Maximizing Value with Minimum Viable Products MVPs

Building products is always doing something that’s never been done before. Anytime you’ve got a team writing code, they’re doing something that’s never been done before. If they weren’t doing something that’s never been done before, you wouldn’t be writing code; you would be buying the product, right? You would be buying the framework; you’d be buying the thing. So, anytime we’re building products, we’re doing unknown things, right? That’s why we have such a high degree of variance in software engineering.

In the business space, there is something that kind of equates to that, or an analogy within the business context, and that’s entrepreneurship. If you’ve got an idea for a company that delivers some kind of value, regardless of how that value is achieved, then you need to go and find somebody to invest in that idea. That works almost the same within the context of an organisation as it does in the context of me. If I wanted to go start a new business, I would probably need to go speak to the bank. I might need to go find some venture capitalists. I need to convince them. I need to find people in my company; I need to convince them that it’s a good idea that needs to be promoted up to a level of somebody with some spending power. That person with some spending power offers me the ability to go try this out, but I have to convince them.

New ideas, things that have never been done before, are not guaranteed. They’re not guaranteed to work; they’re not guaranteed to have the return on investment that you expect them to. If you’re in business, you already understand this concept, right? I believe my numbers might be slightly off, but I believe that 70% of new businesses fail within the first year, and 30% of the leftovers fail within five years. I think I might be underestimating the second part, the five-year part; it might be more than that. So, there is a risk profile to everything you do when you’re investing money in a possible outcome within your business or external to your business.

You need to figure out what your risk profile is for investment in things that have never been done before, and you need to gauge the return that you’re expecting to get on the investment that you’re putting in. This is exactly how products should be funded as well. If we’re going to fund a product in that way, and I’m going to be the one funding that product, I want to see value and market fit as quickly as possible, right?

So, what we tend to do is run lots of very small experiments. I might come to you and say, “I need some money.” Sorry, I’m switching personality here. I’m going to come to you and say, “I need some money. I want to try out this idea. Hi, I’ve got this idea for a database of movie stars,” right? And you’re like, “How are you going to make me some money?” Because you want to return on your investment. So, I’m thinking, “Well, I really, I’m the geek, right? I want to have a database of all the movies with all of the actors.”

Then you can ask, “Can we have the budget for the movie in there? Can we have how much each of the actors made?” Suddenly, you’ve got an idea of who you could sell this product to, right? What its possibilities are going forward into the future with a little bit of ideation from that geek idea of having a movie database, and IMDb is born, right? Which is massive now. If you don’t know, IMDb has two halves. It has the public free half, which is where we just go look up movies, but then it sells inside of the industry and can give directors, for example, who are looking for a leading man or a leading lady, data on who can make my movie the most money.

“Oh, it’s The Rock. Well, I can’t afford The Rock. Who’s within my price range for the budget for my movie that I can get that’s going to maximise the amount of money?” They pay IMDb a lot of money for that story. That’s not something that was within anybody’s budget; it was a venture capitalist effort, right?

So, you want to be thinking within the context of your business. You want to be thinking more like entrepreneurs, thinking more like venture capitalists. What’s your return on investment? If you put money into this product or project, what do you get? How much do you get? What’s the longevity of what you get? What’s the story for this? I want to see a business plan. I want to see a lean canvas on what this looks like. Have people thought about the different aspects of it before I’m going to let them spend some money?

Even once they’re spending money, I want to see a little thing. I want to see a proof of concept, right? Let’s get something out there, see what it looks like, get it in front of some other people. Does it resonate with people? Does it seem like it’s going to provide value? Okay, what’s the next stage? What’s the next bigger thing we can do? What’s the next experiment that we can run?

I talk about it as hypothesis-driven engineering practices. Lots of folks out there use the phrase MVP. They’re mostly, I see MVP, minimum viable product, being kind of misused. Minimum viable product is a proof of concept that you throw away at the end before you build the real product. I know lots of people use it as a small experiment, so whatever context you use it within. But we want to be running lots and lots and lots of small experiments. We know lots of experiments are going to fail, but we’re going to find the ones that work, and then we’re going to invest more in those, invest less in the ones that don’t work.

That’s how you stay within budget: lots of little experiments that have a hypothesis. What are you going to do? What do you expect the world to be like afterwards? How are you going to measure whether you’ve been successful? We want to see the numbers, and we’re going to cull anything that is not living up to its expectation unless we can be convinced otherwise, right? There’s heart as well as data in building your products and staying within budget.