Revolutionize Your Retail with Google Cloud's Retail AI

Table of Contents

1. Introduction
2. The Impact of Good Recommendations and Search Systems
3. Consequences of Maintaining a Good Recommendation and Search System
4. Google Cloud Platform's Retail AI
5. Ingesting Data into Retail AI
6. Model Training in Retail AI
7. Customizing Retail AI Models
8. Pros and Cons of Retail AI
9. Conclusion
10. Frequently Asked Questions

Google Cloud Platform's Retail AI: Revolutionizing Retail

Retail AI is a managed service provided by Google Cloud Platform that helps retailers integrate search and product recommendations into their Website. In this article, we will discuss the impact of implementing a good recommendation and search system into a retailer's website, the consequences involved in maintaining a good recommendation and search system, and how Google Cloud Platform's Retail AI can help retailers overcome these challenges.

The Impact of Good Recommendations and Search Systems

According to Insider Intelligence, a market research company, implementing personalized recommendations on a home page can increase the chances of a user clicking on a recommendation by 60%. Placing recommendations across segments can increase revenue up to 30%. In merchandising, the increase in sales results in fewer non-moving garments, which results in less recycling. To put it into perspective, if a company is making a million dollars in revenue and can place the right recommendations for users, the revenue might go up to 1.3 million dollars.

On the other HAND, bad search recommendations can have a negative impact on a retailer's website. At least 25% of customers leave the site right away if the first set of recommendations doesn't match their expectations.

Consequences of Maintaining a Good Recommendation and Search System

Maintaining a great recommendation system requires a team of data scientists and data engineers and multiple pipelines running to serve the recommendation. This can be challenging for companies with hundreds or thousands of employees, all of which are focused on getting into the market as early as possible.

As for the search system, most of the search systems are built on top of either Apache Solar or Elasticsearch. The problem here is the overhead in infrastructure. Not just that, it also comes with a heavy investment.

Google Cloud Platform's Retail AI

Google Cloud Platform's Retail AI comes with powerful recommendations and product search capabilities. The dashboard for the retailer shows all the APIs enabled, and the catalog part shows the catalog that has been ingested. Retail AI allows us to ingest three different catalogs, and we can directly import catalogs via the console itself.

The data tab of Retail AI shows two segments: catalog and events. The catalog data typically includes product ID, title, price, stock availability, categories, and other tags attached to it. The events tab shows all the list of events that got added to your website. There are multiple different types of events that can be ingested into Retail AI, such as add to basket, detailed page view, home page view, and purchases.

Ingesting Data into Retail AI

Ingesting data into Retail AI is straightforward. To import a new set of catalogs, You just have to click on the import button and choose between product catalog or user events. You can directly sync your Merchant Center to your retailer, or you can have it in a Google Cloud or in your BigQuery. BigQuery is the data warehouse service provided by Google Cloud Platform, and GCS is the storage service provided by Google.

Model Training in Retail AI

Once the data is ingested, we can start training the model. Retail AI allows us to Create seven different types of models, such as recommended for you, others you may like, frequently bought together, and similar items. The model training will demand a minimum level of data that has to be present. Depending on the model, the data requirements will be different.

Customizing Retail AI Models

Retail AI models can be customized Based on business objectives. We can choose between click-through rate or conversion rate. The difference between click-through rate and conversion rate is that click-through rate measures itself by whether the user clicked on the product or not. On the other hand, the conversion rate model will take it as a positive sign only if the user ended up buying the product.

Pros and Cons of Retail AI

Pros:

• Powerful recommendations and product search capabilities
• Easy to ingest data and train models
• Customizable models based on business objectives

Cons:

• Requires a team of data scientists and data engineers
• Heavy investment in infrastructure

Conclusion

Google Cloud Platform's Retail AI is revolutionizing the retail industry by providing powerful recommendations and product search capabilities. Ingesting data and training models is easy, and the models can be customized based on business objectives. However, maintaining a great recommendation and search system requires a team of data scientists and data engineers and a heavy investment in infrastructure.

Frequently Asked Questions

Q: What is Retail AI? A: Retail AI is a managed service provided by Google Cloud Platform that helps retailers integrate search and product recommendations into their website.

Q: What are the consequences of maintaining a good recommendation and search system? A: Maintaining a great recommendation system requires a team of data scientists and data engineers and multiple pipelines running to serve the recommendation. As for the search system, most of the search systems are built on top of either Apache Solar or Elasticsearch. The problem here is the overhead in infrastructure.

Q: What are the pros and cons of Retail AI? A: Pros: Powerful recommendations and product search capabilities, easy to ingest data and train models, customizable models based on business objectives. Cons: Requires a team of data scientists and data engineers, heavy investment in infrastructure.