Providing recommendations enables the retailers to increase sales and to dictate and respond to trends. The customer’s past behavior or the series of the product characteristics are under consideration. Besides, various types of data such as demographic data, usefulness, preferences, needs, previous shopping experience, etc. go via the past data learning algorithm.
Knowledge of the present items in the basket along with all likes, dislikes, and previews is beneficial for a retailer in the spheres of layout organization, prices making and content placement. The analysis is usually conducted via rule mining algorithm.
Focusing on the detection of anomalies in the warranty claims. Powerful internet data platforms speed up the analysis process of a significant amount of warranty claims. This is an excellent chance for the retailers to turn warranty challenges into actionable intelligence.
Having a right price both for the customer and the retailer is a significant advantage brought by the optimization mechanisms. The price formation process depends not only on the costs to produce an item but on the wallet of a typical customer and the competitors’ offers.
Inventory, as it is, concerns stocking goods for their future use. Inventory management, in its turn, refers to stocking goods in order to use them in time of crisis. The retailers aim to provide a proper product at a right time, in a proper condition, at a proper place. In this regard, the stock and the supply chains are deeply analyzed.
Customer sentiment analysis is not a brand-new tool in this industry. However, since the active implementation of data science, it has become less expensive and time-consuming. Nowadays, the use of focus groups and customers polls is no longer needed. Machine learning algorithms provide the basis for sentiment analysis.
Merchandising has become an essential part of the retail business. This notion covers a vast majority of activities and strategies aimed at increase of sales and promotion of the product.
In retail, customer lifetime value (CLV) is a total value of the customer’s profit to the company over the entire customer-business relationship. Particular attention is paid to the revenues, as far as they are not so predictable as costs.
The detection of fraud and fraud rings is a challenging activity of a reliable retailer. The main reason for fraud detection is a great financial loss caused.
When testing different strategies for the customer, you want to know how well each option is doing, without loosing too much potential customers, revenues or interactions. A/B or multi-choice (aka multi-arm) selection strategies help understanding which idea works best.
- auto-complete,
- auto-suggest,
- auto-compare,
- auto-search,
- auto-navigate
Auto-complete, the first generation assistant, simply uses the reverse index (aka word list) to look up words matching the characters the user has entered so far. The most relevant suggestions are identified using frequency information from the word list.
Auto-Suggest:Simply looking up words starting with the same letters soon was not good enough anymore. Users wanted to have real suggestions, even if they didn’t know the exact spelling – or would you know how to spell José Manuel Barroso’s last name correctly?
- entity extraction
- feature extraction
- consistency detection
- summary extraction
- image featurization
- auto tagging
- auto classification
- knowledge graph