By now, you know that one of Fuzy’s core goals is to help technology leaders at product-led companies make more informed decisions more quickly––as well as measure the impact of those decisions. Some product decisions produce immediate, quantifiable results that allow product managers and engineering teams to build on successes or course-correct; but that’s not always the case.
Frequently, it’s the higher-level strategic decisions, with the potential for wider and deeper impact, that are difficult (yet perhaps even more important) to measure. For product-led companies, these decisions can make or break momentum––and sometimes the very survival of the business.
To truly understand your customers and their satisfaction levels with your product or service, you need to go beyond standalone traditional customer experience (CX) metrics. For example, relying solely on feedback forms or one-on-one interaction with a customer success manager may not be enough to identify all the problems faced by a particular client or account. Plus, the timing of the feedback also matters; the survey responses may not come back in time for you to intervene with at-risk accounts.
Why optimize decision-making?
It is our goal to improve decision-making through transparency that provides greater context and advances feedback loops. In the current market, that context is no longer a ‘nice-to-have,’ but a critical way to stay agile, avoid surprises, and keep customers and stakeholders happy.
According to Gartner, poor operational decisions made by managers costs firms more than 3% of profits. And more staggering still, McKinsey research found that managers at a typical Fortune 500 company waste more than 500,000 days a year on ineffective decision making, equal to $250 million in wages annually.
Clearly, optimizing decision-making is a path toward better business outcomes in companies of all sizes. To learn more about decision-making in PLG organizations (and how you can avoid making bad decisions), we sat down with Senior Director of Product Management at Auctane Irina Tyshkevich. Irina has seen the business of technology from almost every angle and now guides the respective ships at ShipStation and Shipping Easy.
In this interview, she shares her product management philosophy and perspective on how to make great decisions, as well as the (sometimes invisible) cost of making poor ones––from simple feature releases to big bets.
Q: Your career history includes everything from consulting to product leadership, so you have seen this process from multiple angles. Give us your philosophy on making product decisions.
A: It's providing the right framework that is flexible enough to change with time. That means defining some consistent ways that you are prioritizing work––saying these are the four lanes we look at, and we can pivot the amount of gravity that we give each lane, depending on where we are as a business. I like to keep it pretty consistent in terms of the RICE framework, and changing that with whatever fits your needs. It has to be flexible enough so that you can adapt if your business changes to a different priority but it's not a complete overhaul of how you're doing product development as a team.
I heavily rely on data, so a lot of decisions are data-driven and each one of those decisions need to be measurable after deployment so you can learn. (What is the total addressable market? What is the amount of support tickets that this would solve? How does that directly map to your revenue?) But there is an art and science to this, with the opportunity for ‘art’ to drive what happens within the actual product development space. If your goal is to grow by X percent, you then get to answer: How do we be innovative and creative about the solutions that we're building?
Q: Say more about the art and science of product management. How do you balance them in decision-making, and what part of the process do they encompass?
A:. The science allows you to get signals of where there’s opportunity and measure the efficacy of the solution. Art allows you to be creative at every step of the way: How can I tell a story with this data? What inspiration can I pull from to build the right solution? What has my previous experience taught me that can help me slice and dice these results to uncover new insights?
What makes a product manager really impactful in analyzing the data is understanding the customers and the business. Without understanding the why behind these numbers, you will not be able to draw accurate and actionable insights. What drives a customer in your industry to convert and use your software? It’s important to bring in your cross-functional leaders such as Sales, Marketing, and Support to supplement the data insights with the business and customer insights.
For example, one of the processes that I like to run every quarter is going through our prioritization list with our cross-functional team members. I ask the team, "If you had just $10 to spend across all of these things that we could be doing, how would you spend that money?” Then I compare that to how the Product org would prioritize it. Where there are differences, we talk about them and then adjust from there.
Q: What are some of the effects or fallout of making a good decision versus a bad decision? What are some tips for mitigating the effects of bad decisions?
A: The bad decisions are the ones where you’re not keeping your customer and business in mind. The truly bad decisions are the ones where you're giving your competitors a leg up and you're building brand loyalty for a competing company. To me, that’s the one that's the hardest to come back from.
For most decisions, I wouldn’t categorize them as bad if you’ve learned from them. One of the best ways you can keep a pulse on that is to constantly monitor metrics after a decision is made and to make incremental changes as much as possible so that you're never making a big decision all at once. You should always be learning so that if a metric that you’ve been monitoring is really not going well, you can figure out how to pivot.
Avoid being so heads down for a prolonged period of time, and you're not listening to the external input around you or what your competitors are doing. That can lead to a truly bad product decision, and coming back from it can be very challenging, especially if you have more agile competitors.
Q: Why do the big bad decisions like you’ve just described happen?
A: As we talked about, if your cycle is too long and you’re not getting feedback often, then you’re not failing and learning quickly. Additionally you could be paying attention to not a broad enough signal set. There are two customer sets you can break this into: your current customers versus potential customers. For your current customers: if you’re listening to the 20% of customers that are going to be the loudest versus the 80% that won't be, you will be missing some key information.
It could be that you are very focused on your current customer segment and not listening to what's happening in the market, and you’re not investing in innovation. Part of that is also a sense of security where you think, “We're established, and we've been doing this for a while, and we have X amount of customers, so we're probably fine.”
Every company has to be wary of complacency. This happened to Pandora when Spotify totally took their market away. Facebook took over MySpace in the same way.
Q: What about decisions that are not as detrimental? Are there hidden costs to smaller-scale poor decision-making?
A: Wasted time. What could we have been building that we weren't building during that time? And I think that is OK, as long as you are learning from it and not making the same mistakes over and over. The next question is, “How do you work with the team to make sure that you don't do that?” I'm a huge fan of retrospectives. Most releases need to have a retro that is cross-functional to find out what went poorly. What did we learn? What went well? What could we make sure we don't do the next time? And again, monitoring results.
Just because something is released, doesn't mean you're done. For weeks and months after the fact, what are you learning from it? How did we do with that initiative? Should we go back and revisit that feature because we're actually not seeing the numbers we're hoping to see? Or do you need to pull it and roll back the release?
Q: Do you have a failsafe protocol for avoiding bad decisions before they happen?
A: Back decisions with a hypothesis and data, and get buy-in from your key stakeholders. This includes feedback from your current customers and internal cross-functional teams. Build an open communication culture with your org and customers so that the decisions that you’re making directly correlate to what is happening across the business and your customers.
Q: How can you enhance cross-functional partnerships to improve product decisions and business outcomes?
A: I think about product decision inputs from four perspectives: customer success, sales, marketing, and the business. You should have input from all four of those areas to help drive your product decision making.
For example, let’s take Customer Success. It's really helpful to know what your product is currently doing well for your current customer base. That information is important for continuing your retention and working on improving your activation rates, but you also can't be solely focused on that because then you're going to miss the market and other opportunities.
So when you're thinking about product decision making, it's really important to figure out what is most important to you right now for product development. Is it growing your current customer base, making sure those customers are successful and just keeping steady and growing within that segment? Or is it important for you to expand? One of the biggest pitfalls is finding the right balance of current customers versus potential customers because if you are hyper focused on current customer success and your competitors are not, and then they start gaining in the market, then you're going to lose that opportunity. You're going to pigeonhole yourself to what you're currently doing.
We’ll be exploring cross-functional collaboration more deeply in our upcoming interview with Leslie Jordan, chief product officer at Backflip. She’ll share how she integrates qualitative feedback, quantitative data, team member input, and intuition to make product decisions. You’ll also learn best practices for cultivating an ideal partnership with your colleagues in customer-facing departments.