In a scenario marked by intense competition, multiple sales channels, and increasingly demanding consumers, the strategic use of retail data in 2026 has moved from being a competitive advantage to becoming a decisive factor for sustainable growth in the sector.
More than simply looking at the past, adopting a holistic, data-driven approach allows retailers to forecast demand, manage inventory more efficiently, and personalize offers across both physical and digital retail environments.
Continue reading to better understand the importance of retail data in 2026 and how the use of emerging technologies can help prepare your business for a more efficient year, with smarter decision-making and a strong focus on customer experience.
The Importance of Retail Data in 2026
In recent years, the retail segment has strengthened its leadership in e-commerce operations. According to a PagSeguro study , online retail transactions in Latin America are expected to reach USD 870 billion by 2026, with Brazil ranking among the regional leaders and generating up to USD 435 billion during this period.
This high transaction volume not only reflects the sector’s growth, but also represents a strategic source of data. These insights allow retailers to accurately understand consumer behavior patterns, anticipate demand peaks, and reduce uncertainties that once relied solely on intuition and market experience.
With a well-structured data processing strategy, retailers can:
- Improve operational efficiency by optimizing operations and supply chains, while enhancing decision-making
- Create targeted advertising campaigns and personalized recommendations that increase customer satisfaction
- Develop loyalty programs based on purchase history, supporting customer retention and long-term engagement
- Enhance inventory management and pricing strategies through sales history analysis
An IBM study also revealed that 62% of retailers investing in data and analytics gain a competitive advantage. In addition, retail media advertising based on purchase data is expected to generate billions globally by 2026 , reinforcing the commercial value of consumer data in retail.
For retailers planning their next growth cycle strategically, January becomes the ideal time to review databases, integrate information from different sales channels, and improve the customer journey with a clear focus on 2026. This preparation enables more accurate predictive models and strategic decisions from the very first quarter.
How AI, Big Data, and Analytics Improve Forecasting and Customer Experience
The acceleration of digital transformation has significantly increased data generation across multiple customer touchpoints, including physical point-of-sale systems, e-commerce platforms, mobile apps, and payment methods.
One of the biggest challenges retailers face is turning this massive volume of information into actionable insights that align pricing, availability, and convenience with customer expectations.
In this context, technologies such as Artificial Intelligence (AI), Big Data, and advanced analytics play a central role, enabling more accurate predictive analysis that supports demand forecasting and personalized experiences throughout the customer journey.
Inventory Optimization and Loss Reduction
Balancing availability and cost is one of retail’s greatest challenges. Through precise data collection and analysis powered by Big Data and analytics, retailers can identify optimal replenishment levels and inventory turnover by product, channel, or store.
AI-based analytical models, such as machine learning, help identify underperforming products, predict excess inventory, and support more strategic markdown decisions.
As a result, companies reduce stockouts, optimize inventory allocation across stores and distribution centers, improve both online and offline operations, and increase profit margins. Not surprisingly, research from McKinsey shows that companies using advanced analytics can achieve an improvement of up to 20% in marketing ROI , highlighting the direct impact on profitability.
Personalized Customer Experience
The integration of these technologies also enables the analysis of purchase habits, frequency, preferences, and average ticket size. This allows retailers to personalize communication, offers, and product recommendations, significantly improving the customer journey and overall shopping experience.
Payment Data as a Strategic Asset
Payment data generated from transactions represents a highly valuable yet often underutilized asset in retail. These insights reveal important behavioral patterns such as purchase timing, preferred payment methods, location, and recurrence.
When analyzed intelligently and at scale, payment data helps retailers better understand customer profiles, enabling smart pricing strategies, portfolio diversification, fraud prevention, and a smoother, more secure purchasing experience.
Retail leaders who successfully transform data into strategic actions will be better positioned to grow in 2026, going beyond operational efficiency and profitability. However, this transformation requires system integration, strong data governance, team upskilling, and alignment between technology and business strategy.
