Data mining and e-commerce: How to unlock the treasure trove of your customer data

Data mining definition and distinction from big data

Data mining describes the attempt to find patterns in data and make them usable. These patterns or empirical correlations should be identified as automatically as possible using algorithms. This method is typically used to analyze large amounts of data.

That is why it is sometimes equated with big data. However, big data primarily refers to the processing of large data sets and how technology makes this possible. Data mining, on the other hand, is a tool for making big data usable. It is even applied to huge databases to obtain structured data. Data mining searches the big data pool for usable information, thereby creating knowledge that would be virtually impossible to identify in large data sets without such assistance.

Key benefits of data mining in e-commerce

Data mining can enable accurate predictions regarding buyer behavior in e-commerce , thereby contributing to greater success. The technology makes it possible to draw conclusions about the purchasing behavior of prospects and customers from past purchases. The insights gained in this way can be used in almost all areas of online retail: from acquiring new customers to perfectly addressing existing customers to efficient after-sales measures.

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This makes it increasingly easy to target buyers or leads as if they were well known to the company as individuals —moving away from the customer target group and toward the specific needs, desires, and problems of each individual. This makes communication much more targeted and successful. This is also made possible by the many customer interactions in e-commerce and well-structured product data in online shops. This is because they enable algorithms to become increasingly accurate in their patterns and the forecasts derived from them.

Examples of data mining applications in online retail

Many companies already use data mining to extract valuable information from their data. The method provides important assistance in the following use cases, for example:

• Customer lifetime value forecasts: Knowing the probable return on investment for a customer makes it possible to tailor advertising expenditure for customer acquisition to specific target groups.
• Optimization of cross-selling and up-selling: Data mining can also identify promising patterns in cross-selling and up-selling campaigns, thereby encouraging customers to increase their shopping cart value.
• Customer segmentation: Effective segmentation of customer data leads to a better understanding of customers, laying the foundation for effective marketing measures and strong customer loyalty. The method can identify patterns that go far beyond age and gender, enabling companies to get to know their customers better.
• Campaign optimization: The technology also helps with campaign optimization through improved customer segmentation. This is because it supports the selection of a suitable target audience.
• Demand and sales forecasts as well as price optimization: Data mining enables companies to identify much more clearly when which products will be sold. Corresponding price optimization makes it possible to tailor offers precisely to target groups and specific times.

These data mining methods exist

Patterns can be recognized in various ways. The technology determines different approaches depending on the objective.

CRISP-DM: A standardized workflow

A uniform standard for data mining processes was created back in 2000: the CRISP-DM model (Cross Industry Standard Process for Data Mining) is designed to enable companies from all industries to obtain more accurate results more quickly and use them across sectors. To this end, this standardized process is divided into six phases. Depending on the task at hand, the focus is usually directed to a greater or lesser extent to the respective stages. Their sequence is also not strict, and it is quite common to switch between the individual phases.

The CRISP-DM model according to Shearer shows the cross-industry standard process for data mining (source: own representation based on Shearer¹)

Conclusion: Use data mining to find out what your customer data reveals

Data mining enables you to tap into a wealth of knowledge that you would otherwise not have access to. This technology allows you to analyze your customer data and use it to find out how to increase customer acquisition and retention. However, it is important to take a targeted approach in order to draw reliable conclusions from the data.

Sources: ¹ Shearer C., The CRISP-DM Model: The New Blueprint for Data Mining in: Journal of Data Warehousing (2000)

Frequently asked questions about data mining