Published June 26, 2024

Testing Business Ideas from Concept to Market Using the Design Test Loop

13 min read

Testing business ideas is crucial for ensuring that your concept is innovative and viable in the market. Even the most promising ideas can fail to gain traction or meet customer needs without proper testing. Refine your business model through continuous design and testing.

A great value proposition addresses key customer needs, while a great business model ensures profitability and scalability. We love Awi Lifshitz’s post emphasizing the importance of a great value proposition and a profitable, scalable business model. Awi highlights that the design test loop takes time and effort, but careful development and interaction lead to success. 

The “Design Test Loop” is an iterative process that helps you refine and scale your business model for lasting success. This approach enables entrepreneurs and business owners to improve their ideas based on real-world feedback and data continuously.

Thanks to David Bland, Alexander Osterwalder, and Strategyzer for the Design-Test Loop model!

In this blog post, we’ll dive into the Design Test Loop, exploring each step in detail and providing practical tips for testing your business ideas. Whether you’re a startup founder or an established business looking to innovate, this guide will help you navigate the complexities of business idea validation.

Using a product like Helio simplifies accessing your audience, speeds testing and experimenting, and helps you learn from your hunches.

Understanding the Design Test Loop

The Design Test Loop is a structured methodology for refining and testing business ideas. It involves several iterative steps: Ideate, Prototype, Assess, Hypothesize, Experiment, Learn, and Decide. Following this loop, you can systematically evaluate your business model, identify potential pitfalls, and make data-driven decisions.

Even if a product or service is desirable, it can still fail financially if the business model is not sound. To determine if a business model fits market needs and is scalable and monetizable, one must engage in an iterative process:

  • Ideate and prototype using customer insights
  • Assess the biz model, including market, channels, pricing, and revenue
  • Narrow down promising options
  • Formulate hypotheses based on real-world scenarios
  • Conduct targeted experiments to test these hypotheses
  • Collect and analyze data
  • Use insights to refine the product and business model

1. Ideate and Prototype

Ideation is the first step in the Design Test Loop, where you generate various ideas and concepts. This phase is all about creativity and brainstorming. Gather your team and use techniques like mind mapping, brainstorming sessions, and collaborative tools to develop as many ideas as possible. The goal is to think outside the box and explore various possibilities.

Once you have ideas, it’s time to create prototypes. Prototyping involves turning your ideas into tangible representations that can be tested and evaluated. These prototypes don’t have to be perfect; they must be good enough to convey the concept and gather feedback. Tools like Figma, Sketch, and physical models can be incredibly useful during this phase.

During ideation and prototyping, keep your target audience in mind. Consider their needs, preferences, and pain points. This customer-centric approach will help ensure that your prototypes are relevant and valuable.

Helio Example

Helio supports this process through pretotype testing, where we gather reactions to very early mockups of ideas before investing in development.

Understanding the difference between a pretotype and a prototype is essential. A prototype usually presents a detailed product version that shows its look and feel. They may be non-functional simulations or functional models, focusing more on product feasibility than on market validation.

In contrast, a pretotype addresses the question, “Would people be interested in the product?” rather than “Can the product be built?” This distinction is crucial for efficient product development. We posed this same question to an audience of Small Business Advertisers in the US to test concepts for an ad management platform.

Part of Advent’s pretotyping process involves sketching out representations of their business ideas for team communication and to get feedback from potential advertising customers. We also created a video voice-over that presents their concept sketches and explains the ideas to participants:

We measured reactions to these early pretotype concepts with emotional reaction questions, where participants expressed how they felt about the concept explained in the video using 8 basic emotions, such as Anticipation or Joy:

“I like the options provided to the one creating the campaign, and I like that it is possible to change the parameters of the campaign afterwards, allowing a more dynamic solution.”
– Small Business Advertiser (US)

These emotional gut reactions, along with the qualitative explanations for participants’ feelings, provide strong early indicators of the company’s concept acceptance rate, a key performance indicator that tracks the percentage of initial mockups or prototypes that receive positive feedback from early testing groups. It reflects how well initial concepts resonate with target users before further development. 

View our blog on Pretotyping

2. Assess the Business Model

After creating prototypes, the next step is to assess your business model. This involves thoroughly evaluating various components such as market demand, channels, pricing strategies, and revenue streams. Conducting a feasibility study at this stage is essential. A feasibility study helps determine if your business idea is viable and worth pursuing.

Start by analyzing the market demand for your product or service. Use market research techniques to gather data on your target audience, competitors, and industry trends. Tools like surveys, focus groups, and online analytics can provide valuable insights into market demand and customer behavior.

