While AI can help increase product development throughput dramatically, there’s one resource PMs need for product success that is still limited (spoiler: it’s not eng time).

Many of you probably came across Julie Zhuo’s post on The Death of Product Development, which raises interesting points on how the design-eng-product tripod will evolve given AI’s ability to accelerating coding and design. For my Haas MBA course, we had my friend Jean Hsu (https://jeanhsu.com/) join a fireside chat to discuss the implications (her take on the topic here).

My takeaways from the discussion:

☠️ The MVP is even more irrelevant than it already is. While the concept of the MVP is still important, the practicality of THE MVP has diminished a ton. In this age of AI, when general-purpose LLMs can make rough wireframes and write decent code, the need for a PM to define a single “right” MVP goes out the window.

🤔 INSTEAD, PMs can now define a general strategic direction for multiple potential MPVs and audiences, then experiment with them rapidly.

🧭 HOWEVER, this doesn’t excuse a PM from setting good strategy & direction. As Jean noted, with AI, it’s like a rowing team where “every rower suddenly gets access to a turbo paddle… but without coordination and clear direction, the boat doesn’t go anywhere.”

🤖 WHAT DOESN’T CHANGE WITH AI is the PM’s responsibility to understand customer needs, create relevant value, and set a vision. AI can help, but it is not (yet) great at this, and human empathy (for now) still matters.

💰 THERE’S ONE RESOURCE THAT IS STILL LIMITED. This becomes evident when you consider the implications of faster development = more products. We have lived through this cycle many times; from the internet, to elastic cloud infra, to mobile app stores, the barrier-to-entry has lowered significantly many times. The one resource that hasn’t increased, and in fact has gotten MORE PRECIOUS, is the customer’s attention. Just look at the long tail of unused apps in the App Store to appreciate how lowering the entry cost doesn’t solve the strategy problem. When users become impatient with crappy products, you need to clearly solve a relevant problem for them to pay attention.

WHAT INCREASE IN IMPORTANCE IN THE AGE OF AI, unfortunately, are proof points and the associated gatekeeping. The products that garner our attention aren’t necessarily the best products, but the ones that are able to collect proof points like VC funding, press coverage, app store curation, search ranking, or “relevance” as deemed by “the algorithm.” And it is on this point where I feel the most pessimistic. 🫤

Some variables to consider…

• This is somewhat more true for startups and somewhat less true for large companies with existing products that’s in an iterative improvement phase. That said, which PM hasn’t launched a new feature only to find that adoption was dismal? 😜
• The acceleration is somewhat less true for AI-powered products, given the expense of developing or even prototyping AI. That said, the costs will likely continue to decrease over time, as all tech tends to do.

How To Avoid The Unintended Consequences Of The “One Metric That Matters”.

In this series…

• Part 1: WTF is a Metric? — introduction to the basics of metrics, what it is, and why it matters.
• Part 2: 4 Steps to Defining GREAT Metrics for ANY Product — how to develop a metric from scratch.
• Part 3: WTF is Your Metric Doing to Your Customers? — how to avoid the unintended consequences of the “one metric that matters.”

Beyond this series…

Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Let’s start with a few news stories over the past years…

In 2016, Wells Fargo was fined $185 Million for opening more than 2 Million fake accounts. As a result, 5,300 employees were fired for “Improper Sales Practices” due to Well Fargo’s aggressive sales targets.

In 2017, a Vietnamese-American pulmonologist was violently dragged off United Airlines Flight 3411 when United needed to “re-accommodate” the customers so they could fly four of their own crew members to staff another flight. This shined a harsh light on the common airline practice of overbooking, wherein airlines sell more seats than available to ensure a plane is full at take-off even if passengers cancel last-minute.

In 2018, it became known that Cambridge Analytica had been harvesting data from 87 Million Facebook users for political campaign purposes using 3rd-party Facebook apps. In the years leading up to this, Facebook’s laser-focus on revenue resulted in a 7-fold increase in their stock price over a 5-year period. This was due to ad revenue derived directly from user engagement (read: addiction and outrage) and supported by diverse content developed by 3rd parties who enjoy Facebook’s ability to grant them access to 2 Billion+ users. This is a scandal that gave us both admission from Facebook’s CEO that “It was my mistake, and I’m sorry. I started Facebook, I run it, and I’m responsible for what happens here…” as well as the Zuckerberg Zmirk™:

What do all of these stories have in common?

Metrics: A Double-Edged Sword

There’s an old adage made famous by Bill Hewlett of Hewlett Packard:

You cannot manage what you cannot measure… and what gets measured gets done.

Similar to how social media has shown us people are predisposed to tracking how many “likes” they get on their latest post [1], Bill Hewlett understood the combination of instrumentation, measurement, and goal-setting is a powerful organizational driver and motivator.

Now that metrics have become integral to a company’s or product’s performance management, incredible focus is placed on the key performance indicators (KPIs) we set for ourselves and our teams. On a weekly, daily, hourly, or even minute-by-minute basis, teams obsessively check their analytics suites, dashboards, and leaderboards to see whether their actions have moved the needle on KPIs like daily new user growth, average minutes of engagement per user, or revenue per user.

However, this focus comes at an expense.

While not all teams that rally around a single business-focused metric will become myopic, it’s not difficult to imagine how easily this becomes the case. Managing against the “one metric that matters” runs the risk of tunnel vision while everything else suffer.

This is the double-edged sword of metrics that gives rise to their unintended consequences.

“But We Would Never Do That…!”

I’m sure the 5,300 employees at Wells Fargo thought so as well. It’s safe to say very few of those at Wells Fargo consider themselves “bad people” that wake up each morning with the explicit intent of harming their clients. However, given tough quotas and earning bonuses, that’s exactly what they did.

Unfortunately, no product or organization is immune to unintended consequences [2]. If there is a measurement in a system, then there is incentive to game that system. For all the good a metric can do to bring focus to things that matter to you and your product, how certain can you be that the same metric isn’t creating incentives for anyone (customers, employees, partners, leaders) to cheat as an unintended side-effect?

How To Combat The Unintended Consequences Of Metrics

It is much easier to attempt creating a perfectly un-game-able system when you are able to control every aspect of the experience, such as within the virtual world of a mobile game [3]. However, most products and companies operate in the real world with real world consequences, whether intended or unintended. Absent the ability to design perfect systems, we have to find as many levers as we can to ensure customers aren’t impacted at the expense of metrics that only measure business objectives.

In my years of managing products, I’ve noticed there are consistently a few places where we can introduce change to a complex system, namely a) the decisions that are driven by b) the processes who are defined by c) the people.

At each of these points of leverage, there are opportunities to implement safeguards against the unintended consequences of metrics:

1. Design direct incentives to influence a decision that may end up hurting customers.
2. Establish counter-metric guardrails that are embedded as part of the process.
3. Empower a culture of accountability towards your customer among all people within your community.

More on each below…

1. Design Direct Incentives

Among the simplest methods to encourage good behavior or avoid bad ones is simply to create rules around them. This is an approach where you either create high-cost punishment as a deterrent or a high-value incentive as a reward.

The benefits with direct incentives/disincentives is that they are relatively easy to construct and understand. You can apply rules and incentives on top of any system you’d like, regardless of whether the core of the system is operating efficiently or not. However, by this nature, direct incentives and disincentives can also be band-aids — one-off solutions that overlay on top of the core issues.

Both incentives and punishments can spur people into action that are rewarding in different ways: functionally (“I get/lose something of value, like money”), socially (“I will look good/bad in front of others”), and emotionally (“I feel good/bad having done a good/bad thing”).

However, punishment-based disincentives require that you are proactive and deterministic, which can become inefficient. You have to anticipate and explicitly identify the negative behavior and create rules and punishments as a means to incite fear. If rules are poorly designed or not comprehensive enough, ill-intended parties may find a loop-hole.

In recent years, the popularization of behavioral economics books such as Nudge have brought incentive systems to the forefront of product management and user experience design.

Unfortunately, a poorly designed incentive can also create more problems.

Case In Point… United Flight 3411

For airlines, the systemic pressure to be profitable drives this margin-conscious industry to nickle-and-dime their passengers as well as overbook their flights. Without fixing the fundamental issue of overbooking, a high-reward band-aid is to get passengers to give up their seat in exchange for flight vouchers.

In the case of the Flight 3411 Incident, all the passengers aboard were initially offered US$400 flight vouchers. When no one volunteered, US$800 flight vouchers were offered in their place. Only when no one took them up on the second offer did United select 4 passengers at random, subsequently dragging one of them off against their will.

From United’s perspective, this seemed like a fair way to offer a high-reward incentive. However, this highlights the shortfalls of this band-aid and the need for designing incentives thoughtfully:

• When the US$400 voucher offered, it only took a few moments of silence for the plane full of passengers to second-guess the value. After all, who wants to to be the sucker that singles themselves out to raise their hand and accept the lowest bid?
• By the time the US$800 is offered, everyone was already in no-sucker mode, “I better wait to see if they will offer even more.”

This approach was only functionally rewarding, not at all socially or emotionally satisfactory.

I have observed other airlines practice a much better voucher process. Once on a Southwest flight, they announced to passengers “Raise your hand if you’d like to give up your seat on this plane to receive a US$2,000 voucher!” Inevitably, more than 4 people raised their hands, by design. The airline then gathered the small group and asked… “we only have 4 vouchers, keep your hand up if you’re still interested in giving up your seat for US$1,500… US$1,200… US$800…” until only 4 hands were left.

