9:59:59.000,9:59:59.000
Like a lot of people around the world,

9:59:59.000,9:59:59.000
earlier this summer[br]my friends and I were obsessed

9:59:59.000,9:59:59.000
with the Women's World Cup held in France.

9:59:59.000,9:59:59.000
Here we are, watching[br]these incredible athletes,

9:59:59.000,9:59:59.000
the goals were amazing,[br]the games were clean and engaging,

9:59:59.000,9:59:59.000
and at the same time, outside the field,

9:59:59.000,9:59:59.000
these women are talking about equal pay,

9:59:59.000,9:59:59.000
and in the case of some countries,[br]any pay at all for their sport.

9:59:59.000,9:59:59.000
So because we were mildly obsessed,[br]we wanted to watch the games live,

9:59:59.000,9:59:59.000
and we decided that one of[br]the Spanish-speaking networks in the US

9:59:59.000,9:59:59.000
was the best place for us to start,

9:59:59.000,9:59:59.000
and it wasn't until a few games[br]into the tournament

9:59:59.000,9:59:59.000
that a friend of mine[br]talks to me and says,

9:59:59.000,9:59:59.000
"Why does it feel like[br]everything I'm seeing

9:59:59.000,9:59:59.000
is commercials for makeup and[br]household cleaning products and diets?"

9:59:59.000,9:59:59.000
It did feel a little bit too obvious,

9:59:59.000,9:59:59.000
and I don't know if[br]we were sensitive about it

9:59:59.000,9:59:59.000
or the fact that we were watching[br]with men and boys in our lives,

9:59:59.000,9:59:59.000
but it did feel a little bit too obvious

9:59:59.000,9:59:59.000
that we're being targeted for being women.

9:59:59.000,9:59:59.000
And to be honest there's nothing[br]necessarily wrong with that.

9:59:59.000,9:59:59.000
Someone sat down and looked[br]at the tournament and said,

9:59:59.000,9:59:59.000
"Well, this thing is likely[br]to be seen by more women,

9:59:59.000,9:59:59.000
these women are Hispanic[br]because they're watching in Spanish,

9:59:59.000,9:59:59.000
and this is women content.

9:59:59.000,9:59:59.000
Therefore, this is a great place for me[br]to place all these commercials

9:59:59.000,9:59:59.000
that are female-centric[br]and maybe not other things."

9:59:59.000,9:59:59.000
If I think about it as a marketer,

9:59:59.000,9:59:59.000
I know that I absolutely[br]should not be annoyed about it,

9:59:59.000,9:59:59.000
because this is what marketers[br]are tasked with doing.

9:59:59.000,9:59:59.000
Marketers are tasked with building brands[br]with very limited budget,

9:59:59.000,9:59:59.000
so there's a little bit of an incentive

9:59:59.000,9:59:59.000
to categorize people in buckets

9:59:59.000,9:59:59.000
so they can reach their target faster.

9:59:59.000,9:59:59.000
So if you think about this,

9:59:59.000,9:59:59.000
it's kind of like a shortcut.

9:59:59.000,9:59:59.000
They're using gender as a shortcut[br]to get to their target consumer.

9:59:59.000,9:59:59.000
The issue is that as logical[br]as that argument seems,

9:59:59.000,9:59:59.000
gender as a shortcut[br]is actually not great.

9:59:59.000,9:59:59.000
In this day and age, if you still[br]blindly use a gender view

9:59:59.000,9:59:59.000
for your marketing activities,

9:59:59.000,9:59:59.000
actually it's just plain bad business.

9:59:59.000,9:59:59.000
I'm not talking even about the backlash[br]on stereotypes in advertising,

9:59:59.000,9:59:59.000
which is a very real thing[br]that has to be addressed.

9:59:59.000,9:59:59.000
I'm saying it's bad business because[br]you're leaving money on the table

9:59:59.000,9:59:59.000
for your brands and your products.

9:59:59.000,9:59:59.000
Because gender is such an easy thing[br]to find in the market

9:59:59.000,9:59:59.000
and to target and to talk about,

9:59:59.000,9:59:59.000
it actually distracts you[br]from the fun things

9:59:59.000,9:59:59.000
that could be driving growth[br]from your brands,

9:59:59.000,9:59:59.000
and, at the same time,

9:59:59.000,9:59:59.000
it continues to create[br]separation around genders

9:59:59.000,9:59:59.000
and perpetuating stereotypes.

9:59:59.000,9:59:59.000
So at the same time this activity[br]is bad for your business

9:59:59.000,9:59:59.000
and bad for society, so double whammy.

9:59:59.000,9:59:59.000
And gender is one of those things[br]like other demographics

9:59:59.000,9:59:59.000
that have historically been[br]good marketing shortcuts.

9:59:59.000,9:59:59.000
At some point, however,

9:59:59.000,9:59:59.000
we forgot that at the core[br]we were targeting needs

9:59:59.000,9:59:59.000
around cooking and cleaning[br]and personal care and driving and sports

9:59:59.000,9:59:59.000
and we just made it all a bucket and[br]we said, "Men and women are different."

