product development hypothesis

UX Collective

Ivan Schneiders

Apr 25, 2021

How to create product design hypotheses: a step-by-step guide

(or, how to take down a rampaging hippo in one move).

So, you’ve decided to get your product team running Lean. You might even have a degree in behavioural science and understand the scientific method as if you were suckled on Karl Popper ’s breast, but your team… not so much. Worse still, the minute you start talking about induction and deduction or null and alternate hypotheses everyone’s eyes glaze over and you realise that if this was an experiment you’d be rejecting the hypothesis that ‘Lean will help me make better products’ (and imagine trying to explain that you’d actually be accepting the null hypothesis). Let’s not do that. I’m going to simply walk you through the what, why and how of hypothesis-driven design with a step-by-step guide.

(Cheat code: There’s a one-minute guide at the bottom of this page).

What is a product design hypothesis?

Well, the first thing to accept is that no matter how much research you do, your product is just a theoretical solution to a human need or want that you hope will result in business success. The hypothesis is your guess at why a particular solution will succeed. Once you succeed, and people are buying and using your product it’s no longer a hypothesis. It’s a fact.

Where do hypotheses come from?

I’m lazy, so when it comes to why we need good hypotheses the answer is, crap in means crap out because alchemy is not a thing. We won’t learn anything useful if our hypotheses are not insightful and well-formed. A lot of people that take on the Lean approach seem to think that they should just get in a room ‘ideate’, then throw it out to customers and see what sticks. Do that and chances are we’re going to waste a lot of time and money. Good hypotheses come from good observations . If you want to be more than a one-hit-wonder and develop an ongoing process of product development and innovation you need to find statistically significant problems to solve. This is what observation is for and why research is essential. Once you understand the space you’re working in ideation can begin and it can be systematic and rigorous.

Starting vision

Imagine, for example, that you have discovered that a lot of people suffer back pain as a result of sitting at an office desk and you want to make a product that solves this issue. Once you have formed a good customer problem statement and prioritised your project by mapping its risks , your observations and business goals need to be summed up in a clearly articulated vision of the future. Once you know the general direction your going, you can move into solving problems.

It’s okay to use your imagination

Don’t let being scientific mean that your team turns into a group of soulless empiricists who may have learned how to apply a Vulcan nerve pinch but couldn’t design their way out of a paper bag.

Imagination is essential and has a very important role to play. Use it to diverge, and create as many possible solution ideas as possible. These are your hypotheses.

The actual Step-by-Step Guide starts here…

Step 1: imagine the change you want, and write it down.

What will the world be like for users of your product or service once they have it? This is your outcome, it’s your grand design, your vision for the future in which your product or service is a huge success and peoples lives are transformed. For example:

People who use our product no longer suffer back pain. They regard the product as a necessity and a delight. In fact, significant numbers of customers order more than one and frequently request the expansion of our product range. They love the product so much they basically sell them for us, and in the first year of release, we have sold 300,000 units and have over 30 distributors nationally.

This is your outcome statement. It provides a point of focus moving forward and should direct your investigation in general as well as directing your choice of success metrics for each and every experiment. A well-formed vision will cover the product’s desirability, viability and feasibility.

Step 2: Why is the status quo is the status quo

Now that you know what the world will be like after you succeed you need to ask, ‘What’s preventing the outcome being achieved?’ That is, ‘why isn’t it already how you want it?’

If these causes were removed your outcome would be already a reality. These are the root of your hypotheses. There should be a few of these and they are often multi-layered. There are two parts to this, one is causes and the other is blockers. A cause might be that muscles seize when not moved, a blocker however can be behavioural or situational, like, ‘I’m too lazy to do regular exercise’ ;) This is important because we have to work on things we can affect. The problem we are solving might be that the poorest people in our community have no savings, one cause is low income, and that could easily reduce the ideation to increasing income. However, it turns out that often this segment spends a meaningful amount on lottery tickets. By going beyond simple causes and looking at blockers we will broaden our potential for solutions. And remember,

Behavior is the medium of design — Robert Fabricant

So, back to our back pain problem:

You get the idea. Write as many as you can think of, obviously it’s preferable if these are based on actual research.

Step 3: Dream like a scientist, ideate like a lunatic

Using your imagination effectively doesn’t mean smoking weed and waiting for the muse to magically infer upon you the perfect solution. Conversely, you don’t have to try to be a genius, experimentation will do that work for us and make us look like Einsteins.

Consider each of the obstacles listed above one at a time. What new ways of doing things might we try in order to remove these obstacles? What are the alternatives to the way things are currently done?

Let’s take cause number 2. People sit for too long causing ligaments and muscles to tighten a) They don’t move their muscles enough b) They aren’t sufficiently motivated to move until it’s too late c) They forget to move regularly … as many as you can find

and ideate some solutions…

It’s very easy to start ideating when the problems you’re solving for are specific and granular enough to engage with directly. Broad and abstract goals are very hard to ideate on because the parameters are too vague. For me it’s like being confronted with a blank canvas, it’s all a bit overwhelming and it’s very easy to freeze up creatively. More importantly, you’re now a hair’s breadth from having a rock-solid hypothesis to test.

Step 4: Writing hypotheses

I’m sure all your ideas are amazing, just like mine ;). However, it is statistically possible that some are more, or less, amazing than others. So at this stage let’s agree that it’s still all hypothetical whether or not our ideas will successfully remove the obstacles to our desired outcome. Which brings us to the next step, writing hypotheses.

Take all your ideas and turn them into testable hypotheses. Do this by rewriting each idea as a prediction that claims the causes proposed in Step 2 will be overcome, and furthermore that a change will occur to the metrics you outlined in Step 1 (your outcome).

For example: Massage pads built into a chair that trigger on a schedule and provide the muscle activation equivalent to getting out of the chair will prevent muscle spasm that causes back pain.

(technically this is the actual hypothesis)

which would be translated as I believe that by adding massage pads to an office chair which provide the muscle activation equivalent to getting out of a chair every 30 minutes we will reduce back pain among office workers because the user’s muscles will be sufficiently activated.

(technically this is a prediction. It’ll help you design your test)

The structure of your hypothesis

I BELIEVE THAT <my feature/product/solution>

WILL <direction of change><thing that will change>

FOR <target user>

BECAUSE<reason for change>

Technically the first two lines are a prediction and only the last part (reason for change) is the underlying hypothesis, but it’s practical for us to combine them. In product development, it’s fair to say you could leave out the < reason for change > because you only want to know that it worked and may not care how or why. However, if you approach it in this way and discover from your first experiment that it didn’t succeed, you won’t know why it didn’t work and will be far less effective in getting to the product that succeeds. It also helps to improve your experiment design because we need to isolate the variables quite explicitly.

A digital product design hypothesis might read,

I believe that an app that makes users’ smartphones vibrate every 30 minutes when they are seated will decrease back pain among office workers because they will be reminded to stand more often, thus activating their muscles.

Ours might read:

I believe that a device that attaches massage pads to an office chair will decrease the prevalence of back pain in office workers that suffer sedentary induced back strain because it will activate the muscles and ligaments frequently enough to induce the blood flow and tissue activity that prevents prolonged stasis injuries.

Great work, we’ve got our first testable hypothesis. Do this for the rest of the ideas and when you’re done, don’t try to prove these to be true. Do the opposite.

Step 5: Testing the right thing and testing the thing right

By now you’re probably a bit excited about my massage chair, I know I am. I can already see towering office buildings full of people moaning with pleasure as my massaging office chair edifies their working life. I know my experiment is going to prove me right. This is the best chair ever!

But, you’re probably going to need to convince a bunch of stakeholders that they’re opinions are wrong and you are right. This approach is key to transforming the mindset of everyone in your organisation to one that understands the difference between opinion and fact. The HIPPO (Highest Paid Person’s Opinion) won’t know what hit him or her.

I’m not sure if I trust myself to design a fair test. Experimentation is not only about finding out if people agree with me, even if they’re customers, it’s also about understanding why something works. That way it becomes repeatable, it becomes valuable knowledge. Knowledge helps us avoid bad decisions, like investing millions of dollars in a chair that promises to fix back pain and make people more relaxed only to discover that it doesn’t actually fix back pain any better than what is currently being used. So we really do need to be rigorous in our testing, and one of the most important steps on that road is to make sure you know what you think you’re changing, and this means disproving the current belief or the status quo. To do that you need to know what the status quo is and make sure it’s measurable.

Our hypothesis is wonderful and promises to be a great alternative to the status quo of back pain, stress and general unhappiness. Let’s prove we can do better than whatever’s being used now. If you’re so confident your hypothesis is true then this shouldn’t be a problem at all.

