All posts by Moe Kiss

Why I almost didn’t join the Digital Analytics Power Hour and what I’ve learned since saying yes

Believe it or not, I almost turned down being a co-host on the Digital Analytics Power Hour podcast (DAPH) when Michael and Tim asked me. In fact, not only did I nearly say no, I considered it for several weeks and the whole time I planned to say no.

The truth is, I was scared. I was scared that people would think I was too inexperienced to possibly join this all star cast. I was scared that the topics and guests would be out of my depth. I was scared that I wouldn’t be funny or have good banter. Most of all, I was scared people would say cruel things, that I wasn’t tough enough to endure. I was worried that future employers would hear me utter an opinion in a learning phase, that years later had evolved and I might lose out on a future role.

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funnel to hire more women

Recently, I was discussing gender diversity with a friend in the analytics industry. His organisation certainly doesn’t have anywhere near gender equality, but he vehemently argues that they are “doing everything they can” to hire women, and it’s a “funnel problem”, with only 3% of the candidates who apply being female.

With all due respect to my friend (and we had a long discussion about this!) this is a complete cop-out. This argument suggests that organisations are only responsible for gender diversity as long as candidates are magically delivered to their doorstep. That’s not a commitment to diversity – that’s only agreeing to not discriminate when hiring candidates. Actual commitment to diversity includes expending effort in an attempt to find, hire, train and retain a great, diverse workforce. It involves understanding the various societal reasons why women may only constitute 3% of your funnel, and actively trying to seek out candidates, not to mention changing the funnel for the future. 

There are things that we can do, at every single step of the funnel to increase conversion rate (aka hire more women). Here’s a few practical examples that came to my mind.


10 tried & tested tips for thriving women (and men who support them) in our analytics community

Some of you might have seen a post by Susan Fowler, a recent engineer at Uber, and her experiences there. I have worked in several male dominated industries, and I share some experiences with Susan. However, I want to focus on the positives, as well as a few things that have worked for me. Here are my ten tips for supporting gender diversity in our analytics community.

  1. Use your connections

Use your connections, and never apologise for it. My sister works in analytics as I do, and she is an industry leader. I am exceptionally lucky to have such a great role model. The point is, your connections might get you an interview, which is what happened to me. But, you will be the reason you get the job. Men are great at using their contacts – so do the same. It’s ok to ask for help via an introduction, a technical question or just career advice. The funny thing is, people love helping others, so just ask!

  1. Find or be a good mentor

Mentors are a huge part of my success. Firstly, because when you are learning the ropes, it allows you to test ideas on someone, before pitching them to your boss. It will also provide a place to vent when you are facing a tough problem. Some mentors even become your biggest advocates, championing you for jobs and promoting you to superiors at your company. I personally believe you can never have too many mentors, as different people will play different roles in your journey.

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Analysis of Competing Hypotheses (for Digital Analytics)

People often ask me how I went from working for the government to fashion. Yes, a pretty crazy story. The truth is simple – transferrable skills. And to be honest I don’t work in fashion – I work in analytics for a fashion company.

What I want to share with the digital analytics community is the most valuable analytical technique I learned while in government. It’s called ACH – Analysis of Competing Hypotheses.

Why do I love it? Often we set out to prove something and then are persuaded by certain key pieces of evidence until we confirm what we believed in the first place (aka confirmation bias).

ACH is the opposite – you set out to disprove something. The strength of this approach is that it is difficult to allow your natural biases to influence the outcome of your analysis. Particularly, in cases where you have missing information.

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Advice from a recent digital analyst. What I wish someone had told me (or just that I’d listened better).

Gone are the days of forging a career in digital analytics through trial and error, falling into it, or learning on the fly as we hear many of our industry leaders did. There is an abundance of resources and tools now. So much so it can be overwhelming. Here is what I wish I’d known when I moved into digital analytics.

  1. Have a good answer about why you want to move into digital analytics – yes, this means you will need to give some thought about why digital analytics is for you. Do you love data, are you interested in understanding how people tick, do you have a passion for data visualisation or learning R? If you can answer that well, and you are a quick learner; you will find people are receptive. It is an in demand profession – in that we don’t have enough people for the demand – so people are willing to train you if you have the right attitude.
  2. Join #measure slack. But…. remember to google everything before you ask a question – don’t ask people for help if you haven’t made any effort to find a solution first.
  3. Read a book – old school and controversial. If you have no experience in marketing, particularly digital marketing, start there. It will give you the basics of what is the difference between SEO and SEM (yes I didn’t know what that was when I started…) or what a session is in Google Analytics. A few personal recommendations are Web Analytics 2.0 by Avinash Kaushik or Benjamin Mangold’s recent book Learning Google AdWords and Google Analytics.
  4. The opposite of old school – read a blog. Often books can become outdated but blogs are full of new tips/tricks. My favourites: Simo Ahava for GTM, Adam Greco for Adobe, Lea Pica for data visualisation, Avinash Kaushik for simple explanations of digital analytics and Tim Wilson for all things KPI and measurement planning.
  5. Use free training. Watch everything in the GA Training Academy – get certified in GTM and GA in particular. If you know you’ll be using Tableau – watch their training videos and have a play with their free two week trial. If you need R – a coursera course is for you.
  6. Set up a website. Implement GTM and GA. People will struggle to believe you want to work in analytics if you are not interested enough to do this (I wish I had done this earlier…).
  7. Go to Web Analytics Wednesday (WAW) or any other meetup/event you can find and start making connections. This industry is all about shared learning so it is good to have some people you trust that you can ask questions of.

How we get more women in Digital Analytics

Recently, all in one week, I listened to two podcasts (Digital Analytics Power Hour and Present Beyond Measure), attended a Google female founders meetup and a Women in Focus event all focusing on women in tech and women in digital analytics.

Unsurprisingly, I have been unable to stop thinking about women in tech and what that means for the digital analytics community.

I am not a woman in tech. My professional background is working within a highly technical Government Defence organisation, where, yes, I was a woman. But I’m not technical, right, so I never think of myself as a woman in tech. I might have advised people on highly technical operations – but I can’t do them. So it doesn’t count. Last year I joined a digital analytics company where I am learning to run analysis through R. True, my most visited website each day is stack overflow (hey – I’m learning) but I’m not a woman in tech because I’m not technical.

And then I came upon those two podcasts. And then an event. All in one week. It shifted my perspective on how I think about myself.

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