Investing in Product Management as a craft
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Why we should invest in the discipline as a craft, through deliberate practice, learning loops, and communities.
Investing in our practice
Why we struggle, why it’s still worth the investment, and how to get started
Why we struggle to think of product management as a craft
Product Management is a generalist discipline. If we ever doubt that, examining Martin Eriksson’s Venn diagram definition of the job is a stark reminder: we sit at the intersection of business, design, and technology. We are defined by other disciplines. But we are expert in none. Product designers spend more time than us researching interactions and human behaviours, engineers specialise in technology, and sales and marketing teams spend much more time in the market. Of course, we are conversant in most of these disciplines, we build empathy and interact with people across the organisation on a daily basis, but we are generalists in essence, rarely going deep in more than one area.
So how do we invest in becoming better at what we do? We can try and go very wide, but there is only so much we can cover before spreading ourselves too thin. We can try and go very deep, but we will never be able to invest as much as time on a single domain as our specialist counterparts.
Besides its generalist nature, there are two other reasons why we rarely think of product management as a craft. Let’s look at how we manage our time. As facilitators, we spend a lot of time interacting with others. As champions for customers, we spend a lot of time researching and getting feedback from the market. As the by default go-to-people for a product area, we spend a lot of time fire-fighting whatever needs to be done. This environment is not prone for us to protect time and space for pure learning and investment in bettering our practice, and so a lot of us might not be able to reach that ‘flow state’ popularised by American-Hungarian psychologist Mihály Csíkszentmihályi. We spend a lot of time reflecting and debating as a team, typically through regular retrospectives. However rarely do we carve time to invest in self-reflective and deliberate practice as individuals: thinking through our decisions, observing what worked and what didn’t, inspecting why and trying to abstract learnings.
And third, we often conflate our success as product manager with the performance of our product. Of course, the success of our product should be our first priority, but not to the point where our practice is solely defined by how the product performs at a certain point in time. This confusion is often linked to how emotionally attached we are to the product we are responsible for. Combined with the heavily reliance on quantitative success criteria in the industry (eg. market share, adoption, usage etc…), it can translate, quite insidiously, to a culture of performance amongst product managers. Pushing this analysis to an extreme, we might even find average product managers working in high-growth industries or organisations become complacent, where excellent ones operating in waning industries or companies might self-doubt and not get the credit they deserve.
Why we should invest time and effort
Following American psychologist David McClelland’s initial research in the 1940s, a number of studies have been conducted on goals and human motivations. From recent research, particularly looking at student motivation in higher education, has emerged a socio-cognitive framework that distinguishes two achievement profiles: mastery orientation and performance orientation. The performance motivation is defined by a willingness to outperform others, as a result of a superior skill. Its validation is external and relative to others. The mastery motivation is defined by a belief that success is a result of prolonged effort and deep thoughts on strategies to achieve a particular goal. It calls on reflective learning strategies, and is more resilient to challenges or failures, as the emphasis is put on the process rather than the immediate result.
Excellence. When we look at individuals with the highest level of craftsmanship or artistic excellence, they seem to exhibit a few common traits. They all share an ability to go through a prolonged effort in their practice (eg. musician’s daily scales, swimmer’s laps, baker’s dough kneading). They are deliberate and self-reflective in their practice (eg. artist journalling, dancer analysing recordings, athlete wearing trackers or replaying videos). And they are constantly experimenting (eg. tailor trying new patterns, painter trying new colour mixes). At our own scale, and with humility, each of us can learn from these common attributes of high craftsmanship as we improve our practices.
Resilience. In a market economy, how a product performs is comparative by definition: market shares, differentiation on capabilities, valuation... But being a product manager doesn’t mean we should solely define ourselves based on these indicators, at a certain point in time. The market dynamics might change, the economy might drastically shift, and a certain product might underperform while we launch another. Instead, we should invest in our practice as product managers and ensure that every product cycle is an opportunity to better our crafts. Finding value in the process (and associated learnings) help us be more resilient when the outcomes we were hoping for might not be immediately met.
