ON PRODUCT MANAGEMENT
3 Ways To Turn Failed Experiments Into Competitive Advantages
Your plan A, B (and C) didn’t quite work out, so you’re heading back to the drawing board. Your team is bracing itself for another round of research, trying to evaluate which variables or components to test and iterate as you inch your way towards your next big product or solution.
Here’s a thought: what if most of the work has already been done? Better yet, what if the best solution to today’s challenge lies in an idea you tossed in the trash years ago? What if that information already exists in your knowledge base; that it’s sitting in silos, waiting for you to connect the dots. After all, so much can change, new patterns can form: markets evolve, technology matures, your company’s internal skill set and capabilities grow.
We teamed up with the Flying Saucer Studios team to explore key ways documenting your experiments can give you a competitive edge. Together, we dug up surprising stats and tangible examples that are bound to reframe how you think of your company’s failed experiments
As things stand, despite generating an increasing amount of metrics and data, only a small fraction of organizations know how to break down data silos to connect and contextualize the evidence they’re collecting from disparate, poorly-connected sources. Yet this de-siloing process is essential to understanding how to innovate using old ideas and experiments paired with new, more informed approaches. When that data isn’t properly collected and intentionally turned into actionable insights, it runs the risk of vanishing into the ether and turning into waste. That lost data — and more specifically that lost data aggregated within one centralized dashboard and product roadmap — is actually a goldmine for innovation.
We know…the idea of failing forward has become a bit of a victim of its own success, but it’s worth noting that the expression is often misunderstood or overused. Sure, we all know we shouldn’t be dwelling on failure, but there is a crucial difference between providing moral support to help your team to carry on, and proactively using experimental data to guide, propel and accelerate your research and product development.
Here are 3 ways to reclaim your team’s experimentation history and formalize your learning process to reduce failure rates, dramatically improve your product management capabilities, and methodically iterate on great ideas.
1: Shift the focus
It may sound simplistic, but the first challenge is often the toughest: get out of your own way. The “we tried that and it didn’t work” attitude has to go. Granted, this type of thinking acts as a popular maneuver to avoid looking closely at your shortcomings and mistakes — but it’s also a great way to ensure your best ideas never see the light of day. So how can your product team escape their own shadow? A great place to start is by regularly conducting retrospectives of past launches. Rebalancing the focus to spend more time analyzing what went wrong last time, when your energies are required for the upcoming launch, seems counterproductive until you try it.
Quick Intro: Launch Retrospectives
While the launch of a new product or solution may seem like the time to pop a celebratory bottle and lean back, it’s but one step in your data-gathering journey as you start thinking about your next feature or iteration. While it may be tempting to move full-steam ahead, it’s essential to take the time to dig back into your experimental data, desilo and contextualize information from multiple sources, and intentionally document your lessons learned before moving forward.
“It’s important to rewind all the way back to the beginning to make sure people are thinking about the big picture, and aren’t fixated on the last 2–3 weeks worth of work that may be freshest in their minds.”
- Heather Jean McCloskey
These meetings — designed to provide structure around the gathering and documenting of your company’s institutional knowledge — are often called Launch Retrospectives. They effectively turn your victories and missteps into a competitive advantage. Curious to find out more? Here’s a helpful breakdown from Atlassian and a super useful template from Marie Prokopets.
Research has shown that up to $29.5 billion is invested in R&D for unadopted or underutilized features. With the right data infrastructure and insights, teams can dramatically decrease losses by quickly adapting or abandoning ill-fitted functionalities as they’re tested and improved. Ultimately, the team will develop a better sense for what kinds of projects have the highest likelihood of success, therefore sharpening your ability to avoid spending time on projects with low likelihoods of success.
The best way to find yourself trapped in a Groundhog Day-like cycle and repeat your mistakes day after day? Failing to understand which factors or faulty hypotheses caused you to fail. The only real way to move the needle on those numbers is to shift your focus and reach back into that uncomfortable pool of disappointment. That, as the HBR’s Amy Edmondson points out, means changing your team’s relationship to failure.
“Failure and fault are virtually inseparable in most households, organizations, and cultures. Every child learns at some point that admitting failure means taking the blame. That is why so few organizations have shifted to a culture of psychological safety in which the rewards of learning from failure can be fully realized.”
The good news is: locating your fatal flaws doesn’t have to be ugly, it can simply become a methodical part of your experimentation process — and an essential part of your continuous efforts to build institutional knowledge.
When it comes to product experimentation, most teams focus on the organization’s OKRs, on where they’re going next and what new rock hasn’t yet been turned over. But if a previous experiment or idea could possibly test differently in a new market thanks to new technological capabilities or changing customer expectations, why reinvent the wheel? Your ability to harness the data you’ve already been generating through previous experiments lies heavily on your team’s ability to formally test and examine their strengths and weaknesses through the diligent, searchable documentation of past hypotheses, data points, and experimental variables.
With the right tools and leadership, that skill set can be intentionally taught, practiced and improved. Regularly devoting a little time and energy to conducting carefully planned retrospectives will also make your team more comfortable with the process of failing, and allow them to fine-tune their ability to recognize and document valuable knowledge and insight amidst the rubble.
Pro tip: make sure they are always given plenty of prior notice before the Launch Retrospectives. That way, they’ll have time to formulate their thoughts, prepare a list of priorities and avoid any surprises or accidental finger pointing.