Rewrite the hypothesis as follows: Activating the muscles and ligaments with massage pads in an office chair will not decrease the prevalence of back pain in the target group.

or if there is a current solution that people believe works your null hypothesis will read,

Activating the muscles and ligaments with massage pads in an office chair will not decrease the prevalence of back pain in the target group more than the current method of informing people they need to stand up every 30 minutes.

The reason is that this will direct the design of the experiment to focus on whether or not the solution has a measurable impact or not. It means you’ll need to have a measurement for the status quo, and this is the metric that you are hoping will change when you test your new chair. If it doesn’t change then you need to go back and alter your original hypothesis and try again. It’s as simple as that. If you can’t measure the status quo your hypothesis is invalid because it’s not testable. If this is the case you will need to go back to either step 3 or 4 and write a new hypothesis.

Amazing work, we’re ready to start designing our experiments.

Damn, that looks like a lot of experiments

Remember when I told you I was lazy? It’s still true. We don’t have to test every single idea, and considering resources have limits for most of us, it’s important to reduce the risk of failure by prioritising. I use Uncertainty Mapping to prioritise my experiments.

The 1-minute guide

7. EXPERIMENT DESIGN: Let’s talk about this another time, I’m sure I’ve pushed my luck getting you to read this far (cheatcode users excepted).

Thanks for reading, hope it’s useful.

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product development hypothesis

Hypothesis-driven product management

BY Saikiran Chandha ON DECEMBER 8, 2022

In this guest post, Saikiran Chandha , CEO and founder of SciSpace provides an overview of hypothesis-design testing and why it is quintessential to building new product features.

“You know me, you know me not.”

Hypothesis testing!

If you are into agile product development and design thinking, the above assertion goes without saying.

Yes, hypothesis-driven practices are ideal for building new features. Since the goal is to test the validity of each hypothesis, the uncertainty around the product development process is significantly reduced.

In a way, hypothesis testing helps you make better decisions about your product lifecycle management.

But how do we define a good hypothesis statement based on an idea or a question? What are the tests that we should even consider while defining one? How are we going to design a new feature based on this hypothesis?

Take a pause!

Let’s dive in and learn more about hypothesis-driven product management.

What is product hypothesis?

A product hypothesis is an assumption made within a limited understanding of a specific product-related situation. It further needs validation to determine if the assumption would actually deliver the predicted results or add little to no value to the product.

The crucial factors required to build a clear hypothesis statement are —

Basically, a hypothesis is a prototype where we think that an improvement in the product will give us a bump in terms of user interaction, business revenue, and other metrics.

What is the need for hypothesis-driven feature development?

Hypothesis-driven practices provide a space to brainstorm based on user behaviors, build a hypothesis, and convert them into a significant feature.

The process also gives us a thorough understanding of what, how, and why to prioritize the product’s features.

It is a cost-effective approach as the experimentation and testing is done before production.

It requires minimal effort and fosters the product team’s confidence. Overall, the impact of hypothesis testing has a direct effect on the business goals and revenue streams.

What are the steps involved in the product design hypothesis?

product development hypothesis

Untested assumptions are the biggest threats to product development. Product management teams in every organization must make it a point to highlight the importance of conducting hypothesis-driven experiments.

The product-design hypothesis is an iterative measure that defines and explores assumptions, followed by conducting suitable experiments and validating the outcome based on user feedback.

In this section, I’ve listed down six steps that we should adopt for the smooth start of the new design hypothesis.

Step 1 — Jot down the uncertainties or challenges of the product and brainstorm questions

Whether you’re adding a new feature or improving an existing one, there will always be uncertainties associated with the product.

Jot them down and come up with the possible assumptions that can solve the uncertainties of the product.

For example — Your application is not gaining users on the Google play store. The possible questions could be — How to increase the reach of your app? How to utilize current users to market your app? Would the right marketing strategy suffice? Would a user survey form help? and more.

So, brainstorming different questions to improve the product feature is the foundation of defining an effective hypothesis statement.

Step 2 — How to build a hypothesis statement?

Now that we now have ideas or questions handy, pick the ones that you firmly think add value to the process. Eliminate the least impactful questions.

The possible assumptions or solutions to the shortlisted questions are the real hypothesis for our product. The solutions should not only reflect a product manager’s thoughts. It should support the group decision structure where all the stakeholders are involved in defining a hypothesis.

There are multiple templates available to build hypothesis statements. Choosing the template that correlates well with your product development team and is most comfortable with is a good practice.

However, the simpler one could be —

We believe that {{building the feature}} for {{users}}, addresses their {{user outcome}} and help us achieve {{business outcome}}

At SciSpace, we used the template for one of our latest features, and it looked like this —

We believe that building the “Trace” feature for the researchers, addresses their literature review problem and helps us achieve an increased user engagement rate.

So, it’s crucial to work on the four factors required to frame the hypothesis statement — Problem, Solution or Feature, Users or Target Audience, Impact or outcome.

Step 3 — Segregate the Good Hypothesis from Bad Hypothesis

Practically speaking, testing and validating all the hypotheses would be challenging. We should bifurcate the hypothesis statements based on the hypothesis’s duration, research method, and outcome.

We can bucketize the statements into Good and Bad hypotheses.

An effective hypothesis is accompanied by specific and measurable outcomes. Conversely, a lousy hypothesis provides neither clear nor quantifiable outcomes.

For example, if we have a statement like, Introducing a new literature review feature will increase the user engagement rate, it  is a bad hypothesis as it is not specific or a definite outcome in the statement.

An example of a good hypothesis would be, Introducing a new literature review feature that simplifies reader workflow will increase the average time a researcher spends on our site.

To be more efficient, draft strong hypothesis statements that deliver impactful outcomes with minimal effort. In addition, you can employ supportive tools like the Discovery effort worthiness (DEW) index , which helps you analyze the steps required for product development and design thinking approaches.

Step 4 — Test! Test! And Test!

product development hypothesis

Testing is vital to product lifecycle management as it decides whether the hypothesis is true or false. It gives us a conclusion on whether to accept the hypothesis and push it to the production stage or reject it and revise or tweak the assumptions.

What are the different types of hypothesis testing?

Step 5 — Evaluate the test results and define the next steps

The next step is to analyze the results of your hypothesis and create a roadmap for the new feature rollout.

The assessment can include both qualitative and quantitative approaches. Quantitative analysis includes several combinations —

While qualitative analysis majorly includes user feedback, survey records, stakeholders’ feedback, and more. By correlating all the possible conclusions drawn from the tests, you can define the next action steps.

Step 6 — Conclude the Experiments — Closing the loop

So, here comes the final verdict of the hypothesis testing. If the experiments favored your hypothesis, you can inform your stakeholders and prepare the team for the feature rollout process.

Release the feature to a wider set of audience (100% users). However, feature testing should keep continuing even after the product release. It helps you gain a deeper understanding of your product under different scenarios.

If the hypothesis is false, do not move it to the production stage and reconsider how to solve the problem. Accumulate the data or insights from the experiments, finetune the hypothesis with specific and measurable goals and build a new hypothesis.

Ensure to stay up-to-date with the latest trends, user behavior or demands, and other factors, and update your product on a timely basis to increase customer retention.

To continuously deliver value to users via products, you need to continue running experiments for various hypotheses.

Hypotheses-led product management clearly depicts the product’s journey from making assumptions to conclusions. The conclusion may or may not give the expected results. But the approach forms a learning cycle and helps us improve the product at scale.

Building a good hypothesis statement based on your users’ pain points, testing it, and experimenting with multiple user groups helps you build a robust roadmap for the new product development and feature release. If the hypothesis fails to meet the predicted outcome, there is always room to tweak it, rerun it, and see how the experiment pans out. Thus, to keep the continuous delivery loop running, it is imperative to execute all these steps while planning for a new feature rollout.

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product development hypothesis

Saikiran Chandha

Saikiran Chandha is the CEO and founder of SciSpace — the only integrated research platform to discover, write, publish, and disseminate your research paper. He holds notable experience in research, development, and applications. Forbes, Fortune, and NASDAQ recently captured his entrepreneurial journey.

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7 Step Product Development Process Explained

Steps to develop a new product.

For most seasoned business owners and aspiring entrepreneurs, the product development process often carries a mystical aura.

And when you hear the origin stories of other great ecommerce businesses , it's often clear the journey to a finished product rarely resembles a straight line. For example, when Tina Roth-Eisenberg’s daughter brought home some semi-permanent tattoos she felt were lacking, she mobilized her community of fellow designers to create Tattly .

