Unlock Higher Restaurant Revenue with Data Analytics

Technology has brought in tremendous transformations within the restaurant industry, including its lexicon. Not so long ago terms like data analysis, real-time data, data mining, and metrics were alien to the sector. But today, not only are these used prolifically but more importantly, they’re even considered indispensable in the day-to-day operations of any restaurant, as well as augmenting its overall success.

80% of restaurants are now utilizing technological solutions to help them run various aspects of their business like payments, ordering, reservation, customer analysis, etc. and the general feedback is that these restaurants are continuing to observe an increase in business efficiency. Among these technological solutions, data analysis offered by systems like POS integration is one of the most in-demand.

What is restaurant data analysis?

Restaurant data analysis can, in the simplest of terms, be defined as the process of collecting raw data related to your daily operations and breaking them down into purposeful and actionable insights, which will, in turn, help you make well-informed business decisions.

Industry experts have all hinted at a future that will be data-driven. Data based decisions will dictate everything from a restaurant's marketing activities to operational choices.

Modern restaurant analytics draws from multiple, increasingly integrated data sources:

• POS (Point-of-Sale) Data: Transaction history, item-level sales, payment methods, time-of-day patterns, and sales trends
• Labor & Payroll Data: Scheduling, hours worked, labor cost percentages, employee performance metrics, and productivity indicators
• Inventory Data: Stock levels, supplier costs, waste/spoilage, turnover rates, and ingredient usage patterns
• Customer Data: Visit frequency, average check size, lifetime value, repeat purchase behavior, and preference history
• Delivery & Online Ordering: Order volume by channel, delivery times, third-party platform performance, and digital conversion rates
• Customer Reviews & Feedback: Sentiment analysis, complaint patterns, service quality indicators, and competitive benchmarking

Marketing & Promotion Data: Campaign performance, redemption rates, customer acquisition cost (CAC), and channel effectiveness

Not surprisingly because by using restaurant data analytics to your advantage, you can generate much higher revenue for your restaurants. Here’s how:

Menu optimization

With data analysis, you can not only evaluate which dishes are the best and worst selling, but also the 'why' behind this.

It's not always true that the dish that sells best is also your most profitable. There can be instances when a lot of customers order a certain item on your menu, but none of these customers return to you for repeat business.

Effective menu optimization begins with implementing a data-driven menu engineering framework that categorizes menu items based on their profitability and popularity:

• Stars (High profit margin, high popularity): These items should be prominently featured on your menu, potentially with slight price increases as they have proven demand.
• Plow Horses (Low profit margin, high popularity): Consider reformulating these items to improve margins while maintaining their appeal, or use them strategically as loss leaders.
• Puzzles/Cash Cows (High profit margin, low popularity): These items deserve more visibility through strategic placement, server recommendations, or targeted marketing.
• Dogs (Low profit margin, low popularity): These underperforming items should be candidates for removal or complete repositioning.

Customer service

There are more than one way in which data analysis can help build a loyal customer base for a restaurant. From sending out wishes and gift coupons on customers' birthdays and anniversaries to being mindful of their favorite dishes, food allergies, etc. and rewarding your most loyal customers you can treat your diners with some truly personalized services.

Today's technology allows you to gather more than just basic contact details. With precise information like demographics, purchase history and preference, data analysis allows you to build a strong relationship with your customers and today's customers don't shy away from providing these details, in fact, they encourage.

90% of consumers say they're willing to share their behavioral data if they can receive additional benefits from brands.

Advanced analytics enables sophisticated customer segmentation that drives personalized experiences and targeted marketing:

• VIP Segment (Top 20% spenders): Identify these high-value customers through historical spend and visit frequency analysis. Reward them with exclusive perks like priority reservations, chef's table experiences, early access to new menu items, or personalized birthday offers. Data shows that increasing retention of these customers by just 5% can increase profits by 25-95%.
• At-Risk Customers (Previously frequent, now lapsed): Analytics can flag when regular customers' visit intervals increase beyond their normal pattern. This triggers automated win-back campaigns with personalized incentives based on their previous preferences.
• High-Frequency, Low-Spend Loyalists: These customers visit often but spend below average. Analytics can identify upsell opportunities by recommending higher-margin items that align with their established preferences.‍
• New Customers: Personalize the onboarding experience with follow-up communications and track conversion to repeat visitors. Analytics can identify which first-time experiences correlate with higher return rates.

Staff management

For a restaurant, labor cost forms one of the biggest expenses, taking anywhere between 25 to 40% of a restaurant's gross revenue and therefore it would do well to optimize this aspect of the business.

