Restaurant Intelligence: The Gateway to Restaurant Analytics

Katalyst Restaurant Intelligence & Analytics Defined

Restaurant Intelligence: The Gateway to Restaurant Analytics

Welcome back to our series on Katalyst Intelligence and Analytics. In this third installment, we delve into Level #2: RI - Descriptive Analytics (Restaurant Intelligence).

Previously, we discussed what Restaurant Reporting and Analytics entail and described the basic reporting of Level #1: Restaurant Intelligence (RI) - Basic Reporting. Now, we will explore Level #2, RI - Descriptive Analytics, which focuses on identifying, summarizing, and highlighting patterns in current and historical data. This helps companies understand what has happened to date, without analyzing why it happened or predicting future outcomes.

Descriptive Analytics in Restaurants: A Look Back to See Forward

Descriptive analytics is akin to taking a snapshot of your restaurant's past performance. It involves collecting and analyzing historical data to understand past events. In the restaurant industry, this data can come from various sources like Point-of-Sale (POS) systems, Customer Relationship Management (CRM) software, and online reviews.

This underscores the importance of Level #1 of Restaurant Intelligence & Analytics: “garbage in, garbage out!”. Clean data is crucial for effective Descriptive Analytics.

Key Areas of Focus

Sales Performance

An efficient POS system is essential for:

• Daily/Weekly/Monthly Sales Reports: Track total revenue, average ticket size, sales by category (food, drinks, etc.), and sales by payment method.
• Best Sellers Report: Identify top-selling menu items to optimize inventory and menu planning.
• Sales by Hour/Day of Week: Schedule staff efficiently and identify peak times.
• Sales by Customer Segment: Analyze sales based on demographics (age, gender, location) to tailor marketing efforts.

Customer Behavior

A robust loyalty program can provide insights into:

• Customer Demographics: Understand your target audience by analyzing age, gender, and location data.
• Customer Spending Habits: Track average spend per visit, frequency of visits, and preferred payment methods.
• Customer Acquisition and Retention: Monitor new customer acquisition and repeat customer rates.
• Customer Feedback Reports: Analyze customer reviews and feedback to identify areas for improvement.

Operational Efficiency

In-depth algorithms help in:

• Labor Cost: Analyze labor expenses compared to sales, identify peak labor hours, and optimize staffing.
• Inventory Management: Track food and beverage inventory levels, identify slow-moving items, and reduce waste.
• Food Cost: Calculate food cost percentage, identify cost overruns, and optimize purchasing.
• Table Turnover: Measure table utilization, identify peak times, and optimize seating arrangements.

Financial Performance

Advanced algorithms are required for:

• Profit and Loss Statement: Assess overall financial performance, including revenue, expenses, and net profit.
• Key Performance Indicators (KPIs): Track essential metrics like average ticket size, food cost percentage, labor cost percentage, net promoter score, and revenue per average seat hour.

Visualization is Key

To make sense of this information, descriptive analytics often relies on visual representations such as:

• Charts and Graphs: Show trends over time, comparisons, and distributions.
• Tables: Present data in a structured format for detailed analysis.
• Maps: Visualize geographic data, like customer locations or delivery areas.

Examples of Insights

Descriptive analytics can provide valuable insights, such as:

• Identifying peak sales periods to optimize staffing.
• Understanding customer preferences to refine the menu.
• Analyzing inventory levels to reduce food waste.
• Measuring employee performance based on sales data.
• Calculating covers per labor hour, labor cost per department labor hour, labor cost per total cover, and revenue per labor hour.
• Analyzing party sizes to better map table configurations.

In essence, descriptive analytics provides a foundation for understanding your restaurant's performance. By uncovering past trends and patterns, you can make informed decisions about the future.

Stay tuned for the next installment in our series, where we will discuss Level #3: RI - Diagnostic Analytics (Restaurant Intelligence), the final step in the restaurant intelligence phase.

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