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Hypothesis-Driven Development

Hypothesis-driven development (hdd), also known as hypothesis-driven product development, is an approach used in software development and product management..

HDD involves creating hypotheses about user behavior, needs, or desired outcomes, and then designing and implementing experiments to validate or invalidate those hypotheses.

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Why use a hypothesis-driven approach?

With hypothesis-driven development, instead of making assumptions and building products or features based on those assumptions, teams should formulate hypotheses and conduct experiments to gather data and insights.

This method assists with making informed decisions and reduces the overall risk of building products that do not meet user needs or solve their problems.

How do you implement hypothesis-driven development

At a high level, here’s a general approach to implementing HDD:

• Identify the problem or opportunity: Begin by identifying the problem or opportunity that you want to address with your product or feature.
• Create a hypothesis: Clearly define a hypothesis that describes a specific user behavior, need, or outcome you believe will occur if you implement the solution.
• Design an experiment: Determine the best way to test your hypothesis. This could involve creating a prototype, conducting user interviews, A/B testing, or other forms of user research.
• Implement the experiment: Execute the experiment by building the necessary components or conducting the research activities.
• Collect and analyze data: Gather data from the experiment and analyze the results to determine if the hypothesis is supported or not.
• If the hypothesis is supported, you can move forward with further development.
• If the hypothesis is not supported, you may need to pivot, refine the hypothesis, or explore alternative solutions.
• Rinse and repeat: Continuously repeat the process, iterating and refining your hypotheses and experiments to guide the development of your product or feature.

Hypothesis-driven development emphasizes a data-driven and iterative approach to product development, allowing teams to make more informed decisions, validate assumptions, and ultimately deliver products that better meet user needs.

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6 Steps Of Hypothesis-Driven Development That Works

One of the greatest fears of product managers is to create an app that flopped because it's based on untested assumptions. After successfully launching more than 20 products, we're convinced that we've found the right approach for hypothesis-driven development.

In this guide, I'll show you how we validated the hypotheses to ensure that the apps met the users' expectations and needs.

What is hypothesis-driven development?

Hypothesis-driven development is a prototype methodology that allows product designers to develop, test, and rebuild a product until it’s acceptable by the users. It is an iterative measure that explores assumptions defined during the project and attempts to validate it with users’ feedbacks.

What you have assumed during the initial stage of development may not be valid for the users. Even if they are backed by historical data, user behaviors can be affected by specific audiences and other factors. Hypothesis-driven development removes these uncertainties as the project progresses. 

Why we use hypothesis-driven development

For us, the hypothesis-driven approach provides a structured way to consolidate ideas and build hypotheses based on objective criteria. It’s also less costly to test the prototype before production.

Using this approach has reliably allowed us to identify what, how, and in which order should the testing be done. It gives us a deep understanding of how we prioritise the features, how it’s connected to the business goals and desired user outcomes.

We’re also able to track and compare the desired and real outcomes of developing the features. 

The process of Prototype Development that we use

Our success in building apps that are well-accepted by users is based on the Lean UX definition of hypothesis. We believe that the business outcome will be achieved if the user’s outcome is fulfilled for the particular feature. 

Here’s the process flow:

How Might We technique → Dot voting (based on estimated/assumptive impact) → converting into a hypothesis → define testing methodology (research method + success/fail criteria) → impact effort scale for prioritizing → test, learn, repeat.

Once the hypothesis is proven right, the feature is escalated into the development track for UI design and development. 

Step 1: List Down Questions And Assumptions

Whether it’s the initial stage of the project or after the launch, there are always uncertainties or ideas to further improve the existing product. In order to move forward, you’ll need to turn the ideas into structured hypotheses where they can be tested prior to production.  

To start with, jot the ideas or assumptions down on paper or a sticky note. 

Then, you’ll want to widen the scope of the questions and assumptions into possible solutions. The How Might We (HMW) technique is handy in rephrasing the statements into questions that facilitate brainstorming.

For example, if you have a social media app with a low number of users, asking, “How might we increase the number of users for the app?” makes brainstorming easier. 

Step 2: Dot Vote to Prioritize Questions and Assumptions

Once you’ve got a list of questions, it’s time to decide which are potentially more impactful for the product. The Dot Vote method, where team members are given dots to place on the questions, helps prioritize the questions and assumptions. 

Our team uses this method when we’re faced with many ideas and need to eliminate some of them. We started by grouping similar ideas and use 3-5 dots to vote. At the end of the process, we’ll have the preliminary data on the possible impact and our team’s interest in developing certain features. 

This method allows us to prioritize the statements derived from the HMW technique and we’re only converting the top ones. 

Step 3: Develop Hypotheses from Questions

The questions lead to a brainstorming session where the answers become hypotheses for the product. The hypothesis is meant to create a framework that allows the questions and solutions to be defined clearly for validation.

Our team followed a specific format in forming hypotheses. We structured the statement as follow:

We believe we will achieve [ business outcome], 

If [ the persona],

Solve their need in  [ user outcome] using [feature]. ‍

Here’s a hypothesis we’ve created:

We believe we will achieve DAU=100 if Mike (our proto persona) solve their need in recording and sharing videos instantaneously using our camera and cloud storage .

Step 4: Test the Hypothesis with an Experiment

It’s crucial to validate each of the assumptions made on the product features. Based on the hypotheses, experiments in the form of interviews, surveys, usability testing, and so forth are created to determine if the assumptions are aligned with reality. 

Each of the methods provides some level of confidence. Therefore, you don’t want to be 100% reliant on a particular method as it’s based on a sample of users.

It’s important to choose a research method that allows validation to be done with minimal effort. Even though hypotheses validation provides a degree of confidence, not all assumptions can be tested and there could be a margin of error in data obtained as the test is conducted on a sample of people. 

The experiments are designed in such a way that feedback can be compared with the predicted outcome. Only validated hypotheses are brought forward for development.

Testing all the hypotheses can be tedious. To be more efficient, you can use the impact effort scale. This method allows you to focus on hypotheses that are potentially high value and easy to validate. 

You can also work on hypotheses that deliver high impact but require high effort. Ignore those that require high impact but low impact and keep hypotheses with low impact and effort into the backlog. 

At Uptech, we assign each hypothesis with clear testing criteria. We rank the hypothesis with a binary ‘task success’ and subjective ‘effort on task’ where the latter is scored from 1 to 10. 

While we’re conducting the test, we also collect qualitative data such as the users' feedback. We have a habit of segregation the feedback into pros, cons and neutral with color-coded stickers.  (red - cons, green -pros, blue- neutral).

The best practice is to test each hypothesis at least on 5 users. 

Step 5  Learn, Build (and Repeat)

The hypothesis-driven approach is not a single-ended process. Often, you’ll find that some of the hypotheses are proven to be false. Rather than be disheartened, you should use the data gathered to finetune the hypothesis and design a better experiment in the next phase.

Treat the entire cycle as a learning process where you’ll better understand the product and the customers. 

We’ve found the process helpful when developing an MVP for Carbon Club, an environmental startup in the UK. The app allows users to donate to charity based on the carbon-footprint produced. 

In order to calculate the carbon footprint, we’re weighing the options of

• Connecting the app to the users’ bank account to monitor the carbon footprint based on purchases made.
• Allowing users to take quizzes on their lifestyles.

Upon validation, we’ve found that all of the users opted for the second option as they are concerned about linking an unknown app to their banking account. 

The result makes us shelves the first assumption we’ve made during pre-Sprint research. It also saves our client $50,000, and a few months of work as connecting the app to the bank account requires a huge effort. 

Step 6: Implement Product and Maintain

Once you’ve got the confidence that the remaining hypotheses are validated, it’s time to develop the product. However, testing must be continued even after the product is launched. 

You should be on your toes as customers’ demands, market trends, local economics, and other conditions may require some features to evolve. 

Our takeaways for hypothesis-driven development

If there’s anything that you could pick from our experience, it’s these 5 points.

1. Should every idea go straight into the backlog? No, unless they are validated with substantial evidence. 

2. While it’s hard to define business outcomes with specific metrics and desired values, you should do it anyway. Try to be as specific as possible, and avoid general terms. Give your best effort and adjust as you receive new data.  

3. Get all product teams involved as the best ideas are born from collaboration.

4. Start with a plan consists of 2 main parameters, i.e., criteria of success and research methods. Besides qualitative insights, you need to set objective criteria to determine if a test is successful. Use the Test Card to validate the assumptions strategically. 

5. The methodology that we’ve recommended in this article works not only for products. We’ve applied it at the end of 2019 for setting the strategic goals of the company and end up with robust results, engaged and aligned team.

You'll have a better idea of which features would lead to a successful product with hypothesis-driven development. Rather than vague assumptions, the consolidated data from users will provide a clear direction for your development team. 

As for the hypotheses that don't make the cut, improvise, re-test, and leverage for future upgrades.

Keep failing with product launches? I'll be happy to point you in the right direction. Drop me a message here.

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How to Implement Hypothesis-Driven Development

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Remember back to the time when we were in high school science class. Our teachers had a framework for helping us learn – an experimental approach based on the best available evidence at hand. We were asked to make observations about the world around us, then attempt to form an explanation or hypothesis to explain what we had observed. We then tested this hypothesis by predicting an outcome based on our theory that would be achieved in a controlled experiment – if the outcome was achieved, we had proven our theory to be correct.

We could then apply this learning to inform and test other hypotheses by constructing more sophisticated experiments, and tuning, evolving, or abandoning any hypothesis as we made further observations from the results we achieved.

Experimentation is the foundation of the scientific method, which is a systematic means of exploring the world around us. Although some experiments take place in laboratories, it is possible to perform an experiment anywhere, at any time, even in software development.

Practicing Hypothesis-Driven Development [1] is thinking about the development of new ideas, products, and services – even organizational change – as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.

We need to change our mindset to view our proposed solution to a problem statement as a hypothesis, especially in new product or service development – the market we are targeting, how a business model will work, how code will execute and even how the customer will use it.

We do not do projects anymore, only experiments. Customer discovery and Lean Startup strategies are designed to test assumptions about customers. Quality Assurance is testing system behavior against defined specifications. The experimental principle also applies in Test-Driven Development – we write the test first, then use the test to validate that our code is correct, and succeed if the code passes the test. Ultimately, product or service development is a process to test a hypothesis about system behavior in the environment or market it is developed for.

The key outcome of an experimental approach is measurable evidence and learning. Learning is the information we have gained from conducting the experiment. Did what we expect to occur actually happen? If not, what did and how does that inform what we should do next?

In order to learn we need to use the scientific method for investigating phenomena, acquiring new knowledge, and correcting and integrating previous knowledge back into our thinking.

As the software development industry continues to mature, we now have an opportunity to leverage improved capabilities such as Continuous Design and Delivery to maximize our potential to learn quickly what works and what does not. By taking an experimental approach to information discovery, we can more rapidly test our solutions against the problems we have identified in the products or services we are attempting to build. With the goal to optimize our effectiveness of solving the right problems, over simply becoming a feature factory by continually building solutions.

