Retail has always been extremely competitive, and never more so than in this post-pandemic scenario. Over the next year or so, retailers will have to make good their losses from 2020 and accelerate their growth so as to continue being relevant in a world of evolving choices. At the same time, customers continue to expect on-demand service that balances efficiency with empathy – a challenging task.

Retail analytics is a process that helps to provide crucial data for businesses with regards to inventory, sales, supply chain activities, consumer demand, and more. By implementing an analytics-driven retail environment, organizations can make better decisions for procurement, marketing, merchandising, and other operational considerations. This, in turn, enables retailers to create a better buying experience and for identifying opportunities for organizational improvement. What are the benefits and importance of retail analytics? What are some current trends in retail analytics?

While artificial intelligence is becomes a major new tool for marketers, nearly one in two retailers are missing out on leveraging AI marketing to better understand customer behaviours, according to new research. The study, conducted by Forrester Consulting on behalf of Emarsys, looked at the growing role of AI in the retail and e-commerce industries. In an industry that's thought to be going through a'creativity crisis', there's some good news for retail marketers amongst the findings: The study, entitled'Building Trust and Confidence: AI Marketing Readiness in Retail and e-Commerce1', sought to understand if there is still a gap between the readiness of AI marketing solutions to execute on real-time B2C marketing campaigns, and the readiness of marketing tech users and business decision makers to adopt AI marketing technology. Businesses polled across US, UK, Germany, France and Australia had revenues from at least $50m to more than $5bn. The study highlighted that marketers and business decision makers polled believe AI-powered marketing will shift the role of marketing toward more strategic work (79%) and make marketing teams more efficient (86%) and effective (86%), as well as enabling them to focus on value-generating tasks as AI automates workflows (82%) and reinventing the way that marketers work (82%).

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits. Unsupervised machine learning is a paradigm in machine learning where we build models without relying on labeled training data. One of the most common methods is clustering. You must have heard this term being used quite frequently. We mainly use it for data analysis where we want to find clusters in our data.