Have you ever bought something that looked amazing online, but when it arrived was a total disappointment? The quality may have been off, or maybe it didn’t work how you thought it would. It’s frustrating, right?
Well, imagine you’re on the other side of that transaction: you’re on a product team that just spent months developing a new feature or product that flops. There has to be a way to ensure you meet your customers’ expectations, right? That’s where customer validation comes in.
Customer validation is the process of gathering feedback from potential customers to ensure that you’re building something people actually want and need. It’s not about asking your friends or colleagues what they think; it’s about getting honest feedback from real customers representing your target audience. By doing so, you can avoid the disappointment of launching a product that falls flat and, instead, build something your customers will love and continue to use.
In this article, we’ll cover why it’s crucial to your product’s success and how you can implement it in your product development process.
What is customer validation?
Gone are the days when product development teams could rely solely on their instincts to create new products or features. Today’s market is highly competitive, and consumers have high expectations for the products they use.
Customer validation involves testing and gathering feedback from potential customers to ensure that a product or feature solves a real problem, meets a real need, and provides real value. It’s a critical step in the product development lifecycle that can increase the chances of success in the market.
Customer validate methods
There are several methods for conducting customer validation research, including surveys, interviews, user testing, and analytics.
Surveys and interviews allow you to gather qualitative data on customer needs and pain points, while user testing and analytics provide more quantitative data on how customers interact with your product. As part of this gathering and testing process, customer validation also involves creating and testing prototypes or minimum viable products (MVPs) to see how customers respond to specific features or functionality. We’ll go into these in more detail later on.
Key terms you need to know
- Product-market fit refers to the degree to which a product meets the needs and preferences of a specific market. Achieving product-market fit is crucial for the success of a product, as it determines whether or not customers will adopt and continue to use the product over time.
- Hypothesis testing involves creating a hypothesis about a product or feature and testing it with real customers to see if it holds up. Hypothesis testing can help you validate assumptions and ensure that you’re building something that customers need.
Why is customer validation important?
Customer validation ensures your product meets the needs of your target audience and helps it stand out from the crowd. It also saves you time and money by identifying potential issues or roadblocks early on in the development process.
Failing to conduct customer validation can result in significant risks for a product development team. For example, investing valuable time and resources into building a failed product can result in significant financial loss. And it can happen to anyone, from the newest startup to the most established tech giant. Just look at Google Glass, a doomed project from Google that’s since become the motto for failed products.
Customer validation can help teams avoid these types of pitfalls. For a more successful real-world example, we look to Dropbox. Dropbox, a cloud-based file storage and sharing service, used customer validation to grow its user base from zero to over one million in just seven months. The company created a demo video explaining the concept of Dropbox and shared it on Hacker News, which secured them an invitation to join an exclusive startup program. After that, they released a series of videos for their Beta launch. The overwhelmingly positive response helped the team understand that there was a real need for the product. It even allowed them to create a waiting list that quickly grew into a large user base.
By understanding your target audience, teams can make data-driven decisions that are more likely to provide real value to their customers.
To summarize, customer validation:
- gives you evidence there’s a market for your product
- makes it easier for teams to iterate early on, which improves your chances of success
- saves resources because you’re not building a product no one wants
- helps you get buy-in from internal and external stakeholders
- focuses your team
Why is customer validation crucial for project managers?
The project manager’s role in customer validation is to oversee the process and ensure the team is gathering the right information and using it effectively. Specifically, they need to focus on several key areas, including defining the target audience, selecting the suitable validation methods, and interpreting the results.
This is important for three reasons:
- Defining your target audience helps ensure that the feedback you gather is relevant and actionable.
- Selecting suitable validation methods is important because different methods can provide different data types; some may be more appropriate for your product than others.
- Interpreting the results is critical to making data-driven decisions about your product.
The customer validation process
Customer validation typically occurs during the product design process, after the initial research and ideation stages have been completed. The earlier you begin, the better — and you definitely don’t want it to happen after you’ve started designing the product.
Here’s a step-by-step guide.
Step 1: Define your target audience
This involves creating a detailed profile of your ideal customer, including their demographics, psychographics, and other relevant information. It’s a vital part of the customer discovery process, which involves asking many questions about your users and their needs.
You can use various techniques to help you turn all this data into something understandable. User personas, for example, are a great way to represent your target audience accurately.
You’ll note your assumptions and develop a hypothesis as part of this stage.
A hypothesis allows you to test the underlying beliefs and assumptions that drive your product development. By testing hypotheses, you can validate or invalidate assumptions, avoid investing in unnecessary features or products, and ensure that resources are used effectively.
Step 2: Select your validation methods
There are several options here. Each method has its advantages and disadvantages. The choice of method will depend on the specific goals and constraints of the project.
- Surveys are commonly used to ask customers a basic series of questions about your product or idea. You can run them online, via email, or in person, and they can help you gather large amounts of data quickly. But they’re not without their limitations: low response rates and biased results are common, and the feedback is on the more basic side of things.
Top tip: When conducting surveys, it’s important to use open-ended questions that allow customers to provide detailed responses rather than just checking boxes.
