Episode 38: How to become more confident with numbers

You always hear generic advice to be more data-driven or “what gets measured gets moved’.

People say this because they use their opinions (vs a conclusion rooted in reality) to make their point. Having actual data makes it harder (not impossible) to make claims without anything to back it up.

While this makes a ton of sense, it was only a few years ago that I faced a ton of anxiety when I was looking at data. I was worried the way I was measuring was incorrect, my conclusions about the data were wrong, or both.

Here’s what I did:

Start with the end in mind

For me, essay writing was always really hard. I struggled with the intro and finding the right flow/transition into the main body. I tried starting with an overall outline and then going into the intro, but I would always get stuck.

Then one day I got the advice to start with the end first. I would write a conclusion of what I wanted the tldr to be and then work backward. Starting from the end helped me go from short bursts of writing for multiple days to multi-hour writing sessions.

I take the same approach with any analysis I’m starting. What is the hypothesis I’m trying to prove or disprove? I start with my own opinion of what I think the answer is and then remind myself to look at the data objectively (this is where the second pair of eyes are super helpful) to prove or disprove what I believe.

Find a thought partner

Going back to first principles - anytime I don’t know how to do something well, my first step is to find someone I know who does that thing well and start talking to them about their approach.

Finding someone you believe is data-driven - even when the data is about something else - will help you solve problem #1 (not sure if what you’re doing from a methodology standpoint is correct).

Finding someone at your organization working on projects that help move the data you’re looking at will solve problem #2 (not sure if your conclusions are correct).

This process is meant to be repetitive - talk to someone you believe has expertise to help you get the confidence to begin and then find someone to help pressure test your takeaways.

Keep it simple

My definition of mastery of a topic is when I can scale the complexity up or down depending on who I am talking to/the context they have without losing the conversation.

Analysis is no different. My roles in the past have focused on win/loss data - i.e., where do we win and lose and why?

Everyone in the organization had an opinion on our top competitors, but rarely did anyone know the actual numbers. The first thing I do for competitive data is calculate the number of wins and losses by competitor by quarter.

If this data isn’t readily available - e.g. we aren’t capturing this from sales teams then my first project is solving that. Every other priority becomes secondary because the raw count of wins and losses quarter or quarter is the baseline.

We can draw many conclusions on why it goes up or down, whether that’s due to something we can control (e.g., product, pricing, messaging) or something we cannot (seasonality of buying decisions, macro environment, etc.).

It’s extremely simple to calculate but powerful. And 9/10 organizations that I talk to don’t know this number.

Scaling the complexity up could look like calculating weighted average counts (considering the average relative to the volume by competitor) or zooming into wins and losses at particular stages where you believe the funnel is most leaky. Fundamentally, though, it’s still about the raw count.

Share widely and often

Once you have confidence in your approach, validated with at least one other person, and successfully explained your analysis, the easy part is done. 

While this has been the heaviest lift for you (and the most anxiety-ridden in my case), the next step of the process is much harder - you need to share this data widely and often. 

In my case for competitive data, I share with the following stakeholders (each time in their context) 

• Executive teams (breaking out by key takeaways and recommendations) 

• Sales reps (breaking out wins and losses by region) 

• Sales leadership (breaking out counts by their teams) 

• Product/Engineering (breaking out wins and losses by product) 

• Technical teams (breaking out wins and losses by the type of technical evaluation) 

• The whole company (sharing high-level trends by quarter) 

Each stakeholder above cares about the data, but only if it’s shared within their context. I learned their context by spending the time to review the data they have regularly (forcing myself to overcome the fear of getting it wrong) and by sharing it with their teams often. Each time I get feedback on how they’d like to see the data in slightly different context or even different data altogether. 

All of these requests get written down and prioritized, and then I repeat the whole process again. The first time, it’s hard. You may not be able to answer any follow-up questions. The second time, you can guess a few follow-ups and be more prepared, but you’ll get stumped again. It took me 5-6 times to clearly and confidently deliver a presentation on win/loss data. 

It’s not that I no longer had any follow-up questions I couldn’t answer, but I began feeling more comfortable with not being able to answer in that moment without doubting myself/my abilities. 

No one is born with this ability to memorize or analyze. But if you spend more time than anyone else at your company thinking about a problem - even if you don’t fully understand it - you’re still the expert. 

As always, feedback is a gift and I welcome any/all feedback on this episode. See ya next week 👋 !

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