What we cover in Episode 18
All of the guests on this podcast to date have been men!
Why is that? Is it a reflection of Scott and I's networks? A representation of the industry, or something else.
We caught up with Sophia Eng, an expert in growth and entrepreneurship to talk about women in growth, including her article:
We talk about the biological differences between women and men, and how you should account for these when designing web experiences.
We also cover the randomness of growth experiments, how it's hard to predict a winner or loser, and how you can use that to develop a culture of growth within your company.
Women in Growth
Instead of reading a summary of Sophias points on women and minorities in growth, I would highly recommend listening from minute 15 to 25 of the podcast, and reading the article we cite above.
Some of the data points Sophia shared during the episode:
- Women only make up 25% of the workforce in computer and math roles
- A study from 2015 showed the average women programmer makes nearly 30% less than a male programmer
Sophia's article on ThinkGrowth is an excellent example of putting yourself out there for something you believe in, which at times can be scary.
Yeah, it was really scary. I actually wrote this whole thing, and by Sunday night I just wanted to delete the whole thing. I wanted to shut down my computer and not even hit publish, but I hit publish because I had to do it for my girls, and I don't know why that was the first thing that came to mind. They need to know what their mom stood for. When I go, right, I want to think about the legacy I left behind, and those were the words that came from their mom.
Women and men differ in their purchasing habits
There are intrinsic differences in how men and women shop for good and services.
"There are more connections between the left and the right lobes of our brains, so the corpus callosum for women, which basically means that we're able to handle a lot of information, and we want a lot of information and experience is important to us, and we're very emotion based, not to say that men aren't, but we make decisions based on emotions.
For men, there are more connections between the front and the back lobes of the brain. So they're more wired for efficiency and are more singular focused."
You can use these differences to craft better web experiences for each gender. For example, in Google Analytics you can segment the audience to your best converting landing pages by male and female.
As an example, Sophia uses these insights to better optimize e-commerce websites:
I found that most women want better experiences, and the best websites that get this right for women earn their loyalty as women are hyper buyers. So once you get their loyalty, they love the experience. Women are 22 percent more likely than men to come back and buy their product or service regardless of the price, quality, or convenience.
The randomness of experiments can help to create a culture of growth in your company.
How many times has the result of a growth experiment surprised you?
I've been humbled by a lot of tests. I think that's part of being in what we're doing, and being so good at what we do is just you have to be okay with being proven wrong.
It rarely matters if your growth experiment succeeds or fails, what matters is you learned something. Remember, tests are another form of research. They help you gather data and form a definite opinion on something. They can help you and others in the company make the right strategic decisions.
So, how can you use experiments to get more people from your company involved in growth?
One way is to encourage people to guess the result of your different experiments. That's precisely how Sophia and her team evangelized growth at InVision.
We had some coins or fake money to put into so that people could vote, and that was how we got everybody outside of just the marketing department and optimization who are looking at this data and really trying to understand what everybody else was thinking.
It means people are not only interested in the experiments themselves, but invested in the result so will end up understanding what the learning was from each test.
The podcast provides a more in-depth look at these topics, so if you enjoyed reading the above, please do give it a listen.
And until next time,
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