Method category: Generative market research
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​How to Use This in GLIDR
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Data Mining is a research tool for uncovering overall trends and large-scale behaviors across many data points from your users. 

In GLIDR, you can run Research and then add a particular data set as a piece of Evidence. You can attach a spreadsheet or other file to the data, and write your observations and information about the data in the Notes and Key Insights sections. Use the Analyze phase of Research to track what you learned from the Data Mining exercise and how these learnings will impact your project overall.

Learn more about each of those aspects of GLIDR:

Article excerpted from The Real Startup Book
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Data mining uses statistics from large amounts of data to learn about target markets and customer behaviors. This method can make use of data warehouses or big data.

Data mining can start with the results from a few questionnaires. However, it is more effective to use a large dataset. Identifying the source information (where you get the data) and extracting key values (how you pick the data points) are important to get quality results.

Data mining is best used for pattern discovery in customer perceptions and behaviors. It is useful in understanding your customers and/or your target market.

For example, you can identify the profile of potential buyers or customers by running email campaigns and gathering the results. This data can help in customer acquisition efforts.

You can also gather customer information by sending out customer satisfaction questionnaires or feedback forms. Alternatively, you can track customer behaviors or mouse clicks on your websites. By combining these two data points, you can determine customer behavioral links between reported satisfaction and actual usage. This can identify key drivers for customer loyalty and churn.

Depending on the amount of data that you need to crunch and data points that you want to discover, it will take 2-3 hours to a few weeks. You should pick one or two of the most important data points to start the learning process.

You can either acquire outside (industry or market) data or distill your own (customer or product) data. Once you identify the area that you want to test:

In data mining, data matters but perspective matters more. There is a saying, “Garbage in, garbage out.” But as human beings, we tend to see what we want to see and draw conclusions based on our own biases.

To counter these biases, you can:

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