As 2026 begins, retailers face a stark reality: despite a decade of data investments, a significant portion of valuable information remains unseen and disconnected from daily decisions. Dubbed “dark data,” this customer and operational intelligence exists within organizations but cannot be easily accessed or applied. SAP Emarsys research reveals that over half of brands struggle to use transactional and behavioral data effectively due to its spread across fragmented systems. Industry analysts report 70-80% of a CIO’s budget is spent maintaining siloed legacy systems rather than unlocking insights.
The Dark Data Problem in Retail Operations
From an operational standpoint, this fragmentation limits agility at a time when speed and precision matter most. A COO perspective points to dark data as an execution risk, not a technical inconvenience. When insight is delayed or incomplete, decisions slow down, teams work with partial context, and problems are addressed only after surfacing in lagging indicators like declining revenue.
Retailers suffer from a lack of connected insight, leaving early warning signals buried until churn, lost revenue, and inefficiencies emerge. Customer loyalty exemplifies this risk. SAP Emarsys’ latest Customer Loyalty Index shows that trust alone does not secure long-term commitment. While 67 percent of consumers say they trust a favorite brand, 61 percent will switch for a better price, and nearly half will leave after a single poor experience.
Customer Loyalty at Risk
Dissatisfaction rarely appears explicit. Customers disengage gradually—opening fewer emails, visiting less, reducing spend incrementally—before relationships are lost. Retail teams acknowledge the problem is not customer volatility but visibility. Commerce platforms may show declining order values, marketing tools reflect reduced engagement, and service systems hold unresolved issues. Without a unified view, no one sees the full progression of the customer relationship.
Industry Trends Intensifying the Challenge
Throughout 2025, AI tools became widespread across retail operations, introducing new complexity with the data volumes they generate. Consumers demand greater transparency around automated decisions, pressuring retailers to understand data foundations. Customer journeys risk becoming more fragmented as shoppers expect consistent experiences across channels, yet many retailers struggle to structure and integrate this data for timely action.
Progress exists. Molton Brown’s consolidation of systems through SAP Commerce Cloud and SAP Emarsys allowed the company to connect engagement across channels more effectively. The result was a 20 percent increase in repeat purchases, a fivefold increase in email-driven revenue, and stronger omnichannel performance overall.
Technology providers like SAP CX highlight how an intelligence layer connecting insights across marketing, service, commerce, and revenue functions can drive value when built on consistent, high-quality data. Advanced analytics and automation only deliver value when structured on reliable foundations. Emerging trends like smart consumer agents further raise the stakes, as these tools depend on clean information to function effectively.
In discussions with U.S. retail leaders, priorities for 2026 include reducing disconnected systems housing customer and product data, narrowing focus to metrics indicating intent or disengagement, and ensuring service insights reach marketing and commerce teams. Finally, explainability has become essential as customers demand clarity around automated decisions.
The opportunity for 2026 is not to gather more information, but to make better use of what retailers already have. Many of the signals that predict customer behavior and operational risk already exist within their systems. What is missing is alignment, shared visibility, common definitions, and integrated pathways that allow insight to move quickly across the organization. This shift requires investment in data architecture and cross-functional collaboration, moving beyond isolated analytics projects toward operational intelligence that informs decisions in real time.
