1 00:00:01,595 --> 00:00:03,518 Like a lot of people around the world, 2 00:00:03,542 --> 00:00:06,143 earlier this summer my friends and I were obsessed 3 00:00:06,167 --> 00:00:08,643 with the Women's World Cup held in France. 4 00:00:08,667 --> 00:00:11,518 Here we are, watching these incredible athletes, 5 00:00:11,542 --> 00:00:15,101 the goals were amazing, the games were clean and engaging, 6 00:00:15,125 --> 00:00:17,893 and at the same time, outside the field, 7 00:00:17,917 --> 00:00:19,893 these women are talking about equal pay, 8 00:00:19,917 --> 00:00:23,518 and in the case of some countries, any pay at all for their sport. 9 00:00:23,542 --> 00:00:27,059 So because we were mildly obsessed, we wanted to watch the games live, 10 00:00:27,083 --> 00:00:30,851 and we decided that one of the Spanish-speaking networks in the US 11 00:00:30,875 --> 00:00:32,893 was the best place for us to start. 12 00:00:32,917 --> 00:00:35,851 And it wasn't until a few games into the tournament 13 00:00:35,875 --> 00:00:38,393 that a friend of mine talks to me and says, 14 00:00:38,417 --> 00:00:40,559 "Why does it feel like everything I'm seeing 15 00:00:40,583 --> 00:00:43,833 is commercials for makeup and household cleaning products and diets?" 16 00:00:44,917 --> 00:00:46,768 It did feel a little bit too obvious, 17 00:00:46,792 --> 00:00:49,018 and I don't know if we were sensitive about it 18 00:00:49,042 --> 00:00:52,184 or the fact that we were watching with men and boys in our lives, 19 00:00:52,208 --> 00:00:54,184 but it did feel a little bit too obvious 20 00:00:54,208 --> 00:00:57,226 that we're being targeted for being women. 21 00:00:57,250 --> 00:01:00,809 And to be honest there's nothing necessarily wrong with that. 22 00:01:00,833 --> 00:01:04,018 Someone sat down and looked at the tournament and said, 23 00:01:04,042 --> 00:01:07,684 "Well, this thing is likely to be seen by more women, 24 00:01:07,708 --> 00:01:10,601 these women are Hispanic because they're watching in Spanish, 25 00:01:10,625 --> 00:01:11,893 and this is women content. 26 00:01:11,917 --> 00:01:15,268 Therefore, this is a great place for me to place all these commercials 27 00:01:15,292 --> 00:01:18,393 that are female-centric and maybe not other things." 28 00:01:18,417 --> 00:01:20,101 If I think about it as a marketer, 29 00:01:20,125 --> 00:01:22,768 I know that I absolutely should not be annoyed about it, 30 00:01:22,792 --> 00:01:26,059 because this is what marketers are tasked with doing. 31 00:01:26,083 --> 00:01:29,726 Marketers are tasked with building brands with very limited budget, 32 00:01:29,750 --> 00:01:32,184 so there's a little bit of an incentive 33 00:01:32,208 --> 00:01:34,184 to categorize people in buckets 34 00:01:34,208 --> 00:01:36,059 so they can reach their target faster. 35 00:01:36,083 --> 00:01:37,434 So if you think about this, 36 00:01:37,458 --> 00:01:38,934 it's kind of like a shortcut. 37 00:01:38,958 --> 00:01:42,167 They're using gender as a shortcut to get to their target consumer. 38 00:01:43,292 --> 00:01:47,226 The issue is that as logical as that argument seems, 39 00:01:47,250 --> 00:01:50,559 gender as a shortcut is actually not great. 40 00:01:50,583 --> 00:01:53,809 In this day and age, if you still blindly use a gender view 41 00:01:53,833 --> 00:01:55,393 for your marketing activities, 42 00:01:55,417 --> 00:01:57,542 actually it's just plain bad business. 43 00:01:58,333 --> 00:02:01,893 I'm not talking even about the backlash on stereotypes in advertising, 44 00:02:01,917 --> 00:02:05,101 which is a very real thing that has to be addressed. 45 00:02:05,125 --> 00:02:08,434 I'm saying it's bad business because you're leaving money on the table 46 00:02:08,458 --> 00:02:10,226 for your brands and your products. 