The Gist: Pivot that habitual and collective thinking pattern from “we tried that, it didn’t work, what’s next?” to “what did we learn from that?” and you’re on your way.
2. Formalize your learning
You’re not failing forward if you’re not learning. So, once you’re all set up with a repository of searchable and centralized data, your ideas and understanding of why they failed will transform from mere assumptions to concrete business hacks. Now your team is ready to take a look at your findings and truly learn from them. MIT Sloan’s Jeanne Ross suggests that goal setting without proper methods for testing and learning will get you nowhere.
“Instead, companies should focus organizational energy on hypothesis generation and testing. Hypotheses force individuals to articulate in advance why they believe a given course of action will succeed. A failure then exposes an incorrect hypothesis — which can more reliably convert into organizational learning.”
What may come to light while looking at your previous launches through this lens is that your idea was golden, but the environment simply wasn’t ready for it. Often times, the reason a good idea fails has little to do with the idea itself and everything to do with a range of variables surrounding your product at the time it launched.
Apple is brimming with poignant examples of how to fail forward. The iPod, for example, was not the first MP3 player on the market, there had been several other attempts by other companies that each failed. But let’s be clear: the fault didn’t lie with the product. It had everything to do with an inhospitable environment that was unable to support the product when it launched — consumers had to first find the right platform and technology to download, manage and upload their music to their devices. Understanding that gap is what inspired the idea for iTunes. Rolling out iTunes created a fertile marketplace in which an mp3 player could not only thrive but become a household item.
When you’re product testing, your previous experiments are fertile ground for insights, but too much navel-gazing can prove harmful without the right tools. If you’re working on a budget or within a team with limited bandwidth, efficiency is key to keeping your teams productive and opted in.
The Gist: Once you’re all on board with a sound tool for collecting your data, begin formalizing and iterating your learning process by leveraging empirical evidence that speaks directly to your challenges. With that in hand, you’ll be ready to head back to the drawing board with purpose, visibility, and confidence.
3. Centralize your data
Imagine this: you’re a gymnast. To become #1, you’re practicing the same thing again and again. As you train, your focus is on nailing those landings — developing the muscle memory to transition perfectly into every little movement and rotation. Meanwhile, your coach is keeping a close eye on you for tiny movements and variations that can be improved and built upon. That way, they can pinpoint what went wrong in post-mortem and you can keep your focus on high-value exercises and movements that truly affect your performance — or your bottom line.
Steve Jobs knew what he was talking about when he said you must “connect the dots backward” but there’s also a lot to be said for keeping the right eyes on the prize. How we recommend you examine those dots without losing your focus is by connecting your data silos.
Today’s product teams juggle a dizzying number of software tools and platforms that aren’t necessarily built to communicate. As a result, most companies leave valuable information buried deep in their company’s data silos, and fail to make informed decisions as they experiment and grow. Instead, agree on one centralized system of intelligence like GLIDR, where your vital product strategy insights can be collected and analyzed. You can also set up hacks with tools like Zapier, which allows you to create silo-busting automations for over 1500+ apps so that they can accurately and systematically communicate information, gather findings and highlight data points as you go.
With the right structure and analysis in place, your team will be able to properly identify and document key findings and lessons learned before you move on to the next round. But without the proper tools and processes in place to harness, centralize and contextualize your siloed, unstructured data; you’ll be starting back at square one every time, and losing precious R&D resources without improving your project management capabilities or constructively building on internal knowledge.
That’s where a tool like GLIDR comes in. GLIDR is product management software that allows your team to make data-driven decisions on what to build, from roadmap through discovery, launch, and iteration. Carefully designed to help teams leverage their experimentation history data, maximize potential and reduce failure rates, GLIDR allows you to meticulously document your research, experiments, and launches. By providing the infrastructure to form clean data sets that can be compared and contrasted against one another, the platform helps you identify which of your ideas get traction and which marketplace variables may have proven inhospitable to your idea at the time of experimentation. Have any of those variables changed or been eradicated since? It may be time to revisit that idea.
Since product managers are tired of having to jump between many different apps to get the information they need, GLIDR helps by importing key data from apps like Intercom, Google Chrome, and (soon!) Jira.
The Gist: What you’re looking for when it comes to reducing your failure rate is a central system of product intelligence and process that can gather all of the evidence and information generated through your experimentation, and produce searchable, data-backed results. Unlike that well-intended shared spreadsheet of yours, the right product management tool allows you to confidently make decisions knowing nothing has fallen through the cracks.
However intuitive or prolific you may be, if you don’t have a solid system in place for documenting, organizing and sharing your team’s experiments, you’re not improving on your idea or your product management capabilities nearly as effectively as you could be.
Often our very best ideas are covered by the weeds in our own backyard. Reach down in there and make friends with your failures — or at the very least, learn to extract as much value and institutional knowledge from it as you can.
“Product management is about insights and judgment, both of which require a sharp mind. Hard work is also necessary, but for this job, it is not sufficient.”
- Marty Cagan, Inspired: How To Create Products Customers Love
With the help of the right tools and buy-in from your innovative teammates, your company will open itself up to countless new opportunities for experimentation, iteration, and seemingly obvious new solutions.
Curious to give GLIDR a go and start turning all the work you’ve already done into an innovation gold mine? Click here to sign up for your hassle-free trial.