Autonomy. Adopting a mastery mindset for our craft of product management also reminds us that we are responsible for our learnings. While product performance goals might be set by others, while others’ achievements are somewhat out of our control, the time and effort we invest, and the benefits we reap are fully in our hands. We can observe and orient our own progression based on the learning goals we want to reach, and actively seek training, guidance, or ownership of product areas that help us achieve them.
How we should start
There are three starting points to this journey. This first section just introduced the value in investing in deliberate, self-reflective and mastery-oriented practice. This allows us to strive for excellence, build resilience, and restore autonomy in our learnings. In the next section, we’ll be examining how we make product decisions and the mental models underpinning them. We will see how it enables us to make every product experiment loop a learning loop, to develop a better awareness of when to use what decision model, as well as expand and refine our library of models. In the last section, we will look at the benefits in having a community of practice as product managers.
Optimising for learnings: towards a unified theory
Examining how we make decision, how we build models, and why product cycles matter so much
Experimentation has become the most popular approach to modern product management. We define success metrics. Based on a number of quantitative or qualitative signals — market, user behaviours, technology trends etc… — we formulate hypotheses. We then make decisions that we believe have the highest chances to affect the defined metrics. As we collect more data points, we can refine our next hypotheses, and guide the next decisions. Rinse and repeat.
This empirical approach may seem very cold and mechanical. But that would be overlooking a profoundly key step in this loop: the human decision.
Every single decision is based on a set of rules, whether conscious or unconscious. That’s true for every decision, however small: should we add this feature? is this in a scope or not? should we build or buy? should we prioritise this? Now, one might ask: what underpins these decision rules? Well, it’s a set of mental models. “All models are wrong, but some are useful” says British statistician George E. P. Box. And they are useful in that they help us navigate uncertainty and contain complexity.
For example, a decision to include a feature in the scope of an MVP, might be influenced by three decision rules: a reasonable ratio of effort to value to users, the fact that the feature can validate a key experiment hypothesis, and an acceptable level of risk. These decision rules are underpinned by a model, in this case lean product development.
Let’s invoke an adapted version of the double-loop learning model invented by Chris Argyris.
Investing in our practice of product management as a craft starts with examining the mechanics of how we make decisions. This indeed allows us to differentiate product experimentations — with their successes and failures — to our learnings as individuals, to ensure we make the most of each cycle to reflect and analyse our thought process. When a particular initiative doesn’t yield the results we were expecting, we shouldn’t immediately shy away from it and quickly dismiss it to jump onto the next one. On the other hand, when a product experiment is successful, we shouldn’t only celebrate it and consider the result as a given. Instead, we should take a step back, move away from our instinctive aversion to failed experiments or attachment to successful experiments, and make sure we reflect, analyse, and learn from the process that led to that particular outcome. This maximises our chances of repeating successes and preventing new failures.
Understanding how we build mental models
Once we’ve acknowledged how we learn and how decisions are made, the question then becomes: how do we improve our mental models over time?
Well, this is where an extra step kicks in: was the observed outcome expected, in accordance with the mental model that underpinned the decision? If that’s the case, the model is rewarded and our belief in its validity is increased. If not, the model is penalised and our belief in it might start to crumble. This reward mechanism is often unconscious: we might sometimes catch ourselves saying we “like this model” or “dislike this model”, even if we can’t even quite articulate why. It’s often the case that we have signals in favour or against of a model, but express as if it was a preference. Understanding this double-loop might help shift the conversation away from preference and towards pertinence. The beauty of mental models is not only in their validity in a particular domain, but that they can be applied to other domains and contexts. It’s often referred to as a model’s ‘fertility’.
Towards a unified theory: tacit knowledge, techniques, frameworks, and principles
Let’s now unbox a bit what’s contained in this mental model black box. A lot could be said and debated here, but we can assume that it falls in four categories: tacit knowledge, techniques, frameworks, and principles.
