Implementing the Feature / Product Fit Playbook
Product managers need to create aligned, unified value for customers and for the company. Ultimately, your customers expect a single unified product, not a loose assortment of unrelated features that happen to sit within the same application.
Therefore, product managers need to layer on the concept of “feature / product fit” to evaluate whether their proposed initiatives are the right next steps to take.
After all, product managers are measured by the return on investment that they generate for the company. If you ship a feature that doesn’t have feature / product fit, you will cause company metrics to drop, and therefore will be creating negative ROI.
Below, we provide a playbook for how product managers can incorporate feature / product fit into their prioritization frameworks, as well as how to find and measure their progress towards feature / product fit.
How product managers should prioritize with feature / product fit in mind
We need to layer in “company business value” into our evaluation processes for “which features to ship next.”
The three core business levers for a product are acquisition (gaining new customers), retention (keeping customers with us), and monetization (capturing revenue from customers).
When features don’t fit the product’s value proposition, they’ll see a combined decrease across these three value levers. When they do fit, they’ll see a combined increase across the value levers.
Of course, occasionally a feature might cause one of the levers to decrease in isolation - but, when viewed more holistically, it actually benefits the business.
As an example, when moving from a “free ad-supported product” to a “premium subscription product” model, you’ll see a loss in retention but you’ll see an increase in monetization for the business.
These tradeoffs are okay! As long as the overall impact is beneficial across the three levers, your feature is doing the right thing.
After all, it would be pretty unreasonable to ask that we don’t lose a single free user when we move from being a free product into becoming a paid product.
Therefore, in whichever prioritization framework we use as product managers - whether we use RICE or something else - we shouldn’t just limit ourselves to “how will my feature move my team’s north star metric.”
Instead, we also need to ask the question “how will this feature move the company’s north star metric?”
Then, once we’ve shipped our functionality, we can’t only look at our own team’s metrics. Instead, we need to look at the metrics of the company as a whole to determine whether we’ve accidentally caused harm to other features within the broader product portfolio.
From a qualitative perspective, it’s also incredibly helpful to sit in with CSMs (customer success managers) during QBRs (quarterly business reviews) to understand whether customers are complaining about features that aren’t working well together.
The point is, product managers need to incorporate a view on the company’s overarching business value into the work that we plan to ship and the work that we’ve already shipped.
By doing so, we will naturally start to prioritize better for shipping business impact, and that helps us align our features’ value propositions with our products’ value propositions.
Unlocking synergistic product value through feature / product fit
PMs need to build synergy to create a snowball effect (also known as the flywheel effect). That is, each feature that your company ships should create exponential value, rather than linear value.
Let’s take Amazon Prime as an example. Amazon Prime originally drove free shipping for the Amazon e-commerce platform. But, Amazon Prime now also yields lots of other benefits across Amazon’s other products:
Apparel: Try before you buy
Grocery: free delivery
Amazon Photos: unlimited free photo storage
Amazon Music: free access
Audible: free audiobooks
Prime Video: free movie and show streaming
Prime Gaming: free online games and in-game bonuses
Twitch: free subscriptions to online content creators
Prescriptions: free delivery
As soon as anyone signs up for Amazon Prime for a core benefit, they are now incentivized to look at Amazon’s other offerings, which is a powerful ability to cross-sell.
By doing so, Amazon creates increased stickiness and retention, decreases customer acquisition costs, and creates stronger brand presence.
In essence, this is the power of multiplicative product value. Amazon Prime unlocks value across all of Amazon’s products, and all of Amazon’s products drive users to take advantage of Amazon Prime.
If your features are just creating “linear value” (i.e. they stand alone and do not drive positive impact for other features), you’re not creating value across the product portfolio.
In essence, you’re not building true products. Your “engine of value” is simply to ship more and more functionality, in the hope that each feature will help you capture more revenue on its own.
This mindset is more reflective of project management or delivery management rather than truly strategic product management.
So, we need to keep in mind that our features need to fit the broader product portfolio to drive synergistic, multiplicative value across the entire feature set of the company.
As we prioritize our work, we should not simply consider the “direct linear value” of our functionality. We also need to assess how our functionality will feed back into the product portfolio to unlock multiplicative value.
As an example, say that we had the following two options:
Ship a feature that increases our feature set’s retention by 10% but doesn’t change product-wide retention at all
Ship a feature that doesn’t increase our feature set’s retention at all, but increases product-wide retention by 2%
Most feature teams will tell you that you should take option 1.
Most product teams will tell you that you should take option 2.
Remember, product management is all about maximizing ROI for our company, not for our feature set!
So, when assessing the expected benefit that your feature will unlock, you should not be assessing it at the level of your feature set - rather, you should be assessing it at the company level.
