Areas where Artificial Intelligence and Machine Learning can influence your Merchandising and Marketing Decisions

Artificial intelligence (AI) is an emerging technology that deals closely with retailers and would be wise to embrace it. As they strive to become better their merchandising and marketing decision, many have already turned to AI and ML. Integrating AI and ML technologies enables retailers to provide their customers enhanced and improved shopping experiences.

Here are 4 areas where AI and ML can can make merchandising and marketing decision making:

1. Assortment
With ability of AI, you can match and compare retailers with their competitors’ and corresponding products. Instead of getting a month’s long process if done manually, soon, you are able to turn to ML to assort your products under various taxonomies at a less amount of time. This technology allows retailers the ability to review many more categories and be more relevant to customers.

Mapping retail products in front of competition done manually or via rule-based engines, will continue to develop, as doing so at scale will be critical.

2. Content
Online retailers are struggling to include more information about their product content to strive confident purchase decisions. Attribute extraction – the stages of extracting an attribute from inputs or manufacturer websites – is called out as attributing products at scale. Machine learning technology zd algorithms are the main source in this area,  need for speed, upgrade and develop sources for attributes continues to grow.

In addition, product images are also form a key part of content. Deep learning technology and algorithms are  used to define images based on the retailer requirements, identifying the fraudulent images and promotional text on images at scale.

3. Pricing
Relying on data from historic sales trends and forecasts to make pricing decisions isn’t enough these days. Retailers has to incorporate the data with demand data available out of the firewall to be able to make more competitive pricing decisions. Allowing algorithms to identify price patterns, allows retailers to make smarter, more timely pricing decisions at scale.

4. Search
Product search requires products to be discoverable in multiple blocks across a catalog or in the taxonomy. Routinely changing of classification for product discovery is a key governance that retailers need to reckon with in the future. This simply can’t be done manually. Instead, a combination of classification techniques and constant learning algorithms integrating with human intelligence will be the future need for many ecommerce retailers.