Tacit knowledge is the least sophisticated class. It is a bucket that contains things that we’ve learned on the way, that guide some of the decisions we make, but that we can’t quite structure and verbalise. We often resort to calling them intuitions, hunches, or referring to our “product 6th sense”. While decisions based on tacit knowledge might lead to expected outcomes, they might not be always repeatable and can’t be shared with others.
Techniques are isolated methods that we collect to solve discrete problems: facilitation (eg. design sprint, six thinking hats), estimation (eg. planning poker), research interview (eg. empathy maps), prioritisation (eg. RICE, dot-voting, stack-ranking), scope (eg. user stories, user story maps, BDD), operations (eg 5 whys), software development (eg. Scrum, Kanban), cost-benefit analysis (eg. net-present value)… They are off-the-shelf repeatable tools for tactical problems.
Frameworks are models that start to contain inner tension and complexity. They are usually more applicable across situations and domains. Because they are usually higher in abstraction level, their fertility is usually at the cost of immediate applicability. Typical examples could be: business strategy (eg. SWOT, BCG Matrix, Ansoff matrix, Porter’s value chain, business model canvas), competitive analysis (eg. Porter’s five forces, PESTEL), product discovery (eg. Jobs-to-be-done, Opportunity-solution trees), product strategy (eg. Kano model, single/double-sided markets, network effect, long-tail , growth loops), market positioning (eg. marketing mix, blue/red ocean), action aggregation (eg. normal distribution), organisational design (eg. matrix, mission-centric squads, Mintzberg’s typology), negotiation (eg. Pareto efficiency, Prisoner’s dilemma)…
Principles sit at the highest level of abstraction. They often attempt to offer holistic and highly cross-domain abstractions. Artistotle defines first principles as “the first basis from which a thing is known”. This school of thought, furthered by Euclid, Descartes, and to some extent Kant, seeks to find root truths for all things. There is a multiplicity of techniques and frameworks. With principles, we seek for generalisation and simplicity. Compact in their forms, principles are very powerful and carry across multiple domains (eg. war strategy, architecture, science and philosophy), but also carry the biggest risk of being misunderstood.
Bettering our crafts has a lot to do with expanding and refining these mental models, as well as the ability to quickly identify which model to use for a given situation.
This growth process is two-fold: additive and transformative.
This process is additive in that we are collecting more techniques, frameworks, and principles for our library of mental models. This is a very natural and satisfying process: have you heard of framework X that Y talks about? Did you know that at trendy startup X they use this technique Y to solve this problem? have you read influential author X’s latest post on how to think about Y?…
This process is transformative in that it changes the nature and relationships inside the library of mental models. Some tacit knowledge might become conscious and become a technique. Some techniques might come together and crystallise into frameworks. And some frameworks might be simplified and verbalised, to unearth first principles. This evolution arises in sequential Copernican revolutions (or ‘epistemological breaks’, if we prefer French science philosopher Gaston Bachelard’s fancier coinage), as new models are not longer additions, but incorporate old ones while making them obsolete in the process.
Product cycles
Now that we’ve built a shared understanding of how we learn as product managers, comes the next natural question: if this is the engine, how can we provide the necessary fuel to feed it?
One might argue that we should aim at quickly increasing the volume and diversity of product experiments. Get as many of these iterative cycles as we can. That’s absolutely true, but it might not be enough. Truly becoming a well-rounded product manager probably requires exposure to the full product lifecycle, as it allows us to stretch all muscles and test all the models in our toolbox: initial discovery, product-market fit validation, initial go-to-market, scaling and expansion, optimisation…all the way to maturing, and retiring (or pivoting) a product. More than lines in the CVs or years of experience, riding these product cycles, and learning as much as possible each time, on the way, is probably the most efficient way to refine our craft.
