This month, we’re focusing on all things Product Data Analytics. Keep an eye out for events, podcasts, blogs, and more!
We always say that the best Product Managers are data-driven. But what does that mean exactly? No digital product can be build, maintained, or disrupted without using data, so surely every PM knows how to use it the right way, right?
You would think! But many mistakes are made with data every day, and sometimes it can be a critically underused resource. This month, we’re talking all about data, and learning from Product Leaders at awesome companies like Google, Uber, and PayPal. But first, let’s talk about why data analytics actually matters for Product Managers, and helps with building better products.
What Does Data Driven Product Management Mean?
You may hear ‘data driven’ in a few different contexts when it comes to Product Management. It may be listed as a necessary characteristic for PMs on job postings, and it may also refer to a product.
A data driven product is one that is backed up by data. The decisions that are made in order to build it are backed up by research and insights, rather than on intuition and guesswork. The same goes for data driven Product Managers, who use data to influence their decision making.
This might sound a little simplistic. After all, aren’t all products and Product Managers data-driven? In an ideal world they would be! But data driven really means data-obsessed. Every opportunity to dig into the data and use it to drive innovation, development, and decision making is seized. When a data driven PM has a new idea for a feature, they don’t just go ahead and start pitching it to leadership. They dig into the data and reveal the truth about how useful it may be.
What Kinds of Data Should a Product Manager Focus On?
We could go into the nitty gritty and talk about each and every type of data point that every single Product Manager could ever need…but there’s far too many of them! Instead, let’s look at the main categories of data that’s useful for Product Managers every day.
There are many types of data points that inform product development. For data driven Product Management, we can lump them together roughly into the following categories:
1. User Data
User research is perhaps the most important type of research that any company can conduct. If you don’t understand your customers, how can you serve them and build a successful product? Short answer — you can’t.
Assumption is the death of customer understanding. With user interviews, usability testing, card sorting, and A/B testing, you can get to know how your customers think, what their behaviors are, and start hypothesizing what their future needs will be.
Using user data helps you to stay user-focused, and ensures that you’re building the right product at the right time. It ensures product-market fit and makes it easier to align product teams on a common goal.
“Why do we need to build this thing this way?” “Because the data shows that the customer likes it that way.”
Types of User Data:
- Online reviews
- NPS scores
2. Product Data
You have to know what’s happening inside your product, as much as you need to understand what’s happening inside your user’s minds. You need to know what the typical user flow looks like, how many people successfully complete onboarding, which features are used more than others, and where people drop off.
This applies whether you have an app, software, or a website. If you can track what goes on inside your product, you absolutely should, as it’ll give you a live view of whether your product is working or not.
Product data can also reveal new insights and provide surprising opportunities for innovation, and perhaps even a pivot that you never would have considered otherwise.
Types of Product Data:
- User flows
- Meta data
- Bounce rates
- Abandonment/adoption rates
3. Market Research
Market research is also absolutely essential to launching a product and setting it up for success. You could build the world’s best snowsuit, but that’s no use in a desert. It’s critical to understand the environment that your product will be expected to thrive in before you can build something that serves the needs of your customers.
One mistake that some companies make is that they conduct market research only at the start of their journey. But the competitive landscape shifts, and before you launch any new product or feature, you need to have a clear picture of what it looks like. You need to understand what your competitors are doing, how you can set yourself apart from them, and what needs are continually being unmet.
Types of Market Research:
- Competitor analysis
- Brand positioning analysis
- Consumer insights
- User segmentation
Qualitative vs Quantitative Data
Atopic debate among Product Managers are the various benefits of both qualitative and quantitative data. It’s not that one type is ‘good data’ and the other type is ‘bad data’, it’s that they both need to be applied in different ways. And you need both to build the best possible data driven product.
Quantitative data: Sometimes referred to as hard data, involves data points that are expressed in numeric values. Includes things like NPS scores, and more typical digital metrics.
Qualitative data: Sometimes referred to as soft data, involves information that can’t be boiled down easily to numerical data. Includes things like app store reviews, recorded conversations with customers, and customer service comments.
You need both types of data to build a product, and combining the two paints a more complete data story.
For more details, check out The Difference: Qualitative vs Quantitative Data
How Product Managers Use Data
Everyone involved in product development uses data analytics to get their jobs done. Marketers use it to create super clickable campaigns, designers use it to craft the best user experience, and leadership uses it to drive innovation and to make the right business decisions.
So how do Product Managers use it?
As well as using data to support various teams in all of these endeavors, PMs also have their own unique way of leveraging data. A hugely important part of a PMs role is to
How To Learn More About Product Data Analytics
To be a great Product Manager, you need to use data, but that doesn’t mean that you necessarily need to be a full-blown Data Scientist. Every Product professional has different strengths, and they may lay outside the sphere of data and logic. If you’re more of a creative thinker with a keen eye for product design and marketing, you can still leverage data to build the best possible product. By working with the designated data professionals at your company, or outsourcing your data analytics, you can be a PM without needing to be a Data PM.
While there are Data Product Manager roles out there, to get started in Product Management, you only need to know how to ask the right questions. That’s far more important than knowing SQL. That being said, to be a truly data-driven PM, you should definitely try to learn as much about data analytics as possible.
But data is a very BIG topic, and trying to learn all of it at once will have you diving through everything from Google Analytics dashboards to machine learning algorithms. So figure out what will be most helpful to you in your day to day. If you have Data PMs at your company, or Data Scientists, ask them for where to get started. Or look at how you handle data each week, and look for opportunities to learn more and dive deeper into the data sets you work with.
To get started and figure out what you need to learn, check out our YouTube channel, where you can find talks from data-loving product leaders teaching you all you need to know. Head over to our Data Analytics for Product Managers playlist. Or try this talk out for size…
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This article was also published on The Product Management Blog, where we share tips, tricks, and industry deep-dives for PMs of all levels.