This improves on the United system in all the key ways:

1. It’s emotionally framed as a game rather than a chore,
2. socially, it sends a strong signal that the reward is in high demand, i.e. no one is left feeling like they were a sucker to taking the deal, and
3. functionally, it ensures the participants value (i.e. the voucher they get) is the maximum amount rather than leaving people guessing whether they accepted too early [4].

As mentioned, even if the incentive design were flawless, it may have avoided the worst of the Flight 4311 Incident but by no means is it fool-proof. As long as airlines operate in a system that is inherently biased towards the business metrics of “airline seat utilization” at all costs, some version of this story will continue to repeat. Therefore, it’s necessary to consider additional measures.

2. Establish Counter-Metric Guardrails

Since business success such as revenue and profitability are much easier to measure than the holistic health and well-being of your customer, it is almost natural that a typical collection of metrics leans heavily towards the health of the business rather than the health of your customer. This is especially true for public companies that are evaluated based on “maximizing shareholder value.”

In order to counteract this tendency, we need to find clever ways to create a collection of counter-metrics. For every business health metric that is established, there should be a counter-metric established to a) recognize the metric’s potential positive AND negative impact to customers and b) balance out its effects.

This idea of a counter-metric is not new. The concept has likely existed for a long time, but became a part of the product management lexicon through mentions by author and Facebook Product Design VP Julie Zhuo, who uses the term “counter-metric” in 2016 and beyond, with some usage through other writings (note: almost everyone I know who speaks of “counter-metrics” has worked at Facebook at some point).

A simplified process to develop a counter-metric:

1. Recognize your product exists to create customer value AS WELL AS business value. I covered this in Part 2 as the first step of creating a metric.
2. Anticipate trade-offs a single metric may create. An example of how to do this is to imagine, in the extreme scenario, what would happen if only the single metric is achieved.
3. Balance out the trade-off by introducing another metric that measures the other side of the story. An example of a common trade-off in user acquisition marketing: quantity often comes at a cost of quality, so ensuring a counter-metric exists to measure quality (conversion rate/churn) will help balance the incentives to maximize a quantity metric (new users per day) [5].

The usage of counter-metrics in your product is a fundamental improvement over the “band-aid” incentive approach because it includes the checks-and-balances in the definition of success for your product. Because so much of the product development process is driven by metrics (quarterly goals, OKRs, etc), baking counter-metrics into your dashboard can create accountability in an explicit and transparent way. This provides a robust foundation for product managers, system designers, and decision-makers to optimize and advocate for competing metrics while maintaining a healthy balance.

Case In Point… Facebook-Cambridge Analytica

While we don’t have visibility into how Facebook actually uses metrics and counter-metrics, we can assume the key business driver is revenue growth (easy to measure), which should be balanced with a counter-metric for privacy and customer data security like a “privacy score” (hard to measure). Such a score would uphold the integrity of privacy and prevent future “Cambridge Analytica” incidents [6][7]. A privacy score may look something like this at various levels:

1. App-Level/Developer-Level Counter-Metric —an app- or developer-level privacy score can measure and show the degree of possible private data exposure when a user approves a 3rd-party app or developer. With this, a) users should see the score when they install a 3rd-party app so they can decide whether they want to share data, b) Trust & Safety moderators could flag apps or developers that have abnormally low scores, and c) developers would be incentivized to keep a good privacy score by only asking for the data permissions they need.
2. User-Level Counter-Metric — a user-level privacy score can measure the total privacy exposure for a given user, aggregated across all installed apps. Paired with a privacy dashboard feature, users can see how their exposure compares to other average users as well as which apps have the most impact.
3. Platform-Level Counter-Metric — a platform-level counter-metric can measure the total privacy exposure on a platform level, aggregated across all users and apps.

Nothing is fool-proof — as people who mean to do harm will always find a way — but counter-metrics as a guardrail could arguably have flagged suspicious activities by Cambridge Analytica and enabled Facebook to move more quickly to ban them before it was too late. More generally, a counter-metric would make it harder for bad actors to thrive as well as limit the time and scope of negative consequences.

3. Empower A Culture Of Accountability

Culture is one of the most powerful, intangible forces in a community. As defined, culture is a “set of shared attitudes, values, goals, and practices that characterize an institution or organization.” In this sense, a culture of accountability equates to an established set of norms around what’s right and an expectation that someone will call out bad behavior when they observe it. See something, say something.

Culture is incredibly powerful [8]. If you can build a culture of doing the right thing and provide mechanisms within your ecosystem to promote that culture, you have a built-in safeguard.

Culture starts with leadership articulating values (establish “what we believe is right and well”), modeling behavior, and sharing and repeating the message with consistency. Subsequently, ensuring that everyone within the community can hold stewardship over the culture and is empowered to act on behalf of the shared values creates robust accountability (e.g. the power to act in the interest of the customer by giving refunds without further approval or report bad behavior in a safe and secure manner without fear of repercussions). If the bad behavior is observable, it should be called out and publicly addressed as soon as possible [9][10].

Changing culture is hard. It’s difficult to succinctly articulate culture, and even more difficult for people to adopt a new set of norms. And while culture takes a long time to establish, it only takes an instance to destroy. Bad behavior seen at the leadership level is utterly demoralizing; few things are as deflating and demotivating as seeing respected leaders knowingly take a nefarious position or turn a blind eye [11].

Absent of the culture of accountability, bad behavior can go unnoticed and unchallenged. Given the pressure of business objectives and “success at all costs,” bad behavior may even be encouraged.

Case In Point… Wells Fargo Fake Accounts

The opening of fake accounts was a symptom of the aggressive sales culture enabled by the highest level of the organization. If revenue KPIs keep going up, anything goes.

When wrongdoing is pervasive, you begin to feel like it’s okay. You may even think it might be bad if you don’t participant. Social pressure, FOMO, etc. is very real. The problem is exacerbated when metrics are tied to individual performance. In the Wells Fargo case, not only were employees given impossible quotas on the number of accounts they needed opened (stick), they were compensated monetarily for their efforts (carrot).

While Wells Fargo taken steps to “transform” for the better, the aggressive sales culture reportedly remains. Unfortunately, getting rid of 5,300 people doesn’t change the core culture. Per the New York Times article: “sales incentives have changed, not disappeared.” Even more damning, the article reports “there is a general fear of retaliation for speaking out,” a clear sign the culture has more room for improvement.

The Interplay Between Incentives, Counter-Metrics, And Culture

As it may already be apparent, the three levers across incentives, counter-metrics, and culture are not mutually exclusive. In fact, application of all levers is likely required to guard against unintended consequences on behalf of customer well-being — each operating to advance customer interests in a different way.

Direct incentives establish the norm of what “good” behavior looks like.

Counter-metrics help consistently measure and align high-level business and customer objectives.

A culture of accountability reinforces incentives and metrics.

For Wells Fargo to change their culture, it would require a holistic re-engineering of incentive structures by decoupling individual compensation from business metrics by going commission-free (removal of perverse incentives), a change of guard throughout the leadership as well the implementation of a whistle-blowing mechanism (top-down culture commitment), and a refocus on customer financial well-being (counter-metric).

For United, instead of the crew being praised for their ability to maximize profitability, the company would need to strengthen their values around customer service (culture), surface and regularly refer to 3rd-party rankings of customer satisfaction (counter-metric), and find ways to reward employees who go above and beyond (direct incentives).

For Facebook, it seems to be on its way to establishing a culture of privacy at the leadership level with its latest initiatives, continuing robust and consistent use of counter-metrics, and rethinking how its partners and users can access data.

As a product manager and product leader, you wield immense power in setting the metrics, and therefore the goals of your team’s success.

I recently had the privilege of inviting Mary Berk, Product Manager at Facebook / tech industry veteran / PhD in Ethics, to guest lecture in my MBA course. One of the most pertinent takeaways was this:

Design is an exercise in power. Use it well… When you put a product out there, you are co-creating the world… Take the challenge to think beyond your immediate product goals, to the world you’re a part of and shaping. When you put a product out there, you are co-creating the world.

As a product manager it’s important to recognize how products impact customers. Businesses can’t exist simply to maximize shareholder value, as this emphasis can lead to detrimental customer impact. As Jeff Bezos articulated as far back as 1999: “…what matters to me is we provide the best customer experience… in the long term, there is never any misalignment between customer interest and shareholder interest.”

Per the law of unintended consequences, you will never be perfect in anticipating all the possible outcomes, but leveraging some options above to improve the culture, broaden the KPIs, and design better incentives are some ways to start.

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

NOTES

[1] I personally experienced this a few years back when fitness trackers were the #1 holiday stocking-stuffer. The simple quantification of the number-of-steps-per-day had me circling the block on my way home from work to hit the 10,000 steps arbitrary, preset health goal, even though I had no idea what the benefits of 10,000 steps may be, or whether there was a difference between my 9,900 steps or the 10,000 goal.

[2] A well-documented case of well-intended but detrimental metric in education policy is the standardized testing and test score systems that drive the American education system. Over the course of many years, education in the US had increasingly relied on the standardized test score to manage everything from school funding to admissions to teachers’ salary to policy-making.

[3] Back in my days PM-ing mobile games, the entire cross-functional team was always hyper-sensitive to the systems of measurements and incentives we created for our players. To that end, we maintained a key mindset: If there is an opportunity for a player to gain an unfair advantage, there is a 100% chance someone will leverage that loophole and cheat (and likely be proud of it too). It may sound paranoid, but it’s the only way to ensure the game is fair and no single player can “game the system”.

