Prashant Mahajan
Anyone mad enough to launch a new product already knows the stats surrounding product failure. According to Harvard Business School professor Clayton Christensen, over 30,000 new products are introduced yearly, and 95% fail.
However, even the 5% don’t always hit their mark, with many not doing precisely what customers need and others failing to meet their target KPIs. We call it the product death cycle, a repetitive cycle that emerges when, despite listening intently to customers and diligently building the features they ask for, products still struggle to gain traction.
It’s an all-too-familiar scenario familiar to many product experts, and the irony is that most product experts are doing exactly what they’ve been taught: listening to their customers.
On the bright side, the vicious product death cycle doesn’t need to repeat itself. There are ways companies can better understand their customers’ pain points to develop the right products that solve the right problems at the right time. It’s about understanding the core fundamentals.
Make sure you’re solving the right problem
Regarding product design and development, solving the right problem might seem obvious. We’ve all heard of a solution for a problem — look at the Segway transporter device. However, the reality is that successful products and services succeed because they solve a specific problem that is well demonstrated. The challenge is really and truly understanding customer pain points.
I found this in my startup’s early days: We were engaged by a division of a large multinational company. As a young, motivated startup, it was a dream customer, and we dove in headfirst to build an excellent product for them. But after we’d developed the solution, they wanted something else because they felt their priorities had changed.
I think it’s something many startups can empathize with — you get a contact at a great company, and they tell you their problems. You build a product or solution to solve that problem, but it doesn’t work out as planned.
What often transpires is that the company realizes too late that its product needs to be solving the right problem or that the product doesn’t quite hit the mark. It may have, for example, a 20% adoption rate when the company was aiming for 80%. Or, the company may misunderstand where they are going wrong internally.
Understand the variables involved in executing everything well
Executing everything well is about more than just the product and overall experience. Execution isn’t one big thing, but lots of small things all coming together. The current economy, people, the market, and the industry situation are out of your control. However, product development, marketing, sales, and customer support are not. All elements must fit together and all are equally important.
Too often, when something doesn’t work out, you hear of product blaming marketing, or sales blaming the product, or someone will blame what’s happening in the world. But it’s never just one thing — everything works together to create a great overall experience.
Stay in for the long haul
This ties in with the final fundamental aspect needed to avoid failure: great timing. This is the hardest to get right. There have been so many products that solved the right problem and were almost perfectly executed but lacked the right timing.
Google Glass is an excellent example of this, and I often wonder whether companies that are successful now, such as Uber and Airbnb, would have achieved the same success if they had launched earlier. And then there’s Netflix, which was operating for years before becoming a household name, all thanks to the technology infrastructure needed to deliver high-quality streaming becoming available.
Stop guessing and use the AI available to you
The good news is that AI and data-driven initiatives can help companies move on from guesswork to avoid product failures. While AI’s predictive abilities may help in the long run, the more tactical, hands-on, data-driven analytics side of things will help in the initial transition.
AI functionality, such as sentiment analysis, can help companies better understand what their customers’ real needs are. Natural language processing is also available to help product managers understand customers’ feelings. Machine learning can help to process market trends and customer needs.
AI will also help product teams better measure what success looks like. In the best-case scenario, they can improve on a successful product foundation. In the worst case, it means they pinpoint micro failures faster and pivot more agilely before too much time and resources are wasted. Either way, AI can help to make more informed product decisions. The tools are there for companies to learn how to leverage and apply them meaningfully to understand what their customers want and need.
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