203 00:08:54,816 --> 00:08:58,674 So these are just a few examples on categories where we found 204 00:08:58,674 --> 00:09:01,145 that gender was not the best place to go, 205 00:09:01,145 --> 00:09:03,928 and actually the argument is that 206 00:09:03,928 --> 00:09:06,775 at this point it's not even a feminist push, 207 00:09:06,775 --> 00:09:07,924 it's just we got used it. 208 00:09:07,924 --> 00:09:09,482 We got used to using gender, 209 00:09:09,482 --> 00:09:12,191 and it's important for us to start finding ways 210 00:09:12,191 --> 00:09:14,917 to measure other things about consumers 211 00:09:14,917 --> 00:09:16,931 so that we don't revert back to gender. 212 00:09:16,931 --> 00:09:19,311 I am not naïve, 213 00:09:19,311 --> 00:09:21,321 and I know there's still going to be appetite 214 00:09:21,321 --> 00:09:22,628 and certain ease around using gender, 215 00:09:22,628 --> 00:09:24,298 but at least this warrants conversation. 216 00:09:24,298 --> 00:09:26,422 In your business, you have to inquire, 217 00:09:26,422 --> 00:09:29,035 is this really the best lens for me to grow. 218 00:09:29,035 --> 00:09:32,916 So, if you are, like me, a person that is in business 219 00:09:32,916 --> 00:09:36,811 that I am constantly worried about what is my role 220 00:09:36,811 --> 00:09:39,643 in the broader societal discussions, 221 00:09:39,643 --> 00:09:42,226 if you're listening to your business 222 00:09:42,226 --> 00:09:43,105 and you hear things like, 223 00:09:43,105 --> 00:09:45,035 "Oh, my target are women, my target are men, 224 00:09:45,035 --> 00:09:47,910 this goes to young girls, young boys," 225 00:09:47,910 --> 00:09:49,823 when it's that gender conversation, 226 00:09:49,823 --> 00:09:51,359 unless you are working, again, 227 00:09:51,359 --> 00:09:55,482 in a very specific, gender-specific product category, 228 00:09:55,482 --> 00:09:57,277 take this as a warning sign, 229 00:09:57,277 --> 00:09:59,875 because if you keep having these conversations, 230 00:09:59,875 --> 00:10:02,392 you will keep perpetuating stereotypes of people 231 00:10:02,392 --> 00:10:04,879 and making people think that men and women are different. 232 00:10:04,879 --> 00:10:07,405 But because this is business, and we're running a business 233 00:10:07,405 --> 00:10:08,654 and we want to grow it, 234 00:10:08,654 --> 00:10:11,644 at least kind of challenge your own instinct to use gender, 235 00:10:11,644 --> 00:10:15,435 because statistics say that you're probably not choosing the best variable 236 00:10:15,435 --> 00:10:17,447 to target your product or service. 237 00:10:17,447 --> 00:10:19,941 Growth is not easy at all. 238 00:10:19,941 --> 00:10:22,102 What makes you think that growth is going to come 239 00:10:22,102 --> 00:10:25,138 from going into market with such an outdated lens like gender? 240 00:10:25,138 --> 00:10:28,283 So let's stop doing what's easy and go for what's right. 241 00:10:28,283 --> 00:10:30,413 At this point, it's not just for your business, it's for society. 242 00:10:30,413 --> 00:10:32,046 Thank you. 243 00:10:32,046 --> 99:59:59,999 (Applause)