With data analysis, you can figure out which time of the day and week requires more staff and who are your best and worst-performing employees. This information can help you optimize productivity by creating a strategic floor layout, where you place servers based on their strengths.

Effective labor optimization requires tracking these key performance metrics:

• Labor Cost Percentage: Calculate total labor costs divided by total revenue. While targets vary by restaurant type, full-service restaurants typically aim for 30-35%, while quick-service targets 25-30%. Track this metric by day, shift, and even hour to identify optimization opportunities.
• Sales Per Labor Hour (SPLH): Measure revenue generated per hour of labor invested. This metric helps identify your most productive shifts and staff members, allowing you to schedule your strongest performers during peak revenue periods.
• Overtime Percentage: Track both planned and unplanned overtime to identify scheduling inefficiencies. Every percentage point of unplanned overtime typically represents 1-2% in avoidable labor cost.‍
• Turnover Rate: High employee turnover increases training costs and reduces service quality. Analytics can reveal which shifts, managers, or scheduling practices correlate with higher retention.

Marketing campaigns

Marketing activities make up a large chunk of a restaurant's total expenditure. According to a study, companies that grew 31 to 100% or more, year on year, spent an average of 50.2% of their revenue on marketing, while those that spent an average of 16.5% of their revenue on marketing grew one to 15% year over year.

However, many restaurant owners aren't professional marketers, and oftentimes end up making erratic decisions that not only lead to financial losses but also harm the brand. With data analysis, you can be assured that you have all the necessary facts and figures required to reach out to the right audience and customize your communication to appeal to individual preferences.

Implementing a data-driven marketing approach involves this proven workflow:

1. Audience Segmentation: Use customer data to divide your base by behavior patterns (visit frequency, average spend, cuisine preferences) and demographics. This allows for targeted messaging rather than generic promotions.
2. Campaign Testing: Run A/B tests with different offers, messaging, and channels on small customer cohorts before full deployment. Measure both redemption rates and the incremental spend generated.
3. ROI Tracking: Calculate campaign ROI using the formula: (Incremental revenue attributed to campaign – campaign cost) ÷ campaign cost. This helps quantify the actual return on marketing investments.
4. Attribution Analysis: Connect campaign exposure (email, SMS, social, in-app) to subsequent transactions via customer ID or tracking codes to understand which channels drive the highest conversion.‍
5. Continuous Optimization: Scale successful campaign variants while suppressing underperformers, creating a cycle of continuous improvement.

Future forecast

Data collected over time can also be useful in predicting trends to help you strategize better for the future. Backed by credible data, you'll be in a position to take the best course of action in any given situation. In an industry that's as unpredictable as any, it can be quite challenging for restaurant owners and managers to forecast future sales but with historical data and predictive data analytics, you can organize profitability for the time ahead.

Effective restaurant forecasting encompasses multiple operational areas:

• Sales Forecasting: Predict revenue by day, daypart, and even hour using historical POS data, seasonality patterns, promotional calendars, and external factors like weather and local events. This enables more accurate:
  
  • Cash flow management and working capital planning
  • Realistic target setting for management and staff
  • Strategic promotion planning during predicted slow periods
• Inventory Forecasting: Predict demand for individual ingredients and menu items by analyzing historical sales data, seasonal trends, and promotional impacts. This precision helps:
  
  • Reduce food waste and spoilage
  • Prevent stockouts of popular items
  • Optimize ordering quantities and delivery schedules
• Labor Forecasting: Predict staffing needs based on projected customer traffic and service requirements. This allows for:
  
  • Creating optimal staff schedules weeks in advance
  • Reducing both understaffing (poor service) and overstaffing (wasted labor)
  • Balancing employee preferences with business needs

Ready to Transform Your Restaurant with Data Analytics?

As a final note, restaurant owners can collect, organize, interpret and apply data to provide the best dining experiences. However, while data gathering and analysis can provide a huge boost to your restaurant business, it is also key to remember that it's not going to provide a full-proof triumph on its own. Interpreting those data effectively and implementing actionable strategies based on that interpretation is what's going to bring in the ultimate success.

The restaurant analytics journey begins with understanding your current data landscape and identifying the highest-impact opportunities. Start by auditing your existing systems, establishing key performance metrics for each operational area, and implementing integrated analytics tools that provide real-time insights across your entire operation. Even small, focused improvements in menu engineering, customer segmentation, or labor optimization can deliver significant returns.

Ready to see how integrated analytics can transform your restaurant? Book a free demo with Restolabs today to discover how our restaurant-specific analytics platform can help you unlock hidden revenue opportunities and streamline operations.