Data mining uses statistics from large amounts of data to learn about target markets and customer behaviors.
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, but 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 getting 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 can take from 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:
Data matters, but perspective matters more. Human beings tend to see what we want to see and draw conclusions based on our own biases.
To counter these biases, you can:
1. Get outside help or another pair of eyes to interpret the data.
2. Get two data points that are counter to each other. (In research methodology, that is called the control group and experimental group.)