[4] To really up the social (or emotional) incentive, the airline could have also re-framed the voucher reward with altruism: “We have got 4 crew members here that need to get to Louisville so a plane full of passengers there aren’t left stranded. Can any one of you help them out and get a voucher for your troubles?”

[5] A couple more examples. In freemium monetization: ensuring a depth metric (average revenue per user) is balanced with a breadth metric (% payer). In eCommerce optimization: ensuring efficiency metric (average # of steps to purchase completion) is balanced by an effectiveness metrics (cart conversion/abandonment). The keen analytical eye will note all these examples balance out absolute/volume measurements with relative/ratio measurements.

[6] At the point in time when Cambridge Analytica was extracting private user data through various mechanisms, Facebook’s developers’ terms of service (ToS) likely did not have enough accountability. This is an example where rule-based disincentives can be ineffective without measurement + enforcement. Apple, on the other hand, rules its platform with a consistent iron fist, rigorously reviewing each App Store submission, rejecting apps that violate their terms, and regularly pulling previously-approved apps from the App Store if issues are discovered.

[7] Another social media example would be to develop another counter-metric to user engagement that balances the promotion of polarizing content. This has shown to drive stickiness but has created toxic environments for social media platforms and beyond. Facebook talked about “time well spent” last year and Twitter is making an effort to develop a “conversational health” measurement as their key counter-metric. This can be combined with behavior-influencing incentive features and systems similar to those found in MMO-RPGs to improve overall user engagement wellness.

[8] Nothing keeps a person in line better than knowing they are being watched. 24-hour surveillance a la China and the correlated drop in crime rates is a particularly dystopian example.

[9] When I worked at Ford Motors as an engineering intern years ago, one of the cardinal sins of the factory was: “Never do anything to stop the assembly line.” Around that time, Toyota’s famed quality popularized some of their manufacturing practices, which included empowering any and all factory employees to hit the emergency stop button if they observed a quality issue on the assembly line. This level of empowerment was (and probably still is) unfathomable in US factories.

[10] “Justice delayed is justice denied.”

[11] Culture attracts like-minded people, both for better or worse. Since people hire people like themselves, once the wrong culture is established, you and your team hire more of the same, making it even harder to improve culture. This is the ultimate culture downward spiral.

WTF is your metric doing to your customers? was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

In this series…

• Part 1: WTF is a Metric? — introduction to the basics of metrics, what it is, and why it matters.
• Part 2: 4 Steps to Defining GREAT Metrics for ANY Product — how to develop a metric from scratch.
• Part 3: WTF is Your Metric Doing to Your Customers? — how to avoid the unintended consequences of the “one metric that matters.”

Beyond this series…

Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Metrics matter. Your product’s success is dependent on and defined by metrics. Metrics are a lens into your product’s health and performance. Defining your metrics, therefore, should be as intrinsic to your product development process as defining requirements.

In fact, metrics are one of the most frequently asked product manager interview questions:

How would you define the success metric for feature X?

The Challenge

Developing metrics from scratch can be incredibly daunting, precisely because there is so much at stake. Unless you work in an industry where standard metrics are widely accepted (such as in gaming, e-commerce, or retail), metrics are anything but trivial.

Many product managers start bottom-up: monitoring and collecting the near-infinite number of data points they can get a hold of. However, an obvious truth quickly begins to surface…

…not all data are created equal.

Just because you can track everything doesn’t mean you should. Some data are more valuable than others because they give you a unique insight into your product’s health. Conversely, there is a lot of subpar data that can cloud your judgement.

A Mental Model for Metrics

Let’s step back. Think about your product as a box:

You can start to instrument this box and collect all available data, but this can become an aimless approach characterized by constant trial-and-error.

In order to build a product your users value, you first need to determine the inputs and outputs of this box. Your inputs are anything your users come to you with: their problems and their resources. Your outputs are the circumstances resulting from the use of your product. These become your goals.

But this is not a black box. Most products require the user take actions within the product to extract value and achieve their goals.

Quantifying and measuring these actions gives you insights into how (and how many) users are getting value from your product. When you track these actions over time, they become your metrics.

Once you start to collect data for your metrics, you can then put them to work towards evaluating your product. The results of your evaluations can inform any number of adjustments in your goals, actions, or even metrics themselves.

You can start to see that this very simple model can be applied in any number of ways:

• Defining success metrics for a new product
• Determining a new feature’s success and impact
• Re-vamping metrics for an existing product
• Fielding interview questions about metrics

For each step, here’s a bit more detail on how to do this.

The GAME Framework

The GAME framework is a 4-step process that can define metrics for any feature or product. For the remainder of the post, I will refer to a hypothetical “product” but feel free to substitute in “feature” whenever a “product” is discussed.

Step 1: Articulate Your Goals

This is obvious, but you should always start by defining your goals for your product. Your goals are critical to ensuring that you maintain a purpose-oriented strategic mindset. They also serve as tangible markers for revisiting and validating directional correctness throughout the metrics development process. Placing goal-setting as your first step forces you to be top-down, which is critical for developing metrics. A bottom-up approach is suboptimal because it relies on intuition and can often lead to analysis paralysis.

To define your goals, ask yourself the following questions:

• User Goals: How will my users benefit from my product? What problems do my users want my product to solve for them? How will my users interact with the product? How will my users feel when they use my product? What is my vision for how this product will integrate in my users’ life?
• Business Goals: What are the tactical or strategic business benefits? Increase revenue? Decrease cost? Be more competitive? Enter a new market? What does my business look like if my product is successful?

Note that for many of the best products, user and business goals align. These goals are often two sides of the same coin.

Once you have this set, you are ready to get in on the action.

Step 2: List the Actions That Matter

The next step is to define your user actions, or more precisely, all the actions you want your users to take within your product. This should start as a qualitative list. Don’t worry about the numbers or whether they are trackable yet.

Here are some example questions you may ask yourself using the common ARM metrics framework. Pick the set of questions that align with your goals:

• Acquisition & Activation: How will my prospective users hear about my product? What actions do my prospective users have to complete to become a user of my product? What actions do my users have to take to get value from my product? At what point is my product solving a real problem for my users?
• Retention & Engagement: What gets my users to come back to my product? What do my users do when they are engaged with and interested in my product? What are the actions that provide my users repeat value? How often does it make sense for my users to take those actions? Daily? Weekly? Monthly? Once in a long while?
• Monetization & Revenue: What form of “payment” does my user purchase with, money or time? What actions do my users take for my product to start charging? If my product is free, what are the reasons my users will opt to pay for it? Do they pay me directly or does someone else pay me when my users take action (e.g. ad/affiliate)? Do they pay me for every action (transaction) or do they pay me periodically (subscription)?

You can also use metrics frameworks like AARRR to flush out the actions. The key to doing this well is to be comprehensive but not exhaustive: have the broadest coverage, but don’t bother differentiating the minutiae. For example, if your acquisition user action is “newsletter signup,” don’t worry (yet) about listing each newsletter sign-up interface.

Step 3: Define Your Metrics

You are now ready to turn each desired user action (qualitative) into a measurable, trackable value (quantitative). Note that if you haven’t already done so, bring engineering and data teams in at this stage to vet your metrics and provide technical guidance on the feasibility of collecting/storing the desired data.

Here are some major decisions for how to count each action:

• Direct vs. Proxy: Can the action be directly tracked? Or do you have to use a proxy to measure the action? For example, you can measure “clicks” directly, but you may need to proxy “views” with values like scrolling.
• Individual vs. Aggregate: Can you group many actions for an overview, then separate out as slices for later analysis? For example: total revenue → revenue by product line → revenue by individual product.
• Magnitude vs. Ratio: Does it matter more for you to measure the overall magnitudes of the action? Or should you track as a comparison using a ratio, whether it be a rate (per time) or a normalizing factor (percentage, per user, etc)? For example, you can measure “total revenue,” or you can measure “revenue per day” and “revenue per payer,” which will give you very different perspectives on the same data.
• Intrinsic vs. Heuristic: Can you derive more knowledge from the measure intrinsically? Or do you have to rely on heuristics for the metric to be valuable? For example, while “daily active user” (DAU) may be intrinsically useful for B2C apps, a heuristic like “number of paying users who were active in the last 30 days” may be more appropriate for B2B SaaS products that are expected to be used monthly.

For most, this is the hardest step in the framework. Some of your decisions are informed by strong intuition, while others are informed by technical constraints or further data analysis. Often you won’t know for certain whether a metric is working until some iterations.

At this stage, you can also begin to weed out vanity metrics. Tweak the metrics that can be “gamed,” i.e. artificially inflate or deflate through clever design/engineering or malicious user intent. For example, inflating page views by using pagination or slide shows.

Step 4: Evaluate your Metrics

The best way to ensure your metrics are providing you the correct insights into your product is to test and iterate. You simply will not know how a metric behaves until you start collecting data.

The most import evaluation you need to make is the functional usefulness of the metric. You can check for usefulness by monitoring the metric for any false-positives or false-negatives. For example, if the metric drops, does it signal a real problem in the product? Conversely, if an issue surfaces, is the metric helping you flag this issue? Of course, you would also expect the metric to flag true-positives and true-negatives.

