Artificial intelligence in retail: 6 AI applications for sales success

Be it for specific questions about ingredients, stock levels or discount promotions: By connecting directly to databases and using natural language analysis and text processing capabilities, modern chatbots can handle complex tasks and provide informed answers. These capabilities are revolutionizing B2C, B2E and B2B communications. In addition, by combining the two key technologies of Optical Character Recognition (OCR) and Natural Language Processing (NLP), data can be reliably extracted from physical documents such as delivery notes or invoices, digitally processed and analyzed using data or process mining.

Too much inventory in the warehouse is a poor use of capital. On the other hand, if inventory is too low, sales go to the competition, which is usually just a click away. It is no coincidence that inventory management is a real art in many companies, which can now be significantly improved through AI-based "auto-scheduling". This involves analyzing trends in historical data as well as current influencing factors (days of the week, holidays, weather conditions, etc.). And the environment benefits too: better demand forecasting reduces unnecessary waste, especially for perishable goods.

Tens of thousands of packages leave warehouses in Germany every day - often with a lot of air and little content. AI applications now help to optimally arrange the individual goods in a carton without wasting space. The exact size of the box is also determined automatically. And after the 3D Tetris game, AI-assisted route optimization takes over in combination with fully automated picking.

Product placements along the lines of "customers who bought product A will surely be interested in product B" are already established in retail. But new AI applications not only find obvious combinations like smartphone and phone case, but also hidden correlations in a huge mountain of data. This makes product recommendations even more relevant. As a result, the value of the shopping basket increases in step with customer satisfaction.

The principle that demands drives price is often ignored, especially in retail. Once prices are set, they seem to be carved in stone, even though, for example, demand for home improvement products increases on Saturday mornings while demand for baked goods decreases in the evening. With dynamic price adjustment, however, companies can consider relevant internal and external factors. AI analyzes the data and calculates the optimal price based on all available information - dynamically and in real time.

Recovering lost customers is more difficult than retaining existing ones. This is especially true for subscription models. An AI service developed by Lufthansa Industry Solutions analyzes customer data to predict churn. Customers who are ready to switch can be proactively persuaded to stay with tailored offers or campaigns. In addition, a simulation environment can be used to evaluate how certain offer adjustments would affect the churn rate.