David Barnett, on the other hand, had to teach himself how to use 3D design software so he could prototype PopSockets , the now-popular phone accessory.

On their own, these inspiring stories don’t provide an end-to-end blueprint for product development, but the similarities they share reveal some of the steps founders consistently take on the road to starting a business and shipping a finished product.

Effective product development requires many things: market research, product management, and testing your idea to name a few.

In this guide we'll cover everything you need to know about the product development process as well as some tactics to improve the development process to help you bring your vision to life!

Table of Contents

What is product development?

Product development examples

Product development FAQ

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How to Find and Source a Winning Product to Sell

In less than 40 minutes, let us walk you through how to find product ideas, how to validate them, and how to sell the product once you have an idea you want to pursue.

Product development refers to the complete process of taking a product to market. It also covers renewing an existing product and introducing an old product to a new market. This includes identifying market needs, conceptualizing the product, building the product roadmap, launching the product, and collecting feedback.

New product development (NPD) is a core part of product design. The process doesn’t end until the product life cycle is over. You can continue to collect user feedback and iterate on new versions by enhancing or adding new features.

product development process

There’s no one role that does product development. In any company, whether an early-stage business or an established corporation, product development unites every department, including design, engineering, manufacturing, product marketing, UI/UX, and more. Each group plays an essential part in the process to define, design, build, test, and deliver the product. As you'll see in this guide, the complexities of the product development process makes product management all that more important.

The new-product-development process in 7 steps

New product development (NPD) is the process of bringing an original product idea to market. NPD can benefit greatly from agile software development principles as well. Although the product development process differs by industry, it can essentially be broken down into seven stages: ideation, research, planning, prototyping, sourcing, costing, and commercialization.

Use the following development framework to bring your own product idea to market.

1. Idea generation

Many aspiring entrepreneurs get stuck on the first stage: ideation and brainstorming. This is often because they’re waiting for a stroke of genius to reveal the perfect product they should sell. While building something fundamentally “new” can be creatively fulfilling, many of the best ideas are the result of iterating upon a product already in the wild.

The SCAMPER model is a useful tool for quickly coming up with product ideas by asking questions about existing products. Each letter stands for a prompt:

By considering these prompts, you can come up with novel ways to transform existing ideas or even adapt them for a new target audience or problem. Using insights from business analysis can also be helpful to better understand the opportunities in the market.

If you’re still looking for your aha moment, we also put together a list of sources for coming up with your own product ideas , from analyzing online marketplaces and product descriptions for inspiration to reinventing historical trends.

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2. Market Research

With your product idea in mind, you may feel inclined to leapfrog ahead to production, but that can become a misstep if you fail to validate your idea first.

Product validation ensures you’re creating a product people will pay for and that you won’t waste time, money, and effort on an idea that won't sell. There are several ways you can validate your product ideas, including:

However you decide to go about validating your idea, it is important to get feedback from a substantial and unbiased audience as to whether they would buy your product. Be wary of overvaluing feedback from people who “definitely would buy” if you were to create your theoretical product—until money changes hands, you can’t count someone as a customer. Asking advice from your family and friends (unless they have prior experience) is also something to avoid.

You may want to run a feasibility study or an assessment of whether your proposed idea is worth investing in—or not.

Product validation ensures you’re creating a product people will pay for.

Validation research will also inevitably involve competitive analysis . If your idea or niche has the potential to take up market share, there are likely competitors already operating in that space.

Visiting your competitors’ website and signing up for their email list will allow you to understand how they attract customers and make sales. Asking your own potential customers what they like or dislike about your competitors will also be important in defining your own competitive advantage.

The information compiled from doing product validation and market research will allow you to gauge the demand for your product and also the level of competition that exists before you start planning. Research is a critical part of product development so definitely don't neglect it!

3. Planning

Since product development can quickly become complicated, it’s important to take the time to plan before you begin to build your prototype. At this stage, it can often be helpful to have a clear product roadmap.

When you eventually approach manufacturers or start looking for materials, if you don’t have a concrete idea of your product’s design and how it will function, it’s easy to get lost in the subsequent steps.

The best place to begin planning is with a hand-drawn sketch of what your product will look like. The sketch should be as detailed as possible, with labels explaining the various features and functions.

 product sketches

You don’t need a professional quality drawing since you won’t be submitting it to a manufacturer at this stage. However, if you are not confident that you can produce a legible diagram that will make sense of your product, it is easy to find illustrators for hire on Dribbble , UpWork , or Minty .

Try to use your diagram to create a list of the different components or materials you will need in order to bring the product to life. The list does not need to be inclusive of all potential components, but it should allow you to begin planning what you will need in order to create the product.

For example, a drawing of a purse design could be accompanied by this list:

Along with the components, you should also begin to consider the retail price or category your product will fall into. Will the product be an everyday item or for special occasions? Will it use premium materials or be environmentally friendly? These are all questions to consider in the planning phase since they will help guide you through not only your product development process but also your brand positioning and marketing strategy.

The packaging, labels, and overall quality of your materials should be considered as well before you continue to the sourcing and costing stages. These will have an effect on how you market your product to your target customer, so it’s important to take these aspects of your product into consideration during the planning phase too. Again, having a clear product roadmap can help ensure you're moving forward towards your goal os releasing your product into the world.

4. Prototyping

The goal of the prototyping phase during product development is to create a finished product to use as a sample for mass production.

It’s unlikely you will get to your finished product in a single attempt—prototyping usually involves experimenting with several versions of your product, slowly eliminating options and making improvements until you feel satisfied with a final sample. Minimum viable product is a great standard to hold yourself too.

product development hypothesis

Prototyping also differs significantly depending on the type of product you are developing. The least expensive and simplest cases are products you can prototype yourself, such as food recipes and some cosmetic products. This do-it-yourself prototyping can also extend to fashion, pottery, design, and other verticals, if you are lucky enough to be trained in these disciplines.

However, more often than not, entrepreneurs will work with a third party to prototype their product. In the fashion and apparel industry, this usually involves working with a local seamstress (for clothing and accessories), cobbler (for shoes), or pattern maker (for clothing). These services usually can be found online by Googling local services in the industry.

Most large cities also have art, design, or fashion schools where students are trained in these techniques. Administrators from these university or college programs can usually grant you access to their internal job board, where you can create a request for prototyping help.

For objects like toys, household accessories, electronics, and many other hard-exterior objects, you may require a 3D rendering in order to make a prototype. Artists or engineers who are trained in computer-aided design and drafting (CAD) software can be contracted to do this using UpWork or Freelancer . There are also user-friendly online tools such as SketchUp , Tinkercad , and Vectary for founders who want to learn how to create 3D models for themselves.

product development hypothesis

To get a 3D design turned into a physical model, makers used to have to get molds made for each part. Molds are typically expensive and involve setup fees for things like tools and dies that are used to cut and shape pieces of plastic and other hard materials.

Luckily, with the innovation of 3D printing, designs can be turned into physical samples at a much lower cost with a quicker turnaround time.

Chris Little, the founder of Wintersmiths , prototyped his line of barware using 3D Systems On Demand Manufacturing . Little explains that he was able to do so on a budget and within a few days’ time. Alex Commons of Bulat Kitchen recommends Hubs , which he used to prototype a knife, paying around $30 per 3D-printed model.

product development hypothesis

You’ll also want to start testing a minimum viable product (MVP) at this stage. The MVP is a version of your product with just enough functionality for early customers to use. It helps validate a product concept early in your product development process. It also helps product managers get user feedback as fast as possible to iterate and make small, incremental improvements to the product.

Startups release the MVP to early customers then run experiments to gauge interest, test price sensitivity and messaging, and more. It begins the feedback process to bring ideas and suggestions based on customer needs. This allows you to create iterations of the product and build something more valuable for your target market.

5. Sourcing

Once you have a product prototype you’re satisfied with, it's time to start gathering the materials and securing the partners needed for production. This is also referred to as building your supply chain : the vendors, activities, and resources needed to create a product and get it into a customer’s hands. During this stage of product development, project management is crucial.

While this phase will mainly involve finding manufacturers or suppliers , you may also factor storage, shipping, and warehousing into your choice.

In Shoe Dog , a memoir by Nike founder Phil Knight, the importance of diversifying your supply chain is a theme emphasized throughout the story. Finding multiple suppliers for the different materials you will need, as well as different potential manufacturers, will allow you to compare costs. It also has an added benefit of creating a backup option if one of your suppliers or manufacturers doesn’t work out. Sourcing several options is an important part of safeguarding your business for the long term.

During product development, each journey to a finished product is different.