Different behavior with your metrics may inform iterations in each of your GAME steps:

• Evaluating Metrics: Is the data trending in the way you expected? Is the metric stable over time (i.e. not wildly fluctuating)? Is the metric flagging issues with your product and prompting further analysis? Are these the correct data points to collect? If you answer “no” to any of these questions, you want to reconsider how you developed your metrics.
• Evaluating Actions: Are these actions reflective of product, user, or business goals? Are your key actions telling the correct story about user behavior? Does your action set cover all emerging user behaviors? Are your actions safe from being “gamed” by your users? If you answer “no” to any of these questions, you want to redefine the set of key actions.
• Evaluating Goals: Is the metric correlating to business or user success? Are your users happier when your metrics are positive? If you answer “no” to any of these questions, you want to revaluate how your metrics relate to your goals.

Ask yourself these honest questions and remember that you are looking for your metrics to measure the vital signs that give you an actionable indication of health and performance.

Applying GAME: Facebook’s Newsfeed Example

Disclaimer: I have no insider information from Facebook. This section is pure speculation. 😇

The Facebook Newsfeed comes up as one of the most common interview questions, particularly because it has the right mix of ubiquity, complexity, and opaqueness.

Let’s apply the framework step-by-step:

Goals for the Newsfeed can be broken down between user and business goals:

• User Goal: I want to be able to see the latest of what my friends are up to without going to each of their profiles.
• Business Goal: Users are only coming to Facebook when they have a reason to: they are looking to post or they have been messaged/mentioned. We want to improve engagement by creating an evergreen content discovery experience.

Actions taken on the Newsfeed can be collected into the following qualitative list when considering “engagement” as the primary goal for both user and business:

• Logging into Facebook to look at Newsfeed
• Posting to Newsfeed (text, photo/video, photo/video album, live video)
• Viewing Newsfeed items, including watching a video
• Scrolling through the Newsfeed
• Clicking on Newsfeed items (content, name/photo of the poster)
• Commenting or replying to a comment
• Liking a Newsfeed item or a comment
• Sharing a Newsfeed item to your own or someone else’s wall (person, page, group), or as a private message
• Saving a post (yes, you can look at saved posts later… who knew?)
• Hiding, unfollowing, reporting, or turning off notifications for a Newsfeed item

Notice how the list is relatively comprehensive but not exhaustive: it covers all of the bases but doesn’t try to split hairs between all the different post, click, or comment types. We can also see how product design around creating or changing actions can be a major lever for impacting engagement.

Metrics is where this gets interesting. In trying develop the most useful, actionable metric, we can vet them through our various decision points:

• Direct vs. Proxy: Most of the actions above can be tracked directly with clicks except for viewing a feed item. For viewing, we may use a proxy to count the number of items viewed, such as “count viewed if 75% of the feed item is in the device viewport.”
• Individual vs. Aggregate: Because our primary goal is measuring engagement, we care about our users engaging with the Newsfeed in general more than any specific action, so we will define a metric with aggregate measures. With that said, some actions (like posting) are more valuable to us, so we may choose to group them differently. For example, we can aggregate Newsfeed posting actions into the first bucket, viewing actions into a second bucket, and interacting actions (inclusive of links, comments, share, blocks) into a third.
• Magnitudes vs. Ratios: Looking at the magnitude of Newsfeed action is relatively meaningless since any given user can generate a great number of actions. It is more useful if we consider either actions-per-day or actions-per-user (or both, i.e. actions-per-user-per-day).
• Intrinsic vs. Heuristic: Unfortunately, there’s no easy measure to determine whether someone is “engaged” or not. Let’s say a hypothetical analysis shows that people who post at least 1 feed item OR view at least 20 feed items OR interact with at least 3 feed items are 2x more likely to come back to Facebook the next day. We can use this data to develop a heuristic around an engagement metric, whereby if a user crosses any of the three thresholds above, they are considered “engaged.”

Pulling this all together, our metric for the Facebook Newsfeed will look something like this…:

…where “engaged user” is a heuristic that’s developed by aggregating both direct and proxy actions and comparing it as a ratio to “total users” on a daily basis.

Evaluations will come in the form of validating many of the assumptions we made above by putting your newly defined metric to work. For example:

• Honing Heuristics: We may find that the definition around “engaged user” is overly permissive and that 90% of our users are regularly considered “engaged,” which doesn’t leave much room for insight or improvement. We dig deeper to find that of the 90%, a majority became engaged through the “view at least 20 feed items” criteria, so we adjust that threshold up to 50 to create a higher bar for an engaged user.
• Disaggregating Actions: We may find that people who take actions against negative posts (hiding, unfollowing, reporting, or turning off notifications) actually correlates inversely to engagement. From this, we can decide to pull these actions out of our “interacting” bucket (bucket 3) and develop a separate metric to monitor negative Newsfeed behavior.
• Shifting Goals: We may find that we’ve done such a great job on engaging users with the Newsfeed that engagement is no longer the primary goal. Instead, we may decide to shift the goal to “monetization,” where we develop ad-related features into the Newsfeed. While engagement may not be the primary goal anymore, we want to continue monitoring the engagement metric to ensure the new monetization features do not have an adverse impact on engagement.

A Case for Defining Your Metrics Before You Need Them

In a “move fast and break things” world, it is tempting to concern yourself with metrics only after the product has launched. However, having metrics defined before you start development can translate to huge benefits because:

• Metrics help you maintain focus
• Metrics set a common goal for team to rally around
• Metrics hold you (the PM) accountable to your users and business
• Metrics enable more objective decision-making during the critical launch period, including triage and prioritization

Flying blind at any stage of your product’s lifecycle will substantially decrease your ability to learn. After all, you can’t improve what you don’t measure. Having the right data up front may be difference between success and failure for a startup!

I hope this has been a useful breakdown on how to develop metrics. Please leave comments if you have any thoughts or ideas about how to improve this post, or was able leverage the tools here to improve your metrics!

Don’t forget to check out Part 1 or Part 3 of this series!

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Hacker Noon is how hackers start their afternoons. We’re a part of the @AMI family. We are now accepting submissions and happy to discuss advertising & sponsorship opportunities.

If you enjoyed this story, we recommend reading our latest tech stories and trending tech stories. Until next time, don’t take the realities of the world for granted!

4 Steps to Defining GREAT Metrics for ANY Product was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

Also check out this piece I wrote for General Assembly on Product Vision!

“Strategy” — one of those words folks like to throw around to sound important. “We need a strategy…” or “what’s the strategy?” an executive may say. Everyone in the room nods in agreement. “Yeah, we need a better strategy.” Except half the room can’t describe what strategy is or how to get one.

Jokes aside, strategy is in fact very important.

For those who don’t constantly think about it or haven’t been exposed to a lot of it, strategy can often be confusing at best and intimidating at worst. Due to its abstract nature, strategy is also difficult to teach and impart on others.

(Negative) Impact of (the Lack of) Strategic Thinking on Teams and Individuals

Strategic thinking is often what sets senior members of a team apart from the junior folks. Within one’s career trajectory, I have observed that acquiring “strategic thinking” skills is among the greatest leaps (i.e. mental shifts) one undergoes.

For a Product Manager, you can get really good at the tactical stuff: requirements gathering, prioritization, sprint management. However if you can’t think strategically, you will be hampered when developing a long-term product vision, avoiding the feature factory, and getting executive buy-in on new product ideas. This can prevent you from becoming a Senior Product Manager or Product Leader.

On the flip side, if you are managing a team of relatively junior employees who lack strategic thinking skills, you will be left filling the gaps. This becomes a repetitive cycle of a) bearing the burden of developing 100% of the strategy for the group, b) validating (and even worse, second-guessing) everyone’s decisions, and/or c) reminding the team to get out of the weeds and see the forest for the trees.

I have experienced both the former and latter situations in my own work. In an effort to train and transform teams to be more strategic, I have developed a simple framework as well as the accompanying analogy and example to help bring more clarity and concreteness to this abstract concept.

The Framework

Before we talk about strategy, it needs to be contextualized within a few other concepts primarily because strategy never exists (or shouldn’t exist) in isolation. Here are the high-level definitions of the different components from a product-centric view:

• Mission: This is a statement that describes the problem you are setting out to solve, typically including who you are solving it for. This is often set at the company level, but can also be defined for a product or product line.
• Vision: This is the idealized solution that addresses the problem you’ve articulated in your mission. A product vision typically has just enough viability to be relatable, but is still devoid of the details that may hamper your imagination. Product vision is also the goal post and rallying point for your product — it paints the picture of how your product will make an impact.
• Strategy: This is a set of principles and decisions — informed by reality (e.g. market forces, laws of physics, data) and caveated with assumptions — that you commit to ahead of development to ensure the greatest likelihood of success in achieving your vision. Your strategy may evolve with the introduction of new data. If there’s a big shift in strategy while the mission and vision remain constant, it’s called a pivot.
• Roadmap: This is the manifestation of your strategy in concrete steps towards your product vision, inclusive of rough milestones and timelines. This, too, often changes given new data. (As I like to say, a roadmap is defunct as soon as it’s published.)
• Execution: This is the day-to-day activities along the path of the roadmap. This is where you do the hard work to build, launch, and iterate, all the while collecting the necessary data to inform any changes in your roadmap and strategy.

As the gradient and size of the diagram above suggests, the mission, by design, is both vague and broad. The further down you go in the stack, the more concrete and narrow these concepts become.

