The POS is no longer just a billing machine
The traditional Point of Sale is going through a transformation.
A POS used to record transactions and apply pre-configured discounts. Today, for fashion, luxury and lifestyle retail, that is no longer enough. AI is becoming essential.
It starts with capturing customer data, then applying an Artificial Neural Network (ANN) to analyse customer behaviour. The result is a POS that understands the customer before any sales activity begins.
The AI-assisted customer journey
Five steps, from the first conversation to the billing counter.
- Identify the customerThe salesperson takes the customer’s phone number and enters it on a tablet or mobile device.
- Retrieve the profileThe device is connected to Retail Clouds and pulls the customer’s details, including preferences and purchase patterns.
- Get recommendationsThe salesperson requests a recommendation, and the system suggests products using predictive analytics.
- Show the right productWith stock visibility across all locations, the salesperson offers the right product at the right price and discount.
- Complete the saleThe customer moves to the billing counter and completes the transaction.
A complete outlet-management solution
A modern POS should cover the whole store day, from opening the doors to closing them. It must also be part of an omnichannel platform, able to fulfil orders that come in from every other sales channel.
How we built it: four stages
From raw sales history to predictions available in every sales channel.
Data
Extract, clean and load customer history from all sales channels, which sits in a MySQL database.
Embedding
Model customer behaviour events and train a Transformer to produce customer embeddings. Training used data we generated for fashion customers over a period of time.
Predictions
Build all predictions from the embedded vectors. One Transformer handles them all, instead of separate neural networks such as MLPs.
REST API
Expose predictions through a RESTful API so they are available wherever they are needed.
What the model predicts
Seven predictions, all produced from one customer vector.
Purchase prediction
Customer segmentation
Churn prediction
Product recommendation
Next-best-offer prediction
Customer lifetime value
Personalized campaigns
How do you know the AI is any good?
Modern retail software must be able to capture customer behaviour and turn it into predictions. To judge whether a vendor’s model is well built and well trained, ask for an evaluation license and validate its performance yourself.
Under the hood
PyTorch: how the model learns
PyTorch turns raw customer events into tensors, passes them through the embedding model, and trains it by repeating one loop: predict, measure the error, adjust the weights.
The Transformer: one model, every prediction
A Transformer reads a customer’s events as a sequence. Self-attention lets every event look at the others, so a late purchase can be understood in light of what the customer browsed earlier. The result is one customer vector, and a single model reads it to produce every prediction.
Bringing AI customer insights into Retail POS
Customer intelligence is most useful at the point of interaction. Store associates can use purchase history and AI-generated predictions to support product recommendations, personalized offers and better-informed conversations with customers.
With Retail POS Software for Modern Stores and Retail Chains, retailers can connect store billing and outlet operations with customer management and the wider retail platform. Building customer insights into the POS workflow brings data-driven decisions closer to the actual sale.