The steps of the scientific method are to:

• Make observations
• Formulate a hypothesis
• Design an experiment to test the hypothesis
• State the indicators to evaluate if the experiment has succeeded
• Conduct the experiment
• Evaluate the results of the experiment
• Accept or reject the hypothesis
• If necessary, make and test a new hypothesis

Using an experimentation approach to software development

We need to challenge the concept of having fixed requirements for a product or service. Requirements are valuable when teams execute a well known or understood phase of an initiative and can leverage well-understood practices to achieve the outcome. However, when you are in an exploratory, complex and uncertain phase you need hypotheses. Handing teams a set of business requirements reinforces an order-taking approach and mindset that is flawed. Business does the thinking and ‘knows’ what is right. The purpose of the development team is to implement what they are told. But when operating in an area of uncertainty and complexity, all the members of the development team should be encouraged to think and share insights on the problem and potential solutions. A team simply taking orders from a business owner is not utilizing the full potential, experience and competency that a cross-functional multi-disciplined team offers.

Framing Hypotheses

The traditional user story framework is focused on capturing requirements for what we want to build and for whom, to enable the user to receive a specific benefit from the system.

As A…. <role>

I Want… <goal/desire>

So That… <receive benefit>

Behaviour Driven Development (BDD) and Feature Injection aims to improve the original framework by supporting communication and collaboration between developers, tester and non-technical participants in a software project.

In Order To… <receive benefit>

As A… <role>

When viewing work as an experiment, the traditional story framework is insufficient. As in our high school science experiment, we need to define the steps we will take to achieve the desired outcome. We then need to state the specific indicators (or signals) we expect to observe that provide evidence that our hypothesis is valid. These need to be stated before conducting the test to reduce the bias of interpretation of results.

If we observe signals that indicate our hypothesis is correct, we can be more confident that we are on the right path and can alter the user story framework to reflect this.

Therefore, a user story structure to support Hypothesis-Driven Development would be;

We believe < this capability >

What functionality we will develop to test our hypothesis? By defining a ‘test’ capability of the product or service that we are attempting to build, we identify the functionality and hypothesis we want to test.

Will result in < this outcome >

What is the expected outcome of our experiment? What is the specific result we expect to achieve by building the ‘test’ capability?

We will have confidence to proceed when < we see a measurable signal >

What signals will indicate that the capability we have built is effective? What key metrics (qualitative or quantitative) we will measure to provide evidence that our experiment has succeeded and give us enough confidence to move to the next stage.

The threshold you use for statistical significance will depend on your understanding of the business and context you are operating within. Not every company has the user sample size of Amazon or Google to run statistically significant experiments in a short period of time. Limits and controls need to be defined by your organization to determine acceptable evidence thresholds that will allow the team to advance to the next step.

For example, if you are building a rocket ship you may want your experiments to have a high threshold for statistical significance. If you are deciding between two different flows intended to help increase user sign up you may be happy to tolerate a lower significance threshold.

The final step is to clearly and visibly state any assumptions made about our hypothesis, to create a feedback loop for the team to provide further input, debate, and understanding of the circumstance under which we are performing the test. Are they valid and make sense from a technical and business perspective?

Hypotheses, when aligned to your MVP, can provide a testing mechanism for your product or service vision. They can test the most uncertain areas of your product or service, in order to gain information and improve confidence.

Examples of Hypothesis-Driven Development user stories are;

Business story.

We Believe That increasing the size of hotel images on the booking page Will Result In improved customer engagement and conversion We Will Have Confidence To Proceed When  we see a 5% increase in customers who review hotel images who then proceed to book in 48 hours.

It is imperative to have effective monitoring and evaluation tools in place when using an experimental approach to software development in order to measure the impact of our efforts and provide a feedback loop to the team. Otherwise, we are essentially blind to the outcomes of our efforts.

In agile software development, we define working software as the primary measure of progress. By combining Continuous Delivery and Hypothesis-Driven Development we can now define working software and validated learning as the primary measures of progress.

Ideally, we should not say we are done until we have measured the value of what is being delivered – in other words, gathered data to validate our hypothesis.

Examples of how to gather data is performing A/B Testing to test a hypothesis and measure to change in customer behavior. Alternative testings options can be customer surveys, paper prototypes, user and/or guerilla testing.

One example of a company we have worked with that uses Hypothesis-Driven Development is lastminute.com . The team formulated a hypothesis that customers are only willing to pay a max price for a hotel based on the time of day they book. Tom Klein, CEO and President of Sabre Holdings shared the story  of how they improved conversion by 400% within a week.

Combining practices such as Hypothesis-Driven Development and Continuous Delivery accelerates experimentation and amplifies validated learning. This gives us the opportunity to accelerate the rate at which we innovate while relentlessly reducing costs, leaving our competitors in the dust. Ideally, we can achieve the ideal of one-piece flow: atomic changes that enable us to identify causal relationships between the changes we make to our products and services, and their impact on key metrics.

As Kent Beck said, “Test-Driven Development is a great excuse to think about the problem before you think about the solution”. Hypothesis-Driven Development is a great opportunity to test what you think the problem is before you work on the solution.

We also run a  workshop to help teams implement Hypothesis-Driven Development . Get in touch to run it at your company. 

[1]  Hypothesis-Driven Development  By Jeffrey L. Taylor

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HDD & More from Me

Hypothesis-Driven Development (Practitioner’s Guide)

Table of Contents

What is hypothesis-driven development (HDD)?

How do you know if it’s working, how do you apply hdd to ‘continuous design’, how do you apply hdd to application development, how do you apply hdd to continuous delivery, how does hdd relate to agile, design thinking, lean startup, etc..

Like agile, hypothesis-driven development (HDD) is more a point of view with various associated practices than it is a single, particular practice or process. That said, my goal here for is you to leave with a solid understanding of how to do HDD and a specific set of steps that work for you to get started.

After reading this guide and trying out the related practice you will be able to:

• Diagnose when and where hypothesis-driven development (HDD) makes sense for your team
• Apply techniques from HDD to your work in small, success-based batches across your product pipeline
• Frame and enhance your existing practices (where applicable) with HDD

Does your product program feel like a Netflix show you’d binge watch? Is your team excited to see what happens when you release stuff? If so, congratulations- you’re already doing it and please hit me up on Twitter so we can talk about it! If not, don’t worry- that’s pretty normal, but HDD offers some awesome opportunities to work better.

Building on the scientific method, HDD is a take on how to integrate test-driven approaches across your product development activities- everything from creating a user persona to figuring out which integration tests to automate. Yeah- wow, right?! It is a great way to energize and focus your practice of agile and your work in general.

By product pipeline, I mean the set of processes you and your team undertake to go from a certain set of product priorities to released product. If you’re doing agile, then iteration (sprints) is a big part of making these work.

It wouldn’t be very hypothesis-driven if I didn’t have an answer to that! In the diagram above, you’ll find metrics for each area. For your application of HDD to what we’ll call continuous design, your metric to improve is the ratio of all your release content to the release content that meets or exceeds your target metrics on user behavior. For example, if you developed a new, additional way for users to search for products and set the success threshold at it being used in >10% of users sessions, did that feature succeed or fail by that measure? For application development, the metric you’re working to improve is basically velocity, meaning story points or, generally, release content per sprint. For continuous delivery, it’s how often you can release. Hypothesis testing is, of course, central to HDD and generally doing agile with any kind focus on valuable outcomes, and I think it shares the metric on successful release content with continuous design.

The first component is team cost, which you would sum up over whatever period you’re measuring. This includes ‘c $ ’, which is total compensation as well as loading (benefits, equipment, etc.) as well as ‘g’ which is the cost of the gear you use- that might be application infrastructure like AWS, GCP, etc. along with any other infrastructure you buy or share with other teams. For example, using a backend-as-a-service like Heroku or Firebase might push up your value for ‘g’ while deferring the cost of building your own app infrastructure.

The next component is release content, fe. If you’re already estimating story points somehow, you can use those. If you’re a NoEstimates crew, and, hey, I get it, then you’d need to do some kind of rough proportional sizing of your release content for the period in question. The next term, r f , is optional but this is an estimate of the time you’re having to invest in rework, bug fixes, manual testing, manual deployment, and anything else that doesn’t go as planned.

The last term, s d , is one of the most critical and is an estimate of the proportion of your release content that’s successful relative to the success metrics you set for it. For example, if you developed a new, additional way for users to search for products and set the success threshold at it being used in >10% of users sessions, did that feature succeed or fail by that measure? Naturally, if you’re not doing this it will require some work and changing your habits, but it’s hard to deliver value in agile if you don’t know what that means and define it against anything other than actual user behavior.

Here’s how some of the key terms lay out in the product pipeline:

The example here shows how a team might tabulate this for a given month:

Is the punchline that you should be shooting for a cost of $1,742 per story point? No. First, this is for a single month and would only serve the purpose of the team setting a baseline for itself. Like any agile practice, the interesting part of this is seeing how your value for ‘F’ changes from period to period, using your team retrospectives to talk about how to improve it. Second, this is just a single team and the economic value (ex: revenue) related to a given story point will vary enormously from product to product. There’s a Google Sheets-based calculator that you can use here: Innovation Accounting with ‘F’ .

Like any metric, ‘F’ only matters if you find it workable to get in the habit of measuring it and paying attention to it. As a team, say, evaluates its progress on OKR (objectives and key results), ‘F’ offers a view on the health of the team’s collaboration together in the context of their product and organization. For example, if the team’s accruing technical debt, that will show up as a steady increase in ‘F’. If a team’s invested in test or deploy automation or started testing their release content with users more specifically, that should show up as a steady lowering of ‘F’.

In the next few sections, we’ll step through how to apply HDD to your product pipeline by area, starting with continuous design.

It’s a mistake to ask your designer to explain every little thing they’re doing, but it’s also a mistake to decouple their work from your product’s economics. On the one hand, no one likes someone looking over their shoulder and you may not have the professional training to reasonably understand what they’re doing hour to hour, even day to day. On the other hand, it’s a mistake not to charter a designer’s work without a testable definition of success and not to collaborate around that.

Managing this is hard since most of us aren’t designers and because it takes a lot of work and attention to detail to work out what you really want to achieve with a given design.

Beginning with the End in Mind

The difference between art and design is intention- in design we always have one and, in practice, it should be testable. For this, I like the practice of customer experience (CX) mapping. CX mapping is a process for focusing the work of a team on outcomes–day to day, week to week, and quarter to quarter. It’s amenable to both qualitative and quantitative evidence but it is strictly focused on observed customer behaviors, as opposed to less direct, more lagging observations.

CX mapping works to define the CX in testable terms that are amenable to both qualitative and quantitative evidence. Specifically for each phase of a potential customer getting to behaviors that accrue to your product/market fit (customer funnel), it answers the following questions:

1. What do we mean by this phase of the customer funnel? 

What do we mean by, say, ‘Acquisition’ for this product or individual feature? How would we know it if we see it?

2. How do we observe this (in quantitative terms)? What’s the DV?

This come next after we answer the question “What does this mean?”. The goal is to come up with a focal single metric (maybe two), a ‘dependent variable’ (DV) that tells you how a customer has behaved in a given phase of the CX (ex: Acquisition, Onboarding, etc.).

3. What is the cut off for a transition?

Not super exciting, but extremely important in actual practice, the idea here is to establish the cutoff for deciding whether a user has progressed from one phase to the next or abandoned/churned.

4. What is our ‘Line in the Sand’ threshold?

Popularized by the book ‘Lean Analytics’, the idea here is that good metrics are ones that change a team’s behavior (decisions) and for that you need to establish a threshold in advance for decision making.

5. How might we test this? What new IVs are worth testing?

The ‘independent variables’ (IV’s) you might test are basically just ideas for improving the DV (#2 above).

6. What’s tricky? What do we need to watch out for?

Getting this working will take some tuning, but it’s infinitely doable and there aren’t a lot of good substitutes for focusing on what’s a win and what’s a waste of time.

