Automating Data Analysis and KPI Setting: A Case Study Using SQLite and SPSS

In the dynamic arena of data-driven decision-making, marketing teams continually seek efficient methods to extract, analyze, and apply data insights. This article presents a case study demonstrating how a combination of SQLite and SPSS was employed to aggregate data, conduct cluster analysis, and ultimately automate the setting of Key Performance Indicators (KPIs) for six distinct customer types.

The Challenge

The primary challenge was to integrate and analyze data from four distinct tables in a SQLite database and then apply the insights gained to establish relevant KPIs for various customer groups.

Step 1: Data Extraction and Aggregation with SQLite

SQLite, known for its simplicity and effectiveness in handling datasets, was used for data extraction. The process began with extracting data from four different tables. Depending on the SPSS model, you can connect SPSS to the database directly. However, direct connections to databases, especially with complex joins and queries, can be slower in SPSS due to its analytical nature. SQLite’s straightforward syntax makes data manipulation tasks, like joining tables and aggregating data, more accessible.

Joining Tables: SQL queries were utilized to join tables based on common fields. For instance, JOIN operations connected customer details with their respective frequency of visitation. An additional variable representing each of the four tables was included in the dataset.

Step 2: Cluster Analysis Using SPSS

The aggregated data was then exported to SPSS for statistical analysis.
Preparing the Data: Data was cleaned and normalized to ensure accuracy in the analysis. A key focus was on handling missing categorical values, which were assigned a unique value to maintain data integrity.

Two-Step Cluster Analysis: This method was chosen for its ability to handle large datasets and automatically determine the number of clusters, helping identify distinct customer groups based on factors like amount spent and performance metrics.

Step 3: Identifying KPIs for Customer Segments

Based on the clusters identified, KPIs were specifically tailored for each customer group. These KPIs were centered around metrics like purchase frequency, average spending, and customer lifetime value, allowing for targeted marketing strategies.

Step 4: Implementing Automated Reporting

The final step involved setting up an automated reporting system. This system utilized the cluster analysis results to update KPIs dynamically, ensuring that marketing strategies remain aligned with current customer behaviors and preferences.

• Automated Data Sync: SQLite using scheduled scripts/ETL (Extract, Transform, Load).
• SPSS Syntax: This script will run at scheduled intervals, processing the latest data from SQLite. The syntax identifies new clusters and updates the characteristics defining each customer segment, based on the latest data.

Conclusion

This case study highlights the efficacy of using SQLite for initial data handling and SPSS for advanced statistical analysis. By automating the process from data extraction to KPI implementation, marketing teams can efficiently adapt to changing customer patterns, ensuring a more dynamic and responsive marketing strategy.

(Updated Jan. 12, 2024)

Part two for this article will arrive in early February 2024, as we are currently implementing Dashboards to address… Regular Updates, which displays the updated KPIs and other relevant metrics.

Part two is published click here