Beyond MVPs – The concept of a minimum viable product (MVP) is well-known in product development circles. An MVP is a stripped-down, early version of a product that aims to validate key assumptions and hypotheses with actual users. By releasing an MVP quickly and gathering feedback, teams can learn, adapt, and improve their product before investing more time and money into a full-featured product.
However, even simple and minimal MVPs require a significant upfront investment of time and resources to build. An MVP requires making technology choices, engineering infrastructure, designing user experiences, and coding a working (albeit limited) product that can be released. For hardware products, MVPs may require building prototypes and manufacturing an initial set of physical goods. Even for software, MVP development involves overhead that slows learning.
Beyond MVPs
Rather than jumping right to even a basic MVP, are there other ways to validate core hypotheses by directly engaging prospective users? This post explores methods beyond MVPs for testing assumptions and desirability for product concepts without building any initial product at all. We’ll discuss how conducting problem interviews, mocked-up landing pages, and targeted surveys can provide validation faster and at a lower cost than viable products.
The Limitations of MVPs
While MVPs can be tremendously useful, they come with downsides and limitations teams should recognize.
Firstly, MVPs incur significant overhead just to create a basic product. Choices have to be made about foundational technology, architecture, and infrastructure that create path dependency down the line. Designers must mock up user interfaces and workflows. Engineering has to build integrations between different technical systems, database schemas, and APIs, and code out core functionality. For hardware, prototypes must be manufactured in some initial quantity. All this precedes validating whether users even want the product.
By the time validating feedback has been gathered from an MVP, much has already been invested. Changing course based on user feedback may require redoing foundational technical work. Throwing away an MVP is wasteful if fundamental assumptions turn out to be wrong.
Additionally, feedback from a bare-bones MVP may not always be reliable. If a product is too minimal, users will have a hard time properly evaluating it and expressing interest. Key features that drive adoption could be missing from the MVP, leading teams to incorrectly believe there is low demand. Users get only one chance to form a first impression of a product, and an MVP may sour them if the implementation is too limited.
Finally, building an MVP inevitably slows down the process of learning and validating hypotheses. No matter how fast a team executes, an MVP will take weeks or months to build, test, and release. Every day the team sinks further into implementing one specific solution before determining whether users want it. The objective should be to learn as quickly as possible and with minimal wasted effort – which MVPs may not enable.
By considering other options first, teams can validate assumptions faster by going directly to the source – prospective users. This avoids creating any product until key hypotheses are tested. Next, we’ll explore alternatives like problem interviews that enable validation without an MVP.
Validating with Problem Interviews
Rather than building any product, teams can gain key insights by interviewing potential users directly. Through open-ended interviews, teams can learn about user problems, frustrations with current solutions, and needs. This allows validating (or invalidating) assumptions about whether the problem space is worth pursuing.
Some key questions to ask in problem validation interviews:
- What challenges do you face regarding [area]?
- How are you addressing [problem] today?
- What workarounds have you created to deal with [pain point]?
- What frustrates you most about current solutions?
- What would your ideal solution for [problem] look like?
By understanding perceived problems and needs in the customer’s own words, teams can determine if their solution solves pain points before building anything.
For example, a startup building AI-powered calendar assistant software hypothesized busy professionals would love not having to manually schedule meetings. By interviewing their target users, they found most were comfortable booking meetings themselves and a bigger pain point was conflicting meeting times. This allowed them to pivot to solving meeting coordination rather than automated scheduling.
Interviews can provide critical validation or invalidation of assumptions to help teams focus their solutions on real problems experienced by users. The learning is direct from the source and the cost is only a few hours of customer conversations.
Validating with Landing Pages
Another approach is creating a landing page advertising the product concept and benefits without any actual product being built. The page describes product features, visuals, and intended customer segments. Interested visitors can provide their email address to get updates on the upcoming product.
This validation approach tests whether your messaging resonates with users and if they become interested enough to share contact information. Teams can gather demographic data like job roles and company sizes from conversions to understand their target customer profile.
For example, a startup landing page advertised an AI analytics platform to detect manufacturing defects early. By driving targeted traffic ads to the landing page, the team could gauge strong interest from quality engineers at mid-size industrial companies – validating the appeal of the product concept and target user.
Landing pages can be rapidly built using no-code tools and quickly published online. Paid advertising platforms like Google Ads allow driving controlled traffic to landing pages to hit specific user segments. This approach provides demand signal testing and learning with minimal time and engineering investment.
Landing pages are limited in how much users understand an abstract concept vs. the actual usage of a product. However, they are useful for gauging interest in ideas early without requiring months of product development. Landing page data combined with problem interview learnings can powerfully validate product hypotheses before ever fully building an MVP.
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Validating through Surveys
Targeted user surveys represent another methodology for validating product assumptions and desirability without requiring an MVP. Through surveys, specific hypotheses can be tested with quantitative data.
Key aspects to validate with surveys:
- Problem prevalence – % of people affected by the pain point
- Current workaround efficacy – how well existing solutions address the problem
- Desired benefits – which potential features/capabilities do users value most
- Purchase intent – the likelihood of buying based on the product description
Surveys should use clear, unbiased wording so as not to influence responses. They should be distributed to a representative sample of the target market to achieve statistically significant results.
For example, a team building AI analytics software for retail forecasting could survey store managers about current methods used, pain points experienced, and perceived value of different capabilities to quantify opportunity size.
While useful for gathering data-driven validation, surveys do have limitations. Respondents may have a hard time evaluating hypothetical product concepts versus hands-on usage. Still, surveys provide quantitative validation that can complement problem interviews and landing page data.
Connecting Validations to Ideas and Execution
These techniques allow teams to validate user problems, solution desirability, and willingness to pay without requiring an MVP product. They provide direct feedback from prospective users faster, cheaper, and with less bias than a limited product.
Teams should use validation learnings to focus their solution capabilities on verified user problems and desired benefits. Don’t get wedded to preconceived product ideas – let user feedback guide what you build.
Remember that while these techniques test assumptions before devoting resources to an MVP, they cannot guarantee market success. Users often cannot accurately express their needs or predict their true usage of a product until they can actually experience it. Validation is about reducing risk – not eliminating it.
Once core assumptions have been validated, it makes sense to invest in an MVP to provide users with hands-on experience and observe actual usage behaviors. Use the MVP to continue validating assumptions, learn about bugs and usability issues, and improve the product.
Think of validation as an ongoing process through MVPs and product enhancements – not a one-time event.
Beyond MVPs: Conclusion
Validating product assumptions is a crucial activity, but investing in building an MVP too early comes with downsides. MVPs require significant time and resources that could be wasted if core hypotheses turn out wrong.
This article discussed alternatives beyond MVPs that enable validating problem/solution fit directly with users first:
- Problem interviews reveal users’ needs and desires in their own words to validate if the problem space is worth pursuing.
- Landing pages quickly test demand and interest in the product concept without engineering investment.
- Surveys collect quantitative data on target users’ pains and desired benefits to validate assumptions.
Each technique provides fast learning without the overhead of MVP development. They allow pivoting product ideas based on user insights before committing to a specific solution MVP.
MVPs still provide value later in the process once core assumptions are tested. But by utilizing these techniques first, teams can validate direction earlier with less wasted effort. They complement MVPs by expanding the product validation toolbox for faster, more informed learning.
By testing demand before building supply, these approaches help ensure product investments are grounded in real user needs.