The image below shows a working CX map for a company (HVAC in a Hurry) that services commercial heating, ventilation, and air-conditioning systems. And this particular CX map is for the specific ‘job’/task/problem of how their field technicians get the replacement parts they need.

For more on CX mapping you can also check out it’s page- Tutorial: Customer Experience (CX) Mapping .

Unpacking Continuous Design for HDD

For the unpacking the work of design/Continuous Design with HDD , I like to use the ‘double diamond’ framing of ‘right problem’ vs. ‘right solution’, which I first learned about in Donald Norman’s seminal book, ‘The Design of Everyday Things’.

I’ve organized the balance of this section around three big questions:

How do you test that you’ve found the ‘Right Problem’?

How do you test that you’ve found demand and have the ‘right solution’, how do you test that you’ve designed the ‘right solution’.

Let’s say it’s an internal project- a ‘digital transformation’ for an HVAC (heating, ventilation, and air conditioning) service company. The digital team thinks it would be cool to organize the documentation for all the different HVAC equipment the company’s technicians service. But, would it be?

The only way to find out is to go out and talk to these technicians and find out! First, you need to test whether you’re talking to someone who is one of these technicians. For example, you might have a screening question like: ‘How many HVAC’s did you repair last week?’. If it’s <10,  you might instead be talking to a handyman or a manager (or someone who’s not an HVAC tech at all).

Second, you need to ask non-leading questions. The evidentiary value of a specific answer to a general question is much higher than a specific answer to a specific questions. Also, some questions are just leading. For example, if you ask such a subject ‘Would you use a documentation system if we built it?’, they’re going to say yes, just to avoid the awkwardness and sales pitch they expect if they say no.

How do you draft personas? Much more renowned designers than myself (Donald Norman among them) disagree with me about this, but personally I like to draft my personas while I’m creating my interview guide and before I do my first set of interviews. Whether you draft or interview first is also of secondary important if you’re doing HDD- if you’re not iteratively interviewing and revising your material based on what you’ve found, it’s not going to be very functional anyway.

Really, the persona (and the jobs-to-be-done) is a means to an end- it should be answering some facet of the question ‘Who is our customer, and what’s important to them?’. It’s iterative, with a process that looks something like this:

How do you draft jobs-to-be-done? Personally- I like to work these in a similar fashion- draft, interview, revise, and then repeat, repeat, repeat.

You’ll use the same interview guide and subjects for these. The template is the same as the personas, but I maintain a separate (though related) tutorial for these–

A guide on creating Jobs-to-be-Done (JTBD) A template for drafting jobs-to-be-done (JTBD)

How do you interview subjects? And, action! The #1 place I see teams struggle is at the beginning and it’s with the paradox that to get to a big market you need to nail a series of small markets. Sure, they might have heard something about segmentation in a marketing class, but here you need to apply that from the very beginning.

The fix is to create a screener for each persona. This is a factual question whose job is specifically and only to determine whether a given subject does or does not map to your target persona. In the HVAC in a Hurry technician persona (see above), you might have a screening question like: ‘How many HVAC’s did you repair last week?’. If it’s <10,  you might instead be talking to a handyman or a manager (or someone who’s not an HVAC tech at all).

And this is the point where (if I’ve made them comfortable enough to be candid with me) teams will ask me ‘But we want to go big- be the next Facebook.’ And then we talk about how just about all those success stories where there’s a product that has for all intents and purpose a universal user base started out by killing it in small, specific segments and learning and growing from there.

Sorry for all that, reader, but I find all this so frequently at this point and it’s so crucial to what I think is a healthy practice of HDD it seemed necessary.

The key with the interview guide is to start with general questions where you’re testing for a specific answer and then progressively get into more specific questions. Here are some resources–

An example interview guide related to the previous tutorials A general take on these interviews in the context of a larger customer discovery/design research program A template for drafting an interview guide

To recap, what’s a ‘Right Problem’ hypothesis? The Right Problem (persona and PS/JTBD) hypothesis is the most fundamental, but the hardest to pin down. You should know what kind of shoes your customer wears and when and why they use your product. You should be able to apply factual screeners to identify subjects that map to your persona or personas.

You should know what people who look like/behave like your customer who don’t use your product are doing instead, particularly if you’re in an industry undergoing change. You should be analyzing your quantitative data with strong, specific, emphatic hypotheses.

If you make software for HVAC (heating, ventilation and air conditioning) technicians, you should have a decent idea of what you’re likely to hear if you ask such a person a question like ‘What are the top 5 hardest things about finishing an HVAC repair?’

In summary, HDD here looks something like this:

01 IDEA : The working idea is that you know your customer and you’re solving a problem/doing a job (whatever term feels like it fits for you) that is important to them. If this isn’t the case, everything else you’re going to do isn’t going to matter.

Also, you know the top alternatives, which may or may not be what you see as your direct competitors. This is important as an input into focused testing demand to see if you have the Right Solution.

02 HYPOTHESIS : If you ask non-leading questions (like ‘What are the top 5 hardest things about finishing an HVAC repair?’), then you should generally hear relatively similar responses.

03 EXPERIMENTAL DESIGN : You’ll want an Interview Guide and, critically, a screener. This is a factual question you can use to make sure any given subject maps to your persona. With the HVAC repair example, this would be something like ‘How many HVAC repairs have you done in the last week?’ where you’re expecting an answer >5. This is important because if your screener isn’t tight enough, your interview responses may not converge.

04 EXPERIMENTATION : Get out and interview some subjects- but with a screener and an interview guide. The resources above has more on this, but one key thing to remember is that the interview guide is a guide, not a questionnaire. Your job is to make the interaction as normal as possible and it’s perfectly OK to skip questions or change them. It’s also 1000% OK to revise your interview guide during the process.

05: PIVOT OR PERSEVERE : What did you learn? Was it consistent? Good results are: a) We didn’t know what was on their A-list and what alternatives they are using, but we do know. b) We knew what was on their A-list and what alternatives they are using- we were pretty much right (doesn’t happen as much as you’d think). c) Our interviews just didn’t work/converge. Let’s try this again with some changes (happens all the time to smart teams and is very healthy).

By this, I mean: How do you test whether you have demand for your proposition? How do you know whether it’s better enough at solving a problem (doing a job, etc.) than the current alternatives your target persona has available to them now?

If an existing team was going to pick one of these areas to start with, I’d pick this one. While they’ll waste time if they haven’t found the right problem to solve and, yes, usability does matter, in practice this area of HDD is a good forcing function for really finding out what the team knows vs. doesn’t. This is why I show it as a kind of fulcrum between Right Problem and Right Solution:

This is not about usability and it does not involve showing someone a prototype, asking them if they like it, and checking the box.

Lean Startup offers a body of practice that’s an excellent fit for this. However, it’s widely misused because it’s so much more fun to build stuff than to test whether or not anyone cares about your idea. Yeah, seriously- that is the central challenge of Lean Startup.

Here’s the exciting part: You can massively improve your odds of success. While Lean Startup does not claim to be able to take any idea and make it successful, it does claim to minimize waste- and that matters a lot. Let’s just say that a new product or feature has a 1 in 5 chance of being successful. Using Lean Startup, you can iterate through 5 ideas in the space it would take you to build 1 out (and hope for the best)- this makes the improbably probable which is pretty much the most you can ask for in the innovation game .

Build, measure, learn, right? Kind of. I’ll harp on this since it’s important and a common failure mode relate to Lean Startup: an MVP is not a 1.0. As the Lean Startup folks (and Eric Ries’ book) will tell you, the right order is learn, build, measure. Specifically–

Learn: Who your customer is and what matters to them (see Solving the Right Problem, above). If you don’t do this, you’ll throwing darts with your eyes closed. Those darts are a lot cheaper than the darts you’d throw if you were building out the solution all the way (to strain the metaphor some), but far from free.

In particular, I see lots of teams run an MVP experiment and get confusing, inconsistent results. Most of the time, this is because they don’t have a screener and they’re putting the MVP in front of an audience that’s too wide ranging. A grandmother is going to respond differently than a millennial to the same thing.

Build : An experiment, not a real product, if at all possible (and it almost always is). Then consider MVP archetypes (see below) that will deliver the best results and try them out. You’ll likely have to iterate on the experiment itself some, particularly if it’s your first go.

Measure : Have metrics and link them to a kill decision. The Lean Startup term is ‘pivot or persevere’, which is great and makes perfect sense, but in practice the pivot/kill decisions are hard and as you decision your experiment you should really think about what metrics and thresholds are really going to convince you.

How do you code an MVP? You don’t. This MVP is a means to running an experiment to test motivation- so formulate your experiment first and then figure out an MVP that will get you the best results with the least amount of time and money. Just since this is a practitioner’s guide, with regard to ‘time’, that’s both time you’ll have to invest as well as how long the experiment will take to conclude. I’ve seen them both matter.

The most important first step is just to start with a simple hypothesis about your idea, and I like the form of ‘If we [do something] for [a specific customer/persona], then they will [respond in a specific, observable way that we can measure]. For example, if you’re building an app for parents to manage allowances for their children, it would be something like ‘If we offer parents and app to manage their kids’ allowances, they will download it, try it, make a habit of using it, and pay for a subscription.’

All that said, for getting started here is- A guide on testing with Lean Startup A template for creating motivation/demand experiments

To recap, what’s a Right Solution hypothesis for testing demand? The core hypothesis is that you have a value proposition that’s better enough than the target persona’s current alternatives that you’re going to acquire customers.

As you may notice, this creates a tight linkage with your testing from Solving the Right Problem. This is important because while testing value propositions with Lean Startup is way cheaper than building product, it still takes work and you can only run a finite set of tests. So, before you do this kind of testing I highly recommend you’ve iterated to validated learning on the what you see below: a persona, one or more PS/JTBD, the alternatives they’re using, and a testable view of why your VP is going to displace those alternatives. With that, your odds of doing quality work in this area dramatically increase!

What’s the testing, then? Well, it looks something like this:

01 IDEA : Most practicing scientists will tell you that the best way to get a good experimental result is to start with a strong hypothesis. Validating that you have the Right Problem and know what alternatives you’re competing against is critical to making investments in this kind of testing yield valuable results.

With that, you have a nice clear view of what alternative you’re trying to see if you’re better than.

02 HYPOTHESIS : I like a cause an effect stated here, like: ‘If we [offer something to said persona], they will [react in some observable way].’ This really helps focus your work on the MVP.

03 EXPERIMENTAL DESIGN : The MVP is a means to enable an experiment. It’s important to have a clear, explicit declaration of that hypothesis and for the MVP to delivery a metric for which you will (in advance) decide on a fail threshold. Most teams find it easier to kill an idea decisively with a kill metric vs. a success metric, even though they’re literally different sides of the same threshold.

04 EXPERIMENTATION : It is OK to tweak the parameters some as you run the experiment. For example, if you’re running a Google AdWords test, feel free to try new and different keyword phrases.

05: PIVOT OR PERSEVERE : Did you end up above or below your fail threshold? If below, pivot and focus on something else. If above, great- what is the next step to scaling up this proposition?

How does this related to usability? What’s usability vs. motivation? You might reasonably wonder: If my MVP has something that’s hard to understand, won’t that affect the results? Yes, sure. Testing for usability and the related tasks of building stuff are much more fun and (short-term) gratifying. I can’t emphasize enough how much harder it is for most founders, etc. is to push themselves to focus on motivation.