Strategy within this framework bridges the gap between what you aspire to be and what you are doing.

In other words, strategy represents the set of guiding principles for your roadmapping and execution tasks to ensure they align with your mission and vision.

Still too vague? Let’s apply the framework to an analogy.

The Analogy

Let’s say you are planning a road trip. Here’s how each of the concepts above break down:

• Mission: Road trip across the continental US from the Atlantic Ocean to the Pacific Ocean.

At this stage, you have a very high-level “problem” that needs a “solution,” but there are a lot of different ways to solve the problem.

• Vision: Drive a Subaru Forester from Portland, Maine to San Diego, California.

Now you have a clearer idea of what the road trip will be like. And with a bit of key descriptors like “Subaru Forester,” “Portland, Maine,” and “San Diego,” you can begin to paint a mental picture of the trip, making it relatable (e.g. in order to convince someone to join you on the trip). However, we’re still not quite ready to start driving.

• Strategy: Design a route that minimizes time in large cities and maximizes time around national parks. If possible, also try to save time and gas with the most direct route.

As you can see, strategies are decisions you make at the onset of your journey to structure your route, but you are also injecting constraints and preferences into the equation. As constraints go, you have limited resources (time, money for gas, etc.). As preferences go, you want to see the great outdoors (you’re in a Subaru Forester after all). With the analogy above, you have effectively prioritized your preferences over your constraints. Agreeing on the strategy up-front also allows you and your trip partners to discuss any future adjustments using a common baseline.

• Roadmap: This is your turn-by-turn directions, the literal map for your road trip route.

With the strategy defined, you can now plan your route with pretty concrete expectations of where you will be at any given point in time. This is also the point you get to choose what degree of detail you want to plan for — from the rough number of miles you want to cover each day (Agile velocity anyone?) to exactly where and when you want to have each of your meals. Note the more specific you are, the more likely you will have to adjust your plan.

• Execution: This is the actual drive for the road trip.

At this point, you’re using your roadmap as your guide and checking regularly to make sure you’re still on track. If something comes up during the drive (e.g. road closure due to snow), you can revisit and adjust your roadmap (take a detour), and perhaps add to or adjust your strategy as well (avoid smaller roads through higher-elevation areas, even if it takes us into a city).

Here’s an interesting point: you can absolutely start driving without a strategy. However, just like building a product without a strategy, you will spend way more time trying to find your way around, perhaps back-tracking, making a major decisions under time constraints, and getting into disagreements with your trip partners. Sure, some spontaneity could indeed be very fun and exciting. However, if you’re going on a trip without a strategy, you’re almost certainly going to end up incurring some costs down the road (pun intended).

The Example

Moving this framework back to the product world, I want to provide a relevant and concrete example using Tesla. I choose Tesla because a) Elon Musk is rad, b) Musk and Tesla have been unusually public and transparent about their strategy, and c) Tesla is a rare example of a company that has followed-through on their strategy with execution that is down to the “T” (pun also intended). This puts them into a godly territory that is almost difficult to believe.

• Mission: “Tesla’s mission is to accelerate the world’s transition to sustainable transport.” (This was recently updated when Tesla merged with SolarCity.)
• Vision: To summarize, Tesla’s vision is to reduce vehicle carbon emissions through the advent of electric vehicles.
• Strategy: This is the famous “Master Plan:” 1) build sports car, 2) use that money to build an affordable car, 3) use that money to build an even more affordable car, 4) while doing above, also provide zero emission electric power generation options, and then finally 5) don’t tell anyone.

You can see the strategy Tesla and Musk developed has some constraints built into it: a) costs/risk of product development and b) product cost vs. adoption. Musk knew he needed to proof viability (MVP) with a small, costly, and niche electrified Roadster. However, the Roaster would only need to generate enough revenue to carry-on with the mission and vision, plowing all the revenue back into the company to make progressively more affordable and mass-market vehicles.

While we unfortunately don’t have insights on their exact roadmap, it’s clear their execution has been going fairly well as evident by their vehicle production volumes and pre-order demand.

The Rubber Meeting the Road

You may ask: who does all of this work? Depending on the level you’re at, you may have done many or most of these activities already. Here are some typical examples of folks who work on these components:

• Mission: CEO, VP of Product, Executive Team, Board
• Vision: VP of Product, Director of Product Management, Senior/Lead PM
• Strategy: VP of Product, Director of Product Management, Senior/Lead PM, PM
• Roadmap: Senior/Lead PM, PM, maybe Associate PM
• Execution: Senior/Lead PM, PM, Associate PM

If you are a PM and you lack either visibility or alignment into the mission of the company or the vision for the product, it is an imperative you either develop one yourself and get buy-in, or ask for leaders within your organization to articulate them.

That said, unless you’re very early in your career, you should always be striving to think strategically.

I hope this has been a useful primer for those who are new to strategy!

For those who are well-versed in strategy, I also hope this gives you a structure that ties your experiences together and allows you share this with your teams and colleagues as a means of achieving shared vocabulary.

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Hacker Noon is how hackers start their afternoons. We’re a part of the @AMI family. We are now accepting submissions and happy to discuss advertising & sponsorship opportunities.

If you enjoyed this story, we recommend reading our latest tech stories and trending tech stories. Until next time, don’t take the realities of the world for granted!

WTF is Strategy? was originally published in HackerNoon.com on Medium, where people are continuing the conversation by highlighting and responding to this story.

Interested in learning more about PMing? Check out an earlier piece I wrote on WTF is a Product Manager!

I get regular inquiries, either from my students or folks within my network, on how one can transition into product management. I recently decided to see what other folks have also said on the topic as well. As expected, I found a host of great advice from a variety of folks who have been down the same path. Perhaps the best and most consolidated advice is presented in the following Quora thread:

How do I become a product manager?

What is the normal path to becoming a product manager?

If you haven’t looked here already, spend a bit of time going over the top 10 answers.

Tactics & Skills: Advice from the Internet

One of the trends struck me about the thread: the advice is overwhelmingly skills-based or tactically-oriented.

For example, the tactics:

“…work on a side project that gives you the experience in shipping a product…” — Kevin Lee

“…go to a small company — Look for a position vacuum (a product that is not managed actively that you could take on in your spare time) — start doing the work…” — Alex Kouts

And the skills:

“…Agile Software Development vs. the Waterfall Model (development)… Marketing… Software development cycle… Basic business concepts…” — Catherine Shyu

However, the general consensus is that no “normal” path exist to becoming a product manager.

This got me thinking about pathing for the range of people who want to become product managers.

The Issue with Pathing for Aspiring Product Managers

The pattern I noticed when speaking to aspiring PMs: they get stuck quickly after starting their research into the PM role. This is usually because they don’t have the necessary resources or connections to either gain the skills or complete the tactics. When the steps seem unattainable and you don’t have anyone to model after, what are you to do?

Key Steps Along the Path

Breaking down the anatomy of any generic career transition will provide some insights on how you may best forge a path. Here are the typical steps:

It’s important to understand that the first two steps are in your control, while the latter two steps depend on someone else choosing you. The latter two are gates.

Most advice will have you focus on these gates, because those are the transferrable, concrete, generically advisable skills and tactics. Getting the interview is a tactical process (get your foot in the door at your current work, network, referrals) while getting the job is primarily a game of skills (displaying knowledge or expertise during the interview). Hence the advice on Quora is primarily skills-based or tactically-oriented.

While investing in skills and tactics will always help you grow and improve, skills outside of the context of a path are continuous and unending. In order to identify a path, you will have to determine where you are now, how far your goals is, and whether you have a direct path towards your goal.

Do You have a Direct Path?

If you are “lucky,” you know someone like you who has become a PM, e.g…:

• … you are an engineer with product interest/business acumen, or…
• … you have an MBA, or you are a management consultant, or…
• … you work closely with a technical product team at your current company.

If you’re really really lucky, you are an engineer who got their MBA and became a consultant at McKinsey, then joined a small startup as a business operations person working closely with the product team. You have a direct path into product management.

When Creating Your Own Indirect Path is the Only Option

But if you’re not in those buckets — you need to travel an indirect path.

To you, who may not be fortunate enough to be in or around tech, reading about how there’s no normal path — and skills, and tactics — is helpful but insufficient.

You don’t currently work closely with a product team, or know many PMs, or have the resource or opportunities to start side projects with ease. Your path is amorphous, anxiety-inducing, and filled with uncertainty.

In this scenario, you may be left with the option of applying to random PM roles, which promptly leads to getting serially rejected. After being turned away a few too many times, the reality sets in: you may not become that PM at the amazing start-up like you’ve been dreaming of — at least not right away. If you have enough conviction not to be driven away by the fear of uncertainty or pain of rejection, there’s still hope: you can forge your own path.

Forging Your Own Path

Forging a path is what pioneers do when they are told their obstacles are insurmountable. But with any worthwhile goal, the crux is always the means, not the ends. Almost by definition, your path will not be the shortest or the fastest route. However, if you are steadfast, determined, and willing to take a few more steps than others, you will eventually become a product manager. This is the long-term investment necessary.

Because every path is custom, this is not going to be a one-size-fits-all plan. Forging your own path means taking the two steps you control and making choices that are unique to your circumstance, not just waiting for someone to choose you.

Making Your Own Choices That Take Advantage of Your Assets

Your personal path depends most heavily on the point of origin: you.

The choice of what jobs to look for and what jobs to apply towards should take into account assets you probably have.

