Getting the most out of product discovery
As a product team the ultimate goal isn't the features you ship or how often it's clarity about what to do and why it matters. Almost any team's culture can ship things; the truth is, not all of them are valuable.
Shipping things that matter is another story. Valuable things are harder work. Achieving work that adds value requires clarity. Product leadership also depends on clarity.
As a product leader, one of the most important things you can do to achieve clarity is regularly bring clear and action-oriented insights back to your stakeholders and collaborators. I hear from product leaders, designers, and researchers in the field about how there's lots of research happening but it's not always being acted on.
Usually this isn't because people in your company are lazy, or uninterested but because they face friction to take action. To get others to take action on the insights you bring to light, you need to ensure they're set up for action and ease of access.
Idea in brief
- Product discovery is meant to clarify future decisions; Discovery that doesn't inform actions is never going to be valued
- Getting people to take action on Discovery is about making sure that results are clear and action oriented
- You can shape your insights to make them easier to take action on
- As a product leader, make sure you orient teams towards action
Discovery's value is in informing decisions
Developing modern products is complex. Making informed decisions is one of the places it's easy to get it wrong. Pace, chaos and conflicting signals can cause teams to compete for attention or feel forced to split time between things.
As a product leader, the best thing you can do to escape "The Build Trap" is demonstrate a clear ability to make informed decisions. Signal to your team and your organisation that clear bets and supporting evidence are valued over quick quick tricks and cutting corners.
Clear results make decisions easier to understand
Clarity takes a long time to establish, but results in a culture that learns to value evidence in decision making. Product discovery is a shared practice across product, design and engineering that helps teams uncover complexity and inform decisions. Lots of authors write about this, but one of the most easy-to-understand and learn from voices on the subject is Teresa Torres. Teresa's Framework, the Opportunity Solution Tree is just one model for informing decisions. However, the lesson it teaches is invaluable, show your work.
Product leadership and Discovery
As a product manager, it's not your job to define the product, and then hand-off “the requirements” to a designer and engineers. That's a basic anti-pattern. It's been written about extensively by Marty Cagan and others. I own't belabour that point here.
What I will say is that the work of product people is "designing the conditions for good decision making," and I've yet to find a better alternative to talk about the work I do.
How to shape product discovery insights for action
Insights that are set up for action have a few key things in common. I've pulled the characteristics that make insights stand out to executives in the rest of this post.
How is your discovery work supporting the key initiatives and goals at your company? If it's not, work on getting some alignment on how Discovery activities could support the next big bet, or inform refining existing products.
Let's say you're worried about driving up good reviews for your food delivery app: Dropoff, you might start by asking what the biggest cause of bad reviews is.
You might start with a baseline insight that looks something like this:
Lots of customers said they're frustrated by the reliability of delivery times.
Chances are you'd frame it a little better, but as the article continues, I'll show you how to add a few key characteristics to your insights to make them more effective for taking action on.
When you pull insights from research make sure they stand out. 1% in some categories means a lot and in others it means nothing. Increasing your average sales by 1% is a big deal in some industries, and it could be chump change to a startup focused on 5x-25x growth. Make sure you're speaking to your audience by understanding what they think of as significant.
This means you'll have to do some digging and build an understanding of your industry and averages so you can talk the talk with your partners at work.
3. Outcome oriented
When you pull and frame your insights, it's not enough to say that they exist. Tie them to an outcome people care about. Help people understand why it matters by painting a picture. Shape your insight for action by helping people envision an outcome that could be achieved if they're willing to invest.
If your work involves something like increasing the attachment rate of a product, make sure you're supporting that by painting a picture of the future you're working towards when you share research insights.
Time is money, as they say. So, how will acting on this insight save or make me money? Your job as a product person is to focus on how taking action is better and smarter than doing nothing. Paint a picture of why acting now is more important than other things.
Trust is crucial in product work. The best way to build and maintain trust is to curate, but never modify your insights. Focus like a laser on the areas where decisions are being made and pull things relevant to those problems, but never be afraid to share the truth.
Research must be seen as a neutral arbiter of truth in decision making. Your goal as a product leader is to ensure that research is helping inform decisions, but not make them.
When you pull insights from research, always tie it back to something important and put that front-and-center of your presentations, written materials and executive summaries.
If the company is betting on customers buying more self care and stay-at-home products, don't pitch or bring up insights about travel. Focus your discovery results like a laser on the decisions at hand.
Make sure you bring data, quotes and other relevant details to add just the right specificity.
Here's an example I made up by for our food delivery app, DropOff. Building on the previous bits of advice and context in the examples above, you might build your insights into something that look like this.
64% of customers said they're frustrated by the reliability of delivery times. In recent app reviews, we see 80% of one star reviews are tied to delivery times. We can explore how to achieve more reliability for our estimated delivery times in our future tests.
So, this is a pretty condensed example. It's pretty good, but missing a few things. Let's break it down:
"Explore how to achieve more reliability...in future tests" is pretty ambiguous. While it paints a picture of the work that will be done, it doesn't tell the stakeholder what they'd get for the work. Focus on painting a clearer picture of the ultimate goal. Let's revisit and focus on a clearer insight from the same problem.
We've been trying to increase positive customer reviews of our app in the app store. [Relevance]
In app reviews over the last 90 days [Specific], we see 80% of one star reviews are tied to delivery times. [Contextual]
In fact, here's a quote from a recent one star review that shows what customers think of the delivery time reliability problem:
"What's the point of having food outlets in your app if you can't service them with drivers? I ordered from a restaurant 2 miles from me and the time of delivery was pushed back multiple times. This happened to every order I've made in the past two months. The chat people apologise profusely but I feel sorry for them and accept their apology but nothing changes. One star service, I will not use the app again." [Specific, Unbiased]
We can explore how to achieve more reliability for our estimated delivery times. Since our focus for the next quarter is increased positive reviews, this is one problem we could focus on to achieve that. [Timeliness, outcome oriented]
I pulled the quote from a real app's reviews and modified to protect the identities of all parties. In the example above, the insight is tied to a bigger outcome, a possible desired state: more reliability.