Now that we have a better grasp of how to prioritize with feature / product fit in mind, how exactly do we find feature / product fit?
How to find feature / product fit
First, a quick caveat: in discussing feature / product fit, we’ve taken product / market fit for granted.
If your product doesn’t have product / market fit in the first place, then getting to feature / product fit doesn’t matter! You need to ensure that your product is already a strong fit with the market that it’s targeting.
If you need a crash course on how to find product / market fit, here are some solid resources:
Product / market fit (Marc Andreessen)
The real product / market fit (Y Combinator)
Understanding product / market fit (UXPlanet)
Assuming that we already have product / market fit, let’s talk about locking in feature / product fit. Qualitatively, the way for us to find feature / product fit is to conduct customer interviews and to review the incoming feedback that we’re getting from users.
We should look for the following insights:
What is missing in the product offering right now, and isn’t being solved by integrations or partnerships?
What is causing users to move to competitors?
Where is the product full of unsolved friction?
Each of these insights helps us determine what kinds of unresolved challenges our users have. In turn, if we dig into these problem areas, our features will naturally fit into the product’s value proposition.
And, in terms of organizational best practices, make sure that your feature KPIs actually ladder up into the company north star metric.
As an example, let’s pretend that you’re the product manager for video captions at YouTube. For this thought exercise, let’s assume that you’ve set a KPI for “number of video captions that users consume.”
Well, YouTube’s north star metric is “total time spent watching videos.”
Therefore, you absolutely don’t want to ship a product where you send “text transcripts of recommended videos into your email inbox.”
Why not?
After all, if you shipped text transcripts into people’s inboxes and people read them, wouldn’t you be boosting the number of video captions that users consume?
Here’s the problem: while you might have really awesome email engagement, you’re pulling users off of the YouTube platform.
Your new feature is pushing users to engage with their email instead of engaging on the YouTube platform. When users can read the entire caption in their email, they have no incentive to go to YouTube.
That then causes users to not visit YouTube, which then drops the company north star metric of “total time spent watching videos.”
So, remember, as you seek feature / product fit, make sure that your KPIs logically ladder into the company’s overall north star metric.
Don’t limit yourself just to your team’s north star metric, which is too narrow of a view for “what defines success for the company and its customers.”
How to measure feature / product fit
Of course, feature metrics are typically leading indicators, and company metrics are typically lagging indicators.
Most of the time, it’s quite hard to actually see the long-term attribution of your features and how they impact company metrics.
So, how exactly do we measure feature / product fit if we can’t see it immediately in the metrics?
Here’s how. We can repurpose the “product / market” fit question proposed by Rahul Vohra to determine how valuable our features are within the broader product portfolio.
Ask users “how would you feel if you could no longer use this feature within the product?”, and provide these three options:
Very disappointed
Somewhat disappointed
Not disappointed
Then, measure the percentage of users who answer “very disappointed”, and track this metric over time. Your goal is to maximize the number of users who would be “very disappointed” if your feature disappeared one day.
Here, the qualitative feedback will react much faster than the quantitative metrics will.
Even better, you can then add on more questions to further refine your understanding of feature / product fit for your customers and users, such as:
Why would you feel the way that you responded above (very disappointed, somewhat disappointed, or not disappointed)?
What type of people do you think would most benefit from this feature?
What is the main benefit you receive from this feature?
How can we improve this feature for you?
Using this knowledge, we can swiftly double down on our investments for features that users clearly find valuable.
And, we can also safely sunset the features that users find lacking in value.
After all, the more we can eliminate non-useful features, the less engineering maintenance we need to pay, and the easier it is for users to get to the features that really matter to them.
If you’re looking for additional help with finding or measuring feature / product fit, consider reserving a time slot with our experienced PM coaches.
Closing thoughts
Product managers must pursue feature / product fit for any net-new functionality that they’d like to ship.
Without feature / product fit, even if they crush their targeted success metrics, they’ll wind up destroying value for the customer and for their business overall.
PM managers must also seriously consider feature / product fit as part of their core strategy.
Directors of Product need to ensure that they haven’t given the “go signal” to a simple laundry list of functionality. Rather, as product leaders, we need to give our PMs the space to kill features that don’t fit the product portfolio’s overall value proposition.
To wrap up this second part of our guide on feature / product fit, we now have a clear framework for assessing feature / product fit.
As a reminder, in part 1, we defined what feature / product fit is, as well as what happens when product teams devolve into simple feature teams.
In the final part of this guide, we’ll share real examples of good feature / product fit and bad feature / product fit, so that we can bring this framework to life.
Thank you to Pauli Bielewicz, Siamak Khorrami, Goutham Budati, Markus Seebauer, Juliet Chuang, and Kendra Ritterhern for making this guide possible.