There’s certainly a relationship and, as we transition to the next section on usability, it seems like a good time to introduce the relationship between motivation and usability. My favorite tool for this is BJ Fogg’s Fogg Curve, which appears below. On the y-axis is motivation and on the x-axis is ‘ability’, the inverse of usability. If you imagine a point in the upper left, that would be, say, a cure for cancer where no matter if it’s hard to deal with you really want. On the bottom right would be something like checking Facebook- you may not be super motivated but it’s so easy.

The punchline is that there’s certainly a relationship but beware that for most of us our natural bias is to neglect testing our hypotheses about motivation in favor of testing usability.

First and foremost, delivering great usability is a team sport. Without a strong, co-created narrative, your performance is going to be sub-par. This means your developers, testers, analysts should be asking lots of hard, inconvenient (but relevant) questions about the user stories. For more on how these fit into an overall design program, let’s zoom out and we’ll again stand on the shoulders of Donald Norman.

Usability and User Cognition

To unpack usability in a coherent, testable fashion, I like to use Donald Norman’s 7-step model of user cognition:

The process starts with a Goal and that goals interacts with an object in an environment, the ‘World’. With the concepts we’ve been using here, the Goal is equivalent to a job-to-be-done. The World is your application in whatever circumstances your customer will use it (in a cubicle, on a plane, etc.).

The Reflective layer is where the customer is making a decision about alternatives for their JTBD/PS. In his seminal book, The Design of Everyday Things, Donald Normal’s is to continue reading a book as the sun goes down. In the framings we’ve been using, we looked at understanding your customers Goals/JTBD in ‘How do you test that you’ve found the ‘right problem’?’, and we looked evaluating their alternatives relative to your own (proposition) in ‘How do you test that you’ve found the ‘right solution’?’.

The Behavioral layer is where the user interacts with your application to get what they want- hopefully engaging with interface patterns they know so well they barely have to think about it. This is what we’ll focus on in this section. Critical here is leading with strong narrative (user stories), pairing those with well-understood (by your persona) interface patterns, and then iterating through qualitative and quantitative testing.

The Visceral layer is the lower level visual cues that a user gets- in the design world this is a lot about good visual design and even more about visual consistency. We’re not going to look at that in depth here, but if you haven’t already I’d make sure you have a working style guide to ensure consistency (see  Creating a Style Guide ).

How do you unpack the UX Stack for Testability? Back to our example company, HVAC in a Hurry, which services commercial heating, ventilation, and A/C systems, let’s say we’ve arrived at the following tested learnings for Trent the Technician:

As we look at how we’ll iterate to the right solution in terms of usability, let’s say we arrive at the following user story we want to unpack (this would be one of many, even just for the PS/JTBD above):

As Trent the Technician, I know the part number and I want to find it on the system, so that I can find out its price and availability.

Let’s step through the 7 steps above in the context of HDD, with a particular focus on achieving strong usability.

1. Goal This is the PS/JTBD: Getting replacement parts to a job site. An HDD-enabled team would have found this out by doing customer discovery interviews with subjects they’ve screened and validated to be relevant to the target persona. They would have asked non-leading questions like ‘What are the top five hardest things about finishing an HVAC repair?’ and consistently heard that one such thing is sorting our replacement parts. This validates the PS/JTBD hypothesis that said PS/JTBD matters.

2. Plan For the PS/JTBD/Goal, which alternative are they likely to select? Is our proposition better enough than the alternatives? This is where Lean Startup and demand/motivation testing is critical. This is where we focused in ‘How do you test that you’ve found the ‘right solution’?’ and the HVAC in a Hurry team might have run a series of MVP to both understand how their subject might interact with a solution (concierge MVP) as well as whether they’re likely to engage (Smoke Test MVP).

3. Specify Our first step here is just to think through what the user expects to do and how we can make that as natural as possible. This is where drafting testable user stories, looking at comp’s, and then pairing clickable prototypes with iterative usability testing is critical. Following that, make sure your analytics are answering the same questions but at scale and with the observations available.

4. Perform If you did a good job in Specify and there are not overt visual problems (like ‘Can I click this part of the interface?’), you’ll be fine here.

5. Perceive We’re at the bottom of the stack and looping back up from World: Is the feedback from your application readily apparent to the user? For example, if you turn a switch for a lightbulb, you know if it worked or not. Is your user testing delivering similar clarity on user reactions?

6. Interpret Do they understand what they’re seeing? Does is make sense relative to what they expected to happen. For example, if the user just clicked ‘Save’, do they’re know that whatever they wanted to save is saved and OK? Or not?

7. Compare Have you delivered your target VP? Did they get what they wanted relative to the Goal/PS/JTBD?

How do you draft relevant, focused, testable user stories? Without these, everything else is on a shaky foundation. Sometimes, things will work out. Other times, they won’t. And it won’t be that clear why/not. Also, getting in the habit of pushing yourself on the relevance and testability of each little detail will make you a much better designer and a much better steward of where and why your team invests in building software.

For getting started here is- A guide on creating user stories A template for drafting user stories

How do you create find the relevant patterns and apply them? Once you’ve got great narrative, it’s time to put the best-understood, most expected, most relevant interface patterns in front of your user. Getting there is a process.

For getting started here is- A guide on interface patterns and prototyping

How do you run qualitative user testing early and often? Once you’ve got great something to test, it’s time to get that design in front of a user, give them a prompt, and see what happens- then rinse and repeat with your design.

For getting started here is- A guide on qualitative usability testing A template for testing your user stories

How do you focus your outcomes and instrument actionable observation? Once you release product (features, etc.) into the wild, it’s important to make sure you’re always closing the loop with analytics that are a regular part of your agile cadences. For example, in a high-functioning practice of HDD the team should be interested in and  reviewing focused analytics to see how their pair with the results of their qualitative usability testing.

For getting started here is- A guide on quantitative usability testing with Google Analytics .

To recap, what’s a Right Solution hypothesis for usability? Essentially, the usability hypothesis is that you’ve arrived at a high-performing UI pattern that minimizes the cognitive load, maximizes the user’s ability to act on their motivation to connect with your proposition.

01 IDEA : If you’re writing good user stories , you already have your ideas implemented in the form of testable hypotheses. Stay focused and use these to anchor your testing. You’re not trying to test what color drop-down works best- you’re testing which affordances best deliver on a given user story.

02 HYPOTHESIS : Basically, the hypothesis is that ‘For [x] user story, this interface pattern will perform will, assuming we supply the relevant motivation and have the right assessments in place.

03 EXPERIMENTAL DESIGN : Really, this means have a tests set up that, beyond working, links user stories to prompts and narrative which supply motivation and have discernible assessments that help you make sure the subject didn’t click in the wrong place by mistake.

04 EXPERIMENTATION : It is OK to iterate on your prototypes and even your test plan in between sessions, particularly at the exploratory stages.

05: PIVOT OR PERSEVERE : Did the patterns perform well, or is it worth reviewing patterns and comparables and giving it another go?

There’s a lot of great material and successful practice on the engineering management part of application development. But should you pair program? Do estimates or go NoEstimates? None of these are the right choice for every team all of the time. In this sense, HDD is the only way to reliably drive up your velocity, or f e . What I love about agile is that fundamental to its design is the coupling and integration of working out how to make your release content successful while you’re figuring out how to make your team more successful.

What does HDD have to offer application development, then? First, I think it’s useful to consider how well HDD integrates with agile in this sense and what existing habits you can borrow from it to improve your practice of HDD. For example, let’s say your team is used to doing weekly retrospectives about its practice of agile. That’s the obvious place to start introducing a retrospective on how your hypothesis testing went and deciding what that should mean for the next sprint’s backlog.

Second, let’s look at the linkage from continuous design. Primarily, what we’re looking to do is move fewer designs into development through more disciplined experimentation before we invest in development. This leaves the developers the do things better and keep the pipeline healthier (faster and able to produce more content or story points per sprint). We’d do this by making sure we’re dealing with a user that exists, a job/problem that exists for them, and only propositions that we’ve successfully tested with non-product MVP’s.

But wait– what does that exactly mean: ‘only propositions that we’ve successfully tested with non-product MVP’s’? In practice, there’s no such thing as fully validating a proposition. You’re constantly looking at user behavior and deciding where you’d be best off improving. To create balance and consistency from sprint to sprint, I like to use a ‘ UX map ‘. You can read more about it at that link but the basic idea is that for a given JTBD:VP pairing you map out the customer experience (CX) arc broken into progressive stages that each have a description, a dependent variable you’ll observe to assess success, and ideas on things (independent variables or ‘IV’s’) to test. For example, here’s what such a UX map might look like for HVAC in a Hurry’s work on the JTBD of ‘getting replacement parts to a job site’.

From there, how can we use HDD to bring better, more testable design into the development process? One thing I like to do with user stories and HDD is to make a habit of pairing every single story with a simple, analytical question that would tell me whether the story is ‘done’ from the standpoint of creating the target user behavior or not. From there, I consider focal metrics. Here’s what that might look like at HinH.

For the last couple of decades, test and deploy/ops was often treated like a kind of stepchild to the development- something that had to happen at the end of development and was the sole responsibility of an outside group of specialists. It didn’t make sense then, and now an integral test capability is table stakes for getting to a continuous product pipeline, which at the core of HDD itself.

A continuous pipeline means that you release a lot. Getting good at releasing relieves a lot of energy-draining stress on the product team as well as creating the opportunity for rapid learning that HDD requires. Interestingly, research by outfits like DORA (now part of Google) and CircleCI shows teams that are able to do this both release faster and encounter fewer bugs in production.

Amazon famously releases code every 11.6 seconds. What this means is that a developer can push a button to commit code and everything from there to that code showing up in front of a customer is automated. How does that happen? For starters, there are two big (related) areas: Test & Deploy.

While there is some important plumbing that I’ll cover in the next couple of sections, in practice most teams struggle with test coverage. What does that mean? In principal, what it means is that even though you can’t test everything, you iterate to test automation coverage that is catching most bugs before they end up in front of a user. For most teams, that means a ‘pyramid’ of tests like you see here, where the x-axis the number of tests and the y-axis is the level of abstraction of the tests.

The reason for the pyramid shape is that the tests are progressively more work to create and maintain, and also each one provides less and less isolation about where a bug actually resides. In terms of iteration and retrospectives, what this means is that you’re always asking ‘What’s the lowest level test that could have caught this bug?’.

Unit tests isolate the operation of a single function and make sure it works as expected. Integration tests span two functions and system tests, as you’d guess, more or less emulate the way a user or endpoint would interact with a system.

Feature Flags: These are a separate but somewhat complimentary facility. The basic idea is that as you add new features, they each have a flag that can enable or disable them. They are start out disabled and you make sure they don’t break anything. Then, on small sets of users, you can enable them and test whether a) the metrics look normal and nothing’s broken and, closer to the core of HDD, whether users are actually interacting with the new feature.

In the olden days (which is when I last did this kind of thing for work), if you wanted to update a web application, you had to log in to a server, upload the software, and then configure it, maybe with the help of some scripts. Very often, things didn’t go accordingly to plan for the predictable reason that there was a lot of opportunity for variation between how the update was tested and the machine you were updating, not to mention how you were updating.

Now computers do all that- but you still have to program them. As such, the job of deployment has increasingly become a job where you’re coding solutions on top of platforms like Kubernetes, Chef, and Terraform. These folks are (hopefully) working closely with developers on this. For example, rather than spending time and money on writing documentation for an upgrade, the team would collaborate on code/config. that runs on the kind of application I mentioned earlier.