When looking for suppliers, there are plenty of resources both online and in person. While it may seem old fashioned, many business owners choose to attend trade shows dedicated to sourcing. Trade shows like Magic in Las Vegas provide the opportunity to meet hundreds of vendors at once—to see, touch, and discuss materials and build a personal relationship with suppliers, which can be valuable when it comes time to negotiate prices.

During the sourcing phase, you will inevitably come across the decision of whether to produce your product locally or overseas. It is a good idea to compare the two options, as they each have their own advantages and disadvantages.

Buying from Alibaba is one way to do so, as it's the most commonly used sourcing platform for overseas production. Alibaba is a marketplace for Chinese suppliers and factories, where you can browse listings for finished goods or raw materials. A popular way of using Alibaba to find a manufacturer is to look for listings with similar products to your own and then contact the factory to see if they can produce your specific design.

After research, planning, prototyping, and sourcing is done, you should have a clearer picture of what it will cost to produce your product. Costing is a business analysis process where you take all information gathered thus far and add up what your cost of goods sold (COGS) will be so you can determine a retail price and gross margin.

Begin by creating a spreadsheet with each additional cost broken out as a separate line item. This should include all of your raw materials, factory setup costs, manufacturing costs, and shipping costs. It is important to factor in shipping, import fees, and any duties you will need to pay in order to get your final product into the customer’s hands, as these fees can have a significant impact on your COGS, depending on where you are producing the product.

product development hypothesis

If you were able to secure multiple quotes for different materials or manufacturers during the sourcing phase, you can include different columns for each line item that compare the cost. Another option is to create a second version of the spreadsheet, so you can compare local production versus overseas production.

Once you have your total COGS calculated, you can come up with a pricing strategy for your product and subtract the COGS from that price to get your potential gross margin, or profit, on each unit sold.

7. Commercialization

At this point you’ve got a profitable and successful product ready for the world. The last step in this methodology is to introduce your product to the market! At this point, a product development team will hand the reins over to marketing for a product launch.

If you don’t have the budget for expensive ads, don’t sweat it. You can still run a successful go-to-market strategy by using the following tactics:

Read more: 25 Alternatives to Running Paid Ads to Promote Your Business

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The product development cycle will naturally vary by industry, so let’s take a brief look at what you might have to consider across three of the largest and most well-established industries: fashion and apparel; beauty and cosmetics; and food and beverage.

These three industries have relatively straightforward paths to product development, thanks to the many well-documented case studies that can be used for inspiration.

Fashion and apparel

In the fashion industry, product development usually begins the old-school way: with a hand-drawn sketch or the digital equivalent made using a program like Procreate .

A sketch is then developed into a sample using a pattern maker or seamstress. During the prototyping phase, a size set is created, which means a range of samples with different measurements for each size you want to sell. Once the size set is finalized, it is put into production.

Rather than make the product, some fashion and apparel businesses choose print on demand to produce their clothing in the beginning. Print-on-demand allows you to upload designs to a third-party app that connects your store with a warehouse and screen-printing facility. When an order is placed online, your design is printed on an existing stock of t-shirts, sweaters, or various other items on offer, creating a finished product without the need to design the entire garment.

Other factors to consider:

Beauty and cosmetics

The beauty and cosmetics industry includes a wide range of products that is constantly expanding, due to wellness and self-care trends. From makeup to bath products to skincare, many beauty brands are focusing on all-natural ingredients and sustainability, which makes it easier to prototype a product on your own using everyday ingredients.

White labeling is also popular in the beauty and cosmetics industry. It’s the process of finding an existing product or manufacturer, then packaging and branding the products they already produce. Whichever route you decide to take, mass manufacturing for cosmetics is usually done by working with a lab and a chemist to make sure quality stays consistent at scale.

Food and beverage

Food and beverage products are among the easiest to start developing at a low cost and from the comfort of your own home. Creating a new energy bar can be as simple as buying ingredients and tweaking the recipe in your own kitchen, like Lara Merriken did when she started Lärabar .

In order to move from recipe to packaged goods you can sell in stores or online, you will need to find a commercial kitchen that is licensed to produce food and has passed a health and safety inspection.

These kitchens are usually set up with ovens and cooking equipment to accommodate large batches, but if you are considering mass production and packaging, a co-packer or co-manufacturer might be a better option. These are manufacturing facilities that specialize in processing raw materials and producing food and beverage products at scale.

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Winning your at your own product development process .

During product development, each journey to a finished product is different and every industry has its own unique set of quirks involved in creating something new.

If you find yourself struggling to figure it all out, remember that every product that came before yours had to overcome the same challenges. From product management, to prototyping, and marketing your product to the world, there's a lot of moving parts!

By following these steps as you undergo your own product development process, you can break down the overwhelming task of bringing a new product to market into more digestible phases.

No matter what you’re developing, by putting in all the necessary preparation—through researching, planning, prototyping, sourcing, and costing—you can set yourself up for a successful final product.

Illustration by Pete Ryan Design by Brenda Wisniowski

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What is the product development process.

The product development process refers to the step a business takes to bring a product to market. It can be a completely new product, renewing an old product, or introducing an existing product to a new market. It involves concept development and testing, prototyping, costing, and commercializing the product by marketing it online.

How can I come up with new product ideas?

What is the difference between product development and product management?

Product development refers to the conceptualization and creation of a new product of the product life cycle. It involves a specific product strategy to bring an idea to life. Product management is responsible for guiding all product teams toward creating a usable product customers will buy as well as the product roadmap. These two departments work together to plan and build the product roadmap that’ll bring the product to market.

What are the 7 stages in the product development process?

About the author.

Mary-Rose Sutton

Mary-Rose Sutton is a Digital Marketing Consultant who enjoys helping Shopify merchants grow their business.

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What is Product Development?

Product development typically refers to all stages involved in bringing a product from concept or idea through market release and beyond. In other words, product development incorporates a product’s entire journey.

Standard Stages of Progress in Product Development

There are many steps to this process, and it’s not the same path for every organization, but these are the most common stages through which products typically progress:

Identifying a market need.

Products solve problems. So identifying a problem that needs solving (or a better way of being solved) is where this journey should begin. Conversations with potential customers, surveys, and other user research activities can inform this step.

Quantifying the opportunity.

Not every problem is problematic enough to warrant a product-based solution. However, the pain it causes and the number of people or organizations it impacts can determine whether it’s a worthy problem to solve and if people are willing to pay for a solution (be it with money or their data).

Conceptualizing the product.

Some solutions may be obvious, while others may be less intuitive. Here’s where the team puts in the effort and applies their creativity to devising how a product might serve its needs.

Validating the solution.

Before too much time is spent prototyping and design, whether the proposed solution is viable should be tested. Of course, this can still happen at the conceptual level. Still, it is an early test to see whether the particular product idea is worth pursuing further or if it will be rejected or only lightly adopted by the target user.

Building the product roadmap.

With a legitimate product concept in hand, product management can build out the product roadmap , identifying which themes and goals are central to develop first to solve the most significant pain points and spark adoption.

Developing a minimum viable product (MVP).

This initial version of the product needs just enough functionality to be used by customers.

Releasing the MVP to users.

Experiments can gauge interest, prioritize marketing channels and messages, and begin testing the waters around price sensitivity and packaging. It also kicks off the feedback loop to bring ideas, complaints, and suggestions into the prioritization process and populate the product backlog.

Ongoing iteration based on user feedback and strategic goals.

Get the Product Development Roadmap Checklist ➜

Product Development is Not Product Management

When you understand product development this way, you can see that it is not synonymous with product management , although many people mistakenly use the terms interchangeably. Indeed, product development does not refer to a single role at all.

In some organizations, “product development” may be shorthand for the implementation team, comprised primarily of developers, engineers, and possibly quality assurance.

But, when it comes to the house’s personnel side, it should instead view it as more of an overall process or method for bringing products to market, which involves many teams across a company, including:

Essentially, it encompasses everyone involved, from idea generation through to customer delivery. Each of these groups plays an essential role in the process, defining, designing, building, testing, or delivering the product.

How to Create a Product Development Plan in 3 Steps

Not to be confused with a project plan, a product development plan encompasses the overarching journey from idea to market. It should include and engage as many stakeholders as possible to ensure all of their specific needs, requirements, and concerns are being considered (if not addressed).

1. Start with a product vision.

It begins with a product vision, which aligns everyone around the shared objective for this product. This is followed by a product mission—the ultimate purpose of the product, who it is for, and what it does for them. Finally, it establishes some guiding principles for the work to come.

