Using Customer Experience Data in Restaurant Management

In the restaurant world, it's almost an unwritten rule: prioritise large parties. Larger parties can be a “vibe” with folks having more drinks and courses, right? However, one insightful group I recently spoke with dared to challenge this convention. When they dug into their data, they discovered a surprising truth: tables of two actually generated higher average spend per person than larger groups (+4 people). This wasn't just a fluke; the data consistently showed that smaller groups had a higher average spend per head. Next, they went a step further and found that smaller tables were turned faster, with larger parties dwelling 27 minutes longer on average (with less spend per head!). With this knowledge, they altered their table allocation strategy on their booking platform with an alert set up to release all table inventory 48 hours ahead of time to ensure they’re operating at a high utilisation rate. This reminded me that it’s critical to challenge our assumptions as the data may tell a different story! Are you making assumptions about your customers, or are you letting the data guide your strategy?

What we once celebrated as 'data-driven' was really just data-curious. I recall an article we wrote in 2012 about how smaller restaurant chains could compete with industry giants through what we then deemed innovative digital loyalty programs. We called it a "Moneyball" approach—using scrappy tactics to punch above your weight class. Back then, we considered it a breakthrough to connect social media engagement with loyalty rewards. When a guest tweeted about their order, a restaurant could respond not just with thanks, but with action—loading a reward directly onto their loyalty card. The focus was on program enrollment and transaction-based rewards. Success meant getting guests signed up and coming back to earn their next free item. In reality, transactional loyalty programs were just the warm-up act for today's comprehensive guest data platforms. Here’s how: From program-centric to guest-centric ⬅️ Then: Focus on loyalty program members and their point balances ➡️ Now: Focus on all guests and their individual preferences, regardless of program status From reactive rewards to proactive personalization ⬅️ Then: Rewarding guests after they engage ➡️ Now: Anticipating and offering guests’ favorite orders, based on historical data and behavioral patterns From transaction tracking to experience orchestration ⬅️ Then: tracking purchases to award points and trigger rewards ➡️ Now: Using comprehensive guest data to personalize everything from menu recommendations to ordering experiences across all channels Here’s how our earlier example would play out today: 2012: Guest tweets about their order → restaurant responds with a free reward → guest returns to redeem 2025: Guest orders → System notes preference and ordering patterns → Next time they open the app, menu item is prominently featured alongside complementary items they're likely to enjoy → If they haven't ordered in their typical timeframe, they might receive a personalized message about a limited-time offer → The experience feels curated, not automated The best restaurant brands today aren't just running loyalty programs; they're building comprehensive guest data platforms that make every interaction feel like coming home to your favorite neighborhood spot. The "Moneyball" approach has evolved, but the underlying truth remains: the scrappy operators who use data will always have a competitive edge.

The Hospitality Data That Nobody Uses Most hospitality brands think they understand their guests because they track the usual performance numbers. That's surface level. The real insight sits in the behavioral data that almost every property collects without even realizing it. I'm talking about the tiny signals that reveal what guests actually want, what slows them down, what annoys them, and what inspires them to spend more. This data is everywhere, and almost nobody uses it in a meaningful way. Here’s where the opportunity lives. Your guests tell you how to increase revenue through their patterns. How long they linger in the lobby. When they return to their room. Where they avoid walking. When they browse your app and immediately exit. How often they pass a restaurant without looking inside. These behaviors are not random. They're emotional decisions, and they're loaded with financial implications. When you understand the emotion behind the behavior, your ROI becomes predictable instead of reactive. If you want to turn this into real growth, start analyzing the data that shows friction. Look at the moments when guests cluster in certain areas and ask why. Look at the times when guests repeatedly ask your team the same questions. That means your communication failed somewhere. Look at what time people naturally crave food or drinks and match your promotions to their real patterns instead of pushing offers on your preferred schedule. This is behavioral revenue management, and it works every single time because it is built on truth, not on assumptions. Here’s a tactic almost no one uses. Review app engagement curves daily. If guests only stay on your app for a few seconds, that tells you your design is not helping them complete the actions that matter. Fix those pathways and you will see more bookings for experiences, more upgrades, more outlet spend, and more repeat visits. Another tactic is to study foot traffic through your public spaces. If a hundred people walk past the bar and only three sit down, the issue isn't demand. It's energy, layout, lighting, or service. Fixing that can double revenue in a week without touching your marketing budget. A weekly behavioral insights meeting should be mandatory. Bring one insight from tech, one from operations, one from F&B, and one from housekeeping. Compare patterns, not opinions. You'll start seeing emotional blind spots that cost you money. The fixes are simple, but the impact is immediate. This is how you create ROI from intelligence instead of luck. The brands that win over the next decade will be the ones that understand behavioral data better than their competitors. Not the ones with the loudest campaigns. Not the ones with the prettiest videos. The winners will be the ones who know what their guests feel, when they feel it, and why they act the way they act. That's where real revenue comes from. --- If you like the way I look at the world of hospitality, let’s chat: scott@mrscotteddy.com

🔥 Your casual dining restaurant has a frequency problem. You're not alone. 80% of casual dining brands are seeing fewer visits per guest compared to 2019. YoY comps are down across the board at casual concepts. But here's the thing... Your data already holds the key to fixing this. You just haven't unlocked it yet. A regional chain boosted their frequency by 22% in just 90 days using their existing customer data. Here's a peek at what was discovered: 1. The "Golden Hour" Pattern We found that guests who visit during specific 2-hour windows are 3x more likely to become weekly regulars. But hardly anyone was targeting these periods. 2. The Menu Multiplier Customers who try items from 3+ menu categories in their first two visits have 40% higher lifetime value. Yet most marketing focuses solely on entrees. Think offering a cross menu offer! 3. The Social Proof Secret Guests who dine with 3+ people are 2.5x more likely to return within 14 days. We turned this insight into a game-changing promotion. What about a group dining incentive? The best part? All of this data was sitting in their POS system, loyalty program, and reservation platform. They just needed the right lens to see it. Want to know how we turned these insights into actionable campaigns that drove real results? Drop a 🍽️ in the comments if you want to learn more! #RestaurantMarketing #CustomerRetention #DataAnalytics #Hospitality #CustomerExperience P.S. This isn't theory. These are real results from real restaurants serving real guests. The future of dining is data-driven, and the pioneers are already feasting on the results.

You can't manage a business in real-time using six-week-old data. 📈 Many operators manage their business by looking backwards: Last week’s sales, last month’s P&L, and last quarter’s guest feedback. The typical P&L reflects what happened many weeks ago, so by the time you're reading the numbers, they’re irrelevant. Even worse, they’re largely the outcome of decisions made even longer ago. 
 The alternative is measuring inputs in real-time - the things that predict what's coming before it shows up in your results. Training and capability signals. Attendance, completion and assessment scores tell you whether standards are being reinforced or quietly eroding. When training slips, service quality follows shortly after, long before it shows up in revenue or reviews. Demand and sales pipeline. Forward reservations, group enquiries, sales conversion rates and cancellation trends show you what revenue is likely to arrive, not what already has. If the pipeline softens, the outcome is already predictable weeks in advance. Operational and guest-experience indicators. Table touches, complaint frequency, menu availability and service readiness expose execution issues in real time. These signals tell you exactly where performance is drifting, and crucially, where to intervene before the damage compounds. These data points aren't as easy to measure, which is why they're often underutilised but getting these right can become your competitive advantage.