What you should be doing, then, is:

1. Asset: identifying unique trades, knowledge, expertise, or connections
2. Choice: focusing on finding and applying to roles either within product management or adjacent to it

You should figure out what assets you have that other PM candidates don’t, and which jobs and companies are likely to value them.

I have so far seen two effective paths for anyone looking to become a PM via an indirect path:

1. Get a PM job in an industry in which you have expertise
2. Get a non-PM job in a company you want to be a PM for

Apply Strategically: A Product-Market Fit Problem

Crafting your strategy means targeting the right company in addition to the right role. You can consider this as a product-market fit problem. You are the product, and you can improve the product as much as you want in the areas you think matter (skills), but you’re not going to get a breakthrough selling the right product to the wrong market (role/company). As many have written about, picking your market is as much a part of success as building your product.

There are a lot of different tips and tricks you can use. I have written up a few different tactics that can help you fulfill your strategy here.

Examples of Custom Paths

Some examples of using your assets to apply to specialized companies, even if they’re not your top industry choices, are the following:

• Your photography hobby ->camera or photo management or portfolio hosting companies
• Your gaming habit-> gaming or e-sports or game app companies
• Your physics degree -> any hardware startup
• Your personal banking work experience-> consumer and B2B fintech

Alternatively, you can apply to companies that aren’t as likely to consider you for a PM role, and rather focus on a fitting, fulfilling role that also helps you transition in — i.e. setting yourself up for a direct path:

• Current IT Project Manager at large infrastructure company -> IT Project Manager at startup -> Infrastructure PM at the same startup
• Current Investment Banker -> finance role at a consumer startup -> Data PM at the same startup
• Current Graphic Designer at marketing agency -> Visual Designer at startup -> Consumer-facing PM at the same startup

Make sure that if you are taking these multi-step routes, the organization you’re joining is open to or has a history of internally transferring employees from other roles into product management.

Prioritize Your Target List to Hedge Your Paths

Once you begin to start to forge your paths, you may find multiple paths are plausible. Undoubtedly, while some of your shorter paths are a stretch, longer paths are more realistic and attainable.

Thoughtful and deliberate prioritization will be key to your job search and application process. Depending on your assets and preferences, you will want to rank the available job opportunities with key considerations, e.g. job title desirability, level of interest in the industry, job role fit, industry fit, etc.

Below is an example of someone who is currently an IT Project Manager looking to get into a consumer startup:

You will notice the desirability of each role is rank-ordered, which typically has a negative correlation with the likelihood of you getting a role. The key here is to spend a deliberate, non-zero amount of time shooting for those stretch jobs.

As you apply to these jobs, you will want to feed new data back to continuously evaluate your assets against your choices to make sure your assumptions about each job opportunity is valid.

At any point, when you have determined that it is not yet the right time for you to go for the top jobs, you can begin to go down the list for roles that are more attainable.

Some additional prioritization considerations:

• Especially with a career transition, any product title will make you a more desirable product management candidate the next time around, so be sure to give additional weight to those roles.
• If you have connections to certain companies that can earn you a warm referral, those should become high priority immediately.
• Depending on how much runway you have, i.e. whether you’re in need of a job right this moment or whether you have a bit of time for transition, you can break groups of jobs into tiers, e.g.: apply to jobs 1–5 over the next 30 days; if it’s no going well, move on to 5–8, etc.

I hope these points above will help you get closer to your dream job.

Good luck!

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Forge Your Own Path Into A Product Management Career was originally published in All Things Product Management on Medium, where people are continuing the conversation by highlighting and responding to this story.

Getting your proverbial foot-in-the-door is among hardest steps when are looking to transition to a new career.

Most career transition advice have you focusing either building specifics skills or following certain tried-and-true tactics to make sure someone chooses you, but I have argued that making the right decision on where you should apply to is just as important.

Being selective in your application is one of the most important choices you can make in your career transition process. Just as any company have their choice in a pool of candidates, you have your choice in the pool of jobs.

I believe optimizing your selective application strategy can significantly increase your likelihood of getting an interview. When it comes to choosing which company you want to apply to become a PM, I have found the following considerations helpful:

1. Founding/Leadership Team
2. Product Team History and Composition
3. Industry (relative to your experience/skills)

The key is to look for companies where each of the characteristics above works in your favor, increasing their likelihood of take a chance on you even if you don’t have the perfect background or experience.

1. Founding/Leadership Team

Key: look for companies run by people like yourself.

Here’s the inevitable truth: people hire those who are similar to themselves (partially explains why there’s a lack of diversity in tech…). However, you can use this fact to your advantage in your job search. Look for…:

• … founding/leadership teams with work experience/education that looks like yours
• … founding/leadership teams that tends towards being first-time executives

For example, a Founder/CEO who was previously a marketer is far more likely to believe someone with a marketing background will succeed in a PM role.

Similarly, someone who’s founding a company for the first time at age 25 will naturally be more risk tolerant and willing to take chance on someone else who may not be deeply experienced. A young, disruptive, intelligent problem-solvers will bias towards hiring other young, disruptive, intelligent problem-solvers in spite of (or perhaps even because of) their lack of experience.

How: do some research on LinkedIn and find all the executives at your target company. Especially check to see if the background of the CEO or the head of product have something in common with you. You can also check Crunchbase to learn about a startup leadership team, assuming they have been updating their company profile.

2. Product Team History and Composition

Key: look at what phase the product team is in.

Here is some of the stuff you want to find out:

• How many PMs do they have on staff?
• What is the background of various PMs?
• Who do the PMs report to?

You can quickly find out some key information, such as…:

• … whether the team only hire people with prior experience
• … whether the team favor internal transfers from other departments
• … whether the team has management bandwidth or a deep bench to mentor someone without PM experience.

Depending on the company and their inception/origin story, a PM role is either ingrained in their DNA as a key value-add role, or it is a organic gap that they could no longer be filled with the existing cross-functional team. Companies with deeply experienced PMs will also have the ability to hire and train people without prior experience. Knowing this will help you position your application.

How: Again, LinkedIn is the method-of-choice for this type of research. Do a search of [company name] + “product” and see how many current and former employees pop up. There’s a chance that some people haven’t put their title on LinkedIn, or the search misses it. Assume there are a few more PMs at a company in addition to the list you have found. Chances are, any company with more than a handful of PMs will have a organizational structure to handle management overhead.

3. Industry (relative to your experience/skills)

Key: look at what customers they serve and problem they solve for their customers (and how your experience/skills can give them a unique advantage).

This one is a bit more obvious — leverage your experience and expertise on a specific market or customer segment to land that PM gig. Questions to consider:

• Which industry is the company in?
• What are they trying to solve?
• Who are they solving those problems for?

If you are a sales person, you have a greater chance being considered for a PM role at a company where sales people are the customers (e.g. CRM) or sales people are a key part of a purchase customer decision (e.g. B2B). Same goes for bankers going into to fintech PM-ing, instructional designers going into edtech PM-ing, or game testers going into mobile games PM-ing.

These are some considerations I have previously found to be helpful in strategically optimizing a job search. Give this a try please let me know if you have found the same level of success as I have.

Good luck!

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Email addiction is real. You’ve probably read about it here, here, or here. Chances are you have experienced or are now experiencing email addiction. The symptoms include:

• Having your email app opened/pinned or push notifications on at all times
• Checking, responding to, or composing work email every free minute of every day
• Using email as your default swiss-army-file-storage-to-do-list-mass-comm-project-tracking-problem-solving-dispute-mediating tool
• Falling behind on emails just as soon as you get to the bottom of your inbox

Why is this bad?

Doing emails feel like work, but email not work. Email is meta-work: task is meant to enable further productivity. However, email often steals away valuable time instead. Getting stuck on the email hamster wheel have negative impacts like:

• Making you feel productive with small dopamine hits, but isn’t actually helping you get work done (thanks, cult of inbox zero!)
• Favoring urgency (quick reaction) instead importance (big impact) due to the instantaneousness of the format
• Sending emails often creates more work

This last point is important. I’ve often found myself “catching-up on emails” on weekends, and while I felt like productivity, the only things that weekend emailing produced were more weekend emails. Coworkers see your emails on the weekends, respond, and just like that, you have more emails. It’s a vicious cycle of meta-work.

How to Get Off the Hamster Wheel Without Losing Control

I’m going to miss an important email from my boss.

Someone is going reply to a mass email chain with an idea before I can.

Coworkers are going to think I’m lazy.

These among many other (substantiated and unsubstantiated) fears are probably popping up as you read. Through personal experience, I was able to get off that hamster wheel safely without any of the fears above manifesting.

Follow the 5 steps to email bliss (make sure you take all 5 steps; many people I have spoken to fail because they only take step 3).

Step 1: Set Expectations with People Around You

This is the first and most critical step: tell your manager, team members, and other immediate coworkers that you will be only be checking emails at specific times. The key here is to make sure they understand that even though you aren’t on emails, you are still responsive and available. To do this, I had set the following expectations:

1. I will only check emails 2–3 times a day, but you should still expect me to respond to most emails within 24-hours
2. For anything timely that can’t wait until the next day, reach me via IM (e.g. Google Hangouts, Slack, HipChat)
3. For anything truly urgent, call or text me

This series of escalation steps made it clear to everyone around me that I am still readily available. However, these expectations require that everyone thinks critically about priorities when trying to figure how best to reach me.

Of course, if you aren’t already, you will have to be extra responsive via IM or text.

