Glossary

AI in Retail

What is AI in Retail?

AI in retail covers applications along the entire retail chain: sales forecasting and stock planning, personalised product recommendations, dynamic pricing, fraud detection in online shops and customer service via chat. Retailers can reduce overstock and shortages and answer enquiries more quickly. This requires clean data and careful handling of customer information. Example: a chat assistant answers questions about delivery times and returns around the clock.

How KI.ordo helps

KI.ordo offers, among other things, a Shopify integration and a support agent that answers customer questions from stored knowledge and cites its sources.

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Topics

Retail
E-commerce
Customer service

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Related terms

Personalised marketing

Personalised marketing means tailoring messages, offers and timing to individual customers or small groups rather than addressing everyone in the same way. It draws on information such as past purchases, interests or the reason for contact. AI can turn this into suitable copy, for example an email that refers to a recently purchased product. It is important to use only data for which consent has been given and to leave out sensitive details.

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Fraud detection

Fraud detection covers methods that identify suspicious transactions, orders, accounts or applications at an early stage. Rules and learning models look for warning signs such as differing delivery and billing addresses, unusual quantities, conspicuous time pressure or disposable email addresses. It is used in e-commerce, banking, insurance and payment services. As false alarms can affect genuine customers, hits should be reviewed by people rather than rejected automatically.

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Customer service automation

Customer service automation means having software handle recurring customer enquiries fully or partly – for example through chatbots, suggested replies or pre-sorting tickets. Common questions about opening hours, delivery status or returns can be answered around the clock, while the team gains time for difficult cases. Reliable knowledge sources and an easy hand-over to people are essential.

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Predictive analytics

Predictive analytics uses historical data, statistics and machine learning to estimate likely future events. It answers questions such as: which customers are about to cancel? How much stock will be needed next month? When is a machine likely to fail? The results are probabilities, not certainties. Their value depends heavily on data quality and on whether the predictions actually feed into decisions and processes.

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forecasting
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