With growing demand, AI in retail enables the industry to understand the pattern and behavior of consumer’s demand
Every year, the ‘End-of-Season’ sale garners huge profits for e-commerce and retail industries. Customers are provided with a variety of options to choose from. The planning, marketing strategy, and execution for such sales require a year-long process. However, the most daunting task relies on the retailer, i.e. to understand customers’ demand and preferences.
A plethora of data regarding the demands of the consumers and customers’ feedback is available to the retailers. However, due to the complexity of the ‘mixed-data’ analysis with limited technology, often the partial picture of customers’ feedback is presented to the retailer. This becomes difficult to understand the pattern in which the customer demand works. As most often only positive feedback is displayed in front of the retailer and the scope of improvement becomes limited.
For any business to succeed, understanding customers’ demand is necessary. It not only helps in estimating the amount of improvement that can be deployed while delivering and developing a product but also enables the retail industry to provide new variation to these demand.
However, manually this entire process becomes time-consuming and laborious, especially at times where the customer demands and feedbacks are an all-time high. Thus, this complex task could be simplified with the help of Artificial intelligence.
AI Text Analytics in Understanding NPS
A report by Mckinsey Insight states that AI can generate US$1.4 trillion to US$ 2.6 trillion value in marketing and sales across the global business and US$1.2 trillion to US$2 trillion in supply chain management.
AI Text Analytics is a process of examining a large amount of data, by accessing the customers’ feedback in the form of text, emails, and online chat, without any data setup and maintenance.
Net Promoter Score (NPS) is an index that ranges from -100 to +100 and measures the willingness of customers to measure or promote a company’s products and services.
The AI-text analytics in NPS understands the co-relation between feedback and scores and helps retailers to detect fluctuations within NPS.
AI-text Analytics in Customer’s Behaviour
AI-text analytics analyzes customers’ behavior. With the help of an online chat program, it enables them to display their demands, feedbacks, common questions and answers, without interacting with them directly. This helps the retailer in understanding the shift towards customer’s behavior, their demands, the pattern of what they need, the customers’ emotions, and helps to make data-driven decisions for improving customer services.
AI in Managing Logistics
Any retail chain has a pile of data regarding the extensive supply and demand chain. It includes a pool of customer and consumer data to understand their behavior.
However, most often this supply and demand chain governs with old data forecasts, thus not giving an insight into the new demands of consumers. As a result, the customer is provided with limited options from the company. This disengages the customers to continue the services of the company.
AI shifts the focus of the retailer from historical forecasts to the forecasts through demands, thus enabling variations within the customer choices. It also becomes imperative for new strategies that can be applied to manage customers’ demand.