Pipeline Automation

Most teams with a continuous pipeline orchestrate something like what you see below with an application made for this like Jenkins or CircleCI. The Manual Validation step you see is, of course, optional and not a prevalent part of a truly continuous delivery. In fact, if you automate up to the point of a staging server or similar before you release, that’s what’s generally called continuous integration.

Finally, the two yellow items you see are where the team centralizes their code (version control) and the build that they’re taking from commit to deploy (artifact repository).

To recap, what’s the hypothesis?

Well, you can’t test everything but you can make sure that you’re testing what tends to affect your users and likewise in the deployment process. I’d summarize this area of HDD as follows:

01 IDEA : You can’t test everything and you can’t foresee everything that might go wrong. This is important for the team to internalize. But you can iteratively, purposefully focus your test investments.

02 HYPOTHESIS : Relative to the test pyramid, you’re looking to get to a place where you’re finding issues with the least expensive, least complex test possible- not an integration test when a unit test could have caught the issue, and so forth.

03 EXPERIMENTAL DESIGN : As you run integrations and deployments, you see what happens! Most teams move from continuous integration (deploy-ready system that’s not actually in front of customers) to continuous deployment.

04 EXPERIMENTATION : In  retrospectives, it’s important to look at the tests suite and ask what would have made the most sense and how the current processes were or weren’t facilitating that.

05: PIVOT OR PERSEVERE : It takes work, but teams get there all the time- and research shows they end up both releasing more often and encounter fewer production bugs, believe it or not!

Topline, I would say it’s a way to unify and focus your work across those disciplines. I’ve found that’s a pretty big deal. While none of those practices are hard to understand, practice on the ground is patchy. Usually, the problem is having the confidence that doing things well is going to be worthwhile, and knowing who should be participating when.

My hope is that with this guide and the supporting material (and of course the wider body of practice), that teams will get in the habit of always having a set of hypotheses and that will improve their work and their confidence as a team.

Naturally, these various disciplines have a lot to do with each other, and I’ve summarized some of that here:

Mostly, I find practitioners learn about this through their work, but I’ll point out a few big points of intersection that I think are particularly notable:

• Learn by Observing Humans We all tend to jump on solutions and over invest in them when we should be observing our user, seeing how they behave, and then iterating. HDD helps reinforce problem-first diagnosis through its connections to relevant practice.
• Focus on What Users Actually Do A lot of thing might happen- more than we can deal with properly. The goods news is that by just observing what actually happens you can make things a lot easier on yourself.
• Move Fast, but Minimize Blast Radius Working across so many types of org’s at present (startups, corporations, a university), I can’t overstate how important this is and yet how big a shift it is for more traditional organizations. The idea of ‘moving fast and breaking things’ is terrifying to these places, and the reality is with practice you can move fast and rarely break things/only break them a tiny bit. Without this, you end up stuck waiting for someone else to create the perfect plan or for that next super important hire to fix everything (spoiler: it won’t and they don’t).
• Minimize Waste Succeeding at innovation is improbable, and yet it happens all the time. Practices like Lean Startup do not warrant that by following them you’ll always succeed; however, they do promise that by minimizing waste you can test five ideas in the time/money/energy it would otherwise take you to test one, making the improbable probable.

What I love about Hypothesis-Driven Development is that it solves a really hard problem with practice: that all these behaviors are important and yet you can’t learn to practice them all immediately. What HDD does is it gives you a foundation where you can see what’s similar across these and how your practice in one is reenforcing the other. It’s also a good tool to decide where you need to focus on any given project or team.

Copyright © 2022 Alex Cowan · All rights reserved.

LETSGO Game

Building A Music Engine

Hypothesis Driven Development

If there’s one thing that sends me, its putting a random word in front of “Driven Development” and preaching it as the best way to write software.

Ye oldheads will live n’ die by Test Driven Development (TDD).

There’s also Behavior Driven Development.

Hell I’ve pitched Superpower Driven Development as a framework. (Figure out what your teams “superpowers” are- what they’re really good at- and lean hard into those things).

Well color me excited when I was researching DORA metrics because I came across a new one:

Hypothesis-Driven Development

How to Implement Hypothesis-Driven Development | Thoughtworks

Practicing Hypothesis-Driven Development is thinking about the development of new ideas, products and services – even organizational change – as a series of experiments to determine whether an expected outcome will be achieved. The process is iterated upon until a desirable outcome is obtained or the idea is determined to be not viable.

www.thoughtworks.com

They be putting the word Hypothesis in front of Driven Development!

Without even clicking on anything I like the sound of it.

Immediately you envision a more “scientific approach” to software development.

Science is good, therefore Hypothesis Driven Development has to be good, right?

So there’s this thing called User Stories.

It’s a sentence that structures a set of work for software developers.

“ As a <role> I can <action>”

“ As a user I can create a new account”

It’s an Agile thing - Agile being arbitrary set of processes for writing software.

HDD morphs this User Story into a Hypothesis.

This Hypothetical User Story is constructed as:

“ We believe that <some action> will result in <some outcome> as measured by <some metric>.”

“ We believe that the ability to create an account will result in more accounts being created as measured by the total number of accounts.”

… Ok, look.

Maybe there’s not a 1:1 correlation here with a Certified Agile ™ user story.

Buuuut, in my most august opinion, user stories are sus to begin with.

Just make the damn account creation feature, amirite?

Here’s a better example:

“ We believe that overhauling the First-Time Experience user flow will result in increased user retention as measured by FTE completion and Day-3 retention metrics.”

Now that is a badass statement.

… It defines a domain of work without micromanaging.

… It has a simple value proposition.

… It has a clear set of metrics to measure that value.

A Measure of Success

This feeds into so many positive potentials.

For you SRE weirdos, those metrics forms the basis of a Service Level Indicator (SLI).

…That means you’re baking reliability into the design stage of a feature.

It also gives the team working on this feature a powerful measurement of value .

As soon as the feature hits prod, your team gets realtime customer feedback on their effort.

You push, FTE and D3 goes up:

Your devs see the effect their effort is having in real time.

That is way better than the feedback of “ we closed X number of tickets this sprint .”

It’s way more actionable too:

If D3 didn’t go up as expected, it indicates a problem you can investigate.

The team doesn’t need to wait for some external validation-

They’ll be pushing a fix before external teams even realize there’s a problem.

And those metrics are what you are communicating to your stakeholders.

Stakeholders, generally, are result driven.

They shouldn’t care how you do something.

They’re likely more focused on how successful you are at doing it.

By defining an exact metric of success, you have a unambiguous anchor for all communication with your stakeholders.

And the best part: it’s the exact same anchor you’re using internally.

If your team and your stakeholders are talking about the same thing, it’s way easier to stay aligned.

Much more aligned than a team internally tracking the number of tickets closed.

If everyone’s incentives are aligned, your teams become way more effective.

Grains of Salt

I’ll be honest, I haven’t tried this.

And I don’t really care whether teams run “Certified Hypothesis Driven Development.”

The real point is to add measurement definitions in the design phase of your development lifecycle.

It doesn’t matter how your teams build- just pull the analytics discussion into the design phase.

If you measure everything you do, you will know exactly how successful you have been.

And that will probably make you more successful.

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‼️ Hard Conversations

Practicing Hypothesis-Driven Development in Azure DevOps

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I like to think of Scrum Product Owners as mini-CEOs of their product. As such, they should be empowered to drive value in any direction that they desire. This often requires a Product Owner to hypothesize about an outcome and then run an experiment to prove or disprove it. Rather than just building features blindly, a Professional Product Owner bases their decisions on data.

Hypothesis-Driven Development

Hypothesis-Driven Development (HDD) is a complementary practice that incorporates an experimentation-based approach to product development. With HDD, each Product Backlog item (e.g. feature or user story) begins with a clearly defined hypothesis that predicts how this new capability will impact user behavior or achieve specific outcomes. The results from these experiments guide the next steps: whether to iterate, pivot, or abandon - all based on actual data rather than just guessing.

“Test-Driven Development is a great excuse to think about the problem before you think about the solution. Hypothesis-Driven Development is a great opportunity to test what you think the problem is before you work on the solution.” Kent Beck, creator of eXtreme Programming

HDD, based on Lean principles, can enhance Scrum's iterative nature by ensuring that the development of risky/costly items is based on empirical evidence and real user feedback, fostering a more focused and adaptive product development process.

Practicing Hypothesis-Driven Development

There are several ways to practice HDD and craft a hypothesis. I like the format put forward in this Thoughtworks post by Barry O'Reilly as it's akin to the user-story description format ...

Creating a Hypothesis work item type in Azure DevOps

The first step is to create a custom work item type in your Azure DevOps project. You will need to have the appropriate permissions to do this. Refer to this page for more information. I will, of course, create an inherited process based on the Scrum process, naming it Hypothesis-Driven Scrum and adding a new Hypothesis work item type ...

Next, I'll add the supporting fields and make any other tweaks (such as hiding the Description field) ...

I'll also edit the Backlog levels and include the new Hypothesis work item type on the Backlog items backlog ...

This is an optional step, but if you don't do this, then you will need to create a custom work item query to retrieve all of your Hypothesis work items. You might want to do this anyway so that you could have a dedicated dashboard showing the various hypotheses.

Forming and testing a hypothesis

Next, I'll create a new Azure DevOps project (or convert an existing one to use the new process). This provides me access to the new work item type, which I can start using to form, track, and manage my hypotheses, such as this one ...

Should you practice HDD with every item in the Product Backlog? I wouldn't. But, for those tricky, risky, and expensive features, I would definitely consider this approach, or at least starting to think in terms of hypothesis (and the expected, measurable outcomes), to help ensure that the product aligns with user needs as well as business and product goals.

Leveraging empirical evidence to make informed decisions? Sounds like good Scrum to me!

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Maslow’s Hierarchy of Needs

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Key Takeaways

• Maslow’s hierarchy of needs is a motivational theory in psychology comprising a five-tier model of human needs, often depicted as hierarchical levels within a pyramid.
• The five levels of the hierarchy are physiological, safety, love/belonging, esteem, and self-actualization.
• Lower-level basic needs like food, water, and safety must be met first before higher needs can be fulfilled.
• Few people are believed to reach the level of self-actualization, but we can all have moments of peak experiences.
• The order of the levels is not completely fixed. For some, esteem outweighs love, while others may self-actualize despite poverty. Our behaviors are usually motivated by multiple needs simultaneously.
• Applications include workplace motivation, education, counseling, and nursing.

What is Maslow’s Hierarchy of Needs?

According to Maslow (1943, 1954), human needs were arranged in a hierarchy, with physiological (survival) needs at the bottom, and the more creative and intellectually oriented ‘self-actualization’ needs at the top.

Maslow argued that survival needs must be satisfied before the individual can satisfy the higher needs. The higher up the hierarchy, the more difficult it is to satisfy the needs associated with that stage, because of the interpersonal and environmental barriers that inevitably frustrate us.

Higher needs become increasingly psychological and long-term rather than physiological and short-term, as in the lower survival-related needs.

1. Physiological needs are biological requirements for human survival, e.g., air, food, drink, shelter, clothing, warmth, sex, and sleep.

Our most basic need is for physical survival, and this will be the first thing that motivates our behavior. Once that level is fulfilled, the next level up is what motivates us, and so on.

