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.
Product catalogs often have little metadata or are not properly indexed by the search engine.
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.
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.
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.
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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