16 April 2024
To continue from the heading it’s not that the changes are happening in the future of retail, technology in the retail landscape is currently changing the field, with data science and artificial intelligence influencing how businesses interact with their customers and how customers interact with businesses.
Prominent among this technological influence are AI its prediction abilities and the emergence of hyper-personalization, where data analytics and machine learning are creating customized shopping experiences. As businesses want to retain their customers, they are increasingly looking at the option of creating personalized experiences for their customers, with the hope that it might help them achieve the goal.
Exploring this option is also Chandrakanth Lekkala, a data scientist who is working on retail personalization and making use of technology to predict customer behaviour. He has implemented a seasonally aware, AI-driven personalized marketing campaign which achieved improvements in customer engagement, including a 45% increase in email open rates and a 60% boost in click-through rates, leading to a 28% boost in campaign-driven sales.
Speaking of personalization, one of his most important developments in retail personalization is the implementation of a personalization platform integrating seasonal trends for a Fortune 500 retailer. The platform seamlessly blends online and offline shopping by consolidating data from multiple touchpoints.
Retailers can now create a 360-degree view of each customer’s journey. This approach has led to significant improvements in customer lifetime value (how much value a customer brings), with some retailers reporting increases of up to 22% year-over-year and an increase of 42% in customer engagement across various channels.
The seasonal forecasting that he implemented (where the seasonal patterns of consumption were taken into consideration), improved inventory management accuracy by 30% and reduced stockouts by 25% during peak seasons. The combination of these approaches- customer segmentation, lifetime value prediction model, and incorporating seasonal purchasing patterns, enabled targeted retention efforts that reduced churn rates by 35% for high-value customers.
The personalization was not limited to predictions, dynamic pricing algorithms that consider both individual customer preferences and seasonal trends achieved revenue increases of 12% while maintaining high customer satisfaction levels. Additionally, the implementation of an AI-powered product recommendation system led to a 40% increase in cross-sell opportunities and a 25% improvement in average order value.
The results came in with its challenges. One challenge was integrating data from various sources into one coherent source, which was solved by building a data lake architecture that successfully consolidated these diverse data streams. Another challenge was predicting the changes in customer behaviour and seasonal patterns, which was solved by implementing a hybrid forecasting system that combined traditional methods, Prophet, and deep learning models such as LSTMs and TCNs, as well as machine learning.
Further, in today’s digital age, privacy concerns and data security remain important. Solutions, such as federated learning approaches, are developed to balance personalization without centralizing sensitive customer data.
He has also shared his knowledge via authoring various articles such as “Machine Learning Model Serving at Scale” and “Self-Refine Prompting” in 2023. Additionally, he conducted a comprehensive evaluation of different approaches in time series forecasting, published in the Journal of Artificial Intelligence, Machine Learning and Data Science in 2024, which can be an important source in understanding seasonal forecasting techniques in retail.
Further, when asked about the current trends in the industry, he tells us that he believes that the future lies in hyper-personalization, where every interaction is tailored not just to broad customer segments, but to individual preferences, behaviours, and even real-time contexts. Also to add to the twist, there will be recommendations for products even before the customer is himself/herself aware of what they want.
By looking ahead, he notes that the integration of edge computing and federated learning promises to enhance real-life analytical abilities while building trust in a personalized experience. “The key to success is the ethical use of data and maintaining a balance between personalization and privacy,” he tells us. “The retailers who can master this balance of technology, data, and customer trust while accurately predicting and adapting to seasonal trends will be the leaders in the next era of retail.”