The human body cannot function optimally if physiological needs are not satisfied. Maslow considered physiological needs the most important as all the other needs become secondary until these needs are met.

Once an individual’s physiological needs are satisfied, the need for security and safety becomes salient.

2. Safety needs –  people want to experience order, predictability, and control in their lives.

Safety needs can be fulfilled by the family and society (e.g., police, schools, business, and medical care).

For example, emotional security, financial security (e.g., employment, social welfare), law and order, freedom from fear, social stability, property, health, and well-being (e.g., safety against accidents and injury).

After physiological and safety needs have been fulfilled, the third level of human needs is social and involves feelings of belongingness.

3. Love and belongingness needs   refers to a human emotional need for interpersonal relationships, affiliating, connectedness, and being part of a group.

Examples of belongingness needs include friendship, intimacy, trust, acceptance, receiving and giving affection, and love.

This need is especially strong in childhood and can override the need for safety, as witnessed in children who cling to abusive parents.

4. Esteem needs are the fourth level in Maslow’s hierarchy and include self-worth, accomplishment, and respect.

Maslow classified esteem needs into two categories: (i) esteem for oneself (dignity, achievement, mastery, independence) and (ii) the desire for reputation or respect from others (e.g., status, prestige).

Esteem is the typical human desire to be accepted and valued by others. People often engage in a profession or hobby to gain recognition, which gives them a sense of contribution or value.

Low self-esteem or an inferiority complex may result from imbalances during this level in the hierarchy.

Maslow indicated that the need for respect or reputation is most important for children and adolescents and precedes real self-esteem or dignity.

5. Self-actualization needs are the highest level in Maslow’s hierarchy, and refer to the realization of a person’s potential, self-fulfillment, seeking personal growth, and peak experiences.

This level of need refers to what a person’s full potential is and the realization of that potential. 

Maslow (1943, 1987, p. 64 ) describes this level as the desire to accomplish everything that one can, and  “to become everything one is capable of becoming”.

Individuals may perceive or focus on this need very specifically. For example, one individual may have a strong desire to become an ideal parent.

In another, the desire may be expressed athletically. For others, it may be expressed in paintings, pictures, or inventions.

Although Maslow did not believe that many of us could achieve true self-actualization, he did believe that all of us experience transitory moments (known as ‘peak experiences’) of self-actualization.

Such moments, associated with personally significant events such as childbirth, sporting achievement and examination success), are difficult to achieve and maintain consistently.

Maslow posited that human needs are arranged in a hierarchy:

“It is quite true that man lives by bread alone — when there is no bread. But what happens to man’s desires when there is plenty of bread and when his belly is chronically filled? At once other (and “higher”) needs emerge and these, rather than physiological hungers, dominate the organism. And when these in turn are satisfied, again new (and still “higher”) needs emerge and so on. This is what we mean by saying that the basic human needs are organized into a hierarchy of relative prepotency” (Maslow, 1943, p. 375) .

Maslow (1954) proposed that human beings possess two sets of needs. This five-stage model can be divided into deficiency needs and growth needs. The first four levels are often referred to as deficiency needs ( D-needs ), and the top level is known as growth or being needs ( B-needs ).

Deficiency needs

Deficiency needs concern basic survival and include physiological needs (such as the need for food, sex, and sleep) and safety needs (such as the need for security and freedom from danger).

Behaviors associated with these needs are seen as ‘deficiency’ motivated, as they are a means to an end.

Deficiency needs arise due to deprivation and are said to motivate people when they are unmet. Also, the motivation to fulfill such needs will become stronger the longer they are denied. For example, the longer a person goes without food, the more hungry they will become.

Maslow (1943) initially stated that individuals must satisfy lower-level deficit needs before progressing to meet higher-level growth needs.

However, he later clarified that satisfaction of a need is not an “all-or-none” phenomenon, admitting that his earlier statements may have given “the false impression that a need must be satisfied 100 percent before the next need emerges” (1987, p. 69).

When a deficit need has been “more or less” satisfied, it will go away, and our activities become habitually directed toward meeting the next set of needs we have yet to satisfy. These then become our salient needs. However, growth needs continue to be felt and may even become stronger once engaged.

Growth needs

Growth needs are more psychological and are associated with realizing an individual’s full potential and needing to ‘self-actualize’. These needs are achieved more through intellectual and creative behaviors.

Growth needs do not stem from a lack of something but rather from a desire to grow as a person. Once these growth needs have been reasonably satisfied, one may be able to reach the highest level, called self-actualization. Growth needs are achieved more through intellectual and creative behaviors.

Every person is capable and has the desire to move up the hierarchy toward a level of self-actualization. Unfortunately, progress is often disrupted by a failure to meet lower-level needs.

Life experiences, including divorce and the loss of a job, may cause an individual to fluctuate between levels of the hierarchy.

Therefore, not everyone will move through the hierarchy in a uni-directional manner but may move back and forth between the different types of needs.

The expanded hierarchy of needs

It is important to note that Maslow’s (1943, 1954) five-stage model has been expanded to include cognitive and aesthetic needs (Maslow, 1970a) and later transcendence needs (Maslow, 1970b).

Changes to the original five-stage model are highlighted and include a seven-stage model and an eight-stage model; both developed during the 1960s and 1970s.

• Biological and physiological needs – air, food, drink, shelter, warmth, sex, sleep, etc.
• Safety needs – protection from elements, security, order, law, stability, freedom from fear.
• Love and belongingness needs – friendship, intimacy, trust, and acceptance, receiving and giving affection and love. Affiliating, being part of a group (family, friends, work).
• Esteem needs – which Maslow classified into two categories: (i) esteem for oneself (dignity, achievement, mastery, independence) and (ii) the need to be accepted and valued by others (e.g., status, prestige).

Cognitive needs – knowledge and understanding, curiosity, exploration, need for meaning and predictability. Cognitive needs drive our pursuit of knowledge and understanding. For instance, a student’s desire to understand complex mathematical theories, a traveler’s curiosity about diverse cultures, or an individual’s quest for life’s deeper meanings all exemplify these needs. Meeting these needs facilitates personal growth, comprehension, and a deeper understanding of life and its complexities.

Aesthetic needs – appreciation and search for beauty, balance, form, etc. Fulfilling these needs leads to a deeper sense of satisfaction and harmony in life, as individuals seek environments and experiences that are pleasing and resonant with their sense of beauty. This involves the appreciation and pursuit of art, music, nature, and other forms of aesthetic expression. Fulfilling these needs isn’t just about physical beauty but also the emotional and psychological satisfaction derived from experiencing order and elegance.

• Self-actualization needs – realizing personal potential, self-fulfillment, seeking personal growth, and peak experiences. 

Transcendence needs – A person is motivated by values that transcend beyond the personal self. Beyond self-actualization, they represent the human desire to connect with a higher reality, purpose, or the universe. This level emphasizes altruism, spiritual connection, and helping others achieve their potential. Individuals seek experiences that move beyond personal concerns, aiming to achieve a deep sense of unity, understanding, and belonging within the vast expanse of existence. Examples of transcendence needs include mystical experiences and certain experiences with nature, aesthetic experiences, sexual experiences, service to others, the pursuit of science, religious faith, etc.).

Self-Actualization Needs

Instead of focusing on psychopathology and what goes wrong with people, Maslow (1943) formulated a more positive account of human behavior which focused on what goes right. He was interested in human potential, and how we fulfill that potential.

Psychologist Abraham Maslow (1943, 1954) stated that human motivation is based on people seeking fulfillment and change through personal growth. Self-actualized people are those who are fulfilled and doing all they are capable of.

The growth of self-actualization (Maslow, 1962) refers to the need for personal growth and discovery that is present throughout a person’s life. For Maslow, a person is always “becoming” and never remains static in these terms. In self-actualization, a person comes to find a meaning in life that is important to them.

As each individual is unique, the motivation for self-actualization leads people in different directions (Kenrick et al., 2010). For some people, self-actualization can be achieved through creating works of art or literature; for others, through sports, in the classroom, or within a corporate setting.

Maslow (1962) believed self-actualization could be measured through the concept of peak experiences. This occurs when a person experiences the world totally for what it is, and there are feelings of euphoria, joy, and wonder.

It is important to note that self-actualization is a continual process of becoming rather than a perfect state one reaches of a “happy ever after” (Hoffman, 1988).

Maslow offers the following description of self-actualization:

“It refers to the person’s desire for self-fulfillment, namely, to the tendency for him to become actualized in what he is potentially. The specific form that these needs will take will of course vary greatly from person to person. In one individual it may take the form of the desire to be an ideal mother, in another it may be expressed athletically, and in still another it may be expressed in painting pictures or in inventions” (Maslow, 1943, p. 382–383).

Characteristics of Self-Actualized People

Although we are all, theoretically, capable of self-actualizing, most of us will not do so, or only to a limited degree. Maslow (1970) estimated that only two percent of people would reach the state of self-actualization.

He was especially interested in the characteristics of people whom he considered to have achieved their potential as individuals.

By studying 18 people, he considered to be self-actualized (including Abraham Lincoln and Albert Einstein), Maslow (1970) identified 15 characteristics of a self-actualized person.

Characteristics of self-actualizers :

• They perceive reality efficiently and can tolerate uncertainty;
• Accept themselves and others for what they are;
• Spontaneous in thought and action;
• Problem-centered (not self-centered);
• Unusual sense of humor;
• Able to look at life objectively;
• Highly creative;
• Resistant to enculturation, but not purposely unconventional;
• Concerned for the welfare of humanity;
• Capable of deep appreciation of basic life-experience;
• Establish deep satisfying interpersonal relationships with a few people;
• Peak experiences;
• Need for privacy;
• Democratic attitudes;
• Strong moral/ethical standards.

Behavior leading to self-actualization :

• Experiencing life like a child, with full absorption and concentration;
• Trying new things instead of sticking to safe paths;
• Listening to your own feelings in evaluating experiences instead of the voice of tradition, authority or the majority;
• Avoiding pretense (“game playing”) and being honest;
• Being prepared to be unpopular if your views do not coincide with those of the majority;
• Taking responsibility and working hard;
• Trying to identify your defenses and having the courage to give them up.

The characteristics of self-actualizers and the behaviors leading to self-actualization are shown in the list above. 

Although people achieve self-actualization in their own unique way, they tend to share certain characteristics.  However, self-actualization is a matter of degree, ‘There are no perfect human beings’ (Maslow, 1970a, p. 176 ).

It is not necessary to display all 15 characteristics to become self-actualized, and not only self-actualized people will display them.

Maslow did not equate self-actualization with perfection. Self-actualization merely involves achieving one’s potential. Thus, someone can be silly, wasteful, vain and impolite, and still self-actualize. Less than two percent of the population achieve self-actualization.

Applications & Examples

Workplace organizations and employee motivation.

The theory applies to organizational structures and the motivation of employees. To enhance performance, the organizational culture and HR strategies must address and fulfill the needs of employees.

HR strategies, including compensation, benefits, job design, training, cultural development, and performance evaluations, can be tailored to cater to Maslow’s hierarchy of needs (Jerome, 2013).

1. What can managers do to motivate employees with physiological needs?

At the foundational physiological level, organizations should provide wages that sustain a decent standard of living and comprehensive benefits, ensuring employees can comfortably cater to necessities such as food, shelter, and medical care.