47 00:02:10,250 --> 00:02:13,351 Because gender is such an easy thing to find in the market 48 00:02:13,375 --> 00:02:15,268 and to target and to talk about, 49 00:02:15,292 --> 00:02:17,601 it actually distracts you from the fun things 50 00:02:17,625 --> 00:02:19,851 that could be driving growth from your brands 51 00:02:19,875 --> 00:02:21,184 and, at the same time, 52 00:02:21,208 --> 00:02:23,851 it continues to create separation around genders 53 00:02:23,875 --> 00:02:25,643 and perpetuate stereotypes. 54 00:02:25,667 --> 00:02:28,851 So at the same time this activity is bad for your business 55 00:02:28,875 --> 00:02:31,125 and bad for society, so double whammy. 56 00:02:31,875 --> 00:02:35,393 And gender is one of those things like other demographics 57 00:02:35,417 --> 00:02:38,101 that have historically been good marketing shortcuts. 58 00:02:38,125 --> 00:02:39,726 At some point, however, 59 00:02:39,750 --> 00:02:42,226 we forgot that at the core we were targeting needs 60 00:02:42,250 --> 00:02:46,976 around cooking and cleaning and personal care and driving and sports 61 00:02:47,000 --> 00:02:48,726 and we just made it all a bucket 62 00:02:48,750 --> 00:02:50,851 and we said, "Men and women are different." 63 00:02:50,875 --> 00:02:53,476 We got used to it and we never challenged it again, 64 00:02:53,500 --> 00:02:55,101 and it's fascinating to me, 65 00:02:55,125 --> 00:02:57,851 and by fascinating I mean a little bit insane, 66 00:02:57,875 --> 00:03:00,393 that we still talk about this as a segment 67 00:03:00,417 --> 00:03:03,601 when it's most likely carryover bias. 68 00:03:03,625 --> 00:03:06,434 In fact, I don't come to this conclusion lightly. 69 00:03:06,458 --> 00:03:10,226 We have enough data to suggest that gender is not the best place 70 00:03:10,250 --> 00:03:13,684 to start for you to design and target your brands. 71 00:03:13,708 --> 00:03:15,726 And I would even go one step further: 72 00:03:15,750 --> 00:03:20,226 unless you are working in a very gender-specific product category, 73 00:03:20,250 --> 00:03:21,559 probably anything else 74 00:03:21,583 --> 00:03:24,143 you're hypothesizing about your consumer right now 75 00:03:24,167 --> 00:03:26,083 is going to be more useful than gender. 76 00:03:27,917 --> 00:03:30,851 We did not set up to draw this conclusion specifically. 77 00:03:30,875 --> 00:03:32,143 We found it. 78 00:03:32,167 --> 00:03:34,643 As consultants, our job is to go with our clients 79 00:03:34,667 --> 00:03:36,143 and understand their business 80 00:03:36,167 --> 00:03:39,684 and try to help them find spaces for their brands to grow. 81 00:03:39,708 --> 00:03:44,059 And it is our belief that if you want to find disruptive growth in the market, 82 00:03:44,083 --> 00:03:45,851 you have to go to the consumer 83 00:03:45,875 --> 00:03:48,101 and take a very agnostic view of the consumer. 84 00:03:48,125 --> 00:03:50,393 You have to go and look at them from scratch, 85 00:03:50,417 --> 00:03:54,809 remove yourself from biases and segments that you thought were important, 86 00:03:54,833 --> 00:03:57,809 just take a look to see where the growth is. 87 00:03:57,833 --> 00:04:00,768 And we built ourselves an algorithm precisely for that. 88 00:04:00,792 --> 00:04:03,351 So imagine that we have a person 89 00:04:03,375 --> 00:04:05,643 and we know a person is making a choice 90 00:04:05,667 --> 00:04:07,601 about a product or service, 91 00:04:07,625 --> 00:04:10,684 and from this person, I can know their gender, of course, 92 00:04:10,708 --> 00:04:13,934 other demographics, where they live, their income, other things. 93 00:04:13,958 --> 00:04:17,143 I know the context where this person is making a decision, 94 00:04:17,167 --> 00:04:19,184 where they are, who they're with, 95 00:04:19,208 --> 00:04:21,018 the energy, anything, 96 00:04:21,042 --> 00:04:23,101 and I can also put other things in the mix. 97 00:04:23,125 --> 00:04:24,476 I can know their attitudes, 98 00:04:24,500 --> 00:04:26,143 how they feel about the category, 99 00:04:26,167 --> 00:04:27,559 their behaviors. 