AI innovations in retail demand effective data strategies
Other very useful areas within computer vision include image recognition and motion detection. In retail spaces, these tools can be particularly effective to count foot traffic or inventory on display shelves. AI applications can also help customers with recommendations and map out their end-to-end journey from a device to the store, using tools like augmented reality, also made possible by computer vision systems.
NLP systems process human language to enable machines to understand natural conversations. An intuitive example of this comes in the form of human-machine interactions through chatbots and dialog systems.
Over the years, chatbots have become a landmark achievement in retail, especially in customer service roles. They are capable of attending to customer queries, thus slashing human workload and reducing human error.
Chatbots can also be a great resource to understand what customers enquire about. These responses can then be used to make an agile sales strategy based on current demand, or supplement other decision-making in business.
Since most online data is text-based, there are many other use cases of NLP, such as sentiment analysis.
When consumers are shown suggestions that automatically align with their preferences, they are more likely to enjoy the shopping process. AI-powered personalization tools hold the key to understanding which products a customer can easily be persuaded to buy, which is essentially the power to bridge the gap between want and need. In fact, more than 35% of Amazon’s consumer purchases are credited to its recommendation engine, which has been a critical part of its success.
Predictive systems are also widely used in sales forecasting as well as for price and demand predictions and inventory and supply chain optimization. Similarly, machine learning (ML) algorithms can be of great help when predicting product performance and demand, based on a range of factors. Purchase history, location of the customers, upcoming holidays and seasonal purchases are some factors that can be accounted for by the algorithms.
Furthermore, with available data on sales, customer demographics and distance from competitor outlets, AI applications can also predict optimal locations for outlets. Data and AI also allow for the convergence of digital and in-store sales strategy.
Clearly, AI innovations are beginning to make retail experiences more seamless, personalized and engaging. But how can retail businesses map out a strategy to tap each of these innovations?
A data strategy to deploy computer vision systems requires a large number of pictures and videos.
