These days, it’s hard for a company to get by without understanding what their target market wants. And for most tech companies, failing to grasp a clear understanding of customer needs, requirements and pain points can be fatal.
With so much competition in the tech field thanks to new solutions being designed every day, companies need to be sure that there’s a need and desire for their product before they even start building it.
Lean manufacturing methodologies are often implemented to not only gauge the user interest, but determine what needs to be done to improve the product and give customers exactly what they want, plus determine whether people would be willing to pay for the product and how much.
The processes of creating a prototype, A/B testing and validated learning help today’s young tech startups determine the direction that they need to take with their product, making data-driven decisions in line with user feedback. There are many benefits of using this methodology for a company, including reducing waste, reducing time and effort put into creating features that customers don’t want or need, and getting a product that the market wants out there faster. Today it’s not the biggest that wins, but the fastest. The sooner you can get a product that there’s a demand for out there, the better.
So, what are today’s successful tech companies doing to make it happen?
Arguably, there’s no change, growth or improvement without learning. Any development process that seeks to be better or faster needs to include learning as a vital part of the process. Taking a lean startup approach when developing a tech product is no exception.
Validated learning is the part of the lean startup processes that allow us to determine how close each iteration is to providing end-user satisfaction. For a lean startup, success is not measured by the number of units that you manufacture, but by the measurable responses that you get from target users.
Applying validated learning to your product development process has several clear advantages over a traditional product cycle:
- Faster development: When feedback and learning mechanisms are more focused, your startup’s product will approach a final release state a lot faster.
- Actionable metrics: The final product version will be more closely aligned to user needs when learning is based on measurable data and metrics.
- Reduced costs: Improved learning leads to faster and more efficient development. In turn, this leads to fewer resources and time wasted throughout the development journey.
- More agility: The validated learning approach leads to agile working and rapid MVP development, leading to greater flexibility as an organization.
How does it help to build scalable products?
In an age of rapid growth, products need to be scalable in order to succeed. Throughout the development process, you should not only be focused on today’s user needs but also tomorrow’s, and even further into the future. If your product’s features and/or capacity are fixed, it’s not going to last.
A genuinely successful product is able to grow within market needs. This is where validated learning is so important, as it keeps you alongside these needs and allows you to both understand and anticipate them better.
The first stage of any lean manufacturing process is to create a prototype, also known as a minimum viable product (MVP). The MVP should contain only the necessary features of the product, to provide users with a basic idea of what to expect and help the company determine whether or not it is worth continuing with the project. An MVP should:
- Contain the basic features of the finished product.
- Provide value to the end-user in order to give them a clear idea of what to expect from the finished product, enabling companies to gather data on how successful they expect the product to be.
- It should collect the maximum amount of validated learning about the target audience, with the least effort.
Before you go down the path of developing an MVP, keep the following principles in mind:
- You should have a clear, defined concept; if you are unable to explain it easily, your customer won’t understand it.
- Define the minimum viability: What do you need to learn from your customers? Design the MVP to this goal, and don’t go further. Speed and simplicity are what developing an MVP is all about.
- Build it similar to intent; watching it fail can often be more informative than watching it be successful. You need to drive a reaction with your MVP, so build with learning in mind.
- Iterate quickly: Keeping moving is an important part of the process. Take what you learn from the first MVP, improve on it quickly, and get the next interaction out to gather further feedback.
- Find people who’re passionate about what you’re doing: You don’t need a large sample size; you need opinionated, early adopters or a few people with deep knowledge about what you’re doing to get feedback from.
Click here to learn more about prototyping in the lean manufacturing process at Kettering University Online.
A/B testing allows you to go further and test several different variations of a product in order to determine which one works best. Even the smallest differences can have a large impact on consumer behavior, so testing both variations at the same time allows you to better understand which version is the most popular with consumers.
A/B testing can be useful because simply put, different audiences behave differently. Something that works well for one company might be completely useless for another.
But A/B testing can be a complex process, so it’s important to be careful. If not, you could end up making incorrect assumptions about what people like, and the decisions you based on them can impact your strategy.
Some A/B testing best practices to keep in mind include:
- Only test one thing at a time
- Testing frequently will continuously improve your results
- Be sure to use the right set of tools
- Allow tests to run long enough to collect the information needed to make a data-driven decision
- Bear in mind that sometimes, the results will be inconclusive
- Don’t make assumptions; always use the data collected to drive decisions
- Since every set of customers and company is unique
A/B testing is often used to evaluate landing pages, but it can be a very useful tool when used to develop better product designs, too.
Product teams are constantly pushing small updates to products in response to customer feedback and what’s happening in the market. Proper A/B testing can gather evidence that enables you and your team to determine exactly which design decisions work best for your product. It enables you to learn why certain elements of the experience are having an impact on user behavior, helping you make more informed design decisions and have more specific conversations with stakeholders.
Where can A/B testing be used?
A/B testing is applicable to almost any design decision. In marketing, it’s often applied to:
- Calls to action
- Search ads
But you can test everything that can be changed. In product design, you might consider running A/B testing with two different MVP prototypes, for example. You can use the data generated from each test to determine which version of the product is going to have the biggest impact and decide which design processes to follow next.
But of course, you shouldn’t test absolutely everything using this tool simply because it has the capacity to. Tests should always be focused on the design process decisions that are going to provide the maximum value to you and your users.
How to run A/B testing:
- Use analytics data to identify areas that can be optimized: Your analytics can provide valuable insight into where to start optimizing.
- Define conversion goals: The goal is any action that you count as a conversion; for example, a user expressing interest in purchasing your product, signing up to a mailing list or putting their name down to buy it once it’s released.
- Generate hypotheses: Prepare a list of ideas on how you can improve the current conversion rate. Once the list is prepared, you should review each idea and evaluate it while considering both the expected impact and the difficulty of implementation.
- Create design variations: Begin with the top priority ideas and make the desired changes to an element of your product.
- Run the experiment: Allow real-world users to interact with your design variations and track their progress.
- Analyze results: Once the experiment is complete, it’s time to analyze the results. This will help you determine whether the changes had a positive, negative or no effect on user behavior.
The lean startup product design process often begins with several ideas, each of which is a potentially different direction to choose for the product. The use of validated learning, MVP production, and A/B testing allows you to take small steps in each direction to test the idea. As a result, you can easily use data to confirm whether or not your assumptions and information about your target market are correct or not.