But the second trend was more baffling. It showed that startups often fail because they are unable to recognise a lack of market fit. In these instances, founders overestimate the value of their product features and underestimate the time it takes to find product-market fit.

According to a report from Pendo, 80% of product features rarely get used, yet startup founders are still quantifying feature usage as the best way to understand product value. Meanwhile, founders were unaware that their products with well-used features were sleepwalking toward failure.

If startups want to survive, they’ll need to re-visit the metrics behind what makes successful products and ensure they have access to customer insights and critical analysis to truly understand market needs. The critical question is, how?

What Are Customer Insights?

Firstly, startups need to understand what customer insights are, how to collect them, and how to extract as much value from them as possible (at scale) to produce continuous feedback that better defines market needs.

Customer insights can be found in anything from a recording of a single sales call to tens of thousands of support tickets. They can also exist in churn reasons, NPS responses, win/loss analysis, or industry reports.

This amount of data can quickly become overwhelming, which is often the point where startups can make the mistake of ignoring it. While they make an effort to collect the data, they aren’t in a position to analyse it at scale to build and develop products that align with what the market needs.

Many startups also make the mistake of resting on the laurels of their engineering and technical excellence. However, today, tools to help promote engineering excellence have become so standardised and accessible that startup survival is no longer a question of sourcing great coding talent. Startup survival and success are now a question of empowering companies with better customer understanding to make more informed product decisions.

Put Great Expectations on Your Data

All insights – no matter how they are gathered – need to be organised to ensure important context isn’t lost. Any successful company should be able to analyse customer data and easily identify themes, create digestible summaries from it, or share and present it to key stakeholders.

Companies should also be able to process vast amounts of high-volume, continuous user feedback, and datasets, such as support tickets, app reviews, and Net Promoter core/customer satisfaction scores. The key is to continuously monitor themes in customer feedback, allowing you to respond quickly and avoid large-scale dissatisfaction.

Use AI to do The Heavy Lifting to Better Understand Your Customers

Processing and analysing customer data is one area where AI is a huge unlock, and startups need to get to a stage where they can apply AI to highlight important moments across a range of data. It’s important to remember that AI is not a panacea for everything. Still, it will play a tactical, behind-the-scenes role in customer research and customer experience-related technologies.

Machine learning can uncover and predict patterns and do the heavy lifting in making sense of large data sets quickly. This is what makes it so valuable for tasks like facial recognition and thematic analysis. LLMs will also help generate summaries and answer questions based on your existing data sets.

With AI tools to speed up parts of the customer discovery process, startups can spend more time advancing their understanding of their users, making better decisions that rely less on ‘gut feel’ and more on deep customer insights.

AI Can’t Do Everything: The Future of Your Startup Will Depend on You

While AI and machine learning have shown advancements in areas such as language processing, there are still certain aspects of customer research that it cannot fully replace. AI, for example, struggles with specialised knowledge, industry jargon, and specific contexts that go beyond general internet training data. This limitation can affect transcription accuracy in non-English languages and will make it challenging for AI in niche industries.

It’s also important to remember that while AI can assist with tasks like summarisation and clustering, human understanding and expertise still have value when it comes to interpreting data and generating insights. Product teams will need to play a strategic role in deciding what needs to be better understood to determine the overall direction for product and service development.

Striking a balance between leveraging AI and recognising its limitations will be key. Startups should automate certain parts of the process to speed up analysis and synthesis while applying human expertise to provide context and specialised insight.

While fundraising may get the most headlines, startups won’t survive unless they understand the needs of their customers and overall markets. Blending AI-powered data crunching with human know-how is now more prevalent than ever in understanding what customers really want and driving innovation forward.