100 00:04:27,583 --> 00:04:32,184 So if you imagine this kind of blob of big data about a person -- 101 00:04:32,208 --> 00:04:34,268 I'm going to oversimplify the science here 102 00:04:34,292 --> 00:04:37,684 but we basically built an algorithm for statistical tournaments. 103 00:04:37,708 --> 00:04:42,143 So a statistical tournament is like asking this big thing of data, 104 00:04:42,167 --> 00:04:46,768 "So, data, from everything you know about consumers at this point, 105 00:04:46,792 --> 00:04:49,476 what is the most useful thing I need to know 106 00:04:49,500 --> 00:04:52,518 that tells me more about what consumers need?" 107 00:04:52,542 --> 00:04:55,101 So the tournament is going to have winners and losers. 108 00:04:55,125 --> 00:04:57,518 The winners are those variables, those dimensions, 109 00:04:57,542 --> 00:04:59,893 that actually teach you a lot about your consumer, 110 00:04:59,917 --> 00:05:02,184 that if you know that, you know what they need. 111 00:05:02,208 --> 00:05:05,184 And there's losing variables that are just not that practical, 112 00:05:05,208 --> 00:05:08,393 and this matters because in a world of limited resources, 113 00:05:08,417 --> 00:05:11,809 you don't want to waste it on people that actually have the same needs. 114 00:05:11,833 --> 00:05:13,893 So why treat them differently? 115 00:05:13,917 --> 00:05:16,518 So at this point, I know, suspense is not killing you, 116 00:05:16,542 --> 00:05:18,434 because I told you what the output is, 117 00:05:18,458 --> 00:05:20,893 but what we found over time 118 00:05:20,917 --> 00:05:25,434 is after 200 projects around the world -- this is covering 20 countries or more -- 119 00:05:25,458 --> 00:05:29,309 in essence we ran about a hundred thousand of these tournaments, 120 00:05:29,333 --> 00:05:34,393 and, no surprise, gender was very rarely the most predictive thing 121 00:05:34,417 --> 00:05:36,167 to understand consumer needs. 122 00:05:37,292 --> 00:05:39,184 From a hundred thousand tournaments, 123 00:05:39,208 --> 00:05:41,601 gender only came out as the winning variable 124 00:05:41,625 --> 00:05:43,125 in about five percent of them. 125 00:05:43,958 --> 00:05:46,018 This is true around the world, by the way. 126 00:05:46,042 --> 00:05:48,559 We did this in places where traditional gender roles 127 00:05:48,583 --> 00:05:50,934 are a little more pronounced, 128 00:05:50,958 --> 00:05:53,018 and the conclusions were exactly the same. 129 00:05:53,042 --> 00:05:55,976 It was a little bit more important, gender, than five percent, 130 00:05:56,000 --> 00:05:57,268 but not material. 131 00:05:57,292 --> 00:05:59,476 So let's let that sink in for a second. 132 00:05:59,500 --> 00:06:01,851 No matter how you're looking at a consumer, 133 00:06:01,875 --> 00:06:06,226 most likely anything else is going to be more interesting to you than gender. 134 00:06:06,250 --> 00:06:09,643 There's probably something very important you need to know about them, 135 00:06:09,667 --> 00:06:13,351 and you're getting distracted because you're doing everything based on gender. 136 00:06:13,375 --> 00:06:15,976 And that's why I say you're leaving money on the table. 137 00:06:16,000 --> 00:06:19,059 Gender is easy. It's easy to design advertising based on gender, 138 00:06:19,083 --> 00:06:23,476 it's easy to target people online and on TV based on gender. 139 00:06:23,500 --> 00:06:26,851 But at the end, that's not where the exciting growth will come from. 140 00:06:26,875 --> 00:06:31,101 If you're a food company, for example, it's actually much more interesting to you 141 00:06:31,125 --> 00:06:34,726 to know where people are eating, who they are eating with, 142 00:06:34,750 --> 00:06:37,184 are they very nutritionally oriented. 