Traditional Retail

Artificial Intelligence for Retail

Digitize the management of your physical store. Predict stock shortages, automate inventory, and offer customers an effortless omnichannel service.

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Before

  • ✕Blind inventory management
  • ✕Frequent stockouts on shelves
  • ✕Ignorance of the physical customer's profile
  • ✕Generic campaigns sent to the entire base

With Mobizze

  • ✓Predictive restocking based on sales
  • ✓Real-time low stock alerts
  • ✓Loyalty and dynamic profile creation
  • ✓Hyper-targeted marketing via SMS/WhatsApp

Frequent Problems Solved

Difficulty in Stock Management

Not having the right item when the customer asks and discovering too late that a product has no rotation.

Waste in Promotions

Printing flyers and sending SMS to everyone with the same discount wastes money without maximizing the conversion rate.

Omnichannel Customer Support

Customers ask questions on social media or WhatsApp, but the store team is focused on those physically present.

How It Works in Practice: Workflow Example: Predictive Stock Management

1

POS Sales Analysis

The algorithm pulls weekly sales data and historical delivery times from suppliers.

2

Stockout Prediction

The AI warns: 'Extra Virgin Olive Oil will run out in 3 days at the current sales rate.'

3

Purchase Order Generation

The system compiles the optimal order list from various suppliers.

4

Quick Validation

The store manager clicks approve and the emails/API requests go directly to wholesalers.

Software & Integrations

POS SystemsERP PlatformsHootsuite/SocialWhatsApp Business APIIn-store Cameras for Heatmaps

Required Data

  • POS (Point of Sale) History
  • Supplier database and delivery times
  • Loyalty program contacts

Solution Limitations

The AI provides highly accurate purchasing recommendations, but the final decision to add new product lines (strategic purchasing) must be human.

Human Validation

Automatic price changes in the physical store always require manager validation before tags are printed or sent to e-displays.

Implementation Estimate

4 to 6 weeks to collect data from the ERP and set up predictive models.

Case Study

14% Increase in Average Ticket

"

A shoe store network started analyzing purchase receipts. The AI sent targeted messages: whoever bought suede shoes received an interactive video offering the protective spray 3 days later. Store visits increased and the global average ticket rose 14% automatically.

Sector FAQs

Does AI work for multi-store networks? +
Yes, that's where it shines. It can suggest stock transfers between Store A and Store B instead of making a new purchase from the central supplier.
Is it capable of recognizing the customer entering the store? +
Purely conversational AI, no, but if you use analytical Wi-Fi or the store app, it can detect when the loyal customer enters and fire a discount to their phone on the spot.
Does the system handle complaints and item warranties? +
It can collect customer data and receipt photos via WhatsApp, initiate the RMA process, and just ask the physical employee to confirm and hand over the new one.

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