
Harnessing Data Analytics in the Retail Industry for Actionable Insights – Part 1
- Industry Use Cases
Drowning in Data, Starving for Insights
Retailers possess a wealth of data from multiple data sources, such as point-of-sale systems (POS), loyalty programs, inventory management systems, order management systems (OMS), online shopping carts, and mobile apps. The list is nearly endless. Despite the abundance of data, challenges persist. Many retailers struggle to translate raw data into a meaningful understanding of customer behavior and actionable insights. The volume and complexity of available customer data render manual analysis impractical.
The result is a common paradox in the retail landscape: retailers are often “drowning in data but starving for insight.”
Traditional Business Intelligence (BI) tools have been the primary focus of retail analytics for years. They excel at identifying trends in the business by reporting on historical performance, allowing managers to track Key Performance Indicators (KPIs) such as sales volume, inventory turnover, and gross margin. The challenge is that BI tools are primarily retrospective, often falling short when explaining performance fluctuations or uncovering complex, hidden patterns buried in the data.
Simply knowing what happened is no longer sufficient in the retail sector. Sustainable success demands a more profound understanding: Uncovering the underlying drivers. This is the realm of advanced data analytics: moving beyond surface-level metrics to understand the complex interactions between variables and root causes that influence critical retail outcomes. Advanced analytics solutions are critical for data-driven decision-making in the industry.
Research by McKinsey shows that 71% of consumers expect to deliver personalized interactions, and 76% are frustrated when these expectations are not met. The financial incentives for getting personalization right are substantial in the retail industry. Practical strategies, often powered by loyalty programs, provide significant results:
Achieving this level of impactful personalization hinges on developing a deep, nuanced understanding of the customer by analyzing customer data from various touchpoints, including:
Combining these diverse and often siloed data sets presents a significant hurdle for all but the most prominent retail businesses, due to the complexity of integration, ensuring data quality and consistency, meeting privacy requirements such as GDPR and CCP, and achieving a consistent 360-degree view of the customer.
Traditional approaches to customer analytics and segmentation often rely on broad demographic categories, such as age, gender, location, and income, or basic behavioral metrics, including Recency, Frequency, and Monetary Value – also known as RFM analysis. While these methods provide some value, they fall short in capturing the true complexity and dynamic nature of the system in the retail landscape. The results tend to be:
Shallow segmentation leads to misaligned marketing campaigns and messages, wasted resources, and ultimately, a failure for personalized experiences to provide the in-depth personalization that consumers demand. In fact, only 24% of Americans feel that customer loyalty programs make them feel “special or recognized.”
The combination of the need for highly personalized marketing campaigns and increased consumer-level privacy requirements led global retailer Lawson to leverage the power of Micro-segmentation to create more targeted campaigns, yielding a 12X increase in sales.
To construct highly targeted segments, retailers often rely on two categories of products: Business Intelligence tools (BI) or platforms explicitly designed to manage customer loyalty programs, and build Customer 360 data models.
While Business Intelligence, analytics, and machine learning are critical areas of work for retail businesses of all sizes, the perfect storm created by the complexity of data, the vastness of data, and an accelerating pace of change in business requirements means that all but the most prominent retailers must pick and choose where to dedicate their precious human analytical resources. This “data paradox” is at the heart of the struggle of modern retailers. Although there is an ever-increasing amount of historical customer data, the reality is that an overloaded BI and analytics staff, coupled with tools that were not designed for deep analytics, means that more data simply leads to more cost, more confusion, and more waste.
Traditional BI and SQL querying tools are often employed because they are flexible and can handle data from multiple sources. The challenge of these legacy tools is they were designed for reporting on identified metrics and analyzing data structures and boundaries that are well-known and well-defined. The complexity and breadth of modern retail data make it difficult for these legacy platforms to be effective at deep analysis:
Whether they be Customer360 platforms or loyalty-specific ones, “loyalty” platforms were built to create customer segments in mind. These platforms can be further segmented into two broad categories:
In the first category, we find products such as Salesforce Loyalty Management and the Adobe Experience Platform. These are full-featured enterprise platforms with deep integration capabilities, real-time analysis, and advanced segmentation through centralized data platforms and (often) Artificial Intelligence capabilities. These platforms, while incredibly capable, can be limiting:
Typified by platforms like Antavo Loyalty Cloud or Yotopo, these are tailored primarily towards medium-sized retailers and offer configurable rules-based loyalty with basic segmentation capabilities:
In our next post, you will learn how these challenges can be addressed through the use of advanced analytics techniques using Statistical AI, with the power of automated business driver discovery, driver stacking, and more.