143 00:06:37,208 --> 00:06:40,934 All of those things are actually significantly more powerful and useful 144 00:06:40,958 --> 00:06:43,393 than knowing if a person is a man or a woman. 145 00:06:43,417 --> 00:06:44,809 And that matters, of course, 146 00:06:44,833 --> 00:06:48,101 because then if you're putting your limited budget into action, 147 00:06:48,125 --> 00:06:51,184 then you're better off creating solutions for different occasions 148 00:06:51,208 --> 00:06:53,375 than trying to target women versus young men. 149 00:06:54,917 --> 00:06:57,226 Another example is alcoholic beverages. 150 00:06:57,250 --> 00:07:00,601 Thirty-five percent to 40 percent of consumption in alcoholic beverages 151 00:07:00,625 --> 00:07:02,768 around the world actually happens with women, 152 00:07:02,792 --> 00:07:04,768 but, you know, "women don't drink beer." 153 00:07:04,792 --> 00:07:07,583 Those are the things that we typically hear. 154 00:07:08,583 --> 00:07:12,601 But actually, when a man and a woman are, for the most part, in the same location, 155 00:07:12,625 --> 00:07:15,559 the emotional and functional needs they have at that moment 156 00:07:15,583 --> 00:07:17,143 are very similar. 157 00:07:17,167 --> 00:07:19,268 There's only one exception, by the way, 158 00:07:19,292 --> 00:07:21,059 and the exceptions exist, 159 00:07:21,083 --> 00:07:22,809 where if you have a man and a woman 160 00:07:22,833 --> 00:07:24,143 on a date, 161 00:07:24,167 --> 00:07:26,518 the man is trying to impress the woman 162 00:07:26,542 --> 00:07:28,851 and the woman is trying to connect with the man, 163 00:07:28,875 --> 00:07:31,101 so there's going to be a little bit of tension, 164 00:07:31,125 --> 00:07:32,601 but that's important to know. 165 00:07:32,625 --> 00:07:33,893 We'll take a few dates. 166 00:07:33,917 --> 00:07:36,976 Financial institutions: that's something where we've heard a lot 167 00:07:37,000 --> 00:07:39,059 about the difference between men and women, 168 00:07:39,083 --> 00:07:41,601 but actually talking about men and women as different 169 00:07:41,625 --> 00:07:44,226 is distracting you from the thing that is underneath. 170 00:07:44,250 --> 00:07:47,476 We made it so simple as "women don't like to invest," 171 00:07:47,500 --> 00:07:49,184 "women hate managing their money," 172 00:07:49,208 --> 00:07:51,601 "men are great and aggressive and risk-takers," 173 00:07:51,625 --> 00:07:53,768 but at the end it's not about men and women. 174 00:07:53,792 --> 00:07:55,601 It is actually a different narrative. 175 00:07:55,625 --> 00:07:58,893 It is about, there are people that are excited and energized 176 00:07:58,917 --> 00:08:01,476 and educated to manage their finances 177 00:08:01,500 --> 00:08:02,934 versus people that are not. 178 00:08:02,958 --> 00:08:04,684 So if you change the conversation 179 00:08:04,708 --> 00:08:07,518 from men and women to actually what's underneath 180 00:08:07,542 --> 00:08:10,851 then probably you'll stop being so condescending to women 181 00:08:10,875 --> 00:08:12,559 and you may start serving some men 182 00:08:12,583 --> 00:08:15,643 that are actually shy about managing their finances. 183 00:08:15,667 --> 00:08:17,268 I'll leave one more example. 184 00:08:17,292 --> 00:08:20,476 If I go back to the women that were playing sport at the beginning, 185 00:08:20,500 --> 00:08:24,101 one of the fascinating things we found over different countries, 186 00:08:24,125 --> 00:08:26,434 exploring sportswear, 187 00:08:26,458 --> 00:08:28,518 that if a person is a competitive person 188 00:08:28,542 --> 00:08:30,768 and they are in the moment of action, 189 00:08:30,792 --> 00:08:33,393 the needs are not different between a man and a woman. 