How I went from struggling with math to becoming a data and analytics advocate - Mixpanel
How I went from struggling with math to becoming a data and analytics advocate
Inside Mixpanel

How I went from struggling with math to becoming a data and analytics advocate

Last edited: Feb 17, 2023 Published: Jul 29, 2021
Jaz Broughton Community Manager @ Mixpanel

I’ve struggled with math since high school.

It wasn’t that I was incapable of doing simple algebra here and there. My hangup was the topic in general. I always had a hard time applying any real focus on the abstract shapes and concepts with no tangible connection to what I actually cared about.

I’m sure this was my dad’s philosophy rubbing off on me. He’s been a carpenter my whole life (and a DJ, but that’s a story for another time). As a child, I would hear him saying things like, “You’re not going to have me in a class just learning numbers for the sake of it.”

Well, today I lead the Mixpanel Community, a place full of stat-powered technical people discussing and learning about data tools. I’ve loved this job and the last several years of my career working in numbers—something that 16-year-old me would be surprised to hear.

How did I go from someone who ran from anything math-related to a professional advocate for data-led business practices?

Below is my story about finding a connection to math and the advice I give to people every day who want to overcome their own quant struggles to unlock the power of data and analytics in their work.

Putting faces to the numbers

There was no single lightbulb moment for when I “got” math—just a series of career moments that showed me, over time, that I’m not bad at it. As time went on, the more and more I saw how numbers connected to business and to processes, things became clearer.

I started out as a property manager and administrator in the hospitality industry. As part of the role, I was involved in a six-week project to adjust room rates based on a large dataset (all in spreadsheets). I didn’t set up the math behind it, but I saw the result: the projected revenue from certain rates at certain times. Very cool and very useful! (I’d eventually learn this is the same kind of cause and effect product teams analyze when developing a product.)

Over time, I learned the tools myself—how to pull the right data, how to plug it in, and how to read the results. It was only when I happened into a revenue management role that the next big revelation came: Working with math and data in the real world meant it wasn’t just empty numbers; it was people and price points. 

I could get my head around the numbers in the right context. That not only made math more manageable, but it also made it more interesting.  

Even with that realization, my first foray into tech wasn’t in a data or revenue role; it was in Customer Success (a role I’m still very much passionate about). Then, after a few more years, I would combine the two: the people-focus of Customer Success and the hard numbers of revenue and product analytics.

I could get my head around the numbers in the right context. That not only made math more manageable, but it also made it more interesting.  

My new career in data and analytics

My Customer Success Manager role wasn’t numbers-focused on its face, but I quickly found out how helpful data and analytics could be in the job.

I could sit down, pull up MongoDB, pull in some stats, and run some chart drops. Suddenly I had a better picture of what works and what doesn’t with the customers we were working with. The technology itself is what made me feel more confident with numbers.

Most of the time, success teams rely on phone calls with clients and generous users reaching out to report what’s working and what’s not. But data tools allowed me to see, in black and white, exactly what was happening—and then make some educated guesses around why it was happening. Even if it was just a simple /TRUE/FALSE boolean column in a .csv file, the data gave me what I needed.

Then things took another (favorable) turn.

My first experience with Mixpanel came when I was working with another company. We were doing a rollout with a customer that had 900 employees and I wanted to see how the team had been using the training materials. I went to a product manager and, instead of pulling up a spreadsheet, he pulled up Mixpanel. A couple of clicks later, I had all the information that previously would’ve meant spending a full day in spreadsheets.

I was hooked. 

My badge of honor, after all, isn’t in me doing the calculations myself. It’s all about being able to pull together trends, identify themes, and present options all based on data. Now I knew there was a tool that would help me do all that. 

How to build a data-focused mindset 

Sticking with a data-first approach is something I carried with me into my Customer Success Manager role with Mixpanel, and it’s something I continue to carry in my role as Community Manager. 

What’s most important are the questions you’re asking, not necessarily the (more scary) numbers.

I want to know the numbers: How many people should I invite to this? How many people show up? How long did people stay? Where did they drop off?

If you’re looking for that in your own role, you don’t have to start from scratch. Ask someone (a product manager, a more experienced rep, or even someone from the data science team) to show you how. Then iterate and learn from there. My first step into data and analytics wasn’t onboarding with some fancy tool. I just asked someone to show me how to pull data into a .csv and run some simple analyses.

The same can be true for your foray into product analytics. What’s most important are the questions you’re asking, not necessarily the (more scary) numbers. In nearly every conversation I have with the Mixpanel community, I share my six steps for wrapping your head around analytics:

  1. Role & Purpose: What is the point of your role?
  2. Goal: What does success look like? What are you driving?
  3. Question: What do you need to know? What are you curious about? What remains a mystery?
  4. Report: Which report is best for this? Do you have an understanding of the different reports?
  5. Query: Translate that question into the query. Think of reporting period, visualization, level of detail, or level of summary.
  6. Answer: Get the insight and use it to inform how you approach your goals and fulfill your purpose!

The best product analytics tools make these steps easy, especially when you can copy from your colleagues who have gone before you. 

Creating Community—with the power of data

Here’s the TL;DR version: In the last few years, I’ve managed to turn math and data into work that I genuinely enjoy. 

It’s not that I’ve gotten better at math—it’s that I’ve gotten better at wielding what I know to help people. I can see what people are doing and I can see what people need, even before they raise their hand. With data, a simple post is more than that—it means someone is curious about the maturity of their product and team, whether they’re a solo product manager, an enthusiastic grad or a time-poor startup founder. 

With this approach, I’m helping to make the Mixpanel Community a place for genuine insight, inspiration, and connection for data-curious people. Please come say hi!

For me, looking for the numbers in things has opened up so many new and exciting opportunities, and I hope that my story proves anyone has the capabilities to do the same—the math-averse included. Even dad has finally learned the usefulness of crunching numbers for a good cashflow outlook.

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