Retailers can use Google Cloud AI applications for their business and industry through two distinct use-cases:
- Driving sales with optimized recommendation systems – Retailers can use intelligent recommendation algorithms to drive sales using personalized customer search recommendations on their homepage and mobile app
- Google Cloud AI to support variable sales/traffic volumes – Retailers can use Google Cloud AI to help reduce its computation load and increase product sales
We need to analyze how retailers can use recommendation system framework algorithms to retrieve products based on customer search activity.
Driving sales with the recommendation system framework
Any large retailer or ecommerce enterprise has a vested interest in maximizing revenue by optimizing the virtual shopping experience and recommending the most comparable products.
Managing huge volumes of traffic while optimizing the shopping experience requires extensive use of AI, particularly machine learning and Cloud AI functions.
The idea behind any recommendation system framework is simple: optimize traffic flow while updating and offering relevant products to the consumers that they want to buy. Moreover, to perform these operations in real-time, thereby increasing customer satisfaction, clickthrough rate, and organizational revenue.
Earlier, the retailers used a “relevance of recommendation” function, which could calculate the “degree of similarity between the previous products clicked on and/or purchased with their inventory.” . However, it has now changed the function to include “diversity of recommendation” and “discovery optimization” through the Artificial Intelligence Recommendation engine.
Supply chain, Recommendation, and Logistics are the backbone of any retail business.
The recommendation engine can analyze and capture user behavior in seconds and provide personalized recommendations within milliseconds. Another way to the above is by focusing more on offering customers more diverse products that (also) have a high clickthrough rate.
The model inputs include the following four structures:
- Based on user input (clickthroughs, add-to-cart, etc.), the recall feature can retrieve products that the user may be interested in real-time. A similarity score can be fetched through a query using an algorithm based on your user selection data. Collaborative filtering can filter out items that a user may like based on the actions of both the active user and similar users. This training data could be transferred to the sorting module.
- The sorting feature could be a deep learning algorithm that can use the product data retrieved from the recall module, with a refined set, and can be assigned to a product score based on additional criteria. The primary feature of the sorting module could be the behavior sequence transformer model, which learns the correlation between user behavior sequences and product data extracted from the recall module.
- The mechanism feature can adjust system algorithm strategy, control traffic, optimize user experience, and sort products. The product image similarity calculation could be made using a Convolutional Neural Network, presumably to ensure the product(s) match before presenting it to the end-user in the form of a recommended product.
Finally, the recommendation system can retrieve the products that match the final algorithmic operations performed in the mechanism module. These products could be then placed in various places on the homepage in real-time.
Google Cloud for retail
Google Cloud has a variety of services provided for retailers. These are some google solutions which can help retailers capture digital and omnichannel revenue growth.
- Digital commerce – By migrating your ecommerce platform to Google Cloud it will take advantage of the reliability and scalability of Google Cloud, improving both site performance and customer experience.
- API Management for Retail – It will easily integrate retail systems and channels with Google’s API Management Platform (Apigee) to provide a more unified shopping experience.
- Product discovery solutions – It will improve product discovery with retail solutions that help to increase engagement and deliver personalized experiences for all shoppers.
- Peak season retail premium support – Google Cloud provides a unique experience to the retail customers with services including early capacity planning, architectural reviews, and reliability testing.
- Data warehouse modernization, analytics and reporting – These Google cloud solutions solve today’s retail analytics demands and seamlessly scale your business by moving to Google Cloud’s modern data warehouse. Looker helps retailers power a multitude of data experiences, from modern business intelligence and embedded analytics to workflow integrations and custom data apps.
- Contact Center AI – CCAI delivers exceptional customer service and increases operational efficiency by infusing your contact center with AI-powered experiences and also integrates with omnichannel experiences for quicker and better customer support.
- Retail API and AI image analysis and processing – A huge leap in AI models where complex images are sorted, uses pattern analysis and creates great user intelligence, and helps retailers build faster and sustained business processes and user experiences.