With product vision and mission statements in hand, primary goals for the product can be established. These may be a little fuzzier in the early stages, such as finding product-market fit, but they can rapidly evolve into measurable KPIs or OKRs. These measurable targets help shape which features, enhancements, and capabilities the product needs to achieve them.

2. Craft a roadmap.

Download the Product Roadmap Kit ➜

3. Implement the roadmap for maximum impact.

Once the product roadmap is agreed upon, it’s time to make things happen. Implementation teams can create schedules, break down significant themes into sprints, and generate iterations of the product. This creates a feedback loop from customers, the sales team, and support, identifying new opportunities, pointing out shortcomings, and shining a light on areas to hone, improve, and expand.

From here, it’s a cycle of reviewing data, synthesizing feedback, and continually updating the product roadmap while grooming the product backlog to ensure every development cycle is utilized for maximum impact.

How Do Product Roadmaps Fit Into Product Development?

Whether you start at the conceptualization stage or first try to identify and validate a market need—you will want to have a system in place for prioritizing, summarizing, and capturing your product’s key objectives and significant themes.

The ideal tool for this early-stage planning is a product roadmap designed to strategically and visually convey your high-level product. So why is it essential to build your roadmap visually? There are several reasons to do so, but here are the two primary benefits:

1. With a visual product roadmap, you and your team can more easily refer back to the product strategy you agreed on and quickly reacquaint yourself with those high-level objectives to make sure you’re still on track.


Contrast this visual, easy-to-review roadmap with a typical spreadsheet-based roadmap loaded with features and to-do items arranged in no particular order, and you can understand why dedicated roadmapping software makes all the difference.

A visually appealing roadmap can also help a product manager present its strategic goals and plans more compellingly to the company’s executives and other key stakeholders.

Earning this buy-in is often necessary to secure organizational approval to move ahead with new product development. Therefore, it makes sense to give your product roadmap every advantage you can before presenting it to your stakeholders.

What is Agile Product Development?

Agile product development is another term you might hear often. This refers to the familiar product development concept we described in the introduction—all steps involved in delivering a product to the market—including agile software development principles, such as rapid iteration based on user feedback.

Download the agile product manager's guide to building better products ➜

The benefit of the agile framework is that it allows an organization to shorten the cycle from brainstorming through actually launching a product—because the product team intentionally pushes out versions of the product (starting with its early-stage MVP) much more quickly and with much fewer updates and improvements in each release. In addition, this allows the team to enlist the feedback of actual users to make the product better incrementally.

A More Literal Definition of Product Development

Finally, you might also encounter a far more narrow definition of product development, describing the product’s actual development: This would be the coding stage in software or manufacturing in a physical product).

When it comes to software, development teams can create and maintain their product development roadmaps to prioritize, summarize, and communicate their plans to build and ultimately release the product. For example, below is a product development roadmap template that your team can use to stay on track during the development process.

Product Development Roadmap Template by ProductPlan

Takeaways: Where the Magic Happens in Agile

Product development is the hard part. It’s where bright ideas collide with reality and where utopian visions of the future crash into the limitations of technology and headcount that separates dreamers from doers.

To avoid a promising product vision from faltering in the face of challenging work and difficult hurdles, roadmap strategies should be tightly coupled with Agile planning to optimize the work being done. To learn more about how rapid iteration can stay true to long-term objectives, check out this webinar .

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Six Myths of Product Development

The fallacies that cause delays, undermine quality, and raise costs

Reprint: R1205E

Many companies approach product development as if it were manufacturing, trying to control costs and improve quality by applying zero-defect, efficiency-focused techniques. While this tactic can boost the performance of factories, it generally backfires with product development. The process of designing products is profoundly different from the process of making them, and the failure of executives to appreciate the differences leads to several fallacies that actually hurt product-development efforts.

In this article, the authors, an HBS professor and a consultant, expose these misperceptions and others. They look at six dangerous myths:

1. High utilization of resources will make the department more efficient.

2. Processing work in large batches will be more economical.

3. Teams need to faithfully follow their development plan, minimizing any deviations from it.

4. The sooner a project is started, the sooner it will be finished.

5. The more features a product has, the better customers will like it.

6. Projects will be more successful if teams “get them right the first time.”

The authors explain the negative effects these “principles” have when applied to product development, offer practical guidelines on overcoming them, and walk readers through a visual tool that will help them keep projects on track.

product development hypothesis

Most product-development managers are always struggling to bring in projects on time and on budget. They never have enough resources to get the job done, and their bosses demand predictable schedules and deliverables. So the managers push their teams to be more parsimonious, to write more-detailed plans, and to minimize schedule variations and waste. But that approach, which may work well in turning around underperforming factories, can actually hurt product-development efforts.

Although many companies treat product development as if it were similar to manufacturing, the two are profoundly different. In the world of manufacturing physical objects, tasks are repetitive, activities are reasonably predictable, and the items being created can be in only one place at a time. In product development many tasks are unique, project requirements constantly change, and the output—thanks, in part, to the widespread use of advanced computer-aided design and simulation and the incorporation of software in physical products—is information, which can reside in multiple places at the same time.

The failure to appreciate those critical differences has given rise to several fallacies that undermine the planning, execution, and evaluation of product development projects. Together, we have spent more than 50 years studying and advising companies on product-development efforts, and we have encountered these misconceptions—as well as others that arise for different reasons—in a wide range of industries, including semiconductors, autos, consumer electronics, medical devices, software, and financial services. In this article we’ll expose them and offer ways to overcome the problems they create.

Fallacy 1: High utilization of resources will improve performance.

In both our research and our consulting work, we’ve seen that the vast majority of companies strive to fully employ their product-development resources. (One of us, Donald, through surveys conducted in executive courses at the California Institute of Technology, has found that the average product-development manager keeps capacity utilization above 98%.) The logic seems obvious: Projects take longer when people are not working 100% of the time—and therefore, a busy development organization will be faster and more efficient than one that is not as good at utilizing its people.

But in practice that logic doesn’t hold up. We have seen that projects’ speed, efficiency, and output quality inevitably decrease when managers completely fill the plates of their product-development employees—no matter how skilled those managers may be. High utilization has serious negative side effects, which managers underestimate for three reasons:

They don’t take into full account the intrinsic variability of development work.

Many aspects of product development are unpredictable: when projects will arrive, what individual tasks they’ll require, and how long it will take workers who’ve never tackled such tasks before to do them. Companies, however, are most familiar with repetitive processes like manufacturing and transaction processing, where the work doesn’t change much and surprises are few and far between. Such processes behave in an orderly manner as the utilization of resources increases. Add 5% more work, and it will take 5% more time to complete.

Processes with high variability behave very differently. As utilization increases, delays lengthen dramatically. (See the exhibit “High Utilization Leads to Delays.”) Add 5% more work, and completing it may take 100% longer. But few people understand this effect. In our experience with hundreds of product-development teams, we have found that most were significantly overcommitted. To complete all projects on time and on budget, some organizations we worked with would have needed at least 50% more resources than they had.

High Utilization Leads to Delays

The curve below is calculated using Queuing Theory, the mathematical study of waiting lines. It shows that with variable processes, the amount of time projects spend on hold, waiting to be worked on, rises steeply as utilization of resources increases. Though the curve changes slightly depending on the project work, it always turns sharply upward as utilization nears 100%.

It is true that some variability is the result of a lack of discipline, and that some product-development tasks (like designing components for an airplane prototype or performing clinical trials) include more-repetitive work. But even if some of the work is predictable, when it’s combined with other unpredictable work, you will see queuing problems.

They don’t understand how queues affect economic performance.

High utilization of resources inevitably creates queues of projects. When partially completed work sits idle, waiting for capacity to become available, the duration of the overall project will grow. Queues also delay feedback, causing developers to follow unproductive paths longer. They make it hard for companies to adjust to evolving market needs and to detect weaknesses in their product before it’s too late. Ironically, these problems are precisely the ones that managers think high utilization will allow their teams to avoid.

Even when managers know that they’re creating queues, they rarely realize the economic cost. Although that cost can be quantified, we’ve found that the vast majority of companies don’t calculate it. Managers need to weigh queue costs against the costs of underutilized capacity in order to strike the right balance.

In product development, work-in-process inventory is predominantly invisible.

Manufacturing queues consist of physical things, and when inventory in a factory doubles, it’s obvious. That’s not the case in product development, where inventory largely consists of information, such as design documentation, test procedures and results, and instructions for building prototypes. When inventory doubles in an engineering process, there are no physical signs. Moreover, because accounting standards require most R&D inventory to be carried at zero value, financial statements give no indication of serious inventory excesses in product development.