Next, assess your channels and distribution strategies. How will you reach your customers? What platforms and methods will you use to deliver your product or service? Consider both online and offline channels and evaluate their effectiveness.

Pricing and revenue models are also critical components of the business model assessment. Experiment with different pricing strategies to determine which one best aligns with your target audience and market positioning. Additionally, analyze potential revenue streams to ensure your business can generate sustainable profits.

By thoroughly assessing your business model, you can identify potential challenges and opportunities, ensuring that your idea has a strong foundation before moving to the next phase of the Design Test Loop.

Helio Example

Helio’s survey capabilities form a foundation for learning from a market audience. You can delve deeper with a competitor review to learn how the market is already receiving products.

Helio enables you to conduct competitor analysis by comparing user feedback on different homepages. This comparison helps you identify areas for improvement and differentiate your site from the competition.

To illustrate how you can use Helio to test landing pages, we analyzed five prominent CRM providers—HubSpot, Zoho, Zendesk, Salesforce, and 

We conducted five independent tests focused on user motivation, ability, and clarity of prompts. Distinct patterns emerged, highlighting the strengths and weaknesses of each platform. The image above shows the framework we used to evaluate the results from each test.

This type of competitive review testing can significantly affect a company’s market fit score, which quantifies the alignment between product features and identified market needs. It helps determine how well the proposed product fits into the existing market landscape.

View the Competitive Review Case Study

3. Formulate Hypotheses

After assessing your business model, it’s time to formulate hypotheses. A hypothesis is an assumption you can test to validate or invalidate your business idea. In this context, hypotheses are educated guesses about how your business model will perform in the real world.

To formulate effective hypotheses, identify the key assumptions underpinning your business model. These assumptions could relate to customer behavior, market demand, pricing strategies, or any other critical aspect of your business. For example, you might hypothesize that your target customers are willing to pay a premium for a particular feature of your product.

Ensure that your hypotheses are specific, measurable, and testable. Instead of vague assumptions, create clear statements that can be validated through experimentation. For instance, “We believe that offering a free trial will increase conversion rates by 20%.”

Clearly defining your hypotheses sets the stage for targeted experiments that provide meaningful insights into your business model’s viability.

Helio Example

Hunches! Most of our Helio testing starts with hunches, which are educated guesses or assumptions about how the target audience will receive a new product or idea.

It’s a preliminary hypothesis about market preferences, product features, or effective messaging that we must test and validate through feedback from potential customers. Hunches guide the creation of concepts for testing and help identify key areas to focus on during the testing process.

4. Conduct Targeted Experiments

With your hypotheses in hand, the next step is to design and conduct targeted experiments. These experiments are designed to test your hypotheses and gather data on how well your business model performs in real-world scenarios.

One effective method for conducting experiments is through the development of a Minimum Viable Product (MVP). An MVP is a simplified version of your product that includes only the core features necessary to validate your hypotheses. By launching an MVP, you can gather early feedback from customers and make adjustments before investing significant resources into full-scale development.

When designing your experiments, focus on gathering quantitative and qualitative data. Quantitative data, such as conversion rates and user engagement metrics, provides measurable evidence of your hypotheses’ validity. Qualitative data, such as customer feedback and user interviews, offers deeper insights into customer needs and preferences.

For example, if you hypothesized that a free trial would increase conversion rates, you could run an A/B test where one group of users is offered a free trial while another group is not. You can determine whether your hypothesis holds by comparing the conversion rates between the two groups.

Conducting targeted experiments allows you to validate or invalidate your hypotheses with real-world data, reducing the risk of pursuing unviable business ideas.

Helio Example

Multivariate testing allows you to test several conceptual variations at once to surface signals that inform the direction of the design. For example, Indiana University aimed to enhance the user experience on its homepage by determining which visual elements most positively influenced user impressions and satisfaction. To create this effective homepage, the Indiana University team hypothesized that including an animation of their city’s skyline on the page would better align with their desired brand impressions.

The team placed each version of the homepage into a Figma prototype to showcase the animated aspects of the homepage, allowing participants to experience the animations in an interactive environment. After interacting with the Figma prototype, we asked participants a series of evaluative questions to compare satisfaction and brand impressions across the three versions.

We presented three homepage variations to users:

  • Simple, no skyline: Basic design without any background imagery.
  • Skyline V1: Features the city skyline during the day as a background.
  • Skyline V2: Incorporates a vibrant evening city skyline with prominent university branding.

We randomly assigned participants to review one of the three versions and they provided feedback on their satisfaction and overall impression of the page.

