Creating Value through Log Data and Process Mining

   Ji Soo Hwang | Sep 2, 2018 | 4 min read 

The advancement of big data processing technologies is rapidly increasing the volume of data, making the utilization of big data a key focus for each company. One of the key components of big data is log data.

In the past, log data was simply stored or neglected, primarily serving as records generated during the operation of systems. However, with the development of process mining technologies, the utilization of log data has become an important issue.

Therefore, it is crucial to effectively leverage the various and extensive log data generated by various systems to discover new opportunities ahead of others. Let’s explore how to use log data from the perspective of process mining.

First, let’s explore how log data generated by a customer’s actions in an online shopping app can be connected to customer behavior:

A customer engages in the following activities on an online shopping app:

  1. Customer accesses the online shopping app (Access log data is generated).
  2. Customer clicks the login button (Login log data is generated).
  3. Customer clicks the menu button (Main menu navigation log data is generated).
  4. Customer clicks on a specific product category (Product category selection log data is generated).

From this data, it’s apparent that the customer accessed the app, logged in, explored the main menu, and specifically clicked on a product category. Log data accurately represents the customer’s screen navigation sequence, allowing us to understand the customer’s service usage process. Therefore, log data analysis is essential for understanding customers.

Now, how can we utilize this data?

  1. Discover and Define Customer Process Patterns: Use log data to discover and define process patterns followed by customers when using the service. Classify discovered process patterns and create detailed process patterns for different customer groups based on specific criteria. Utilize the analysis results to find various patterns and group customers accordingly.
  2. Combine and Apply Log Data with Other Data: Pay attention to customer-generated events and timestamps in log data, and combine them with other big data sources to create additional value. For example, if log data analysis reveals that specific customers exhibit similar process patterns, combine this information with other data like gender, age, and more to derive group-specific characteristics.
  3. Derive Metrics and Utilize for Marketing: Use patterns of customer behavior to derive metrics and apply them in marketing efforts. By leveraging customer-specific characteristics and process patterns, tailor marketing strategies for each group. Offer personalized services to customers belonging to specific pattern groups, providing optimized and customized experiences.

In this way, log data can be used to discover customer process patterns and extract insights. By combining log data with additional data sources, meaningful results can be generated. In the era of the Fourth Industrial Revolution, data analysis and utilization are essential for decision-making. Those who can use data to discover opportunities and create value are the ones who stay ahead. Utilize process mining with log data to seize opportunities ahead of others.