You have a great idea for a company. You’re excited about it; you know the technology/business model works, and you know the people who can help you realize it. You have examples of when such an idea worked or companies that are successful doing it. That is all you need to start a company, right? You build a great product and people will come – isn’t that what the conventional wisdom says? All you have to do now is find the funding to turn this concept into a product and hire the right team to do it.
Not so fast…
Let’s talk a bit more about the idea. Is it an idea that solves a problem? If so, who are your customers? What pain points do they have and how are they addressing them today? How do these pain points impact their day to day and are they willing to pay to make the pain go away? These are the most fundamental questions every entrepreneur should ask themselves when they are thinking of building a product. How do you go about answering these questions?
There are several ways you can get this information. Design thinking and design sprints are a few methodologies I have used in my experience as a product manager developing e-commerce, data operations, and AI products. I won’t go too much into the merits of the methodologies or compare them but regardless of which one you choose make sure you are following these fundamental steps:
- Identify your ideal customers
- Discover their pain points
- Brainstorm ideas on how to solve for the pain points
- Create a paper prototype/storyboard the solution
- Get feedback from customers
- Decide on a minimally viable product (MVP) by narrowing down the scope and solution
- Check-in with customers throughout the development life cycle
This process differs from the market validation you might have done earlier in the process. Now, you’re looking to build the product. You need to delve deeper when you’re building a product than when you’re in the market validation phase. Building an analytics/AI product adds even more detail and complexity than a pure software product. The additional benefit of this process is you get to engage with your customers, keeping them interested in the process and allowing them to help shape the product.
You can discover potential challenges and customer needs when you investigate the data and details in this process. When I was working on a product using Artificial intelligence (AI) to help with M&A due diligence, our market validation gave us insights and we identified the biggest and earliest pain point in the process. Based on this information we had a preliminary idea of the AI solution and the application design. As we went through the design thinking process and had customers share data with us, we gained additional insights that changed both the design and the AI solution. It was not the simple classification problem we expected, but a more nuanced natural language understanding problem that we had to solve for. Getting this feedback in the design phase rather than later was invaluable. We had to make significant changes to our approach.
Finding customers to give you their valuable time is not easy but the effort to connect and engage with them is important at this stage as the commitment is just time. If you are having trouble finding customers to give you their time, think how much harder it would be to get them to try out your beta product or pay for the it. Think of this as the process of building sales muscles before you are ready to sell.
Next up, consider design. As important as it is to find the right solution, it is just as important to find the right solution design. Consider the user experience. Make sure the product design delights customers, enticing them to engage in the experience. Make your product easy-to-use and keep the learning curve low. Product design should make intuitive sense to the user and not be disruptive to their experience. For example, I have seen websites that have a cancel button where I expect to see a submit button. I click to confirm or complete an action and end up canceling all my activity on the page. It’s one of my biggest pet peeves.
Getting to MVP with a test and iterate mindset
Your next challenge is to decide on the MVP and have a plan to validate and iterate as you go through the development cycle. Testing with prototypes and initial iterations of software with customers is also important to create a commercially viable product. It is a quick way to validate the myriad of decisions you have to make in the development process and move to the next level. The sooner you do it the easier it is to iterate and get it right.
Before you hand out the beta version of your product, develop a clear set of goals and success metrics with instrumented data collection to help you understand what worked and what didn’t. On one of my AI products, looking at the initial data told us the linear workflow customers outlined in the design thinking/protype sessions wasn’t so linear after all and was impacting adoption. This was critical insight that was crucial to get right in the next iteration to increase adoption.
Following product management methodologies can help you produce an MVP with pre-defined criteria for success, helping you get to market faster.
About the author –
Rajitha Chaparala worked in data and predictive analytics before it became the cool buzz word it is today. Her experience in product management building data, predictive analytics and artificial intelligence products spans a wide variety of industries and business functions. She is the head of Data Product Management at ZoomInfo, a leader in go-to-market intelligence solutions that uses Big Data, Natural Language Processing, and Machine Learning for unrivaled data coverage, accuracy, and depth of contacts.