Sales Data Synthesizer Development

A sales data synthesizer helped train an effective AI model to improve the online marketplace's recommendation system.

System structure

Plugin

Services

AI and ML Development
Research
Project management
Consulting

Industry

eCommerce industry

Timeline

3 months

Team composition:

Lena, Project Manager

Pavel, Software Engineer

Daria, QA Team Lead

Cyril, Software Engineer

Our client operates an online marketplace and aims to harness the power of an AI model for improved product recommendations. Have you ever observed how certain online marketplaces seamlessly suggest precisely what you're looking for when you add items to your shopping cart? This is precisely what our client aspired to achieve. However, despite Celadon's expertise in training AI models and marketing automation software, a significant challenge emerged.

Challenge to Develop a Sales Data Synthesizer

Massive volumes of data are required to train an AI model. In this case, we needed historical data about sales to build an algorithm. However, the client wasn’t able to provide sufficient data. However, we suggested to build a sales data synthesizer that would generate the required synthetic data.

Solution With Sales Data Synthesizer Development

We started with sales data synthesizer development. Our data scientists and AI engineers cooperated to develop a sales data synthesizer capable of generating substantial amounts of sales data. Here's the process: we utilized existing historical data and tasked the data synthesizer with producing comparable data in significant quantities.

Consequently, the sales data synthesizer creation played a pivotal role in training a model for an enhanced recommendation system.

The model workflow bears a resemblance to the algorithms incorporated in streaming services and operates in the following manner:

A customer on the marketplace adds a product to their shopping cart.
The algorithms then process this action and the associated data.
Subsequently, they scan other datasets in search of correlations, identifying what other customers have added to their carts or purchased in conjunction with the item in question.
Based on the correlations discovered, the system provides recommendations for additional items that customers are likely to find interesting.

Lena, Project Manager

It is noteworthy that the approach to create a sales data synthesizer and constructing an AI model based on this data has proven instrumental in identifying correlations based on less apparent factors. As a consequence, this led to significantly improved recommendations.

The plugin gave very good results showing an increase in customer satisfaction, better shopping experience, increased sales, and a better understanding of the customers. Such AI models can be trained in both mobile app development services and web app development services.

If you’re searching for a similar solution, Celadon is here to help. Contact us to make your business AI-driven and hence increase KPIs.