The Problems with Finding and Validating Market Problems

Finding and validating market problems are a product team’s most important responsibility. Yet many teams struggle to consistently engage in this best practice.

The reasons may vary, but what we hear most often is that while product teams collect ample data from customers and potentials, they find it difficult to aggregate into meaningful information.

Unstructured data is difficult to decrypt and the process of analyzing it in close detail is rarely a direct route that leads to something new that can be applied in a larger context.

Most important, it can be frustrating to use data collected from this method as a tool to discover the next big thing because each data element seems unique.

Before you can mine data and retrieve meaningful information, you must define the problem. Your understanding of that problem will determine whether you focus on quantitative or qualitative research. It will also determine your sampling process and sample size.

You think you need a large sample size and a significant budget

You may like to collect large pools of data because it makes you feel more comfortable with the analysis and decision-making process. In an ideal situation the data would decide for you, but the time and cost of collecting enough data to reach high levels of confidence aren’t practical. Besides, big data doesn’t always give you the information you need to make good market decisions. Simply having large amounts of data available doesn’t mean you’ll make the right decisions.  

What you are seeking is insighta mental vision or understanding. In more practical terms, it’s discovering something that isn’t clear at first glance.

Can you apply these principles to the activities of finding marketing problems in a systematic way and make them repeatable? Yes. Working with clients for the past two years has provided some valuable results.

Here’s how it works:

  • Target a small to moderate sample size (approximately 20 to 30 respondents)
  • Interlock online survey and in-depth interviews into a discovery and validation sequence
  • Use the blended responses to amplify the small sample size and bring more context to the research

This proves to be a better way to think about market discovery and market validation. As a market-discovery method, insight analysis allows product teams to work with unstructured data and a small sample size to:

  • Test and validate your product direction
  • Verify product roadmap priorities with market data
  • Test and validate the degree that your messaging resonates
  • Test and measure the strength of your value proposition