- Interviews involve having one-on-one conversations with customers about their needs and experiences. Interviews can provide rich, qualitative feedback that surveys cannot, which can help you identify new insights and opportunities. Conversely, they are time-consuming and resource-intensive and may not represent your entire target audience. They work best when combined with surveys.
- Prototype testing involves creating a functional version of your product or feature and asking customers to interact with it. This can help you gather feedback on the usability of your creation before it’s fully developed.
- A/B testing is commonly used in web development. It involves testing two different versions of your product or feature with a subset of your target audience to see which performs better.
- A Minimum Viable Product (MVP) is a version of your product with just enough features to provide feedback for future development. Launching an MVP, you can quickly test your assumptions in the real world, validate or invalidate your hypothesis, and iterate on your product development.
Step 3: Gather and organize
Once you’ve selected your validation methods, it’s time to gather feedback from your target audience. You’ll have a lot of data at this stage, so make sure you organize it well. Digital stores? Good. Bits of paper in a drawer? Bad.
Step 4: Analyze the feedback
After gathering feedback, the next step is to analyze it and identify key patterns and insights.
You can use various data analysis techniques, such as clustering or regression analysis, to identify patterns and trends. You can also use visualization tools, such as charts or graphs, to present the data in an easily digestible format.
Here are some key steps to follow:
- Data cleaning and preparation include removing duplicate data, correcting errors, and ensuring the data is consistent. The cleaned data should be organized into a format that is easy to analyze, such as a spreadsheet or in your diagramming tool.
- Descriptive analysis involves summarizing the data to understand key trends and patterns better. This can include mean, median, and mode for numerical data or frequency counts and percentages for categorical data. This analysis helps identify critical patterns and trends in the data and can serve as a starting point for more in-depth analysis.
- Inferential analysis uses statistical techniques to draw conclusions about the population based on the sample data. This analysis can help project managers determine the statistical significance of their findings and make data-driven decisions. Some standard inferential techniques include hypothesis testing, correlation analysis, and regression analysis.
- Visualization is an essential tool for presenting data in a way that is easy to understand and interpret. Visualization can include charts, graphs, and other visual aids that can help project managers identify key patterns and insights in the data. Visualization is also helpful for presenting findings to stakeholders and other team members.
- Actionable insights are the final step in data analysis. It is time to use the insights gathered to make informed decisions about the product development process. This takes us to our next section.
Step 5: Iterate and improve
The final step in customer validation is to use the insights you’ve gained to iterate and improve your product. This could involve tweaking the design, changing the messaging, or even pivoting to a new product entirely. Using a data-driven approach, you can be confident you’re making the right decisions for your product and customers.
The process of customer validation is not a one-time event. It’s an ongoing process that should be repeated throughout the product development lifecycle.
To iterate and improve your product based on the insights gathered during customer validation, here are some key steps to follow:
- Analyze the data: As mentioned earlier, data analysis is a critical step in the customer validation process — and it remains this way throughout.
- Identify areas for improvement: Project managers should identify areas that need improvement based on the insights gathered during the data analysis. This could include adding new features, improving existing features, or addressing any issues or pain points you identified during the validation process.
- Prioritize improvements: It’s essential to prioritize these improvements based on their impact on the product and your customers’ needs. This will help you focus on the improvements that will significantly impact the product and the customer experience.
- Implement changes: Once you have identified and prioritized the improvements, it’s time to implement them.
- Test and validate: After implementing the changes, testing and validating them with your customers is important. This could involve running additional validation tests, collecting feedback, or monitoring usage metrics to ensure the changes have the desired effect.
- Repeat: The customer validation process should continue throughout the product development lifecycle. By repeating the customer validation process and iterating on the product based on the insights gathered, project managers can continue to improve the product and ensure that it meets the needs of their customers now and in the years to come.
Customer validation: essential tools of the trade
Here are some essential tools to have in your toolkit.
- Project management software, like Backlog, can help you keep track of tasks, assign responsibilities, and collaborate with team members. Choose one that’s intuitive and has all the features you need.
- Diagramming tools, such as Cacoo, can help you create user journeys and other visualizations to understand your customers’ interactions and experiences.
- Chat apps can be a great way to communicate less formally with your team members and customers. These tools make it easy to share information and collaborate with team members in real time.
- Survey tools like SurveyMonkey or Google Forms can help gather customer feedback. These tools make creating and distributing surveys easy and can help you collect data quickly and efficiently.
- User testing tools, such as UserTesting or UsabilityHub, allow you to watch users interact with your product and provide feedback, helping you identify areas for improvement. Meanwhile, tools like Hotjar and Google Analytics are great for showing how users physically interact with your app or site.
Customer validation is a crucial step in ensuring the success of any business venture. It allows project managers, entrepreneurs, and product developers to refine their offerings and create something that meets the needs of their target market to a T.
Managing all of this data can be a daunting task without the right tools, though. And that’s where collaboration tools come in. By using software to collect, organize, and analyze customer feedback, you can streamline the validation process, collaborate easily, and cut down on data admin — leaving you free to focus on what matters: making the best product possible.