6 Essential Skills To Look Like a Product Analyst Rockstar

My boss asked me to write a post on what I believe makes a good analyst.

I told him that it’s hard to explain a process to what I do. It’s not something that is methodical — I just get curious, stay patient, and keep digging.

Simple, right? Well, not quite.

Flesh-out Project Scope

It all starts with the project that you’ve been assigned.

The first step is to break down the needs for each stakeholder, in an effort to understand who is looking at the data and why.

Ask these questions:

• What are they trying to accomplish?
• What problem are they trying to solve?
• What would provide them direction?
• If you asked for this report, what would you want to see?

Identify some hypotheses. That will help guide your search, especially when the problem is so broad.

After you’ve collected a few hypotheses, dedicate yourself to collecting data that either supports or disproves that hypothesis.

I was tasked with understanding what part of our business to focus on. In order to understand that, we must first understand why our customers use our product.

Plan Cross-Departmentally

Working with other departments was key to getting this done quickly.

We needed to hear from our customers, and the most effective way to reach our customers is through surveys paired with incentives. I began reaching out to other departments to see if there was customer information that would be helpful to know.

After collaborating with other departments, I drafted up a survey to understand why they use our product, and which features they use. Using Formstack, I made use of logic, multi-select options, and radio buttons on my form, which allowed me to group the data instead of having free-form responses.

That helped me to first get a sense of the lay of the land — specifically, identifying how many users selected which use case at a glance.

Be Curious

You’ve already outlined which data points are most important, and what data you need for your hypotheses.

Now, it’s time to get curious and live in Excel.

This is the point in the project when curiosity will help you find the correlations and the interesting data points.

Looking at one column at a time, I use filtering, color-coding, and tagging systems to organize my data. I usually start with the data that is the most meaningful to the project and start analyzing one data point at a time.

Before long, your curiosity will get you prioritizing different data points. When I retrieved the use case data, I began looking at the count response for each use case, and started looking at the highest use case response. I would start color-coding responses that were interesting to see if they fit the use case persona, or if they were just an outlier.

Dig In

This is where patience comes into play with curiosity.

It’s hard to be patient, especially if the data isn’t speaking to you just yet. Filters are your best friend. I would filter by use case and plan type, because I wanted to see if there was a correlation between how a customer is using our product versus how much they are paying us.

I recommend spending some quality time with the data and reading each survey response. Take notes while you read each response, review different variables and create a column for any feedback relating to them.

Also, be sure to take a break from the data. It’s hard to see when you’ve been looking at Excel for hours.

Get out and talk about the data. Talk to the stakeholders about your initial findings — they will always have feedback or additional questions that can help further guide your analysis.

Set Realistic Expectations

Finding the answers versus trying to get the project done is a very real problem for all analysts.

It’s hard for us to push back on leadership when this is the case. If there isn’t enough data to prove or disprove a hypothesis, you may need a second round of data collection. As an analyst, finding the answer is more important than making your deadline.

That being said, I’ve always made my deadline, and here’s how: Open communication on any bottlenecks between the data collection to the analysis . I’ve also found it helpful to provide a data synopsis slide and state what data was collected vs. what data was missing.

For each hypothesis I would label them Likely, Unlikely, or Inconclusive, and provide a short description or data that supports my theory.

At the end of the presentation, I always provide an “Action Items” slide on next steps such as additional data collection and analysis to be done. This will help the leadership team understand that there could be more holes to jump down.

Deliver Fearlessly

Being an analyst is challenging — you have to be confident in both your numbers and communication, because only more questions will follow.

You have to take into account every piece of data, but also prioritize it based on relevance and accuracy. You need to accept that the information that you provide may not be what the team wants to hear, but that is what makes analysts so valuable.

With this project, the main findings were that our users primarily use Formstack for internal purposes, and the main feature they valued had been sunsetted 6 months prior to the analysis.

After the presentation of my findings, Product was instructed to revive the feature and dedicate resources to improving functionality. If I hadn’t done the analysis, the feature would have stayed sunsetted, and we would have been missing out on a big opportunity to keep our customers happy.

My advice for analysts is to be curious, be confident, and be fearless — you are helping to solve a problem and provide value, whether it be good or bad news.