When e-commerce saw unprecedented growth in India powered by large-scale mobile penetration, digital connectivity and logistics management there was a strong speculation that it would be the end of the physical-store era.
However, the retail sector has evolved to adopt a phygital model – both digital and in-store aspects incorporated with a digital-first approach to provide a harmonious, omni-channel experience for consumers.
The phygital model evolved in response to shifting consumer demands as retail buyers still visit physical stores and at the same time look up products online on their smartphones for price comparisons and better offers. To perfect the phygital model, retailers need to focus investments on modern storefronts, real-time online shopping, seamless customer experience and upskilling employees to serve customers better.
A modern course for phygital in retail
In a phygital model, CIOs need to analyze large datasets generated from a multitude of sources including third-party-generated data and user-generated data to derive insights, giving rise to concerns such as the ethical use of data and regulations on data privacy.
Organizations need to invest in the latest tools and technologies around data and AI and explore ways to maximize their impact by implementing a well thought out data strategy, strong governance with security and privacy guardrails, and data observability. Having data products built around customers’ journeys through the different channels and touchpoints can not only help improve customer experience but also help the enterprise build a deeper understanding of customer behaviors and preferences.
Engagement touchpoints span from digital channels to various retail stores or micro fulfillment centers in the phygital consumption model. This has increased the role and importance of data at the edge to support operational intelligence in real-time, lower operational costs, and enhance consumer and store associate experiences. Data-driven, intelligent in-store execution to increase operational visibility and efficiency as well as improved customer engagement and storefront associate experience is becoming imperative for survival in the retail industry. Edge architectures facilitate the processing of data at the “store edge,” such as smart shelves, smart freezers, item-level RFID, smart robots, smart check-out as well as traditional store systems like point of sale (POS).
Organizations will also need to invest in their infrastructural and operational transformation journey. They will need to prepare for the transition and changes expected on the cultural, operational and business fronts to justify the technology part of phygital.
VP and CTO, Data and AI Services
Kyndryl
Transforming for success with Data and AI
From the warehouse to the storefront and at all points in between, employees at different levels are involved in decision-making across the enterprise. The success of retail will rely on the availability of quality data in the required quantity for them to support the organization’s strategic plan. The right insights and analysis will drive a better understanding of the consumer, sharpen focus, and design a seamless, simplified, personalized experience overall. Taking fashion retail as an example, analytics and AI can add differentiated value by detecting the latest trends using third-party data, assortment localization and localized markdown optimization or pricing. Similarly, in food retail, advanced analytics and AI can transform the value chain with better food quality control to optimized inventory management and route optimization.
Generative AI is becoming a game-changer for retail with agility in innovation as well as the efficiencies that it can help bring in. Generative AI can help customers and associates achieve hyper-personalization at scale since there is a greater ability to search and identify product attributes, as well as to leverage chat interfaces using natural language. Improved content discovery can help improve supply chain dynamics for in-stock availability. For example, in fashion retail, the fitting rooms of the future will evolve into a phygital world powered by Generative AI / AR technologies. The catalyst to drive Generative AI adoption at scale can come from having a comprehensive Large Language Model Ops (LLMOps) framework that makes it possible for rapid prototyping, pipeline orchestration, cost management and establishing responsible AI guardrails in a simplified manner across the enterprise.
When e-commerce did not kill brick-and-mortar
Buying and shopping trends across various cohort groups indicate that both ecommerce and traditional commerce will co-exist, with different business models. However, regardless of the specific model, success will depend on enhancing both the physical and digital store experiences by adopting the best of each world. From a consumer standpoint, the experience should be captivating at a level where they can seamlessly crossover from one to another unhindered by boundaries.
