Retail is one of the industries that can receive excessively wide range of new opportunities if implements Artificial Intelligence solutions as one of the approaches in omnichannel integration strategies. With the methods of AI, businesses can deliver hyper-personalized customer-retailer interactions, offer the true value of a positive shopping experience as well as attract a larger audience.

Use Cases

Customer segmentation, channel selection

Data mining and predictive analytics can be applied for using data about customers, including descriptions of the customers’ purchase and transaction history, for creating segments of customers. This is important prerequisite for further analysis, for example, for selecting customers for specific sales or marketing channels or for predicting which is the optimal.

Direct marketing

In retail it is essential to identify leads with the highest probabilities for conversion for a certain marketing campaign so that you only would contact the most likely cases up to a limit where the contacting costs would no longer lead to revenue high enough to compensate the costs. Even with the advent of e-mail, direct marketing is necessary, the “cost” here might be that recipients are tired of getting too much spam and might opt out.

Recommendations, cross-selling and up-selling

"People who bought THIS book also purchased THAT one." To make this recommendation, we need to perform calculations for all combinations, so called "frequent item sets", which might happen, and so we need algorithms guiding our search to the most promising directions. This approach is called cross-selling and might complement or even replace traditional cross-selling approaches based on manual rules. Loosely connected to this is up-selling where we try to identify customers who are likely to purchase a higher-valued product or a larger quantity.

Customer lifetime value

Instead of analyzing the historical customer value, many organizations now turn to predict how customers will develop in the future and what their total customer lifetime value will be to guide their sales and marketing efforts. Predictive analytics methods help to identify typical customer value cycles as well as to assign customers and leads to those cycles and determining at which state within their cycle they are.

Personalized design and production

Instead of being produced uniformly, apparels and consumables can be tailored on demand. If we look at fashion and clothing as an example, we could eventually move to fully interactive and customized design and supply in which AI created mock-ups of garments are sold online, made in small batches using automated production, and subsequent changes are made to design based on user feedback.

Logistics and Delivery

Consumers want the fastest possible delivery at the lowest possible price. Retailers want to offer very fast delivery as a competitive option, but must retain an acceptable profit margin. Artificial intelligence enables logistics professionals to cost-effectively accelerate delivery.


  • Get your customers to buy more
  • Improve your customer's experience because you offer them exactly what they need at a better price
  • Extract really valuable insights to improve your inventory turnover, optimize your stock, or predict future revenue