How to Start Using Data Analytics to Predict Orders

1. Centralize Your Data Sources
The first step is ensuring you have the right tools to collect data. Sources include:

• POS systems (sales data)
• Online ordering platforms
• Reservation systems
• Feedback and reviews
• CRM platforms
• Inventory software
  
  Restro Consultants Pvt Ltd (RCPL) helps restaurants integrate these tools into one unified dashboard for better visibility and actionability.
  Chef Shajahan M Abdul notes, “If your data is scattered, your decisions will be too. Centralization is step one to smart forecasting.”

2. Identify Patterns in Customer Behaviour
Look for trends in your historical sales data:

What sells best on weekends?

• Do monsoon months affect coffee orders?
• Are vegetarian dishes trending on weekdays?
• What time slots see the most online orders?
  
  By analyzing these insights, you can predict what customers will likely order on similar future days.
  Chef Abdul shares an example: “One of our RCPL clients saw a 30% rise in paratha orders on Friday evenings. We used that insight to run targeted promotions—and revenue spiked even more.”

3. Optimize Menu Offerings Based on Insights
With predictive analytics, you can identify:

• High-margin items with rising popularity
• Low-performing dishes that can be rotated out
• Seasonal favorites that should be promoted earlier
• This makes your menu not just appealing but also strategic.
  
  Restaurant consultants at RCPL often perform quarterly menu audits using sales and customer data to refine offerings that align with demand and profitability.

4. Personalize Marketing and Offers
If you know a regular guest always orders a butter chicken thali on Thursdays, your CRM can trigger a personalised WhatsApp or SMS reminder with a 10% discount.
Predictive analytics helps automate such personalisation at scale.

Chef Shajahan M Abdul says, “The future of loyalty isn’t points—it’s personalisation. Customers return where they feel understood.”

5. Forecast Inventory & Reduce Waste
By predicting orders, you can also forecast ingredient usage. This helps reduce:

• Overordering perishables
• Food spoilage
• Emergency purchases at higher costs

  Restro Consultants Pvt Ltd (RCPL) sets up innovative inventory systems that align with sales forecasts so that stock levels are accurate and just-in-time.

6. Improve Staffing Efficiency
Staffing too many employees during a slow shift eats into profits. Too few during a rush compromises service.
By predicting high and low traffic hours, you can create smarter rosters, reduce payroll costs, and improve guest experience.

Chef Abdul adds, “It’s not about working harder—working smarter with the right team at the right time.”

How AI Takes Predictive Analytics Further
Many modern platforms now use AI (artificial intelligence) to:

• Predict exact sales volume by item, date, and time
• Recommend automatic reorder quantities
• Suggest combo offers that increase basket size
• Detect anomalies in order patterns (e.g., sudden drop in item popularity)

  RCPL partners with leading tech vendors to bring these AI-powered insights into daily restaurant operations.

  Case Studies from RCPL

• A Hyderabad QSR used AI forecasting to stock only 70% of slow-moving SKUs during weekdays, reducing food waste by ₹50,000 monthly.
• A Bangalore bistro personalised weekend pasta offers to repeat customers via SMS, increasing order frequency by 22% in three months.
• A cloud kitchen chain worked with Chef Abdul to identify the top three dishes likely to trend during the IPL season and promoted them as combo packs, doubling the average order value.