A large pastry distributor came to us with what seemed like two separate problems. First, they were losing €2.3M annually to product waste. Second, they were watching long-term HoReCa clients slowly drift to competitors.

What we discovered changed our approach: These weren’t two problems. They were a single problem, with two faces.

The Waste-Churn Mirror Effect

Here’s the insight that nobody talks about: Every instance of waste is actually a prediction failure that signals future churn risk.

Think about it: When you have excess inventory (waste), it means you overforecasted demand. When customers churn, it means you underforecasted their actual needs Both stem from the same fundamental issue: misaligned prediction of what customers will actually buy.

Why Does This Happen?

Well, our client seems to have been caught in a “Prediction Death Spiral”. While this may sound dramatic, it’s actually quite common. This phenomenon usually has multiple stages:

Month 1: The Overforecast

• Historical data suggests Customer A will order 100 units of Product X
• They order 60 units
• 40 units become waste

Month 2: The Adjustment

• Forecast adjusted down to 70 units for Customer A
• They order 90 units
• Stockout occurs, customer frustrated

Month 3: The Search

• Customer A starts “testing” alternative suppliers
• Orders drop to 45 units from you
• Meanwhile, you’re still holding safety stock

A Few Months Later: The Goodbye

• Customer A places their final order: 0 units
• You’re left with inventory you can’t move
• Total impact: Waste from overforecasting + revenue loss from churn

The Data Patterns That Predict Both

Through our work across retail and distribution, we’ve identified three warning signals that predict both waste and churn risk:

1. Missed Orders

When your historical-based predictions start missing the mark:

• Immediate signal: Growing waste from overstock – the client is not ordering the products they would generally order.
• Future signal: Customer needs are evolving beyond your prediction model

2. Order Volatility Increase

When customer ordering becomes less predictable:

• Immediate signal: Random demand swings from otherwise predictable patterns, with most of them hitting bellow the mark, not above it.
• Future signal: Customer behavior shifting (new supplier testing)

3. Seasonal Pattern Breaks

When established seasonal patterns stop working:

• Immediate signal: Seasonal inventory that you knew you were going to sell becomes waste
• Future signal: Customer’s business model is changing

How Need Prediction Breaks the Cycle

Traditional forecasting asks: “What did they buy before?” Need prediction asks: “What do they actually need next?”

OptiComm.AI‘s approach combines:

• External market intelligence (weather, economic factors, events)
• Cross-customer behavioral analysis (what similar businesses actually need)
• Reality-based workflow integration (your sales team’s customer insights)
• Need prediction vs. demand forecast(anticipating each individual client’s needs, not just inventory levels)

The Business Impact Nobody Expects

When you solve waste and churn together through need prediction, something powerful happens:

Traditional approach:

• Waste reduction = cost savings
• Churn prevention = revenue protection

Need prediction approach:

• Accurate need fulfillment = new revenue creation

Our clients don’t just reduce waste, they discover products their customers need but aren’t ordering. They don’t just prevent churn, they become indispensable by fulfilling needs customers didn’t even articulate.

The result?

• Dramatic waste reduction through accurate need prediction
• Customer relationships that become nearly churn-proof
• Revenue growth from unmet needs fulfillment

The bottom line: Waste isn’t just a cost problem. It’s an early warning system for customer churn.

In the age of AI, the companies that thrive won’t be those with the best historical data—they’ll be those that best predict future customer needs.