Over the last several years, there has been a rapidly growing body of practices associated with using data to create better outcomes for customers of products and services. This is referred to as Data-Driven Product Development (and multiple similar terms).
The Lean Startup increased attention to leveraging data for better decision-making by popularizing the Build-Measure-Learn loop. Digital products and cloud-based services made it easier to instrument, collect usage data, and run experiments like A/B testing that is foundational to trendy Product-Led Growth strategies.
In fact, leveraging data for improving outcomes in product management has become so popular that a new role has been created called Product Operations (aka ProductOps) that largely help organizations create, scale, and leverage data-driven practices.
I am a huge believer in the need to leverage data in product management, I fear that the pendulum is swinging too far towards thinking that data alone is our path to product perfection.
I offer four high-level reasons why relying on hard data alone is not the best approach to product success.
Hard data is not always available.
The reality is that it is not always possible to collect the data necessary to support the desired analysis and decision-making process. When it is possible, it can be too costly or take too long to support the needs of your business.
Issues with data quality and analysis.
An article in the Proceedings of the National Academy of Sciences named “Issues with data and analyses: Errors, underlying themes, and potential solutions” (March 2018) describes major themes for how data analysis can go wrong. These themes include errors in producing data, managing data, statistical analysis, logical flaws, and in communications.
Waiting for more data to help make a decision obvious can frequently slow down business. There is rarely sufficient data to make decisions perfectly clear. It is often about weighing best a variety of unclear inputs and probabilities against one another.
Great data does not equal great decisions.
Finally, good data, even great data on the topic under scrutiny does not always produce expected or desirable outcomes when a decision is made.
If data alone is not the answer, then what can help ensure we make the most out of the data we do have? Further, how do we make progress in the absence of good data?
“All great achievements of science must start from intuitive knowledge. I believe in intuition and inspiration…. At times I feel certain I am right while not knowing the reason.” – Albert Einstein
Some decisions are not going to have good data to support them. This means that product managers often need to rely on something else to make their decision. I refer to that something else as Product Intuition. Whether you call it Product Intuition, common sense, trusting your gut, or relying on a hunch – it has always been key to progress.
Intuition is the ability to understand something immediately, without the need for conscious reasoning. So we are able to put intuition into the context of product management and understand that since it occurs before getting into deep cognitive analysis, it can happen without direct, hard data.
For years, I believed that some product managers have good product intuition and others do not – an immutable fact. This was based on lots of anecdotal evidence. Then, when working on a marketing project to describe how a particular AI technology worked, someone suggested that it was “intuitive”. I researched. Lo-and-behold, this lead me down the rabbit hole of learning something new.
Intuition is rooted in the synthesis of diverse and continuous learning
Intuition can, in fact, be learned.
This begs the question, how can we develop our Product Intuition?
It turns out that there is a bounty of research on intuition and approaches to further develop it. I have consolidated these into 5 practice areas that anyone can use to improve their product intuition.
The foundation for building great products starts with learning about problems and needs that customers in a market face. We should seek to build on that narrow insight with more diverse and continuous learning about how the broader market operates, how your business functions, technology trends and research, stakeholder interests and behaviors, and about evolving practices within product management.
One interesting ability this provides is the ability to pattern match into your own space.
Pattern Matching Examples:
While researching my own master’s thesis (more than a few years ago) I came across a paper by Eric von Hippel from MIT titled “Sticky Information” and the Locus of Problem Solving: Implications for Innovation. This illustrated a pattern replicated across multiple industries in how technology is brought together with the domain expertise to solve problems. In brief, since domain expertise is stickier than tech expertise, there is a frequent motivation to push simplified technology to the domain experts to solve problems over time.
Consumerization of enterprise applications over the past decade has led to predictable expectations by users within a business context. Users expect a minimal degree of usability and functionality within their business apps driven by their experiences with personal software. This lead in large part to the rise of enterprise social network technologies.
Conversely, enterprise expectations from consumers have also driven new advances in personal technology. For example, continuous backup is a basic expectation in the enterprise but came later to home and personal device use. The same holds true with expectations around data security.
Writing requires focus and forces us to clarify our thinking on a topic. Whether you write in a personal journal, publish articles and newsletters, or give talks at your professional events – I find this is a way to force myself to analyze what I have learned and identify connections that were not always obvious.
Learn to Trust Your Gut
Ask Malcolm Gladwell and he will say that you need to put in thousands of hours of practice to master complex mental skills. Once you understand good intuition can be learned, such practice becomes obvious. Here are a few techniques to practice trusting your gut:
Reflect/Hindsight. When you have an intuition in a product decision, write it down and later reflect back on how it turned out when the actual decision was made. Similarly, at the start of the year, make some predictions about your product and market then reflect at year-end.
Two-way Doors. In his 1997 Letter to Shareholders, accompanying the Amazon Annual Report, CEO Jeff Bezos described decisions as one-way and two-way doors. Irreversible decisions are one-way doors. Once you go through you can’t get back. However, most decisions are two-way doors allowing you to walk back through. Practice assessing which decisions are two-way doors and make them with less data, fast, knowing you can always revert.
Start small. Find smaller decisions with fewer downside consequences and trust your gut to make them with limited data. You will suddenly become known as the person that gets things done.
Almost all articles, whether academic or in the popular press, on developing intuition include meditation. I have yet to take up meditation but I do run and think that it has many of the same benefits. Clear your mind of distractions and let it wander. Once it does, I have found some that I suddenly am able to connect the dots or make a pressing decision without the benefit of any new data.
Find creative pursuits to occupy your mind. Much like meditation by spending time painting, drawing, or writing, your mind gets into a state of flow. Effectively unblocking the silos that keep your mental state in an efficient productivity mode all the time. This makes it easier for your mind to synthesize your past learnings and more rapidly make decisions. (Personally, I like building legos. Even if it doesn’t help my intuition at least I had some fun trying.)
Developing a better Product Intuition will improve your outcomes. At times you will make bad decisions when relying heavily on your intuition. However, it is always faster than waiting for data that can also result in incorrect decision-making. Beyond getting good at making decisions in the face of limited data, developing a good product intuition will make you great at learning faster. With every decision – no matter the balance of Data and Product Intuition that goes into it – take the opportunity to learn from the outcome.
If you are interested in learning more and hearing some detailed examples on these practices, please check out my Live Talk: Developing Product Intuition with Sean Sullivan hosted by The Product Mentor recorded on March 8, 2020.