These targeted experiments help a team establish their optimization impact score, which measures the effect of different design variations tested in multivariate experiments on the desired outcome (e.g., conversion rate, user engagement). This score helps identify which variations produce the most positive impact.

5. Learn from Data

Once you have conducted your experiments, learning from the collected data is crucial. Analyzing this data helps you understand whether your hypotheses were correct and provides insights into how you can improve your business model.

Start by organizing and reviewing the quantitative data. Look for patterns and trends that indicate how well your business model performed during the experiments. For example, did the free trial increase conversion rates as expected? If so, by how much?

Next, delve into the qualitative data. Read through customer feedback and interview notes to identify common themes and insights. What did customers like about your product? What challenges or pain points did they experience? This qualitative information can provide valuable context for the quantitative results.

Generally I split the value created for the customers into two categories: hard and soft. Hard value prop is usually highly measurable, has a direct impact on customer satisfaction and is a direct proxy into customer retention Soft value prop is often poorly measurable, has an indirect impact.

Avatar of the person that wrote the post

Jevgeni Kabanov



Based on your data analysis, determine whether your hypotheses were validated or invalidated. If a hypothesis was validated, consider how you can build on that success. If a hypothesis was invalidated, use the insights gained to refine your business model and formulate new hypotheses for further testing.

Learning from data is an iterative process. Each cycle of the Design Test Loop provides new information that helps you continuously improve and refine your business model.

Helio Example

Using templated data frameworks, we can quickly make sense of the data produced by the Helio tests across multiple surveys and designs. We quickly copied the data from the Indiana University skyline testing from each separate Helio test into the framework below:

This side-by-side view quickly reveals which of the variations received the most positive feedback from participants. Brand impressions such as Impact, Discovery, and Innovation all increased compared to version 1. The net positive alignment of skyline version 2 (total positive impressions minus total negative impressions) was much greater than the other versions. Combined with the higher satisfaction, the IU team confidently moved forward with their plans for an animated skyline on the homepage.

The more you learn from your data, the more you can optimize your team’s Insights Utilization Rate, or how effectively the team implements the insights gathered from data analysis into the product design or strategy. This KPI reflects the team’s ability to translate data into actionable changes or improvements.

View the Indiana University Case Study

6. Decide and Iterate

The final step in the Design Test Loop is to make informed decisions based on the data and iterate on your business model. This involves using the insights gained from your experiments to adjust your business model strategically.

If your experiments validated your hypotheses, you might decide to scale up your efforts. For example, if offering a free trial significantly increases conversion rates, you could implement the free trial as a standard part of your marketing strategy. Additionally, you could explore other ways to enhance the trial experience and further boost conversions.

If your hypotheses were invalidated, it’s important to revisit your business model and identify areas for improvement. Use the feedback and data to refine your value proposition, pricing strategies, or customer acquisition methods. Formulate new hypotheses and design new experiments to test these revised business ideas.

Remember that the Design Test Loop is an iterative process. Each cycle builds on the previous one, allowing you to improve your business model continuously. By embracing this iterative approach, you can adapt to changing market conditions, customer preferences, and competitive dynamics.

In conclusion, the Design Test Loop provides a structured and systematic way to test and refine your business ideas. By following these steps—ideate and prototype, assess the business model, formulate hypotheses, conduct targeted experiments, learn from data, and decide and iterate—you can increase the likelihood of developing a successful and scalable business model. Keep testing, learning, and iterating to stay ahead in the ever-evolving business landscape.

Helio Example

After establishing baselines with your current designs, you can test and measure future iterations using the same Helio surveys to understand how your user experience is progressing (or regressing) over time.

The Interaction Matrix is a great tool for establishing a baseline with your current designs and then measuring against that baseline for future iterations. We conducted this iterative testing on a mock-up of an ecommerce brand that sells formal clothing online, called Getup.

The Interaction Matrix framework shows that we first tested Getup’s homepage baseline, which revealed usability issues with many primary and secondary actions, such as Shopping for a Suggested Look.

Primary actions are the most important CTAs on the page and should produce at least 80% success from users. Secondary actions, important but not the main purpose of a page, should achieve at least 70% success. Tertiary actions, as minor as contact links in the footer, should be discoverable by 55% of users on the first click.

After testing your baseline, use the data to make design decisions to drive your new variations forward. With their first round of results in hand, the Getup team continued to test new variations of their homepage actions, immediately seeing success in secondary actions like syncing calendars and watching instructional videos.

The primary action of Shopping a Suggested Look was the last nut to crack, which finally achieved expected success with Version 3 of the homepage.

