The future of commerce is already being massively influenced by AI. In online retail, these tools are already contributing to a significantly improved user experience by offering personalized buying options.
To compete today, retailers must respond to their customers like never before, all while eliminating waste and inefficiencies from their operations. Data can you get there, but making sense of the sheer volume of it takes expertise.
Automated content processing uses Natural Language Processing techniques to extract, normalize and enrich metadata. These tags can be published directly to a search engine ensuring results are relevant.
In online retail, there is a growing challenge in meeting customer expectations with unique customer experiences.
Online product search will let your customers search through the catalog using text, voice, or images. By looking at a user’s past behaviour, the search results can be tuned to each user and so maximize the clickthrough rate.
Sales forecasting takes into account historical sales data, seasonal variations, and other data.
Precise sales forecasts let you automatically determine optimal order quantities and maximize margins.
Dynamic pricing is real-time pricing where the price of a product responds to changes on demand, supply, competition price, subsidiary product prices.
Machine learning can analyse customers' historical data in real-time so that it can respond to demand fluctuations faster with adjusted prices.
Measuring churn is important for retail businesses as the metric reflects the customer’s response towards the product, service, price, and competition.
Machine learning can identify customers at risk of churn ahead of time and retain them with personalized offers and services.
Understanding customer opinion is an important part of making products better.
Tools like sentiment analysis can identify subjective information from product reviews, and automatically classify statements about products or product features as positive, negative, or neutral.
Chatbots can search through large amounts of data to pick out the most relevant information for a customer, irrespective of whether it is a recommendation for a new product or troubleshooting a solution.
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