Customer Segmentation, Behavioral Archetypes, and Personalization

The online shopping revolution has made things both easier and more difficult for consumers. The problem has changed from time wasted travelling to stores to time spent evaluating the options online. We’ve all spent untold hours clicking through page after page of similar products.

The right AI system can change all that with personalization that saves shoppers time and increases revenue for retailers, especially when you factor in the ability to identify patterns like combination and recurring purchases.

Based on demographics, prior purchases, location, wish list churn, customer feedback, and other factors, AI systems can help retailers truly understand what their customers want, validate assumptions, and even uncover surprises. The rewards for the business include increased customer loyalty, higher conversion rates, and larger average order values.

Shelf Monitoring

Without real-time data, it becomes almost impossible to respond to sudden shifts in buyer behaviour, even for experienced planners with finely-honed instincts. Compliance with planning and supplier agreements are also hard to verify.

AI systems can use images and/or video to determine exactly what’s on the shelf, and follow changes as items are purchased. These systems can help eliminate lost sales due to stock-outs that human teams are too busy to detect and identify items that can’t sell because they’re misplaced. Lemay.ai’s solutions will never get tired of assessing product placements and can send alerts when problems are detected.

Inventory Planning

Our defence research projects give analysts tools to make sense of what they see in the data, so commanders have better information. Our work will help protect troops in the future through better organization, training, equipment, and even provide deterrence and risk reduction tools.

For example, one project in signals Intelligence allows us to make sense of radio traffic faster—when operators see changes in the radio spectrum they can be alerted to a potential situation before it has a chance to fully develop.

Sales Route Optimization

Solving the classic ‘travelling salesman’ route optimization problem has always been a massive undertaking, considering the need to crunch the numbers on distances between all possible stops and how time adds up depending on the path between stops. Traditional heuristics would often work to find a good route, if not the best one. But now, businesses need to factor in specific delivery time commitments, traffic conditions, and weather that can impact overall traffic speed, and this has made the task even more daunting.

The problem becomes an opportunity, however, with an AI solution that can not only use raw processing power, but that can swiftly incorporate real-time changes in conditions. Best of all, the solution can ‘learn from experience’ to provide predictive capabilities as well. From interstate and interprovincial delivery routes down to the last mile, Lemay.ai’s solutions will quickly pay for themselves in accumulated optimization savings.

Dynamic Pricing

The travel industry knows that dynamic pricing works. The ability to raise and lower prices, depending on demand, has been helping airlines and hotels ensure that spaces are as fully booked as possible for years.

The same principle holds true in retail, but as always, success relies on paying attention to the details. Identifying when to change prices, determining by how much prices should change, and even predicting shifts in demand can depend on dozens of factors. Those factors, in turn, will depend on demographics and personal preferences. The sheer number of variables makes dynamic pricing recommendation the ideal job for a custom machine learning solution.

Predictive Analytics

Predictive consumer analytics plays a huge role in effective demand and price forecasting for retailers. But considering the data is no longer enough: you need artificial intelligence to adapt to ongoing disruption. Because AI systems can process incoming information in near-real-time and develop a realistic understanding of how the tastes of different customer segments are changing, they can make recommendations that really move the needle on ROI.

But where AI-based predictive analytics really shines is in product design. The value of understanding the response of your customers to product style, colour, and material choices—before final manufacturing decisions are made—can not be underestimated. When the same target audience wants black clothes one year and vibrantly patterned clothes the next, it’s difficult even for experienced product designers to avoid costly mistakes, especially when considering regional preferences.