190 00:08:33,417 --> 00:08:34,726 An athlete is an athlete. 191 00:08:34,750 --> 00:08:38,184 It doesn't matter for men and women, it doesn't matter for old and young, 192 00:08:38,208 --> 00:08:39,476 you are an athlete, 193 00:08:39,500 --> 00:08:41,976 and in the moment of action and extreme competition, 194 00:08:42,000 --> 00:08:43,726 you need this gear to work for you. 195 00:08:43,750 --> 00:08:47,309 So these soccer-playing women have a lot in common with their counterparts. 196 00:08:47,333 --> 00:08:49,143 Out of the field, it doesn't matter. 197 00:08:49,167 --> 00:08:52,101 Out of the field, they may be into fashion, into other things, 198 00:08:52,125 --> 00:08:54,684 but on the field, the needs are not different. 199 00:08:54,708 --> 00:08:58,434 So these are just a few examples on categories where we found 200 00:08:58,458 --> 00:09:01,226 that gender was not the best place to go, 201 00:09:01,250 --> 00:09:03,559 and actually the argument is 202 00:09:03,583 --> 00:09:05,934 that at this point it's not even a feminist push, 203 00:09:05,958 --> 00:09:07,434 it's just we got used to it. 204 00:09:07,458 --> 00:09:08,851 We got used to using gender, 205 00:09:08,875 --> 00:09:12,018 and it's important for us to start finding ways 206 00:09:12,042 --> 00:09:14,226 to measure other things about consumers 207 00:09:14,250 --> 00:09:16,684 so that we don't revert back to gender. 208 00:09:16,708 --> 00:09:18,018 I am not naïve, 209 00:09:18,042 --> 00:09:20,226 and I know there's still going to be appetite 210 00:09:20,250 --> 00:09:22,101 and certain ease around using gender, 211 00:09:22,125 --> 00:09:24,184 but at least this warrants a conversation. 212 00:09:24,208 --> 00:09:26,101 In your business, you have to inquire, 213 00:09:26,125 --> 00:09:29,101 is this really the best lens for me to grow. 214 00:09:29,125 --> 00:09:32,643 So, if you are, like me, a person that is in business, 215 00:09:32,667 --> 00:09:36,643 that I am constantly worried about what is my role 216 00:09:36,667 --> 00:09:39,726 in the broader societal discussions, 217 00:09:39,750 --> 00:09:42,726 if you're listening to your business and you hear things like, 218 00:09:42,750 --> 00:09:44,893 "Oh, my target are women, my target are men, 219 00:09:44,917 --> 00:09:47,601 this goes to young girls, young boys," 220 00:09:47,625 --> 00:09:49,518 when it's that gender conversation, 221 00:09:49,542 --> 00:09:51,059 unless you are working, again, 222 00:09:51,083 --> 00:09:55,226 in a very specific, gender-specific product category, 223 00:09:55,250 --> 00:09:56,726 take this as a warning sign, 224 00:09:56,750 --> 00:09:59,601 because if you keep having these conversations, 225 00:09:59,625 --> 00:10:02,143 you will keep perpetuating stereotypes of people 226 00:10:02,167 --> 00:10:04,851 and making people think that men and women are different. 227 00:10:04,875 --> 00:10:07,684 But because this is business, and we're running a business, 228 00:10:07,708 --> 00:10:09,018 and we want to grow it, 229 00:10:09,042 --> 00:10:11,851 at least kind of challenge your own instinct to use gender, 230 00:10:11,875 --> 00:10:15,393 because statistics say that you're probably not choosing the best variable 231 00:10:15,417 --> 00:10:17,309 to target your product or service. 232 00:10:17,333 --> 00:10:19,559 Growth is not easy at all. 233 00:10:19,583 --> 00:10:21,934 What makes you think that growth is going to come 234 00:10:21,958 --> 00:10:25,351 from going into market with such an outdated lens like gender? 235 00:10:25,375 --> 00:10:28,059 So let's stop doing what's easy and go for what's right. 236 00:10:28,083 --> 00:10:31,601 At this point, it's not just for your business, it's for society. 237 00:10:31,625 --> 00:10:33,018 Thank you. 238 00:10:33,042 --> 00:10:36,042 (Applause)