It is very difficult to fight a problem that you can’t see or measure. Consider the situation at a major pharmaceutical firm. Several years ago its newly appointed head of drug discovery faced a managerial dilemma. Like other senior executives who run large R&D organizations, he was trying to find ways to make his scientists more innovative. He wanted them to experiment more with new chemical compounds that could generate promising new drugs and, at the same time, to eliminate unpromising candidates as early as possible. Experiments with living organisms, however, were the responsibility of animal testing, a department that was not under his control and was run as a cost center. It was evaluated by how efficiently it used testing resources, which naturally led to high utilization. Consequently, the scientists in drug discovery had to wait three to four months for the results of tests that took a little more than a week to perform. The “well-managed” testing organization impeded the discovery unit’s progress.

The obvious solution to such problems is to provide a capacity buffer in processes that are highly variable. Some companies have long understood this. For decades, 3M has scheduled product developers at 85% of their capacity. And Google is famous for its “20% time” (allowing engineers to work one day a week on anything they want—a practice that means extra capacity is available if a project falls behind schedule). However, in our experience this kind of solution is quite hard to implement. As we will discuss, few organizations can resist the temptation to use every last bit of available capacity. Managers reflexively start more work whenever they see idle time.

But there are other viable solutions:

Change the management-control systems.

For the pharmaceutical company, this might involve taking steps to align the objectives of the animal-testing unit with those of the discovery unit. The company could, for example, reward animal testing for prompt responses (measuring time from request to completion of test) rather than resource utilization.

Selectively increase capacity.

Adding extra resources to the areas where the utilization rates are 70% or higher can significantly reduce waiting time. If the pharmaceutical company did this in animal testing, it would obtain feedback on new chemical compounds far faster. In settings where testing is conducted with computer modeling and simulation, increasing capacity is often relatively inexpensive, since it just involves buying additional computer equipment and software licenses.

Limit the number of active projects.

If the pharmaceutical firm couldn’t increase animal testing’s capacity, it could still lower the utilization rate by reducing the number of active projects exploring new chemical compounds. The discipline of putting a hard limit on what goes into a product-development pipeline often results in sharper focus and clearer priorities.

Make the work-in-process inventory easier to see.

One method is to use visual control boards. These can take a number of forms, but the key is to have some sort of physical token, such as a Post-it note, represent the development work (see the exhibit “Typical Work-in-Process Control Board”). A control board should display all active work and show what state each part of the project is in. It should be at the center of the team’s management process. Teams can hold 15-minute daily stand-up meetings around such boards to coordinate efforts and keep work moving.

Typical Work-in-Process Control Board

Control boards make invisible work visible by showing the precise stage that each work item is in. In designing the boards, most teams limit the number of tasks at each stage to prevent delays. This simple board contains features that might be found on a software project that involves a team composed of six to 10 people.

Fallacy 2: Processing work in large batches improves the economics of the development process.

A second cause of queues in product development is batch size. Let’s say a new product is composed of 200 components. You could choose to design and build all 200 parts before you test any of them. If you instead designed and built only 20 components before you began testing, the batch size would be 90% smaller. That would have a profound effect on queue time, because the average queue in a process is directly proportional to batch size.

The reduction of batch sizes is a critical principle of lean manufacturing. Small batches allow manufacturers to slash work in process and accelerate feedback, which, in turn, improves cycle times, quality, and efficiency. Small batches have even greater utility in product development, but few developers realize the power of this method.

One reason is the nature of their work flow. Again, because the information they’re producing is mostly invisible to them, the batch sizes are too. Second, developers seem to have an inherent bias to use large batches—possibly because they incorrectly believe that large batches produce economies of scale.

In a well-managed process, the batch size will balance transaction and holding costs (see the exhibit “How to Determine Optimal Batch Size”). It’s similar to buying eggs at the grocery store. If you buy a 12-month supply on a single trip, your transaction cost is low, but most of the eggs will spoil, increasing your holding cost. If you buy a one-day supply at a time, your spoilage will be low, but your transaction costs will be high. Intuitively, you try to strike a balance between the two.

How to Determine Optimal Batch Size

Changes in batch size affect two primary costs: the transaction cost and the holding cost. As batch sizes become larger, average inventory levels rise, which raises holding costs. But at the same time, transaction costs decrease, because it takes fewer transactions to service demand.

The optimal batch size is the point where the total cost (combined holding and transaction cost) is lowest. When a company operates near this point, small deviations have little impact. For example, if a company operates at less than 20% above or below the optimal batch size, total costs increase less than 3%. So even rough estimates permit a company to capture large economic benefits.

The companies that understand how this works have exploited IT advances to reduce batch sizes, often with astonishing results. Some software companies that used to test large batches of code every 90 days are now testing much smaller batches several times a day. A manufacturer of computer peripherals that used a similar approach with its software group reduced cycle time in software testing by 95% (from 48 months to 2.5 months), improved efficiency by 220%, and decreased defects by 33%. The cost savings were twice as high as the company had expected. Although those results were exceptional, we have found that reducing batch size improves most development projects significantly. Similarly, computerized modeling and simulation tools have dramatically lowered the optimal batch size of experimentation and testing in companies that develop physical products.

By shrinking batch sizes, one company improved the efficiency of its product testing by 220% and decreased defects by 33%.

Fallacy 3: Our development plan is great; we just need to stick to it.

In all our consulting work and research, we’ve never come across a single product-development project whose requirements remained stable throughout the design process. Yet many organizations place inordinate faith in their plans. They attribute any deviations to poor management and execution and, to minimize them, carefully track every step against intermediate targets and milestones. Such thinking is fine for highly repetitive activities in established manufacturing processes. But it can lead to poor results in product innovation, where new insights are generated daily and conditions are constantly changing.

A classic study of technical problem solving done by Thomas Allen of MIT highlights the fluid nature of development work. He found that engineers who were developing an aerospace subsystem conceived of and evaluated a number of design alternatives before selecting one that they judged to be the best. Along the way their preferences changed frequently, as they tested and refined competing technical solutions. This is typical in innovation projects: Testing and experimentation reveal what does and doesn’t work, and initial assumptions about costs and value may be disproved.

Defining customers’ needs can also be hard to do at the outset of a product-development project. When you think about it, that’s not surprising: It isn’t easy for customers to accurately specify their needs for solutions that don’t yet exist. In fact, familiarity with existing product attributes can interfere with an individual’s ability to express his or her need for a novel product. Customers’ preferences can also shift abruptly during the course of a development project, as competitors introduce new offerings and new trends emerge.

For all those reasons, sticking to the original plan—no matter how excellent its conception and how skillful its execution—can be a recipe for disaster. This is not to suggest that we don’t believe in planning. Product development is a set of complex activities that require careful coordination and attention to the smallest detail. However, the plan should be treated as an initial hypothesis that is constantly revised as the evidence unfolds, economic assumptions change, and the opportunity is reassessed. (See “The Value Captor’s Process,” by Rita Gunther McGrath and Thomas Keil, HBR May 2007.)

Fallacy 4: The sooner the project is started, the sooner it will be finished.

As we discussed earlier, idle time is anathema to managers. They tend to exploit any downtime by starting a new project. Even if the task cannot be completed because people have to return to another project, managers reason that anything accomplished on the new project is work that won’t have to be done later. Such thinking leads companies to start more projects than they can vigorously pursue, diluting resources.

This dilution is dangerous. If a company embarks on a project before it has the resources to move ahead, it will stumble slowly through the development process. That’s problematic because product-development work is highly perishable: Assumptions about technologies and the market can quickly become obsolete. The slower a project progresses, the greater the likelihood it will have to be redirected. Indeed, one branch of the military discovered that its cost and schedule overruns were exponentially proportional (to the fourth power) to a project’s duration. In other words, when the original schedule of a project doubled, the cost and schedule overruns increased by a factor of 16.

The importance of reducing the amount of work in process is evident when we look at one of the classic formulas of queuing theory: Little’s Law. It simply states that, on average, cycle time is proportional to the size of the queue divided by the processing rate. Thus, if 20 people are ahead of you in line at Starbucks and the barista is serving five people a minute, you will be served in four minutes. You can shorten the cycle time by raising the processing rate or by reducing the number of jobs under way. In most settings the latter is the only practical choice.

For some product developers the solution has been to rigorously control the rate at which they start work. They match it to the rate at which work is actually completed; carefully manage the number of projects in process; make sure that once a project is launched, it is adequately staffed until it is completed; and resist the temptation to steal resources from an ongoing project to squeeze in new ones.

Fallacy 5: The more features we put into a product, the more customers will like it.