Step 2: Set Your Work Environment Up for Success

The key to this step is to remove all temptations to go into your inbox rabbit hole. There are about a thousand triggers built into our email applications that want you to check them impulsively. Here are some key actions everyone should take immediately:

• Turn off your phone’s email notifications
• Close your email app on your computer/browser

To further ensure you’re setting yourself up for success, try these progressively intense suggestions and tactics:

• Mild email addiction: install apps or browser extensions that allow you to access certain email features (like composing an email) without going showing you the inbox
• Substantial email addiction: install apps or browser extension that totally block certain sites/apps to make it harder to access your inbox
• Severe email addiction: authorize your coworkers and desk mates to make you put a dollar in an “email jar” every time they catch you using email outside of the designated times (works well if a group is fighting addiction together)
• Extreme email addiction: buy a shock bracelet and program it to shock you when you access email (I had a friend who used this to control a Reddit addiction)

Feel free to get creative with whatever can help tilt the scale in your favor. If you’re in a role where quick response to inbound messages is a key metric (e.g. consulting, sales, customer service, BD, account management), consider setting up filters or other systems that parse out the valuable signals from the noise of your inbox.

Step 3: Carve Out Some Flex Time to Check Email

This is the obvious step, though most people jump right to this step without setting up some of the systems above.

For me, checking about 2–3 times during the day worked really well — you can adjust up or down according to the cadence of your role, team, or company. My three time slots were:

1. As much time as necessary first thing in the morning. I like to get in at least 1 hour before my first meeting.
2. Optional 30-minutes mid-day as time permits, preferably after I’ve had lunch so email doesn’t take over any fuel stops (forgetting to eat lunch seems like a growing problem at startups, despite all the free food!).
3. As much time as necessary right after my last meeting of the day.

As you may notice, these are dynamic blocks that work around my schedule. Having a bit of flexibility means you are less likely to break the habit, similar to having a cheat day during a diet.

An added bonus to setting time aside for emails: you will be less compelled to check your emails during off hours, whether it be in the evening at home or in bed first thing in the morning.

Step 4: Tune Your Email Checking to Your Daily Energy Cycles

Once I got into a cadence of specific time slots, I found myself optimizing the effectiveness of each time slot based on my energy level throughout the day. For example:

1. I’m a morning person, so I reserve my big reply-alls for my morning time slot, especially since letting complex problems stew in my mind overnight usually translates to better solutions.
2. Because the optional mid-day check is only 30-minutes, it’s reserved to glancing at my inbox for important threads based on subject lines, thread length, and CC group size. I only open the threads that require my immediate attention.
3. When I have the lowest energy and attention span after a day of meetings, my focus during the last time slot is to read through all of the unread emails to quickly sort/categorize them into 3 buckets: A) emails I need to read but don’t have to respond to, B) emails I can respond quickly to with no more than 3 sentences, and C) emails for which I have to compose a long response. As I read through, bucket A requires no further action, bucket B is responded to on-the-spot, and bucket C is marked for response the next morning after I’m recharged.

Of course, there is always going to be that occasional email that goes into bucket F (i.e. the f*ck bucket) but having a robust system means you can recover from any irregularities quickly without skipping a beat.

Step 5: Optimize Your Email Habits to be Even More Efficient

Once you’ve figured out the cadence and systems that work for you, you can continue to make your process more efficient since the ultimate goal is to waste less time on emails (i.e. effectiveness before efficiency).

Here are some examples of clever apps and UX that I have started to leverage over time:

• Using keyboard shortcuts to quickly batch process emails (archive, mark as important, reply) — this is great for the mid-day or end-of-day email checks
• Taking advantage of the swiping, scrolling, skipping, or nesting features to quickly go through all threads without going back to the inbox for every message
• Using only my phone for the mid-day or end-of-day email checks (even if I’m at my desk) because it keeps me from typing out long responses

Potential Step 0: Fighting the Inertia of the Hamster Wheel with a Catalyst

The more momentum is on your hamster wheel, the harder it will be to get off safely. Lots of pressure from different directions may be used to justify staying on the wheel just a bit longer.

To enable a safe dismount from the hamster wheel, you may need a substantial catalyst, like a set deadline to get away from day-to-day work for a bit. For me, this happened in the form of a 2-week off-the-grid vacation I had been planning for months.

The Results and the Lessons

Putting these habits in place over a few weeks of experimentation yielded a transformative experience for me and my career. Better email habits earned me the necessary room to prioritize and think critically during my work day. I was about to resolve problems and generate insights at a far greater frequency and higher quality than before.

I also found that a good chuck of email threads would resolve themselves even without my input.

Most importantly, the lesson that stuck with me most: things are simply not as urgent as they seem, and it takes a surprising amount of distance to see that.

Now imagine if an entire team or organization adapted these email habits — it would have a multiplier effect on productivity because everyone would be generating and responding to fewer unnecessary emails. Once something like this becomes the organization norm, a team focus everyone’s energy on much larger and more valuable problems.

What won’t be fixed even if you improve your email habits…

At the most fundamental level, email is a method of communication (albeit an ineffective and inefficient medium). For every benefit realized by better email habits, there are probably as many underlying organizational communication issues highlighted by those habits. I was lucky enough to be in a highly functional organization that provided a safe landing from the hamster wheel. However, many bad email habits can be attributed to root causes such as:

• Poor communications: lack of shared vocabulary, mission, or vision among groups of people
• Poor relationships: culture of resolving conflicts with escalations (e.g. looping in an executive for back-up)
• Poor meeting habits: unwillingness to schedule or ineffectiveness in running productive meetings
• Poor responsibility/accountability definitions: over-your-ass behavior to make sure discussions and responses are “on-the-record”
• Poor culture: organizational tolerance for grand-standing, credit-claiming, or general politicking

Even better email habits will not fix the organizational or cultural issues above, but it will enable to see and address the underlying issues.

Tweet at @vincelawco if you find this helpful. I would love to hear from you all on whether these techniques that worked for me also worked for you!

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Doing nothing is always an option. It’s not always a popular option, but it often gets overlooked as an option at all. Especially in times of crisis, the instinct to act is so great that we often forget doing nothing may be the best option. Before you jump into evaluating all of the possible solutions to your problem, it’s worth asking yourself: do you need to take action at all?

The Framework

One of the earliest lessons I received on decision-making frameworks was from an executive at my first PM job. There was a full-blown crisis with our product. Our Chief Product Officer pulled all the product managers into a conference room for triage. He proceeded to draw the following 2x2 matrix on the whiteboard:

The Purpose

Our CPO shared that he had learned this framework from a friend of his who is a surgeon. This is the framework that the surgeon uses to walk patients through tough decisions about a surgery. In the surgeon’s case, she uses the matrix to drive discussions about a patient’s decision to have surgery or not. This helps the patient break a complex decision down into its constituent pieces and make abundantly clear what the trade-offs are for his or her choices.

At any juncture when you have a desire to act, this framework can help you evaluate whether action is necessary, even if you may not know exactly what action needs to be taken yet. By filling out the 2x2 matrix, you start to define a few key components of your decisions. Doing this up front can potentially save valuable time and energy.

The Incredible Value of Best-case and Worst-case Outcomes

When you flush out the best- and worst-case scenarios when doing something or doing nothing, you’re effectively defining the full spectrum of plausible outcomes. The ends of the spectrum becomes the boundaries of your decision — knowing the range enables you take two critical actions:

1. Recognize extreme ends of the spectrum. This has extremely valuable because it allows you to set expectations on the full range of outcomes for your and your team, e.g. see that perhaps worst-case isn’t so bad after all, or conversely that the best-case doesn’t actually resolve your situation.
2. Draw a line on what an acceptable outcome is. Often times there are many solutions that solve a problem. Defining what the acceptable range of outcomes affords you the freedom choose whichever option that makes the most sense based on other optimization factors depending on your situation (cost, time, impact, …etc).

Especially in a crisis situation, taking these key actions will contribute heavily to your decision on whether to do anything at all — and if so, what all your options are and which can resolve the problem most optimally.

An Example

So let’s say I had a skiing accident and tore a ligament in my knee (true story). Since this type of injury does not heal on its own, the only option I have is knee reconstruction surgery.

This is what the 2x2 looks like for knee reconstruction surgery would look like:

You can begin to see the patterns:

• When you do nothing (the two squares on the bottom), the best- and worst-case outcomes are the same, effectively means there’s no upside to doing nothing.
• When you look at both worst-case scenarios (the two squares on the right), the only additional risk of doing something is surgery complications.
• When you look at both best-case scenarios (the two squares on the left), you can see a range of benefits when you choose do something.

From this, you can quickly deduce that doing something has greater value due to the number of upsides (stable, full range of motions, low chance of re-injury) and only one downside (surgery complications) compared to doing nothing.

Priorities and Probability of Success

One of the first things you may note with the example above is that different people may vary in how they prioritize the upsides. For an athlete or an active individual, stability and full range of activities is far more important — knowing this will certainly push the decision towards “doing something.”

What this framework will also prompt the critical questions about the probability of each outcome. Using the knee surgery example again, if your doctor was to tell you that knee surgery has a very high rate of success (i.e. 95%) and surgery complications are very unlikely, this will also push your decision towards “doing something.”

On the flip side, for someone in their 80’s, the probability of surgery complications increases dramatically and the desire to be fully active may be low, so someone in that position may consider opting for “doing nothing.”

