Unlocking Retail Success: Harnessing Unstructured Data for Enhanced Customer Engagement

Retailers worldwide are achieving significant growth in sales and profitability by employing omnichannel consumer engagement strategies across online, mobile and social platforms. This approach enables hyperpersonalization, accurate demand forecasting, and efficient inventory management. These benefits extend to dynamic bundling and pricing management, global supplier management, programmatic procurement, and cost-effective last mile logistics.

These strategies are powered by data analytics, artificial intelligence (AI), machine learning (ML), and generative AI (GenAI), which leverage vast amounts of structured, semi-structured and unstructured data. Industry analysts estimate that 80 percent to 90 percent of enterprise data is now in semi-structured and unstructured formats, creating a "Massive Big Data" ecosystem.

Structured data traditionally included enterprise information such as product, customer, supplier and inventory data. In contrast, semi-structured and unstructured data come from diverse sources, including social media feeds, multimedia content, third-party data, and Internet of Things sensor data from smart stores and logistics.

Enhancing Retail Growth Through Advanced Data Utilization

The evolution of database technologies — from SQL to No-SQL, document, columnar, and vector databases — has enabled the effective use of semi-structured and unstructured data. Retailers can now extract valuable insights from sources like social media, video, IoT sensor data, and third-party applications offering market analysis and supply chain information.

Contemporary retailers are transitioning from traditional data warehouses to data lakes and oceans. Analytics capabilities have progressed from basic trend reporting to advanced forecasting and predictive analytics, facilitated by AI and ML. Real-time data processing allows retailers to capitalize on time-sensitive opportunities, such as delivering hyperpersonalized promotions, managing programmatic procurement and pricing, and optimizing deliveries from dark stores.

The integration of multiple legacy platforms for warehouse, inventory, supply chain, order, pricing, payments and storefront management presents challenges. Effective use of unstructured data is essential for enhanced consumer engagement and leveraging diverse data sources.

Retailers face complexities in their data setups, requiring extensive big data initiatives to achieve business outcomes. Sources of unstructured data include emails, chats, call logs, social media feeds, and audio records from call centers. Additionally, scanned documents, images, videos and other data types carry significant legal, geographic and compliance implications.

Historically, managing and utilizing vast amounts of unstructured data was labor-intensive. However, advancements in AI models have simplified these tasks. Video content, in particular, has become a significant data source, offering insights into product reviews, customer feedback, and social media complaints. The complexity of retail workflows adds another layer of unstructured data to manage.

Unstructured data capabilities enable retailers to predict customer behavior, negotiate supplier contracts, analyze competitors, detect fraud, and manage hyperpersonalized promotions. These capabilities enhance revenues, customer lifetime value, and profits through programmatic procurement and inventory management.

For example, understanding the likelihood of cart abandonment allows retailers to implement strategies to entice consumers to complete purchases, addressing a common cause of revenue leakage. Analyzing unstructured data like social media mentions and customer reviews provides insights into consumer sentiment, preferences and trends. Retailers can adjust strategies proactively based on these insights to maintain customer satisfaction.

Unstructured data is also crucial for gathering product feedback, informing product development cycles, and providing insights into customer demographics. Consumer packaged goods companies rely on this data to understand market performance and gain other valuable insights.

Managing compliance and risks through unstructured data analysis unlocks numerous insights. Sensor data from smart stores optimizes layouts and personalizes recommendations, while unstructured data from customer interactions enhances targeted advertising and brand engagement. In-store experiences benefit from smart assistants powered by extensive knowledge bases.

The Massive Big Data ecosystem's power lies in overcoming data silos within existing systems, which hinder the full integration of stores into supply chains and e-commerce fulfillment. Breaking these silos is essential for deriving benefits from big data analytics.

Algorithmic Decision Making is Rewarding

Modernizing retail data infrastructure is complex but crucial for programmatic buying. Fragmented applications and databases create roadblocks for implementing customer data platforms and achieving a single view of customers and suppliers. Centralized repositories for all data structures (i.e., "data oceans") facilitate access for various functions. Data contracts are essential for managing unstructured data and ensuring data integrity, critical for programmatic buying.

Strategic purchasing involves more than reordering stock; it considers payment periods and local demographics to optimize decisions. Leveraging store networks as warehouses requires a deep understanding of customer behavior to avoid stock-outs. A data-driven approach empowers efficient delivery and fulfillment, revolutionizing the retail ecosystem.

Modernizing the Massive Big Data ecosystem is crucial for long-term success, unlocking new opportunities, fueling innovation, and creating value. Legacy players must adapt swiftly to gain a competitive edge. Traditional retailers must also modernize to fully utilize unstructured data for enhanced consumer engagement and revenue maximization.

The combination of vector databases, large language models, and verticalized GenAI implementations has transformed the Massive Big Data AI space. Data-driven business models spur growth, profitability and customer acquisition. AI applications in demand forecasting, supply chain analytics, programmatic procurement, smart logistics, abandoned cart prediction, customer satisfaction, and loyalty programs are areas where unstructured data plays a pivotal role. A modern approach to analyzing the Massive Big Data ecosystem is essential for retailers to stay competitive.

Padmanabhan Venkatesan is senior vice president and general manager of consumer tech at Persistent Systems, a trusted digital engineering and enterprise modernization partner for global market leaders across industries.