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SPAR Slovenija
SPAR Slovenija is a leading FMCG (Fast-Moving Consumer Goods) supermarket chain with over €1 billion in annual revenue. As a key player in the Slovenian retail market, SPAR sought to leverage data analytics to gain a deeper understanding of customer behavior and drive business growth.
Our partnership with SPAR Slovenija began over a decade ago with the objective of architecting, designing, and developing a robust Data Warehouse (DWH) solution. Over the years, this collaboration expanded to include advanced analytics, customer segmentation, and various predictive models, all of which have become integral to SPAR's business strategy.
Services
Data Science
Project Length
132 Months
Client
Spar
Our Planning Process
SPAR Slovenija faced significant challenges in analyzing their customer data, particularly from their loyalty card program. The company needed insights into customer behavior, purchasing patterns, and preferences to tailor their marketing strategies and improve customer engagement.
SPAR Slovenija set several ambitious goals for their data warehouse and analytics capabilities:
- Centralize and Streamline Data - Consolidate data from multiple sources, including their SAP system, into a single, fast, and reliable data warehouse. Implement robust Master Data Management (MDM) processes to ensure consistency, accuracy, and a single source of truth across all data points.
- Enable Advanced Customer Insights - Develop sophisticated models to analyze customer behavior, segment customers, and predict future trends using well-managed and consistent data.
- Support Data-Driven Decision Making - Empower the business to make informed decisions based on comprehensive and reliable data analysis, underpinned by strong MDM practices that enhance data quality and integrity.
What we did for Spar
Implementation and Methodology
The DWH was designed to connect seamlessly with multiple data sources, with SAP being the primary system. We focused on creating a solution that was both scalable and optimized for speed, ensuring that it could handle large volumes of data efficiently and support SPAR’s business requirements. To maintain high data quality, we developed specific Data Quality (DQ) jobs and reports within the ETL processes. This ensured that data was consistently accurate and reliable, which was crucial for the effectiveness of subsequent analysis and decision-making.
At the time, SAS was chosen for its popularity and robust capabilities in on-premise DWH solutions. Its speed and reliability made it an ideal choice for SPAR Slovenija. Python was introduced for its superior capabilities in statistical analysis and the development of advanced models, such as RFM segmentation and churn analysis. Over the 10 years, the technology stack evolved with the times. SAS was upgraded 2-3 times to leverage new features and enhance performance. We also implemented data archiving solutions to manage growing data volumes effectively. Python was continuously upgraded to its latest versions, ensuring that the analytics models benefited from the latest advancements in data science.
For developing advanced analytics models, we employed standard methodologies such as the SEMMA (Sample, Explore, Modify, Model, Assess) framework. This structured approach allowed us to systematically build and refine models like RFM segmentation, churn analysis, cohort analysis, and more, ensuring they delivered actionable insights for SPAR.
Implementation and Methodology
The DWH was designed to connect seamlessly with multiple data sources, with SAP being the primary system. We focused on creating a solution that was both scalable and optimized for speed, ensuring that it could handle large volumes of data efficiently and support SPAR’s business requirements. To maintain high data quality, we developed specific Data Quality (DQ) jobs and reports within the ETL processes. This ensured that data was consistently accurate and reliable, which was crucial for the effectiveness of subsequent analysis and decision-making.
At the time, SAS was chosen for its popularity and robust capabilities in on-premise DWH solutions. Its speed and reliability made it an ideal choice for SPAR Slovenija. Python was introduced for its superior capabilities in statistical analysis and the development of advanced models, such as RFM segmentation and churn analysis. Over the 10 years, the technology stack evolved with the times. SAS was upgraded 2-3 times to leverage new features and enhance performance. We also implemented data archiving solutions to manage growing data volumes effectively. Python was continuously upgraded to its latest versions, ensuring that the analytics models benefited from the latest advancements in data science.
For developing advanced analytics models, we employed standard methodologies such as the SEMMA (Sample, Explore, Modify, Model, Assess) framework. This structured approach allowed us to systematically build and refine models like RFM segmentation, churn analysis, cohort analysis, and more, ensuring they delivered actionable insights for SPAR.
Final Results
Results and Impact
- Revenue Growth - The insights gained from the analytics models directly contributed to revenue growth by optimizing promotions and cross-sell/up-sell strategies.
- Increased Customer Loyalty - Through precise customer segmentation and tailored marketing strategies, SPAR saw a significant increase in customer loyalty.
- Enhanced Customer Understanding - SPAR developed a much deeper understanding of their customers' preferences and behaviors, allowing them to create more personalized shopping experiences.
The implementation of customer behavior segmentation and other models transformed SPAR’s decision-making processes. Day-to-day decisions became increasingly data-driven, with a shift from relying solely on sales and product data to incorporating comprehensive customer insights. This allowed for more effective and targeted promotions, contributing to overall business growth.
Long-Term Relationship and Lessons Learned
Over the decade-long partnership, our relationship with SPAR Slovenija evolved into one of deep trust and collaboration. We became their first partner for any advanced analytics or data-related challenge, and our agility as a smaller firm allowed us to adapt quickly to their evolving needs.
During the maintenance phase, we provided continuous support by monitoring system performance, optimizing data processes, and implementing necessary updates. We also proactively introduced new analytical models and techniques as SPAR’s business needs evolved, ensuring they stayed ahead in the competitive retail market.
This long-term engagement taught us the importance of building trust, listening closely to client needs, and delivering results swiftly. Our ability to adapt quickly to changes was a key factor in the success of the project, proving that agility is a crucial asset in long-term partnerships.
SPAR Slovenija has recognized the critical role of data analytics in their business strategy. They have established a dedicated sector for advanced analytics, moving beyond its initial home within the marketing department. As they evolve, we will continue to be a key partner in their journey, helping them leverage data to drive innovation and growth.