Data Mining

Overview

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.

In Brief

"Data mining uses statistics from large amounts of data to learn about target markets and customer behaviors."

Helps Answer

• Who is our customer?
• What are their preferences?
• How do they rank planned feature sets?

Tags

• B2C
• B2B
• Customer
• Quantitative

Description

Data mining identifies patterns in customer perceptions and behaviors. Examples include analyzing email campaign results for customer profiles, using satisfaction questionnaires, or tracking website behavior to discover links between reported satisfaction and actual usage patterns.

Time Commitment and Resources

2-3 hours to several weeks, depending on dataset volume and complexity. Focus on one or two critical data points initially.

How To

1. Acquire data (integrate from various sources, if required)
2. Identify data points (determine which data is relevant)
3. Transform and extract data (use business intelligence or database tools)
4. Recognize and search for patterns
5. Draw conclusions or refine by revisiting earlier steps

Interpreting Results

"Data matters but perspective matters more." Counter biases through outside review or control/experimental group comparisons.

Potential Biases

• Confirmation bias
• False positives
• Ignorance of black swans (rare, unprecedented events)

Adapted from The Real Startup Book, edited by Tristan Kromer and published by Kromatic, under a Creative Commons Attribution-ShareAlike 4.0 International License.