Retailers are facing challenges from all directions – from customer behavior to pricing, merchandising, campaigning, store operations and store-level inventory. More than ever, they are searching for ways to better understand customer preferences and behavior, predict demand, optimize inventory and enhance operational efficiency. This is why retailers need to invest in data analytics and advanced technologies such as AI and ML, NLP and deep learning.
Leveraging Technology in Retail
Retail 4.0 has re-shaped the concept of shopping, converging traditional brick-and-mortar and online channels into a digitally enabled ecosystem. Retailers have become technologically aware and are increasingly focused on improving process efficiencies with digital innovation and enhancing customer experience (CX) and operations with the use of advanced analytics. Some are using AIOps to streamline operations and reduce costs.
Digital dashboards and wallets, as well as advancements in self-check-in and shop-floor customer services, are now prevalent across stores, allowing retailers to capture a significant volume of consumer data. Innovative solutions can extract valuable insights from this data and enable retailers to make informed decisions and optimize operations.
Data Synthesis and Analytics for Retail Enterprises
Data is the fuel for modern applications that drive the retail industry. Customer data from multiple sources have become critical in the consumer and pricing analytics value chain. Innovation in data collection through IoT devices, synthesis of data to interpret and uncover patterns, trends and correlations, and the use of predictive analytics have created new ways to predict customer behavior.
Customer data can help retailers make the right product available at the right place and time, which, in turn, drives CX, revenue growth and operational efficiency. The use of data analytics helps retailers gain insights into consumer behavior, sales patterns and KPIs to make data-driven decisions that can improve profitability and customer satisfaction.
Large and digitally mature retailers have been incorporating AI and ML, deep learning models, NLP and other analytics technologies in niche retail operating segments and seeing significant results in cost and process optimization. Retailers know that consumer behavior data and location intelligence can deliver real outcomes in terms of customer retention and growth and new revenue potential.
Transforming Retail Enterprises through Specialized Data and Analytics
The following are common use cases for data and analytics in retail:
- Customer behavior modeling to predict spending
- Automated dynamic pricing models and optimization
- Hyperpersonalized shopper experience
- Omnichannel integration
- Digital assistants through extended reality (AR/VR)
- Shelf and inventory optimization
- In-store AI and location intelligence
- Automated merchandising
Service providers have invested substantially in strengthening their analytics and AI capabilities to help retail clients gain a deep understanding of consumer preferences, optimize inventory management and drive profitability by making informed decisions at scale. Service providers' offerings in AI and analytics will help retailers offer customers a seamless shopping experience, assist with pricing, forecast demand and supply, operate stores with improved decision-making and deliver exceptional CX.
The following are some of the specialized capabilities service providers offer:
- Hyperpersonalized experiences: Service providers leverage large volumes of customer data to help retailers create tailored marketing campaigns, personalized recommendation systems and customized offers. Personalized targeting enhances cross-selling and up-selling opportunities and improves CX, customer engagement and sales. The growing relevance of digital assistants integrated with AI and data analytics has reinvented CX by allowing retailers to offer interactive experiences to customers.
- Customer insights: Customer data has become the lifeline of retailers, helping them understand customer behavior, brand perception and purchasing patterns to drive and implement a customer-centric approach. The use of analytics on this data generates valuable customer insights and helps retailers predict patterns in purchasing decisions, which, in turn, guides them to make informed decisions about products, pricing, branding and inventory.
- Store operations analytics: Service providers have prioritized the use of big data and analytics to improve retail store operations. The use of operational and partner data and the application of AI and ML in advanced analytics provide insights into improving business processes and better planning of shelf management, product substitution, product adjacency and improved in-store experience, thereby ensuring customer loyalty. Service providers are helping retailers leverage comprehensive in-store data for forecasting and managing inventory.
The upcoming ISG Provider Lens™ Retail Analytics Services – Specialist Providers 2024 report explores these challenges to retailers and showcases providers and vendors capable of addressing them. The report also highlights the specific capabilities of players that can help retailers choose the right partner to sustain and grow in the current economic environment.
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