Less overstock, more customer loyalty - with AI as a lever
Goal
Reduce markdown losses through precise demand forecasts, use warehouse capacity more efficiently and at the same time offer each customer the right product at the right time. The goal is a retail sector that no longer relies on gut feeling and experience, but on data-driven decisions - from purchase planning to personalized customer contact.
Solution
With our modular digital platform, we combine sales data, stock levels, weather data, seasonal patterns and customer behavior to create an intelligent overall system. AI models forecast demand at store and SKU level, optimize replenishment and pricing and automatically control personalized campaigns.
Purchasing, store management and marketing work on a shared database - with clear recommendations for action instead of mountains of data. The result: less waste, greater availability of popular items and a customer experience that is actually relevant.
Initial situation
Too much of the wrong thing, too little of the right thing
A medium-sized fashion retailer with 60 stores and a growing online store knows the problem: seasonal goods that are not sold out end up on sale at a 30-40% discount. At the same time, bestsellers are sold out at peak times before replenishment arrives. Newsletters reach all customers with the same content - regardless of whether they last bought running shoes or evening wear.
According to a recent study by Voyado and Retail Economics (2025), 95% of European retailers have already tested AI - but only 5% achieve a clearly measurable ROI. The reason rarely lies in the technology: there is a lack of a reliable database, integration into existing processes and concrete use cases with measurable added value.
This is precisely where prodot comes in: pragmatically, step by step and with a focus on quickly visible results.
Solution concept
Four AI modules that make the difference
1. demand forecasting & stock optimization
AI models analyze historical sales data, seasonality, local events and weather patterns to accurately predict demand at store and item level. Replenishment quantities and distribution logic are automatically optimized - overstocks and shortages are systematically reduced.
2. personalization & next-best-offer
Based on purchase history, browsing behavior and preferences, customers receive individually tailored product recommendations - by email, app notification or directly in the online store. AI-supported personalization demonstrably increases the average order value and the repurchase rate.
3. dynamic pricing
Pricing models react in real time to stock levels, demand, competitor prices and remaining time in season. Markdowns are controlled in a targeted manner instead of being applied across the board - this protects margins and accelerates sales where necessary.
4. store & space optimization
Dashboards provide store managers with a clear overview of performance, stock situation and local demand patterns. Staff deployment, space allocation and action planning are controlled based on data instead of gut decisions.
Pilot phase & results
First measurable results in 90 days
In the proof of concept, two product groups and ten selected stores are initially connected. Sales data, stock information and customer data flow into the platform - initial AI forecasts and recommendations go into test operation after four weeks at the latest.
Success criteria after the POC:
- Forecast accuracy of the demand forecast ≥ 80%
- Measurable reduction in excess stock in pilot stores
- First personalized campaign with demonstrably higher conversion compared to generic mailings
Expected results after 6-12 months
- -20-30% markdown losses due to more accurate demand forecasts
- -15% excess stock with -10% shortages at the same time
- +10-15% revenue through personalized product recommendations (McKinsey, 2025)
- +18% average order value through AI-driven recommendations compared to generic offers
- 2.9× higher marketing ROI compared to retailers without AI personalization
- Significantly reduced inventory costs through optimized replenishment