Getup’s Interaction Matrix demonstrates constant iteration and improvement over time, validating their team’s decisions with user data. This type of testing can greatly contribute to a team’s Iteration Success Rate, which measures the percentage of iterations that result in improved performance metrics compared to previous versions. It assesses the effectiveness of the iterative design and development process in enhancing user experience over time.

View the Interaction Matrix Guide


Testing Business Ideas Resources

Here are some great links. As Nabila Amarsy and Jevgeni Kabanov explained, some business models excel due to clarity in value propositions and scalability. David Bland emphasizes a discovery-driven culture for testing ideas, while Catherine Brown outlines the next steps for value propositions. Figma provides templates for business models and value proposition canvases.

Best Practices for Testing Business Ideas

Tips for Effectively Testing Business Ideas

  • Start Small and Scale Gradually: Begin with a simple prototype or MVP to gather initial feedback. Scale your efforts based on the insights gained.
  • Focus on Key Metrics: Identify and track key performance indicators (KPIs) crucial for your business model’s success, such as conversion rates, customer acquisition cost, and lifetime value.
  • Engage with Your Target Audience: Use surveys, interviews, and focus groups to gather qualitative feedback from your target audience and understand their needs, preferences, and pain points.
  • Run A/B Tests: Experiment with different versions of your product or marketing strategies to determine what works best.
  • Document and Analyze Results: Keep detailed records of your experiments and their outcomes. Use this data to inform future decisions and iterations.
  • Be Open to Pivoting: If your tests reveal significant issues with your business model, be willing to pivot and explore new directions.

Common Pitfalls to Avoid in the Testing Process

  • Skipping the Hypothesis Stage: Always start with clear, testable hypotheses to ensure your experiments are focused and meaningful.
  • Relying Solely on Quantitative Data: While numbers are important, qualitative insights from customer feedback are equally valuable for understanding user behavior.
  • Ignoring Negative Feedback: Pay attention to all feedback, especially negative comments, as they often highlight areas for improvement.
  • Overcomplicating Experiments: Keep your experiments simple and focused. Avoid adding too many variables that can confuse results.
  • Lack of Iteration: One round of testing is rarely sufficient. Continuously iterate based on new data and insights to refine your business model.

Importance of Customer Discovery and Continuous Learning

In this post, we’ve explored the Design Test Loop and its importance in validating and refining business ideas. From ideation and prototyping to conducting targeted experiments and learning from data, each step in the loop helps you systematically improve your business model.

An iterative approach to testing business ideas is essential for minimizing risk and maximizing success. You can develop a robust, scalable business model that meets market needs and drives growth by continuously testing, learning, and iterating.

Customer discovery is a critical component of the Design Test Loop. It involves understanding customers’ needs, preferences, and behaviors through direct interaction and feedback. This ongoing process helps ensure that your business model remains relevant and aligned with market demands.

Continuous learning is equally important. The business environment is dynamic, and staying informed about industry trends, competitor strategies, and customer preferences will help you adapt and innovate. Regularly revisiting and updating your business model based on new insights ensures long-term success.Now is the time to put these principles into practice. Start testing your business ideas using the Design Test Loop, and unlock the potential for lasting success in your entrepreneurial journey.

Testing Business Ideas FAQs

What is the Design Test Loop?
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The Design Test Loop is a structured methodology for refining and testing business ideas through iterative steps: Ideate, Prototype, Assess, Hypothesize, Experiment, Learn and Decide. It helps entrepreneurs systematically evaluate their business models, identify potential pitfalls, and make data-driven decisions.

Why is testing business ideas important?
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Testing business ideas is crucial to ensure that your concept is innovative and viable in the market. Even promising ideas can fail if they don’t meet customer needs or gain traction. Proper testing helps refine and scale your business model for lasting success.

How do you start the ideation process?
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The ideation process begins with generating various ideas and concepts through brainstorming sessions, mind mapping, and collaborative tools. The goal is to think creatively and develop multiple possibilities to be turned into prototypes for further testing.

What is the role of a Minimum Viable Product (MVP) in testing business ideas?
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An MVP is a simplified version of your product that includes only the core features necessary to validate your hypotheses. It allows you to gather early feedback from customers and make adjustments before investing significant resources into full-scale development.

How do you formulate testable hypotheses?
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To formulate testable hypotheses, identify the key assumptions underlying your business model. These assumptions should be specific, measurable, and testable. For instance, hypothesize that a free trial will increase conversion rates by a certain percentage.

What are common pitfalls to avoid during the testing process?
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Common pitfalls include skipping the hypothesis stage, relying solely on quantitative data, ignoring negative feedback, overcomplicating experiments, and not iterating based on new data. Avoid these to ensure effective testing and refinement of your business model.

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