VertexAI and MongoDB for Intelligent Retail Pricing

In today’s competitive retail environment, the ability to quickly adjust pricing in response to market trends, consumer demand, and competitors’ moves is not just an advantage — it’s essential for survival. This is where dynamic pricing comes into play, serving as a strategic tool for businesses to pull in their quest for market dominance. Dynamic pricing goes beyond changing numbers; it’s a strategic approach that reflects the dynamic nature of the market, powered by data-driven insights that enable prices to be adjusted in real-time for maximum effectiveness.

This shift towards a more agile, data-driven pricing strategy underscores a broader trend in the business world: the recognition of data as a foundational element in decision-making processes. By leveraging real-time data, businesses can ensure their pricing strategies are not only responsive to market fluctuations but also strategically aligned with their overall business objectives, thus driving retail competitiveness to new heights.

Let’s uncover how integrating both platforms empowers developers when it comes to delivering best-in-class, data-driven applications.

Google Cloud: A platform for real-time analytics and AI

Google Cloud stands out as a powerhouse in real-time analytics and artificial intelligence (AI), offering the infrastructure necessary for dynamic pricing strategies and other data-driven business approaches. It’s designed to facilitate big data analysis, machine learning, and operational agility.

Built-in tools form the backbone of an effective dynamic pricing strategy. These include Vertex AI for advanced machine learning models following best- in- class MLOps practices, and Pub/Sub for real-time messaging to solve real- time data ingestion.

By harnessing the power of Google Cloud, retailers can analyze vast quantities of data in real-time, from current market trends to customer behavior and competitor pricing. This enables businesses to make informed decisions swiftly, adjusting their pricing strategies to reflect the ever-changing market conditions.

MongoDB: Flexible data modeling and rapid application development

MongoDB complements Google Cloud by offering a high performance document- based database with a flexible data model that allows rapid application development. For pricing data in particular, where there may be different variants for different sizes of store or country, the flexibility allows for the ease of storage of complex or hierarchical data. In addition, polymorphic capabilities allow you to use a single interface to represent different types, making your system more flexible. It also supports scalability as new types can be easily integrated. Lastly, it enhances efficiency by allowing the same operation to behave differently based on the object, reducing code redundancy.

This flexible schema also enables seamless integration with AI models. MongoDB Atlas supports workload isolation, ensuring dedicated resources for AI tasks and smooth operation alongside core application workloads. Additionally, change streams and triggers can be utilized to capture real-time updates in the pricing data, allowing the AI model to be called upon for immediate analysis and adaptation and enabling in-app analytics for retailers to gain a competitive edge.

Figure 1: MongoDB replica set: Workload Isolation

In the dynamic pricing reference architecture, Atlas collections function as an ML feature store. By leveraging the capabilities of MongoDB Atlas as a developer data platform, we are able to embed real-time automated decision-making into our e-commerce applications and reduce operational overhead for both business operations and MLOps model fine-tuning. This is achieved through implementing a streamlined approach to data management, incorporating real-time, automated decision making, workload isolation, change streams, triggers for immediate updates, and seamless integration with AI models.

Dynamic prising microservice overview

Building an event-driven AI architecture leveraging MongoDB Atlas in Google Cloud is straightforward. We can summarize our dynamic pricing microservice by first describing the different components of its architecture, what they are used for, and how they interact with each other:

Figure 2: Description of the different technology components of a dynamic pricing microservice and what they are used for.

Handling data sources

The proposed solution uses Google Cloud Pub/Sub to ingest data sources like customer behavior events in JSON format. Using a technology like Pub/Sub allows for scaling to handle a large number of messages and efficiently distribute them to many subscribers. This is partly because it allows for parallel processing of messages and can be distributed across multiple servers or instances. It is often a fundamental pattern in event-driven architectures, where the flow of the program is determined by events or messages, supporting reactive programming and making the system more responsive and efficient.

Data federation

We’ll use Vertex AI Notebooks to clean the data and train a TensorFlow model. This model will learn the non-linear relation between customer events, products names, and prices, enabling it to calculate the optimal predicted price.

Orchestrating

Using Cloud Functions, we orchestrate the customer events coming from the Pub/Sub topic to be converted into tensors, which are then stored in a MongoDB Atlas collection. This collection acts as a feature store serving as a centralized repository designed to store, manage, and serve features for machine learning (ML) models. Features represent individual measurable properties or characteristics used by ML models to make predictions or decisions.

MongoDB’s document model flexibility paired with the document versioning pattern will allow us to design time-sensitive chunks of events and granularly manage the training datasets for our models.

Serving

The Cloud Function will use the event tensor to invoke our trained model that is served in a Vertex AI endpoint. The model will provide a predicted price score that can then be inserted into our product catalog stored in MongoDB so our e-commerce application can read the price change in real time.

Dynamic pricing architecture: Putting it all together

In the following architecture diagram, the blue data flow illustrates how customer event data is ingested into a Pub/Sub topic. This allows us to make a push subscription to a Cloud Function from the topic. This function orchestrates the data transformation from raw event into a tensor and calls an endpoint to then update the predicted price into our MongoDB product catalog collection.

By using this architectural approach, we can isolate raw events threads and build different services around them, reacting in real time for dynamic pricing or asynchronously for model training. With every component loosely coupled, we prevent the system from crashing completely. Moreover, publishers and subscribers can continue to process their logic without the need for the other components to receive or publish messages.

Figure 3: Dynamic pricing architecture integrating different Google Cloud components and MongoDB Atlas as a Feature Store

For businesses, this translates into more precise and responsive pricing strategies. In the model building and optimization phase, by utilizing TensorFlow within Google Cloud Vertex AI notebooks, retailers can harness the power of deep learning capabilities. The neural network model is capable of analyzing intricate patterns and relationships within large datasets. This is how businesses may capture nuanced market dynamics, customer behavior, and pricing elasticity with greater accuracy, leading to more optimized pricing decisions.

But even the best of the models should be consistently optimized. Maintaining model effectiveness requires continuous adaptation. Regularly evaluating accuracy and performing feature engineering ensures your models stay sensitive to market changes.

This underscores the importance of retraining as a core principle in a continuous improvement data science approach. Using MongoDB Atlas as your operational data layer means that your feature store is always accessible, reducing downtime and improving the efficiency of machine learning operations. On the other hand, cross-region deployments can bring features closer to where machine learning models are being trained or served, reducing latency and improving model performance.

Get started

The integration of Google Cloud and MongoDB presents an easy approach to modernizing dynamic pricing strategies. Leveraging real-time analytics, flexible data modeling, and reactive microservices architecture, it empowers businesses to achieve operational efficiencies and gain a competitive advantage in their pricing strategies. For retailers looking to elevate their pricing strategies, considering a strategic partnership with both technologies is essential.

For a deeper dive into integrating the different components of this architecture, make sure to check our GitHub repository.

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