Product-development teams seem to believe that adding features creates value for customers and subtracting them destroys value. This attitude explains why products are so complicated: Remote controls seem impossible to use, computers take hours to set up, cars have so many switches and knobs that they resemble airplane cockpits, and even the humble toaster now comes with a manual and LCD displays.

Companies that challenge the belief that more is better create products that are elegant in their simplicity. Bang & Olufsen, the Danish manufacturer of audio products, televisions, and telephones, understands that customers don’t necessarily want to fiddle with the equalizer, balance, and other controls to find the optimum combination of settings for listening to music. Its high-end speakers automatically make the adjustments needed to reproduce a song with as much fidelity to the original as possible. All that’s left for users to select is the volume.

Getting companies to buy into and implement the principle that less can be more is hard because it requires extra effort in two areas of product development:

Defining the problem.

Articulating the problem that developers will try to solve is the most underrated part of the innovation process. Too many companies devote far too little time to it. But this phase is important because it’s where teams develop a clear understanding of what their goals are and generate hypotheses that can be tested and refined through experiments. The quality of a problem statement makes all the difference in a team’s ability to focus on the few features that really matter.

When Walt Disney was planning Disneyland, he didn’t rush to add more features (rides, kinds of food, amount of parking) than other amusement parks had. Rather, he began by asking a much larger question: How could Disneyland provide visitors with a magical customer experience? Surely, the answer didn’t come overnight; it required painstakingly detailed research, constant experimentation, and deep insights into what “magical” meant to Disney and its customers. IDEO and other companies have dedicated phases in which they completely immerse themselves in the context in which the envisioned product or service will be used. Their developers read everything of interest about the markets, observe and interview future users, research offerings that will compete with the new product, and synthesize everything that they have learned into pictures, models, and diagrams. The result is deep insights into customers that are tested, improved, or abandoned throughout the iterative development process.

Determining what to hide or omit.

Teams are often tempted to show off by producing brilliant technical solutions that amaze their peers and management. But often customers would prefer a product that just works effortlessly. From a customer’s point of view, the best solutions solve a problem in the simplest way and hide the work that developers are so proud of.

One company that has understood this is Apple. It is known for many things—innovative products, stylish designs, and savvy marketing—but perhaps its greatest strength is its ability to get to the heart of a problem. (See “The Real Leadership Lessons of Steve Jobs,” by Walter Isaacson, in our April issue.) As the late Steve Jobs once explained, “When you start looking at a problem and it seems really simple, you don’t really understand the complexity of the problem. And your solutions are way too oversimplified. Then you get into the problem, and you see it’s really complicated. And you come up with all these convoluted solutions….That’s where most people stop.” Not Apple. It keeps on plugging away. “The really great person will keep on going,” said Jobs, “and find…the key underlying principle of the problem and come up with a beautiful, elegant solution that works.”

Determining which features to omit is just as important as—and perhaps more important than—figuring out which ones to include. Unfortunately, many companies, in an effort to be innovative, throw in every possible bell and whistle without fully considering important factors such as the value to customers and ease of use. When such companies do omit some planned functionality, it’s typically because they need to cut costs or have fallen behind schedule or because the team has failed in some other way.

Deciding what to omit from a product is as important as figuring out what to include.

Instead, managers should focus on figuring out whether the deletion of any proposed feature might improve a particular product and allow the team to concentrate on things that truly heighten the overall customer experience. This can be determined by treating each alleged requirement as a hypothesis and testing it in small, quick experiments with prospective customers.

Development teams often assume that their products are done when no more features can be added. Perhaps their logic should be the reverse: Products get closer to perfection when no more features can be eliminated. As Leonardo da Vinci once said, “Simplicity is the ultimate sophistication.”

Fallacy 6: We will be more successful if we get it right the first time.

Many product-development projects fail to meet their objectives for budgets, schedules, and technical performance. Undoubtedly, poor planning, rigid processes, and weak leadership all play a role. But another cause that’s often overlooked is managers’ demand that their teams “get it right the first time.” Requiring success on the first pass biases teams toward the least-risky solutions, even if customers don’t consider them much of an improvement over what’s already available. Worse yet, teams have little incentive to pursue innovative solutions to customers’ problems.

To avoid making mistakes, teams follow a linear process in which each stage (specify, design, build, test, scale, launch) is carefully monitored at review “gates.” Work on the next stage cannot begin until the project passes through the gate. As the project moves down the line, significant commitments are made and the cost of responding to new insights increases by orders of magnitude. Successful tests in late stages are celebrated, and surprises, no matter how valuable they are, are considered setbacks. Unfortunately, such a linear process flow can cause project overruns because test feedback is delayed, teams cling to bad ideas longer than they should, and problems aren’t unearthed until it’s expensive to solve them.

A tolerance for “getting it wrong the first time” can be the better strategy as long as people iterate rapidly and frequently and learn quickly from their failures. Advances in simulation and rapid-prototyping technologies have made operating in this fashion vastly easier and less expensive.

Practical Guidelines for Overcoming Common Fallacies

A checklist for today’s product-development managers

1. Make queues and information flows visible.

2. Quantify the cost of delays and factor it into your decisions.

3. Introduce resource slack where utilization is highest.

4. Shift the focus of control systems from efficiency to response time.

5. Reduce transaction costs to enable smaller batch sizes and faster feedback.

6. Experiment with smaller batches; you can easily revert to large batches if this doesn’t work.

7. Treat the development plan as a hypothesis that will evolve as new information becomes available.

8. Start projects only when you are ready to make a full commitment.

9. Aim for simplicity: Ask what features can be deleted, not just what can be added.

10. Experiment early, rapidly, and frequently, with computer models and physical prototypes, in controlled and real-life customer environments.

11. Emphasize overlapping and iterative—not linear—process designs.

12. Focus on quick feedback instead of first-pass success.

Consider what we found in a study of 391 teams that designed custom integrated circuits. Teams that followed an iterative approach and conducted early and frequent tests made more errors along the way. But because they used low-cost prototyping technologies, they outperformed (in terms of the time and effort required) teams that tried to get their design right the first time. The teams that faced high prototyping costs invested more effort on specification, development, and verification. And because they did their iterations later in the process—and did far fewer of them—they delayed the discovery of critical problems.

Experimenting with many diverse ideas is crucial to innovation projects. When people experiment rapidly and frequently, many novel concepts will fail, of course. But such early failures can be desirable because they allow teams to eliminate poor options quickly and focus on more-promising alternatives. A crash test that shows that a car design is unsafe, a drug candidate that proves to be toxic, or a software user interface that confuses customers can all be desirable outcomes—provided that they occur early in a process, when few resources have been committed, designs are still very flexible, and other solutions can be tried.

Demanding that teams “get it right the first time” just biases them to focus on the least-risky solutions.

A classic example of the advantages of the “fail early, fail often” approach is Team New Zealand’s surprising victory in the 1995 America’s Cup. To test ideas for improving the keel design, the team used two nearly identical boats: one boat that was modified during the course of the project and a “control” boat that was not. On a daily basis, the team simulated hypotheses on a local graphics workstation, applied those that looked promising to the one boat, raced it against the control, and analyzed the results. In contrast, its competitor, Team Dennis Conner, which had access to more-powerful computers (supercomputers at Boeing), ran large batches of simulations every few weeks and then tested possible solutions on one boat. The result: Team New Zealand completed many more learning cycles, eliminated unpromising ideas more rapidly, and ended up beating Team Dennis Conner’s boat Young America.

What we hope is becoming clear by now is that experiments resulting in failures are not necessarily failed experiments. They generate new information that an innovator was unable to foresee. The faster the experimentation cycle, the more feedback can be gathered and incorporated into new rounds of experiments with novel and potentially risky ideas. In such an environment employees tend to persevere when times get tough, engage in more-challenging work, and outperform their risk-averse peers.

But creating this kind of environment isn’t easy—a topic that Amy C. Edmondson of Harvard Business School explored in “Strategies for Learning from Failure” (HBR April 2011). Failure can lead to embarrassment and expose gaps in knowledge, which can undermine individuals’ self-esteem and standing in an organization. After all, how often are managers promoted and teams rewarded for the early exposure of failures that lead a project to be killed—even though the early redeployment of precious resources benefits the company? This is especially true in organizations that have built a “zero tolerance for failure” or “error-free” (Six Sigma) environment.