9:59:59.000,9:59:59.000
We got used to it and[br]we never challenged it again,

9:59:59.000,9:59:59.000
and it's fascinating to me

9:59:59.000,9:59:59.000
and by fascinating I mean[br]a little bit insane

9:59:59.000,9:59:59.000
that we still talk about this as a segment

9:59:59.000,9:59:59.000
when it's most likely carryover bias.

9:59:59.000,9:59:59.000
In fact, I don't come[br]to this conclusion lightly.

9:59:59.000,9:59:59.000
We have enough data to suggest[br]that gender is not the best place

9:59:59.000,9:59:59.000
to start for you to design[br]and target your brands.

9:59:59.000,9:59:59.000
And I would even go one step further:

9:59:59.000,9:59:59.000
unless you are working in[br]a very gender-specific product category,

9:59:59.000,9:59:59.000
probably anything else

9:59:59.000,9:59:59.000
you're hypothesizing about[br]your consumer right now

9:59:59.000,9:59:59.000
is going to be more useful than gender.

9:59:59.000,9:59:59.000
We did not set up to draw[br]this conclusion specifically.

9:59:59.000,9:59:59.000
We found it.

9:59:59.000,9:59:59.000
As consultants, our job[br]is to go with our clients

9:59:59.000,9:59:59.000
and understand their business

9:59:59.000,9:59:59.000
and try to help them find spaces[br]for their brands to grow,

9:59:59.000,9:59:59.000
and it is our belief that if you want[br]to find disruptive growth in the market,

9:59:59.000,9:59:59.000
you have to go to the consumer

9:59:59.000,9:59:59.000
and take a very agnostic view[br]of the consumer.

9:59:59.000,9:59:59.000
You have to go and look[br]at them from scratch,

9:59:59.000,9:59:59.000
remove yourself from biases and segments[br]that you thought were important,

9:59:59.000,9:59:59.000
just take a look to see[br]where the growth is.

9:59:59.000,9:59:59.000
And we built ourselves[br]an algorithm precisely for that.

9:59:59.000,9:59:59.000
So imagine that we have a person[br]and we know a person

9:59:59.000,9:59:59.000
is making a choice[br]about a product or service,

9:59:59.000,9:59:59.000
and from this person, I can know[br]their gender, of course,

9:59:59.000,9:59:59.000
other demographics, where they live,[br]their income, other things.

9:59:59.000,9:59:59.000
I know the context where[br]this person is making a decision,

9:59:59.000,9:59:59.000
where they are, who they're with,

9:59:59.000,9:59:59.000
the energy, anything,

9:59:59.000,9:59:59.000
and I can also put[br]other things in the mix.

9:59:59.000,9:59:59.000
I can know their attitudes,

9:59:59.000,9:59:59.000
how they feel about the category,

9:59:59.000,9:59:59.000
their behaviors.

9:59:59.000,9:59:59.000
So if you imagine this kind of blob[br]of big data about a person,

9:59:59.000,9:59:59.000
I'm going to oversimplify the science here

9:59:59.000,9:59:59.000
but we basically built an algorithm[br]for statistical tournaments.

9:59:59.000,9:59:59.000
So a statistical tournament[br]is like asking this big thing of data,

9:59:59.000,9:59:59.000
"So, data, from everything[br]you know about consumers at this point,

9:59:59.000,9:59:59.000
what is the most[br]useful thing I need to know

9:59:59.000,9:59:59.000
that tells me more[br]about what consumers need?

9:59:59.000,9:59:59.000
So the tournament is going[br]to have winners and losers.

9:59:59.000,9:59:59.000
The winners are those variables,[br]those dimensions,

9:59:59.000,9:59:59.000
that actually teach you[br]a lot about your consumer,

9:59:59.000,9:59:59.000
that if you know that,[br]you know what they need,

9:59:59.000,9:59:59.000
and there's losing variables[br]that are just not that practical,

9:59:59.000,9:59:59.000
and this matters because[br]in a world of limited resources,

9:59:59.000,9:59:59.000
you don't want to waste it on people[br]that actually have the same needs.

9:59:59.000,9:59:59.000
So why treat them differently?

9:59:59.000,9:59:59.000
So at this point, I know,[br]suspense is not killing you,

9:59:59.000,9:59:59.000
because I told you what the output is,

9:59:59.000,9:59:59.000
but what we found over time

9:59:59.000,9:59:59.000
is, after 200 projects around the world,[br]this is covering 20 countries or more,

9:59:59.000,9:59:59.000
in essence we ran about[br]a hundred thousand of these tournaments,

9:59:59.000,9:59:59.000
and, no surprise, gender was very rarely[br]the most predictive thing

9:59:59.000,9:59:59.000
to understand consumer needs.

9:59:59.000,9:59:59.000
From a hundred thousand tournaments,

9:59:59.000,9:59:59.000
gender only came out[br]as the winning variable

9:59:59.000,9:59:59.000
in about five percent of them.

9:59:59.000,9:59:59.000
This is true around the world, by the way.

9:59:59.000,9:59:59.000
We did this in places where[br]traditional gender roles

9:59:59.000,9:59:59.000
are little more pronounced,

9:59:59.000,9:59:59.000
and the conclusions were exactly the same.

9:59:59.000,9:59:59.000
It was a little bit more important,[br]gender, than five percent,

9:59:59.000,9:59:59.000
but not material.