• Offer comprehensive healthcare benefits – Quality health insurance, dental, vision, mental health coverage, and wellness programs demonstrate you care about employees’ overall health and ability to afford care.
• Subsidize gym memberships – Some companies offer monthly gym subsidies or onsite fitness centers to support physical health and stress management.
• Make the space ergonomic – Ensure workstations, chairs, keyboards, etc. are height adjustable and comfortable to work at for extended periods to prevent bodily strain or injury.
• Pay for wellness services – Some companies offer perks like free annual flu shots, smoking cessation programs, or biometric screenings to proactively address health.

2. What can managers do to motivate employees with safety needs?

For the safety tier, offering job stability, secure working conditions, and equitable compensation is essential. Employees are more motivated when they feel both financially stable and physically safe within their workplace.

• Establish anti-harassment policies and reporting procedures – Ensure strong systems are in place for reporting issues confidentially and without retaliation.
• Cultivate psychological safety – Foster an environment where people feel safe to take risks, make mistakes, and speak up without fear of embarrassment or punishment.
• Define and reinforce ethical standards – Clearly establish and model expected conduct to prevent ethical lapses that undermine security.
• Promote transparency in pay and promotion practices – Clearly communicate compensation structure, advancement criteria, and salary negotiation options to build trust.

3. What can managers do to motivate employees with social needs?

Addressing social needs involves cultivating an inclusive community within the organization. Team-building exercises, social gatherings, mentorship initiatives, and transparent communication can foster a sense of belonging. Motivation is heightened when employees feel appreciated and integrated within their teams.

• Develop mother’s rooms – Providing clean, private lactation rooms supports new mothers’ needs to pump breast milk during work hours.
• Train supervisors in mental health first aid – Equip leaders to recognize signs of depression, anxiety, substance abuse and properly intervene or connect employees with help.
• Develop a mentorship program – Allow experienced employees to mentor newer ones to establish interpersonal bonds and a sense of support.
• Model inclusive language and behavior – Use words and actions that are welcoming and respectful to all groups.
• Share vulnerability and imperfections – Leaders should open up on mistakes, challenges, and lessons learned to humanize the workplace.

4. What can managers do to motivate employees with esteem needs?

To cater to esteem needs, organizations should implement recognition systems, merit-based promotions, and leadership roles.

• Leverage unique talents – Properly designated titles that reflect an individual’s role and status can also be beneficial.
• Make the most of performance reviews – Regular performance evaluations not only offer recognition but also highlight areas for growth, feeding into the employees’ need for esteem. Thoroughly highlight strengths, progress made, and areas of influence.
• Entrust employees with mentoring roles – Having them share knowledge and coach others recognizes their expertise.

What can managers do to motivate employees with self-actualization needs?

For self-actualization, organizations should ensure that job roles align with employees’ talents and passions. By empowering employees, presenting them with challenges, and fostering an environment that encourages innovation, organizations can facilitate their journey toward self-actualization.

• Foster innovation – Dedicate time and resources for experimenting with new ideas without pressure.
• Sponsor continuing education – Provide tuition reimbursement or subsidies for advanced courses and certificate programs. Offer paid time for relevant reading, online courses, conferences, and seminars.

The hierarchy provides a framework for understanding patients as multifaceted human beings.

Patient care should be holistic, not just medical. Nurses must assess and address the spectrum of patient needs – physical, mental, emotional, and social (Jackson et al., 2014; Toney-Butler & Thayer, 2023).

Doing so motivates greater engagement in care, faster healing, and improved outcomes.

• A – Airway: Ensure the patient has an open airway.
• B – Breathing: Assess and support adequate breathing and gas exchange.
• C – Circulation: Evaluate and maintain proper blood circulation.
• D – Decreased level of consciousness: Monitor for any changes in behavior or mental status.

Explain tests, treatments, and medications to patients to relieve anxiety. Keep patient info confidential. Foster a climate of trust through compassionate listening. Prevent medication errors.

• Belongingness – Loneliness impedes healing. Make patients feel welcomed and included. Introduce them to other patients. Allow for family visitation and spiritual practices.

Empower patients in care decisions. Explain care in an easy-to-understand way. Listen attentively to their concerns. Make them feel valued.

• Self-actualization— Align care with patient values and aspirations. Perhaps share motivational stories of those with similar diagnoses who stayed active or provide resources on coping with grief over health changes.

Special Considerations

• Pain Management : While pain is typically considered a physiological need, its priority can vary. Acute, severe pain or pain indicating a life-threatening condition should be addressed immediately.
• Hospice Care : For end-of-life care patients, comfort and quality of life may take precedence over addressing physiological needs.

Maslow’s (1962) hierarchy of needs theory has made a major contribution to teaching and classroom management in schools. Rather than reducing behavior to a response in the environment , Maslow (1970a) adopts a holistic approach to education and learning.

Maslow examines an individual’s complete physical, emotional, social, and intellectual qualities and how they impact learning.

Applying Maslow’s hierarchy theory to the work of the classroom teacher is obvious. Before a student’s cognitive needs can be met, they must first fulfill their basic physiological needs.

For example, a tired and hungry student will find it difficult to focus on learning. Students need to feel emotionally and physically safe and accepted within the classroom to progress and reach their full potential.

Maslow suggests students must be shown that they are valued and respected in the classroom, and the teacher should create a supportive environment. Students with a low self-esteem will not progress academically at an optimum rate until their self-esteem is strengthened.

Maslow’s hierarchy provides a humanistic lens for teaching the whole child.

Maslow (1971, p. 195) argued that a humanistic educational approach would develop people who are “stronger, healthier, and would take their own lives into their hands to a greater extent. With increased personal responsibility for one’s personal life, and with a rational set of values to guide one’s choosing, people would begin to actively change the society in which they lived”.

Here are some ways a teacher can apply Maslow’s hierarchy of needs in the classroom:

• Physiological – Ensure students have access to water, food, restroom breaks, and movement. Allow snacks, flexible seating, and adequate breaks.
• Safety – Maintain an orderly classroom with clear expectations. Prevent bullying. Build trust through consistency and fairness. Allow students to make mistakes safely.
• Belongingness – Facilitate community and collaboration. Foster teamwork through group projects. Learn student names and backgrounds. Appreciate diversity.
• Esteem – Recognize student strengths and progress. Display student work. Empower leadership roles like line leader or tech helper. Praise efforts, not just achievement.
• Self-Actualization – Help students pursue interests creatively. Assign passion projects. Encourage goal-setting. Provide enrichment opportunities. Support challenging oneself.

When these foundational needs are met, students are more motivated to learn and perform well academically. But needs fluctuate. Be observant and nurture needs as they arise. 

Critical Evaluation

The most significant limitation of Maslow’s theory concerns his methodology. Maslow formulated the characteristics of self-actualized individuals by undertaking a qualitative method called biographical analysis.

He looked at the biographies and writings of 18 people he identified as being self-actualized. From these sources, he developed a list of qualities that seemed characteristic of this specific group of people, as opposed to humanity in general.

From a scientific perspective , there are numerous problems with this particular approach. First, it could be argued that biographical analysis as a method is extremely subjective as it is based entirely on the opinion of the researcher.

Personal opinion is always prone to bias, which reduces the validity of any data obtained. Therefore Maslow’s operational definition of self-actualization must not be blindly accepted as scientific fact.

Furthermore, Maslow’s biographical analysis focused on a biased sample of self-actualized individuals, prominently limited to highly educated white males (such as Thomas Jefferson, Abraham Lincoln, Albert Einstein, William James , Aldous Huxley, and Beethoven).

Although Maslow (1970) did study self-actualized females, such as Eleanor Roosevelt and Mother Teresa, they comprised a small proportion of his sample .

This makes it difficult to generalize his theory to females and individuals from lower social classes or different ethnicity. Thus questioning the population validity of Maslow’s findings.

Furthermore, it is extremely difficult to empirically test Maslow’s concept of self-actualization in a way that causal relationships can be established.

It is difficult to tell in Maslow’s theory where the scientific leaves off and the inspiration begins. His theory is seen as more speculative than empirically proven, with a tendency to substitute rhetoric for research.

Another criticism concerns Maslow’s assumption that the lower needs must be satisfied before a person can achieve their potential and self-actualize. This is not always the case, and therefore, Maslow’s hierarchy of needs in some aspects has been falsified .

Through examining cultures in which large numbers of people live in poverty (such as India), it is clear that people are still capable of higher-order needs such as love and belongingness.

However, this should not occur, as according to Maslow, people who have difficulty achieving very basic physiological needs (such as food, shelter, etc.) are not capable of meeting higher growth needs.

Also, many creative people, such as authors and artists (e.g., Rembrandt and Van Gogh) lived in poverty throughout their lifetime, yet it could be argued that they achieved self-actualization.

Psychologists now conceptualize motivation as a pluralistic behavior, whereby needs can operate on many levels simultaneously. A person may be motivated by higher growth needs at the same time as lower-level deficiency needs (Wahba & Bridwell, 1973).

Contemporary research by Tay and Diener (2011) has tested Maslow’s theory by analyzing the data of 60,865 participants from 123 countries, representing every major region of the world. The survey was conducted from 2005 to 2010.

Respondents answered questions about six needs that closely resemble those in Maslow’s model: basic needs (food, shelter); safety; social needs (love, support); respect; mastery; and autonomy. They also rated their well-being across three discrete measures: life evaluation (a person’s view of his or her life as a whole), positive feelings (day-to-day instances of joy or pleasure), and negative feelings (everyday experiences of sorrow, anger, or stress).

The results of the study support the view that universal human needs appear to exist regardless of cultural differences. However, the ordering of the needs within the hierarchy was not correct.

“Although the most basic needs might get the most attention when you don”t have them,” Diener explains, “you don”t need to fulfill them in order to get benefits [from the others].” Even when we are hungry, for instance, we can be happy with our friends. “They”re like vitamins,” Diener says about how the needs work independently. “We need them all.”

Maslow’s theory differs from more purely physiological representations of human motivation because motivation is seen as being not just concerned with tension reduction and survival but also with human growth and development.

While Maslow’s work was indeed relatively informal and clinically descriptive, it did provide a rich source of ideas, and as such, a framework for discussing the richness and complexity of human motivation that goes beyond homeostatic models and other biological models.

Frequently Asked Questions

What are some of the weaknesses of maslow’s theory.

Maslow proposes a positive view of humans, however, it could be argued that this might not be very realistic when considering everyday reality such as domestic violence and genocides.

Furthermore, the hierarchy’s focus on meeting our needs and fulfilling our growth potential reflects an individualistic, self-obsessed outlook that is part of the problem faced by our society rather than a solution.

How many levels are there in Maslow’s pyramid of needs?

There are five levels in Maslow’s pyramid. From the bottom of the hierarchy upwards, the needs are: physiological (food and clothing), safety (job security), love and belonging needs (friendship), esteem, and self-actualization.

Maslow asserted that so long as basic needs necessary for survival were met (e.g., food, water, shelter), higher-level needs (e.g., social needs) would begin to motivate behavior.

Why is Maslow’s hierarchy of needs important?

Maslow’s theory has given rise to a new way to look at people’s needs. For example, Maslow’s hierarchy of needs is widely used in health and social work as a framework for assessing clients’ needs.

Problems or difficult circumstances at one point in a person’s life can cause them to fixate on a particular set of needs, and this can affect their future happiness.

For example, a person who lived through a period of extreme deprivation and lack of security in early childhood may fixate on physiological and safety needs. These remain salient even if they are satisfied.

So even if this person later has everything they need they may nonetheless obsess over money or keeping enough food in the fridge.

