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Retail AI Use Cases for 2021 and Beyond

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With use cases for different verticals getting supported by big players and making headlines across every industry vertical, AI is here to stay. With almost all industries using it in one or more ways, it has brought significant business value to organizations across the world. However, what does it mean for the retail industry. Let's check out:

AI-based recommendations will drive higher conversions

One of the most common and easily implementable use case is recommendation engines which use historical data to suggest items or products to customers. These engines help retailers in gaining richer insights about their customers and can even predict their buying patterns.

For example, if a customer has bought Product A and B in the past, it is highly likely that he/she might be looking forward to Product C which is priced higher than them. Using this data, AI can suggest other accessories or similar products which are popular among users who have bought the same items in the past.

Recently, a web retailer saw his conversions increase by 50% after deploying personalization technology. The web retailer was able to analyze more than 3 billion data points, about the shopping behavior of its customers. The cloud-based AI platform not only helped it to understand consumer behavior but also to make sense of those insights and convert those into actionable intelligence.

Inventory management

AI can help retailers in better managing their inventories as well as control the stock by tracking the product flow across different channels. In a recent report, it was seen that retailers who have adopted AI to manage their inventories have been performing better than others. Retailers following this have seen reduced stock-outs and higher revenues.

Inventory management using AI helps retailers in efficient allocation of optimized resources, making better use of time and money by arriving at the right decisions faster. By using AI to track, analyze and understand the patterns of the inventory across different stores and warehouses, making a holistic view about where to place a product or stock it from reduces idle capital. Retailers are able to optimize their warehouse space as well as reduce time required for physically visiting those locations to check on them physically.

Real-time business insights

Retailers across sectors can use AI-based technologies like computer vision and machine learning to make sense of their data in an easier way. For example, it's possible for retailers to accurately predict demand for items by using images captured in their stores to understand precise time when they are likely to run out.

This can be done by using computer vision technologies which is used for analyzing the image of items on the shelf and then compare it with past sales data. With this, retailers can accurately predict when an area will run out of stock or how much product should be re-ordered. If this data is used in conjunction with the insights about when demand for an item is likely to pick up, retailers can take advantage of such fluctuations and order more stock at that time.

This will help them in achieving optimum utilization of resources and avoid wastage by deciding on optimal levels depending on factors like weather or holidays which can affect buying behavior.

IoT for operational efficiency

IoT helps in turning warehouses into smart environments with real-time data reporting on warehouse activities. Retail logistics using AI is expected to help reduce operating costs by up to 15%.

For example, manufacturers can make use of RFID tags which are integrated with sensors, to monitor the location, temperature and vibrations in the warehouse. Based on these parameters, an optimization algorithm can be run to ensure that product is sent to the right place at the right time which reduces wastage of resources or items getting damaged due to mishandling or weather conditions.

Another real-time use case for AI in retail logistics is detecting theft by using computer vision or similar technologies. As soon as a customer picks up an item, the computer vision technology can capture images of them and then match them with existing data to check for theft.

With the help of real-time data, retailers are able to take more intelligent decisions at all points in time which boosts their productivity by helping them avoid wastage or theft of time, money or stock.

Cutting-edge AI solutions are expected to drive up business intelligence in the field of retail. With more retailers adopting these technologies, it will be interesting to see how they leverage them for their benefit in the coming days.

Conclusion

Using AI-led technologies, retailers are gearing up for providing more personalized experiences to their customers. With the help of these technologies, they also stand to benefit by increasing operational efficiency and gaining new insights on consumer behavior.

Currently, this is only at a nascent stage but these solutions hold great potential, especially when combined with other big data or IoT led solutions. It remains to be seen how retailers leverage these technologies for their benefit in the future and help them gain more from their business operations.


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