Analysing customer behaviour and preferences using AI video analytics
By Atul Rai
The developments in AI have skyrocketed in recent times, and the curiosity to integrate AI-powered solutions has risen among businesses of various scales. The scope of automating processes through AI with a human-error-free perspective also draws a lot of attention. Keeping in mind, it is important to understand how AI technology can be molded to everyday business processes through solutions such as video analytics.
Video analytics might be a buzzing word, but it is not a new technology. Image and video feeds comprise the highest amount of data in the world, called big data. Big data analytics has been prevalent in the field of retail since the 2000s, since the adoption of video surveillance through CCTV cameras. Initially, the retail industry used it more in terms of security, but recent times have witnessed a surge in retail intelligence. Artificial intelligence models are used in big data analysis to present accurate insights that can be used to drive various marketing initiatives by businesses.
Another important factor is that AI video analytics makes making informed decisions easier. Strategies behind marketing plans are backed by insightful data that is used for the benefit of the retail business. The trends that can be traced back to how your retail business performed in the preceding months can help businesses determine what and how things went right or wrong. They can either continue doing what they did for continued success or change the processes that led them downhill.
Determining the demographic needs of a particular region can be drawn through gender identification/classification methods that can be used to design marketing campaigns. Let’s say males from the age group 22-27 years are more into apparel shopping in Northern India compared to males from the same age group in Southern India. Retail marketing heads can re-design their marketing plans to drive more sales in Southern India according to the insightful data received.
Similarly, unique footfall analytics, occupancy visualization, queue management, and customer journey are various other AI-powered use cases that retail businesses can leverage for maximum revenue growth.
Revenue growth is an important basis for any business plan. As such, video analytics can be a critical platform equally supporting revenue growth and loss prevention for retail businesses. Analyzing customer experiences in brick-and-mortar retail has never disappointed retailers. Replacing manual analysis, video analytics uses artificial intelligence to produce human error-free results, ensuring precision and reliability.
In conclusion, the integration of AI-powered solutions in the brick-and-mortar retail industry has the potential to drive revenue growth, improve operational efficiency, and enhance the overall customer experience. As retailers continue to embrace and innovate with AI technology, one can expect to see exciting advancements in the brick-and-mortar retail industry that will shape the future of retail for years to come.
The author is the CEO and co-founder of Staqu Technologies