This, for Maslow, was the root cause of many ‘neurotic’ mental health problems, such as anxiety or depression.

What is at the top of Maslow’s hierarchy of needs?

According to Maslow, the highest-level needs relate to self-actualization, a process by which we achieve our full potential.

Self-actualizing people have both a more efficient perception of reality and more comfortable relations with it. This includes the detection of what is phony and/or dishonest and the accurate perception of what really exists – rather than a distortion of perception by one’s needs.

Self-actualizers accept themselves, others and nature. They are not ashamed or guilty about being human, with shortcomings, imperfections, frailties, and weaknesses.

Nor are they critical of these aspects in other people. They respect and esteem themselves and others.

Geller, L. (1982). The failure of self-actualization theory: A critique of Carl Rogers and Abraham Maslow. Journal of Humanistic Psychology, 22, 56–73.

Hoffman, E. (1988). The right to be human: A biography of Abraham Maslow . Los Angeles, CA: Jeremy P. Tarcher.

Ivtzan, I. (2008). Self actualisation: For individualistic cultures only? International Journal on Humanistic Ideology, 1 , 113–140.

Jackson, J. C., Santoro, M. J., Ely, T. M., Boehm, L., Kiehl, A. L., Anderson, L. S., & Ely, E. W. (2014). Improving patient care through the prism of psychology: Application of Maslow’s hierarchy to sedation, delirium, and early mobility in the intensive care unit.  Journal of Critical Care ,  29 (3), 438-444.

Jerome, N. (2013). Application of the Maslow’s hierarchy of need theory; impacts and implications on organizational culture, human resource and employee’s performance.  International Journal of Business and Management Invention ,  2 (3), 39-45.

Kenrick, D. T., Neuberg, S. L., Griskevicius, V., Becker, D. V., & Schaller, M. (2010). Goal-driven cognition and functional behavior: The fundamental-motives framework . Current Directions in Psychological Science, 19 (1), 63-67.

King-Hill, S. (2015). Critical analysis of Maslow’s hierarchy of need.  The STeP Journal (Student Teacher Perspectives) ,  2 (4), 54-57.

Maslow, A. H. (1943). A theory of human motivation . Psychological Review, 50 (4), 370-96.

Maslow, A. H. (1954). Motivation and personality . New York: Harper and Row.

Maslow, A. H. (1962). Toward a psychology of being . Princeton: D. Van Nostrand Company.

Maslow, A. H. (1970a). Motivation and personality . New York: Harper & Row.

Maslow, A. H. (1970b). Religions, values, and peak experiences. New York: Penguin. (Original work published 1966)

Maslow, A. H. (1987). Motivation and personality (3rd ed.) . Delhi, India: Pearson Education.

Mittelman, W. (1991). Maslow’s study of self-actualization: A reinterpretation.  Journal of Humanistic Psychology ,  31 (1), 114-135.

Neher, A. (1991). Maslow’s theory of motivation: A critique. Journal of Humanistic Psychology, 31 , 89–112.

Tay, L., & Diener, E. (2011). Needs and subjective well-being around the world . Journal of Personality and Social Psychology, 101 (2), 354-356.

Toney-Butler, T.J., & Thayer, J.M. (2023, April 10). Nursing Process. In StatPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK499937/

Wahba, M. A., & Bridwell, L. G. (1976). Maslow reconsidered: A review of research on the need hierarchy theory . Organizational Behavior and Human Performance, 15 (2), 212-240.

Wulff, D. M., & Maslow, A. H. (1965). Religions, values, and peak-experiences. The Journal of Higher Education, 36 (4), 235.

Further Information

• Maslow’s Theories
• Maslow Hierarchy of Needs Infographic Poster
• Hierarchy of Needs
• Maslow Reconsidered: A Review of Research on the Need Hierarchy Theor
• BBC Radio 4 Programme: Maslow and the Hierarchy of Needs
• Questionnaire: Are you self-actualized? How to Write a Psychology Essay

Hierarchy of Needs and Nursing

• A Nursing Diagnosis Using Maslow’s Hierarchy of Needs
• Improving Patient Care Through the Prism of Psychology: application of Maslow’s Hierarchy to Sedation, Delirium and Early Mobility in the ICU
• Maslow’s Hierarchy of Needs Adapted for Nursing (Image)

Hierarchy of Needs in the Workplace

• Organizational Culture, Human Resource and Employee’s Performance
• Improving Workplace Productivity: Applications of Maslow’s Need Theory and Locke’s Goal-Setting

5 Steps to Continuous Measurement

• Continuous delivery
• Digital transformation

Software development is complex, expensive and time-consuming. Every business wants to get the highest return on projects, yet success remains typically grounded in meeting one’s schedule, scope and budget. We argue that different metrics, focused on the business outcomes of the delivered software, are more realistic measures of success. 

Over the last year, we have worked closely with a number of clients to explore different methods of measuring success based on outcomes over output. Outcomes and output are both important, albeit to measure different things. Output is a productivity measure, and outcome is a business measure. We experimented with methods to embed regular quantitative and qualitative measurement into the software development process to measure money earned rather than just story points. We regularly defined and used different business metrics at a story level to get fast feedback against initial goals. Business metrics were also used at a macro level for project governance.

We’re measuring it wrong

Often, we find a project is deemed successful if it delivers all features on time and on budget. However, is a project still successful if it delivered minimal business value? Studies have shown that more than 50% of functionality in software is rarely or never used. That is potentially 50% of resources wasted. Going back to original question, does it matter if something was delivered on time if it won’t be fully utilized?

IT projects regularly focus too heavily on their constraints instead of the value they are delivering. Scope, schedule and costs are easily understood and calculated, but benefits, if measured at all, are usually broad and non-specific. For example, project teams regularly report velocity and burn-up to stakeholders instead of the value they are delivering. Constraints are important, and teams should track them on a regular basis, but they shouldn’t be a measure of success.

We realized that to deliver business value, we need to rethink our approach to metrics. Here are five steps we took on our journey to Continuous Measurement:

#1 Change the Definition of “Done”

In order to measure a project’s success based on the value it delivers instead of its constraints, we had to challenge our established way of working. Traditionally, a feature is considered complete when it has passed all testing and is in production. We questioned this approach and did not count a feature as complete until we had measured its outcomes and learnt from it.

To implement this, we extended our Agile Story Wall and put a column labelled “Measured and Validated” to the right of the “Live” column, which is typically the end of the lifecycle. Adding this to the story wall meant the story was visibly incomplete until we had measured the effect of the feature. Therefore, when a story went live, it was still not complete on the story wall until it had been measured. Consequently, the team became focused on the outcome the story was delivering and not just getting the story live. The whole mindset shifted from delivering features to delivering measurable outcomes.

#2 Use Hypothesis Driven Development

By changing the definition of done, our efforts evolved from delivering what stakeholders thought was the highest priority story, to an experiment to see if the feature delivered value or not. However, the next problem we encountered was validating the story’s value against the initial goals and purpose.

Hypothesis Driven Development  solved this problem. Hypothesis development is derived from the scientific method. For every experiment, a person must make a hypothesis of what is expected to happen, based on research and findings. Afterwards, the experiment’s outcomes are measured against the initial hypothesis to see if it was correct or not.

We adopted a new User Story template to represent the story now being an experiment.  Initially, the most common user story template was:

As a <type of user>, I want <some goal>, so that <some reason>.

However, a user story template to support Hypothesis Driven Development would be:

We believe that <this capability> Will result in <this outcome>; We know we have succeeded when <we see this measurable signal>.

Capability represents what feature we will develop. Outcome refers to the specific business value expected by building the feature. Measurable signal include  the indicators that will illustrate whether the feature built has met the outcome expected. These are qualitative or quantitative metrics that will test the hypothesis in a defined time period.

Hypothesis and measurable signal are determined based on existing business data, persona-driven research, user testing, domain expertise, market analysis and other information. Some examples are:

Each story was measured, once it went live, to gauge its performance against the measurable signal. The results fed back into our product development cycle and influenced future hypotheses and remaining priorities. If a particular change in one part of our application produced unexpected results, we could apply that new real-world data point to other parts.

#3 Shorten your Feedback Cycle

Continuously measuring at a story level against defined hypotheses enabled a fast feedback cycle and quick learning. If a story under-performed against the hypotheses, it either went back into the pipeline to be improved (based on our new learning) or was rolled back. All learning that arose from this process, positive or negative, was critical to the formation of new hypotheses, subsequent story creation, and prioritisation.

Measuring business outcomes gives a development team a foundation for “failing fast” when a hypothesis doesn’t measure up to expectations. By constantly measuring the impact of stories, a team can quantify trends and determine the point at which decreasing return on investment means a project should pivot.

#4 Focus on business-oriented outcomes

As a result of this continuous measurement process, the development team’s focus is shifted from delivering software to a particular specification to delivering business oriented outcomes. This fundamentally aligned software delivery with business strategy and objectives.

Reporting the business measures and outcomes of the stories creates a shared understanding and improved communication among development team members. Specifically, they are able to more effectively communicate what has been delivered. Additionally, business executives can now understand the benefit of what has been delivered and become an advocate for IT.

#5 Implement Continuous Delivery, Design and Measurement

Continuous Delivery  infrastructure and the Continuous Design  processes enable teams to measure quickly and respond to new insights quickly. Continuous Delivery gives teams the ability to deliver frequently and get fast feedback at the push of a button. Continuous Design is the process of regular improvement and evolution of a system as it is developed, rather than specifying the complete design before development starts.

While Continuous Delivery lets us rapidly release features, Continuous Design enables us to iteratively adapt the design. Combining these with Continuous Measurement evolves software delivery. Continuous Measurement and learning is the missing link to this powerful combination, as it enables us to ensure we are building software that meets the business goals.

Continuous Measurement can also be applied to track macro-level progress towards key performance indicators.  This is similar to a traditional burn-up chart, which tracks story points to a target. Knowing that business outcomes are important, as opposed to story points, we decided to focus the burn-up towards our key goal. As an example, if the overall goal of the project is to increase conversion, a burn-up chart could be reported on an iterative basis, illustrating the progress towards the project goal.

This adjusted burn-up chart, showing progress in business terms, is more relevant and understandable across the organisation. The concept can be applied to any sector or industry, using different metrics that are relevant to the business situation and project goal.

The Continuous Measurement approach discussed is applicable for existing software applications, where one can use Continuous Delivery and design to get new features live as soon as possible. This enables Continuous Measurement of the outcomes against the hypotheses.

For a greenfield software project, other techniques (e.g., usability testing) are available to measure potential outcomes and validate hypotheses before the first release. New learning may lead to hypotheses of higher value stories to pursue, or may lead the team to “fail fast” without further investment, cutting losses as compared to a lengthy period of analysis. In any case, measures should be applied at a granular level over the course of the project and not only at the end.

For software projects to be deemed successful, it is important to measure the business impact that the software has achieved and not just use traditional measures of schedule, scope and budget. In order to do this, Continuous Measurement should be an integral part of the software development process.

Each story should have an expected outcome that can be measured and validated within a certain time period. Validating outcomes will generate new insights, which should be incorporated into a fast feedback cycle and influence future development. Tracking the success of the project can be achieved by introducing macro-level burn-up towards key business performance indicators.

This process results in a shared understanding that will shift the focus to align software delivery with business strategy and objectives. Combining Continuous Design and Delivery with Continuous Measurement allows software projects to take a more outcome-focused approach that ensures business goals are not only met, but are also quantified.

Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.

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