Investing in product management as a craft requires true deliberate and self-reflective practice. The very first step is acknowledging how our product experiment loops and our learning loops are distinct but intrinsically coupled. The second step is ensuring we ride enough product cycles to build, test, and refine our mental models through real-world experience. But how ever hard we try, there are only so many products we can be responsible for, or industries we have exposure to. That’s why we should view learning as a collective experience: by investing in a community of practice.
A community of practice, as a way to accelerate learnings.
Why model building is also a collective learning experience: reflecting, reinforcing, refining.
Across ages and societies, artisans and merchants have assembled in guilds and corporations. Regardless of their specialties, shoemakers, drapers, stonemasons, sculptors, bakers, watchmakers… would meet and unite forces. While defending industry interests and many socio-economic dynamics are obviously at play, some deeper motivations may explain this need to come together: advancing their craftsmanship, congregating around a shared feeling of hardship, and passing on knowledge to new generations.
In the recent years, many options for professional training for Product Management have emerged. From formal university courses to accelerated bootcamps, it is now possible to learn fundamentals of the discipline before starting one’s product management career. Despite this variety of offers, so many of us accidentally become product managers and learn on the job, with nothing but practice and the support of the community. In this context, having a network of product managers is vital to introduce newcomers to the discipline, to provide a support network, and to open career opportunities.
But beyond these reasons, in a more self-interested manner, connecting and investing in our ‘community of practice’ (concept formalised by Etienne Wenger) is an invaluable way to accelerate our learnings, advancing our craft, and ultimately becoming better product managers.
Reflecting on our experience
Let’s loop back to a key concept introduced in the first section: deliberate practice. Meeting with other product managers offers us the time and space to reflect back on our product experiences. Every talk we’ve committed to give, every blogpost we draft, every experience we share or hear, however informal, is a golden invitation to honest retrospection and vulnerable introspection: what was the outcome of that decision? what are the causes that led to that? what could I have done differently? what have I learned about how I do product? what can others learn? By forcing us to externalise our thoughts, the act of sharing accelerates our learning. Sharing makes our practice self-reflective and deliberate.
Reinforcing our learnings
Now looking back at the diagram in the second section, one of the most important relationship is the one between the observed outcomes and the mental models. If a given mental model drives a particular decision, and that the observed outcomes in the real-world are what is expected, we then reinforce our belief in said model. Reversely, observing unattended consequences would lead us to question its validity, and depreciate the value we give to it. This reward system helps us sharpen our mental models. Through interactions with the larger community, we can expand the test bed for our models to not only our personal product experiments, but to what other have experienced with different products and in different contexts. It gives us a chance to then stress-test our models with a much larger sample size of cases.
Refining our mental models
Guilds and corporations have been surrounded by an aura of mysticism. Through popular fiction, they often evoke coveted trade secrets, esoteric techniques passed on from generation to generation, or privileged access to traditions. While product management can be somewhat of a dark art, we probably shouldn’t frantically scroll through the scriptures of Medium to seek for any magic compass of prioritisation or holy grail of product vision. However, what we can probably learn from this analogy has to do with a willingness to aggregate and unify knowledge, based on high quality standards and rooted in hands-on experience. By exposing ourselves to new techniques, frameworks, and principles, we can start building more holistic mental models. Referring back to the learning model outlined earlier, communities of practice accelerate this two-pronged growth of mental models: to expand (additive) and refine (transformative) the box that underpins our decision rules, and therefore drive our product decisions.
Investing in product management as a craft requires true deliberate and self-reflective practice. It is an invitation to reflect on our experiences, explore how we learn, and observe how coupled our product experiment loops and our learning loops are. It is then about seeking opportunities to ride as many product cycles as possible to build, test, and refine our mental models through real-world experience. And it is finally an invitation to genuinely connect with a community of practice, to accelerate our learnings. With these key elements, investing in product management as a craft can be a truly rewarding experience of personal growth.
Thanks David for our endless conversations on learning, and Susana Videira Lopes for reviewing early drafts. Thanks Théo, Henry, and Romain for further feedback. 🙌
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