You don’t need more features. You need more data. Data-driven analysis not only save time but also make the product owners see exactly how the users are doing with the product, for words ( interviews and brainstorms ) sometimes lie but actions ( data ) don’t. No more scratching-head time wasted in the guesswork development. The following passage introduces 2 stages to collect data for further and smarter decisions, with the aid of some recommended data tools.
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It seems like magic, some sort of product manager voodoo that can only be tapped if your last name is Zuckerberg, Spiegel, Hansson, or Dorsey. The rest of us are doomed to an endless and expensive loop of trial and failure.
The painful truth is that there is no magic involved. Instead, there is data … a lot of data.
What we fail to realize is that the best product owners are extremely dedicated to making data-driven decisions. They don’t get caught up in an idea, or make impulsive decisions based on emotions. They let the behaviors of their customers drive the development of their products.
Words Lie — Actions Don’t
If you’ve ever read a Business 2.0 book — aka, a startup how-to book — you’ve been exposed to one of the biggest business lies of our time: If you build what your customers ask for, you will get massively rich.
It’s a great starting point for a product, but that is all it is. Once you build your product and get users, continuing to build features that customers ask for is the fastest way to waste money. This is because ninety percent of the time, customers don’t actually know what they want. Their words lie, but their actions don’t.
A customer that sticks with your product for any significant amount of time has a very specific reason. You will also have a cohort of individuals who end up churning after becoming a customer. These reasons come to the surface through behavioral analytics.
Your best customers, the ones who have built a habit with your product, will share similar behaviors. The same is true for the users who end up churning early in their lifecycle.
If you ask your best customers what they want, you might end up building out a new feature that is only used by a handful of them. But if you look at their behavior usage, you are much more likely to build a feature that will enhance why they formed a habit with your product in the first place.
Focus on Product Enhancement
There’s a difference between feature building and feature enhancement. This is where we see the true power of more is less. When you look at how the best products are developed, it’s rare to see a completely new feature. Instead, you see enhancements to existing features.
This looks like making a specific feature easier to access or adding a new integration. It might even look like trashing a few features that pull users away from the core functionality. The only effective way to accomplish this is through the use of in-product usage data.
Building for Data Collection
I would estimate that eighty of the companies that I cross paths with don’t have clear insights into their data or bother to gather it at all. When I dig into the reasons for not collecting data, the conversation generally shifts to why they didn’t think it was important from the beginning.
Let’s be honest. Constructing databases to collect specific user data is not a sexy task. On top of that, many product owners don’t truly know what they are building until they are well into the development process, so how could they know what data to collect?
Where to Start
Users might not know what features they really want, but they know exactly what pain they are trying to solve.
You created your product to solve a very specific pain. Suggestion: Build your strategy for collecting behavioral data around solving this pain. If we examine a typical interaction with a SaaS or PaaS, there are two macro stages in the user’s lifecycle.
The first stage is onboarding — the user finds your product and signs up. This is the single most important stage for accurate behavioral data because this is the most vulnerable stage in the user’s lifecycle. They will either form a habit with your product or they will leave and never return.
As a product owner, you should be looking for the user to interact with all of your main features and a few of your secondary features. For example, if your app is Gmail, you would want your users to send an email, read an email, and explore the settings panel where they can set a custom signature.
This data is easier to collect because it’s directly associated with an account.
The data that is not as easy to collect and is often forgotten is how many people never create a Gmail account because of even earlier product interactions like finding an available email address. This is the data that you need to focus on to enhance the usage of your product and put your effort into building features that your users actually need.
The second stage is continued usage, or customer success. Let’s stick with the Gmail example on this one. Anyone who has ever had an email account knows how easily it becomes a disordered mess. If users are supposed to successfully send and receive emails, they need to be able to navigate their inboxes. To make this possible, Google has created a handful of customer success features, like the SPAM filter or the Promotions tab.
These features don’t just appear out of thin air. They came from watching how users interact with their inboxes . They watched with data.
They saw users get bogged down with interaction alerts from Facebook, deleting most of them without ever opening them and created a social tab. They saw how users would spend large amounts of time searching their inbox for a specific email and created an in-inbox file system. They saw people scanning their inbox and created colorful labels to make identification easy.
Tools to Consider
Every product will need to collect different data, but here is a handful of tools that I trust for collecting and interpreting user data. I would suggest getting to know a few of these tools and investing a few hours into learning will save you thousands of dollars in guesswork development:
Looker — Looker is by far the most advanced SQL data analytics platform on the market. It not only connects relational databases; it also creates amazing visualizations of your data, letting you dig in and focus on what matters.
Marketing Automation — Marketo, Hubspot & Infusionsoft, have powerful tools for pulling specific lists of users based on usage data. This is because it connects with your CRM, like Salesforce, allowing you to see trends among your most successful, and more importantly, lucrative users.
Mixpanel — Mixpanel is the equivalent of Google Analytics embedded into your product. It allows you to gather incredible amounts of in-product data, query that data, and make informed decisions.
Google Data Studio — Google has upped its game in the data world recently. Google’s Data Studio is a powerful tool for those that can’t afford to use Google 360.
Like everything, data is what you make of it. It has the ability to empower you to build a product in an extremely efficient manner or the ability to keep you spinning your tires for days, interpreting millions of inputs that have no business value at all.
Successful product owners learn how to cut through the clutter, the vanity metrics, and the noise to identify exactly what they are looking for. They look with intentionality, and they look with the expectation to learn outside of a bias. By using tools to do the heavy lifting, these owners make the most out of every dollar they spend improving their software experience.
By Jesse Williams