It's 4x more complicated, says Co-Elevate's Brian Balfour.
In Summary: The orthodox wisdom for every startup is to 'build a great product.' This is part of the puzzle, but it’s far from the full picture. Many great products fail to grow and many terrible products take off like a rocket. Likewise, plenty of companies have all the product market fit signals but definitely don’t become a $100M+ unicorn.
The difference between unicorns and those that struggle is that some are able to make 4 pieces of a puzzle fit: Market / Product Fit, Product / Channel Fit, Channel / Model Fit and Model / Market Fit.
Each of these influence each other, so shouldn't be considered in isolation. Whatsmore, they are always evolving, changing and breaking. When that happens, you can’t simply change one element, you have to revisit and potentially change them all.
Start by solving for real user pain. Without that foundation, no amount of optimization will breathe life into your product, says OpenView's Kyle Poyar.
In Summary: You’re in the business of designing a product that you want your users to love. This means obsessing over the features that people actually use and saying ‘no’ to one-off requests that buyers ask for, but are overkill for the majority.
Product usage is the ‘canary in the coal mine’. It predicts when a user is likely to upgrade, expand their purchase and renew their subscription. Companies can then direct product changes and customer support to drive behaviors they know will increase customer lifetime value.
Users don’t want to have to sit through hours of onboarding videos to get started. True 'product-led' businesses guide their users to complete the key functions they need in the context of what they want to do.
To understand why people really buy your product, look for customer progress, says Basecamp's Jason Fried.
In Summary: The most interesting aspect of customer research is understanding the moments and situations people find themselves in before they become customers. It’s not about demographics, it's about the progress they’re trying to make.
There are usually a few events that lead to the decision to buy. Something happens that inspires the initial idea. This is when passive looking begins. Then a second event happens and, all of a sudden, someone needs to make progress. Now they're ready to buy.
Try falsifying instead, says Roger Cauvin.
In Summary: It's common to hear about 'validated learning' and 'validating' product ideas. The assumption is that you have a great product idea, then seek validation from customers before expending resource bringing it to market.
The problem is, when you seek validation from someone, you tend to get it. When talking to prospects, you don’t get reliable information by posing hypothetical questions.
If you want to be scientific in your approach to product decisions, craft experiments and make falsifiable predictions, with the intention of testing (not 'validating') the underlying assumptions.
Instead of asking hypothetical questions, product developers should ask what someone actually did instead of what they would do.
You're in the experience business, says Clearleft's Andy Budd.
In Summary: The commoditisation of digital products happened very quickly. Every category in the Appstore is now swamped with thousands of undifferentiated copycats.
If you look at the majority of category leaders, they have one thing in common. They aren’t really selling products, they're selling solutions to problems. These companies are also selling a brand promise, 'If you use our tool, you can expect a certain level of service.'
The ultimate goal is to deliver an experience. The experience of hanging out in a coffee shop generates more value than the service alone.
People today are increasingly willing to forgo material possessions for individual experiences. In the experience economy, everything eventually comes back to UX.
If you want to build a successful company, your distribution strategy must be a function of your product and your target market, says Ben Horowitz.
In Summary: There's a big difference between viral marketing, inside sales and direct sales. And, if you have multiple products and target markets, you will need multiple sales channels.
Certain products can rely on inherent virality for marketing, but it's not a complete channel strategy as you might miss the opportunity to go after bigger customers with the same product.
For a very small business, 'self-serve' companies like Dropbox or Slack might not require much assistance. But, for a large enterprise that expects major support or integrations, even a seemingly self-serve product will require major assistance.
Invest now or pay later, says Daniel Elizalde.
In Summary: Adding onboarding to your product might not be as exciting as adding new features, but this investment will be returned many times over in conversion, customer satisfaction, and reduced cost.
The correct onboarding format depends on the type of software and your company’s business model. It should help the user quickly understand your product and evaluate if it is a good fit for their needs. Video games provide great, immersive onboarding, even making it fun to discover the product.
The better your UI, the easier it will be for new users to start being productive quickly. You might not completely eliminate the need for onboarding, but it will reduce the investment necessary to get upto speed.
Product Managers are filling the CEO pipeline for tech companies, says McKinsey.
In Summary: Software development needs to be a strategic priority for all companies in today’s digital era. And Product Managers play a pivotal role as the connection between engineering and other parts of the organization.
Product Managers now spend less time writing requirements up front. Instead, they work closely with different teams to gather feedback and iterate frequently. New responsibilities include overseeing APIs as product, owning key partnerships and managing the developer ecosystem.
Product Managers of the future will be analytics gurus, not reliant on analysts for answers to basic questions. They'll be able to spin up a Hadoop cluster on AWS, pull usage data, analyze and draw insights. They'll be adept at applying machine-learning concepts and tools specifically designed to augment their decision making process.