Harnessing Data Analytics in the Retail Industry for Actionable Insights – Part 1

Drowning in Data, Starving for Insights

Why Deep Analytics in Retail is No Longer Optional

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.

Where Traditional Retail Analytics Tools Hit a Wall

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:

• Increased consumer spending with improved customer loyalty: Loyal customers spend more. Data from Accenture shows that 57% of consumers spend more on brands to which they are loyal. Consulting company Bain & Company found that a 5% increase in customer retention translates to a 25% increase in profits.
• Improved Retention and Profitability: Retaining customers is far more cost-effective than acquiring new ones, which is estimated to be 5 to 25 times more expensive.
• Higher ROI: Well-executed loyalty programs consistently demonstrate a positive ROI, averaging around 4.8X the investment amount.

Achieving this level of impactful personalization hinges on developing a deep, nuanced understanding of the customer by analyzing customer data from various touchpoints, including:

• POS Systems: Customer transaction details, customer purchase histories, basket contents, payment methods, historical sales data.
• Loyalty Programs: Membership status, points accumulation and redemption, offer engagement, reward preferences, customer lifetime value.
• Website & Mobile App Analytics: Browsing consumer behavior, page views, time spent, cart additions/abandonments, search queries.
• Customer Relationship Management (CRM) Systems: Customer profiles, demographics, contact history, customer interactions, customer engagement activities.
• Other Data Sources: Social media interactions, survey responses, customer feedback, email engagement metrics, operational metrics, inventory data, and ERP/OMS data.

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:

• Too broad: Customers are often combined who, despite common demographic traits, have different needs, preferences, and motivations.
• Static: Because they are built on historical data, segments often fail to adapt to changing customer preferences, lifestyle changes, external factors like seasonal variations, or economic fluctuations.
• Superficial: Focusing on easily observable characteristics while missing deeper insights hidden in transaction details, cross-channel interactions, or shifts in customer behaviors risks missing essential business drivers.

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.

Why Standard Tools Struggle with Deep Dives

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.

The Human Gap

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.

Business Intelligence & SQL

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:

• Hypothesis-driven approach: BI and SQL tools are akin to using a flashlight. You must know what you are looking for and have a rough idea of where to find it. Hypothesis-driven analysis requires formulating questions that are then proven or disproven, a slow and tedious process that makes it challenging to unearth questions the business did not know it should be asking.
• Complex queries and diverse data: Traditional BI tools often require complex joins, transformations, and manipulations across data sets, creating a multi-step process that is slow and prone to errors.
• Limitations of SQL: While it’s indispensable for querying databases, SQL is not inherently suited for exploratory and statistical data analysis to uncover complex, multi-variate patterns, especially non-linear relationships or interactions hidden within wide datasets containing hundreds of columns.
• Granularity and dynamic handling: While BI and SQL tools can report on granular data, they are primarily meant for aggregated reporting and trend analysis. This means BI tools can miss subtle but potentially critical anomalies or segment-specific behaviors.

Loyalty Suites for Customer Analytics and Segmentation

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:

Enterprise Loyalty Suites

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:

• High Total Cost of Ownership: The costs associated with implementation, licensing, servicing, and data warehousing required to properly “feed” these platforms can escalate into multiple millions per year, making them suitable only for the largest retailers.
• Steep Learning Curve: Due to their extensive feature sets, learning to use them and deriving maximum benefit requires a significant amount of time and dedicated staff, thereby increasing the cost of ownership.
• Prebuilt Segmentation Limits: These platforms favor rules like “bought product X in the last Y days,” but often struggle with time-sensitive, complex behavioral logic, such as “does not shop on Sat-Sun,” which frequently requires workaround logic or custom SQL.
• Temporal Pattern Challenges: Most platforms can’t natively detect or model complex time-series behavior, such as recurring but lapsed purchasing over specific intervals.
• Black Box AI/ML Capabilities: While most of these platforms offer some form of AI/ML capabilities (e.g., Einstein Scoring, Adobe Sensei), these capabilities often involve scoring or lookalike modeling that lack transparency and customizability.

Mid-Market Loyalty Platforms

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:

• Basic rules-based filtering: The use of basic rules-based filtering means that, like with BI tools, the user must make assumptions about the segmentation they want to build and must determine whether that segment is valuable in some other way, creating a test-revise-repeat problem.
• Lack of Native Advanced Capabilities: Platforms in this category don’t support ML-based clustering, lookalike modeling, or regression-based behavior analysis. Segmentation is rule-based with no trend detection or time-series logic.
• No or Limited Time-Based or Frequency-Based Logic: Like their enterprise counterparts, these platforms often lack or have limited capabilities to build segments, such as “buys every 60 days and stops.” The lack of support for comparing customer behavior against prior customer shopping patterns (e.g., “frequency decay” or “recency shift”) limits their usefulness in building well-understood patterns.
• Lack of Exploratory Tools: Perhaps the biggest challenge is that these types of platforms often lack built-in customer segmentation dashboards for discovering latent personas or clusters. Data must be exported to business intelligence (BI) tools for analysis and further exploration.

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.