Optimizing for the Best Solution Option

Once you’ve figured out you need to do something to address a problem, and if you have several solutions you can choose from to fulfill your range of acceptable outcomes, you can begin to optimize for other factors like cost and quality.

Drawing from the knee surgery example one last time, there are actually two common solution options for knee reconstruction. Grossly simplifying the options ( I’m not a doctor), you can choose to: 1) take ligament tissues from somewhere else on your body and transplant it to your knee, or 2) source ligament tissues from a donor. The former procedure takes longer to recover from but yields better results (higher recovery cost, higher-quality outcome) — as such, this is recommended for younger people with more expected years of use for their knees. The latter procedure takes less time and effort to recover from, but your knee will be noticeably weaker than before (lower recovery cost, lower-quality outcome) — this is usually recommended for older folks who is less likely be active.

Application to Product Management

While the most frequent and impactful usage of this framework is with crisis management, the same analysis can be applied to almost any action you take as a product manager. Since one of the core competency for product managers is to say “no,” being able to assess the cost and impact of doing nothing goes a long way in helping keep your teams focused on what’s most important. Since doing nothing is often not a popular choice, being able to articulate your rationale with a framework like this can earn you buy-in from stakeholders more quickly.

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Coaching people through a job offer decision is among the request I receive most frequently from my network (second only to “how do I become a Product Manager?”). The below is a distillation of the responses I’ve given over the years.

You’ve just landed an offer — congrats friend!

However, you’re not super excited about it. It’s not quite the dream job you’re hoping for. You don’t know if you’re going to take it.

You’ve probably envisioned what your dream job looks like. You can see it now: the pinnacle of happiness in your career. You’re working on something you’re passionate about. You’re working with great people. You feel competent about the work you do. You feel fulfilled. Maybe you have a corner office. Maybe this dream job affords you a big house and fast cars.

Unfortunately, this offer is decidedly not for your dream job. It passes the filter for all the common deal-breakers like money, time, and location, but you’re simply not sure about it.

What should you do now?

Most people I know obsess with finding their dream career. They often take this proverb to heart:

“Choose a job you love, and you will never have to work a day in your life.” — Anonymous

It’s certainly a laudable goal. While most of us have a vision (or at least some idea) of what makes them happy, many consider their dream job to be out of reach — whether it be because of a lack of experience, skills, connections, or otherwise. Having a framework that can bring that dream job down to earth is the key to gaining clarity on whether this offer is going to get you closer to your dream job.

What I mean by dream job…

In my mind, a dream job is not some sort of career aspiration like “corner office” or “lots of money.” Assuming again that all the basics (money, time, location) meets the baseline, a dream job to me is a relevant role with a great team in an industry you’re passionate about.

1. Relevant Role — is it the role you want to be in? Do you have the skills for it? Will you be responsible for, or have the opportunity to be responsible for, the activities and decisions you care about?
2. Great Team — who will be your manager and can they be able to mentor you and make you better? Who are your immediate teammates and do you respect their strengths, process, and culture? Who are in leadership positions and do you believe in their vision?
3. Industry You’re Passionate about — are you excited or passionate about the industry? Is the industry growing? Is the company solving a big, painful problem in the market?

It’s worth noting that, by this definition, there are actually many dream jobs. Maybe the job you’re in is one of them (in that case you should probably reject this new offer…). But assuming you’re not already in your dream job, and unfortunately, the offer you have in hand doesn’t fulfill all three requirements — what’s next?

Not perfect, but 2-out-of-3?

If you have an offer in hand and you have positive things to say about 2-out-of-3 of the above requirements, then take the job. Because 2-out-of-3 is a pretty freakin’ good job.

Think hard — how many people among your friends and professional network can say they have all three of the requirements above fulfilled? The fact is, a dream job is hard to come by and a majority of people aren’t there (yet). If your dream jobs, for whatever reason, are not within reasonable reach, finding a gig that gets 2-out-of-3 is the best next step and the closest thing on the path towards your dream job. Some may consider this selling out, but having this framework means taking this offer is a strategic step — a part of your roadmap towards that greater goal.

Heck, there’s even a chance that this 2-out-of-3 job will turn into a dream job through team changes, role transfers, or product pivots over time.

Needless to say, if your offer scores a 1- or 0-out-of-3, run away. Conversely, if your current job is already a 2-out-of-3, consider staying at your current job where you’ve built up relationship and reputation capital and start looking for that dream job instead.

Shouldn’t I shoot for the dream job anyway?

You’re absolutely entitled to look for that perfect job — the one that fulfills all three requirements, even if it seems out of reach. However, know that having the experience, network, or time to score your dream job is a luxury… a luxury most people earn by having made some tough trade-offs and compromises in their past. Unless you get really lucky, you will have to do your time, whether this be taking a less-than-perfect job, going back to school, or taking courses at bootcamps like General Assembly.

Whatever you do, start focusing your efforts towards doing your time with whatever is closest to a dream job — especially if your only alternative is to wait for that perfect one — because any minute you’re not getting highly relevant experience, you’re squandering potential progress towards your dream job.

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

Part 1 of Intro to Metrics

In this series…

• Part 1: WTF is a Metric? — introduction to the basics of metrics, what it is, and why it matters.
• Part 2: 4 Steps to Defining GREAT Metrics for ANY Product — how to develop a metric from scratch.
• Part 3: WTF is Your Metric Doing to Your Customers? — how to avoid the unintended consequences of the “one metric that matters.”

Beyond this series…

Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!

As a Product Manager, picking the right metrics may be the single most important thing you’ll do for your product. Having the right metrics can mean the difference between success and failure, not least is the fact that they MEASURE your success. The metrics will also become the ball you obsessively keep your eyes on. Metrics will likewise drive your team’s priorities, values, and culture.

You may find yourself asking this question:

“What does success look like?”

Every single strategic initiative or decision I have encountered has been accompanied by discussions addressing this question. We fundamentally understand success to be strongly correlated with setting achievable goals with concrete, quantifiable metrics. After all, we can’t improve what we don’t measure. Metrics—whether they be revenue, customer satisfaction, or engagement—become the driving force of our success.

For all their importance, how do I pick the RIGHT metrics? In the course of trying to answer this question, I realized I had to first define what a metric was. It was at this point I also realized that not once in my entire professional life has anyone explicitly told me what a metric is.

What constitutes a metric?

To most, the definition of the term “metric” may seem obvious, even trivial. However, when I attempt to seek out a clear and established definition of what a metric is, I am not able to find anything sufficiently substantive. Some resources related to the topic will mention the term’s definition, but only vaguely and casually in passing. Searching mainstream references like Dictionary.com and Wikipedia only resulted in lengthy articles about the metric system. Most relevant articles will mention something about quantitative measure or performance. They generally leave me desiring more depth and precision.

So, lacking an authoritative definition, I will, here and now, attempt to establish the meaning of the term:

metric

/ˈmetrik/ noun

In the broadest terms, a metric is any collectable, quantifiable measure that enables one to track the performance of an aspect of your product or business over time.

With the goal of being clear-cut and exhaustive about this definition, I would also like to be explicit about what I mean by each of the phrases used in this definition:

• “…collectable, quantifiable measure…” This means a metric must be a value you can somehow collect by instrumenting your product.
• “…track the performance…” You can measure a lot of dimensions of your product, but not all of them are tell-tale of its performance. Metrics must measure components of your product that are relevant to the product’s success (more on this in a subsequent part of this series).
• “…an aspect…” It’s important to note that rarely does one single metric give you a sufficient look into the health of your product. A product will typically have multiple metrics to represent its various aspects, and this suite of metrics together paints a complete picture.
• “…over time.” Metrics should capture changes in your product over time to help you set baselines with past data, to deduce trends, and to enable projections.

For example, a common metric called lifetime value (LTV) is calculated based on two commonly collected data points: number of payers and total amount in revenue they pay over their lifetime. Per the definition above, this metric tracks the performance of your product over time.

How are metrics special?

You may ask yourself: “Based on that definition though, can’t any number be a metric?” Not exactly. To further understand what a metric is, it’s also important to understand what a metric is not:

A “metric” is not an “analysis.”

This is a hard thing to pick apart, because you can analyze a metric, and metrics invariably contribute to your analysis. However, a metric is most commonly concerned with a high-level measure that provides a pulse to your product.

In contrast, an “analysis” (in quotes because I’m yet again referring to the specific context of a very broadly defined word) can any numerical value that is derived to provide insights beyond monitoring performance over time. An analysis can quantify as a trend in your metric for a specific period of time (e.g. LTV has on average increased 5% week-over-week for the past 3 months) or a comparison of your metric across different dimensions (e.g. users who sign up during the weekend are 1.28x more likely to buy during their first visit).

Metrics also tend to be simple combinations of data, i.e. one or two arithmetic transformations from raw data, whereas analysis are typically more involved and sometimes will require intensive calculation or statistical correlation.

A good mental model to apply when distinguishing between a metric and an analysis is this:

A metric will tell you that something is happening, while an analysis will tell you why something is happening.

If your body is a product, your body temperature, heart rate, blood pressure, and glucose levels would be metrics. If any of those metrics appears out of the ordinary, you would then run analyses, like blood tests or CT scans, to ascertain why.

Please check out “Part 2” of this series, where we discussed HOW metrics developed to ensure a success of a product!

Interested in more? Check out my blog at vincelaw.co/blog and subscribe to receive updates right in your inbox!