Layers of Product Discovery
Every Product Manager has a tendency of what data they are most comfortable collecting and analyzing.
If the PM takes the path of least resistance, these tendencies can create blind spots in their data collection.
Check out the Layers of Product Discovery described below to balance their approach to gathering data.
In the graphic below, the importance of the data is represented by the color. The difficulty of obtaining the data is represented by the distance from the PM.
Each layer is more difficult to obtain but commensurately more important.
If a PM, their team or the organization is stuck at a certain layer or restricted from accessing specific layers then it’s time to escalate and solve this problem.
Overview
Gathering data from stakeholders and colleagues within your company is easier but is not enough to create the right product.
Don’t stop there.
In business-facing products, there is a customer at the client that acts as the business buyer of your product who is likely not the end user.
Understanding data from this business buyer is incredibly important.
Don’t stop there.
A product that serves the business buyer might be purchased once but has a danger of not being renewed because it doesn’t deliver value to the end user.
On a regular basis, Product teams need to be looking at the quantitative data that end user interactions create (visits, interactions, purchases, etc). Once setup, this data should be easily accessible to Product teams.
Don’t stop there.
The hardest to obtain and most important data to collect comes from conversations and prototype interviews with the end user.
Let’s explore these layers in more depth.
Data from Internal Facing Colleagues
Data from External Facing Interactions
Data from Business Buyers
Quantitative Data from End Users
Qualitative Data from End Users
Avoiding the Completionist Approach to Product Discovery
Choosing a Targeted Approach to Product Discovery
Data from Internal Facing Colleagues
Sometimes PMs work alone to create product specifications. In a work environment where roles and responsibility are rigidly allocated, PMs might create specifications for the product by themselves.
Other reasons may get in the way of a PM getting data from their coworkers.
Designers may be assigned to the product only after the specifications are created. Engineers may not be brought in to new idea discussions until just a few days before the sprint starts. In a remote world, coworkers may be in different timezones. There might be a strict meeting culture where working sessions that explore new ideas are frowned upon.
If you are an isolated PM, get started on getting insights from the first layer of your immediate coworkers. It is the easiest layer and can provide an order of magnitude greater insight than just working alone.
Data from External Facing Interactions
The next layer up is the external facing interactions that your company is having with customers.
It’s not as easy as chatting up your coworkers on Slack or Teams but it’s just a scheduling click away. (That said, many high performing companies have customer forums on Facebook, Discord or Slack where they can quickly interact with users.)
Every day, there are sales calls, tech support calls, and customer success calls that PMs and others can tap into.
At PowerReviews, I worked with the Head of Customer Success to pair customer success managers with my PMs, engineering directors and data analysts.
Anytime a customer success manager had a customer call, they would include their engineering or product counterpart.
The engineering or product person was expected to mostly just listen in to the call. Over time, the customer success managers got more comfortable with them asking questions and interacting more.
The point of this data is to hear concerns directly from customers. It’s human nature to discount second hand information.
It’s nearly impossible to ignore the data we hear with our own ears.
Note that you are not on the customer call to refute or debate the customer. This is not the time and place for that. Asking for more background or detail is appropriate as long as the questions are well-intentioned and respectful.
As you sit in on these external facing communications, you will also get connected to your external facing coworkers and get insights from them.
Data from Business Buyers
The next step beyond listening in to external facing calls is to understand proactively why customers buy and renew your technology.
Usually after listening into external facing calls, you have built trust within your organization to the point where they will help you set up proactive calls with business buyers.
This might involve a win/loss analysis for recent sales opportunities. It might happen with long-time customers.
In all cases, it should start with a conversation with the business buyer.
You will need to setup a specific conversation with the point of contact at the customer in order to ask the questions that help you understand how they evaluate and value your product offering.
Do not rely on surveys to extract these insights. After you have done many of these conversations, you might use a survey to gather more data points in specific areas.
Along the way, you’ve built up your confidence in interacting with customers to feel good about your ability to extract insights in a conversational setting.
Quantitative Data from End Users
The passive way to observe end users is to view their actions and behaviors with various online analytics systems.
You can also query your internal databases for important milestones that customers have achieved (purchases, logins, etc).
You can send out surveys (though this technique is overused and not useful for new ideas and innovation).
For some Product teams, there are online reviews written about their product. These reviews can be analyzed to find trends and spot emerging problems that other analytics systems haven’t detected.
Every team with access to online reviews should do at least a quarterly analysis of the reviews and rank the problems, opportunities and successes that users self-report.
But don’t stop at passive data collection.
Qualitative Data from End Users
There is no substitute for direct access to end users.
The most important and perhaps most difficult data to collect on a regular basis is qualitative data from end users.
This data explains the “why” behind the “what” you’ve been seeing in the quantitative data. It acts as a doublecheck on the feedback from the business buyer.
Stakeholders and colleagues can provide solution feedback but they can’t truly represent the real world of actual users.
Great solution testing feedback avoids wasting time and energy in the engineering, QA, deployment, customer onboarding, etc processes. Read my series on best practice solution testing.
Avoiding the Completionist Approach to Product Discovery
As I provide you with several possibilities for Product Discovery, do not try to do all of them all of the time.
And don’t work your way through this from bottom to top.
It is not meant to be a linear journey.
These Product Discovery options are a reminder of the diverse ways to understand your product and its impact.
Find the ones that provide the best value to you and your team.
Choosing a Targeted Approach to Product Discovery
I looked at this graphic and quickly circled a few areas teams should focus on getting Product Discovery going.
I didn’t explicitly circle the Internal Facing layer since I’m hoping this doesn’t need to be called out for most teams.
Most teams need to start somewhere, anywhere. Then over time, build up a discipline to continuously do most of the circled items.
You can read related articles in my series on prototype interviewing and my series on analytics.
Summary
It’s the Product team’s job to understand the impact of their decisions at all layers in the stack.
Having diverse sources of data brings confidence to a Product team’s decision making process.
Make sure to conduct Product Discovery in all these layers to create an offering that delivers customer value and works for the business.
Jim coaches Product Management organizations in startups, growth stage companies and Fortune 100s.
He's a Silicon Valley founder with over two decades of experience including an IPO ($450 million) and a buyout ($168 million). These days, he coaches Product leaders and teams to find product-market fit and accelerate growth across a variety of industries and business models.
Jim graduated from Stanford University with a BS in Computer Science and currently lectures at University of California, Berkeley in Product Management.