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HBR’s 10 Must Reads on Innovation

Thomas Alva Edison understood all this. He organized his famous laboratories around the concept of rapid experimentation, locating machine shops for building models close to the rooms where experiments were conducted so that machinists could cooperate closely with researchers. The labs had libraries containing a vast number of volumes so that information could be found quickly; nearby storerooms with ample quantities of supplies; and a diverse workforce of craftsmen, scientists, and engineers. Edison wanted to make sure that when he or his people had an idea, it could be immediately turned into a working model or prototype. “The real measure of success is the number of experiments that can be crowded into 24 hours,” he said.

Advances in information technology, such as computer-aided design, modeling, and simulation, have already allowed companies to make great strides in developing better products in less time and at a lower cost. Many companies, however, have not tapped the full potential of these tools, because their management thinking has not evolved as quickly as the technology: They still approach the highly variable information-generating work of product development as if it were like manufacturing. As advances in IT continue, the opportunity to improve the product-development process will become even greater. But so will the risks for companies that fail to recognize that product development is profoundly different from manufacturing.

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Chris Compston

Jan 16, 2019

Forming Experimental Product Hypotheses

An introduction to forming hypothesis statements for product experimentation, hypothesis statements.

A hypothesis is a statement made with limited knowledge about a given situation that requires validation to be confirmed as true or false to such a degree where the team can continue their investigation and find the best solution to a given problem.

In the most simple form a hypothesis comes as an ‘if… then’ statement, here’s a common example:

Now if this statement proved to be false and the desired outcome is that the flowers will grow faster and live longer we can add more statements to the list to either prove true or false:

Problem Statements

In the above example the problem is clearly that the flowers aren’t growing fast enough. The hypothesis statements were then created and experiments run to attempt to solve this problem. Moving the flowers, growing flowers together and adding nutrients are all experiments to prove which of the solutions will ensure that the flowers grow faster.

It’s important to identify an actual known user or business problem to form hypothesis statements from. Although hypothesis statements are an exercise in educated guessing, without a focus point it’ll be hard to identify when the hypothesis statement has been proven true or false.

The problem statement should follow the ‘How might we…’ (HMW) format and be based on actual user research findings, data driven themes or insights. The HMW format allows the team to both understand that there is a problem to solve and also helps to start the discussion that will frame their potential solution.

Hypothesis Statement Templates

Product Hypothesis statements can come in many different forms so pick what’s most comfortable for the team and business to understand. However they should always include the following key details:

A simple hypothesis statement might look something like this:

We believe that [business outcome] will be achieved if [user] attains [benefit] with [feature]

Something a little more detailed with more of an emphasis on the solution:

We believe that [building this feature] [for these people] will achieve [this benefit] . We will know we are successful when [outcome from the market] .

Forming the Hypothesis

With a user or business problem to solve and a hypothesis template ready it’s time to fill in the statement blanks. As shown in the table below there’s four initial sets of detail to understand (Problem, Users, Benefits, Features) to form the hypothesis statement. The final two parts (Assumptions and MVP) will identify how best to experiment to validate the hypotheses created.

Problems As mentioned previously the business or user problem should be formed as a HMW statement and should be initiated from previous discovery or research work run by the Product and Design teams.

Measures of success are going to be dependent on current business measures, data analysis or direct qualitative feedback from a number of users. These are often that hardest part to define for the hypothesis statements and this is usually due to a lack of business input or, little or late data analysis.

Without good measure of success it’s impossible to know whether the experiments has proven the hypothesis true or false so it’s worth spending time upfront to fully understand them.

Users If the team doesn’t already have personas or an understanding of the main user groups affected by the problem statement then run a proto-persona session. If personas already exist but some more detail around their specific problems in a given situation is lacking then an empathy mapping session would work well. If there’s a genuine lack of knowledge about the way users are interacting with the product or the overall service then run a customer journey or user story mapping session.

Benefits Using the understanding gained from the user identification sessions and the problem statements identified try to understand what benefits users are actually looking for. What are the customer needs for specific points in their journey? Where there are problems highlighted, what is it that users are lacking or trying to achieve but can’t?

Features There’s many ways to ideate on potential solutions, this part of the process should only really be conducted once the former stages have been highlighted. However it’s equally important that past ideas aren’t left to expire, any ideas the team have had in the past should also be presented here. A quick way to get going with ideation is this One Hour Collaborative Workshop .

Filling in the Blanks

Spend time to consider as many possible options for each section in the table. Write down on sticky notes every possible permutation of the problems the team has uncovered, what type of users could be affected by any of those problems, what benefits will improve their experiences and all the solutions from the ideation sessions the team has had.

There are many tools, templates, sessions and canvases that can aid with completing these sections of the table. Here is a non-exhaustive list of things that might help: Gamestorming , Impact mapping , Lean Product Canvas , User story mapping , Customer journey mapping , Event storming , WhoDo , Protopersonas , Empathy mapping .

Completing each section does not have to go in this particular order, it might be that problems are drawn from investigating users and their interactions with the products.

Creating the Statement

With the table now full of ideas and findings start to line up problems and successful outcomes with the users impacted, the benefits they’ll gain and the solutions that will hopefully bring them.

It might be that some details have to be duplicated; maybe a problem affects more than one user type or a benefit can be found for multiple problems. Duplication is fine, just make sure that only one sticky per section exists per row as these will form the hypothesis statement, as is shown here:

At this stage everything is in place to create the hypothesis statement, lets see an example:

Problem statement: ‘ How might we increase the number of overdraft applications on mobile? ’

Template: We believe that [business outcome] will be achieved if [user] attains [benefit] with [feature]

Hypothesis: We believe that an increase in Overdraft applications of 25% will be achieved if customers that often go overdrawn can apply for a flexible short term loan with a quick apply mobile Overdraft feature .

Highlighting Assumptions

This is a pretty bold statement and it’s full of assumptions that have either been validated previously or are completely new and much less understood. The process of designing and building a new feature on mobile is both lengthy and costly to the business, how can we ensure it’s not a wasted effort?

Firstly it’s important to highlight all the assumptions the team has about this statement. Right now without further investigation we can highlight that the user affected might be those that often go overdrawn, but what if there’s many customers of this service want it simply for peace of mind? What if they want a longer term solution to their money problems rather than something short term and more flexible?

Take one hypothesis statement at a time and pick through it and highlight every assumption, whether it’s been validated already or not.

There are a few assumptions in the above statement but one of the riskiest assumptions (i.e. if we go straight into building the feature what could cause the whole thing to fail?) is that users want a way to ‘quickly apply’ for an overdraft.

This could be an assumption from prior research and partly validated, but right now it’s the one thing that if wrong would mean the time and effort it took to build this feature was wasted.

With the list of assumptions pull out the riskiest ones and order them by rank from most risky to least on the table. It’s possible to experiment and validate every single assumption the team have found, but in reality it’s generally only feasible to validate the top few riskiest to allow the team to move forward.

Experiment with MVPs

Now we have a hypothesis statement and identified the riskiest assumptions it’s time to understand how best to experiment with the potential solutions.

I believe that the concept of an MVP can come in two distinct flavours — to validate riskiest assumptions by understanding what the market wants or to deliver limited functionality for fast customer value and business benefit.

Type A: might simply be a paper prototype to test with users, a survey to gain further insight or a concierge or Wizard of Oz MVP . It might need no development work whatsoever and it’s not a basic ‘first release’ with defined future releases already on the backlog. The purpose of this is to ensure that the team can validate their riskiest assumptions before spending any further time or effort to build a real feature.

Type B: can again be a concierge or Wizard of Oz MVP, but equally it could have further analysis, design work and rapid development to get it into the market quickly. This is more useful if the assumptions are less risky (i.e. the team are more confident they’re doing the right thing based on prior knowledge or experience) and speed to market could bring greater business benefits.

It could be that there are multiple possible types of MVP experiment for each of the identified risky assumptions. However it’s always best to first test the riskiest assumption with the least effort MVP before moving on to others.

Now the table is complete we have a hypothesis statement with the riskiest assumptions identified and experiments that can validate whether the assumption is true or not.

Problem: ‘ How might we increase the number of overdraft applications on mobile? ’

Experiment: We intend to validate our riskiest assumption that customers want a quick apply overdraft feature by building a simple one tap application screen that will send user details to an Overdrafts team to follow up and complete the process.

If the experiment shows that the risky assumption was actually validated as true then the team can move on and start to design and build the intended solution with a level of confidence that it will bring both customer value and business benefits.

If however the assumption is validated as false they can move onto the next assumption and reconsider how to solve the original problem. This is a fantastic place to be as the team has just saved the business months of development work and the associated costs.

This is the power of product experimentation using hypotheses.

If you’re looking for more about product, Agile, Lean and design you can follow me on Twitter @ndxcc or read more here .

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How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on December 2, 2022.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Prevent plagiarism. Run a free check.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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