Your restaurant is collecting useful data all the time. But collecting data isn’t the same as being able to act on it.
Your POS system knows what food people bought last night, and your loyalty app knows who comes in every week. Meanwhile, your marketing automation platform can show you engagement, but it doesn’t know who gave your restaurant a 5-star review or what they ate.
When you can connect the dots on all your guest interactions, you can start to personalize restaurant experiences for them. With margins as thin as they are for restaurateurs, integrated data and analytics can mean the difference between barely scraping by and feeling delighted by the month you had.
This guide breaks down restaurant analytics, how to track them, and how to use them to maximize revenue and get diners in the door.
What is restaurant analytics?
Restaurant analytics is the practice of collecting, measuring, and analyzing data generated by a restaurant to understand guest behavior and business performance. Restaurateurs use restaurant analytics to make decisions about their menu, inventory, and promotion strategy.
Restaurant analytics typically spans a few categories of data:
- Traffic and acquisition: percentage of guests from different channels (walk-in, reservation platforms, delivery apps, etc.), cost per new guest
- On-site/on-app behavior: menu items and categories viewed, time spent browsing menu, items added to cart for delivery, cart abandonment, funnel drop-off points (e.g., reservation started but not completed), saved/favorited items or restaurants
- Sales and revenue: conversion rate, average check size, revenue per cover/per guest, sales by menu item or category, discount and promo code performance
- Retention: lifetime value (LTV), repeat visit rate, cohort retention, overall guest retention
- Guest satisfaction: guest satisfaction score, net promoter score (NPS), review response time, first contact resolution rate for complaint handling
- Marketing performance: return on ad spend (ROAS) and return on investment (ROI) by channel, email and text message engagement, attribution across channels
- Inventory and fulfillment: sell-through rate (menu item popularity vs. waste), 86'd items, return/comp rates, delivery times
The restaurant metrics that indicate growth
Not all restaurant metrics are indicators of growth. If growth is your first priority, you’ll want to build a dashboard and generate reports that highlight these metrics. For example:
Restaurant metric | What it is | Why it matters |
|---|---|---|
Sales per cover | Total revenue divided by number of guests served | Separates a busy night from a profitable one: high number of covers with low spend per guest can look good on paper but overwork staff |
Average check size | Typical spend per check across dine-in, pickup, and delivery | Tracks whether up-sells, menu pricing, and combos are working |
Revenue per available seat hour | Revenue earned per seat per hour, combining spend per guest with table turnover | Captures whether you’re winning on volume, on spend, or both |
Daypart performance | Revenue and traffic split by breakfast, lunch, and dinner | Tells you where to allocate promotional spend to bring diners in |
Menu mix and item margin | Sales volume paired with profit margin for each dish | Flags the gap between what's popular and what's profitable, so you can steer guests toward high-margin items |
Repeat visit rate | Share of guests who return within a given window | Measures whether food and service quality are bringing diners back |
Visit frequency | How often any given guest typically returns | Defines what absence looks like for a regular, so you can act before it becomes permanent churn |
LTV | Total revenue a guest generates over the full relationship | Tells you how much a diner is worth, so you can properly price promotions and win-back offers |
Loyalty enrollment and redemption | Sign-up rate paired with how much of their earned rewards guests actually use | Shows whether your loyalty program is collecting usable guest data and whether the rewards are compelling enough to bring them in |
Email and text message revenue | Revenue directly attributed to owned marketing channels | Measures the revenue from channels you can control |
Channel margin | What dine-in, first-party online ordering, and third-party delivery each keep after fees and commission | Reveals which channels are actually worth growing |
How multi-touch attribution works for restaurants
Restaurant analytics isn’t about measuring guest behavior in a straight line. Guests interact with multiple channels before they actually visit your restaurant, and you may want each of those channels to take credit in different ways, depending on your promotion strategy.
This is where omnichannel, linear multi-touch attribution comes in. This type of attribution gives each channel its fair share of credit, without double counting revenue. When you use this type of attribution model, you can avoid pouring budget into the wrong channel just because it was the last one a guest interacted with before coming in.
Instead, you get a more accurate, balanced view of what’s really influencing visits. You start to see not just what channels influenced guests, but how much credit each one deserves. Rather than giving all the credit to the last interaction, omnichannel, multi-touch linear attribution evenly distributes credit across all meaningful touchpoints.
But keep in mind that multi-touch attribution only works if you can track interactions to the same guest. This is challenging for restaurants where a walk-in guest, a phone reservation, an OpenTable booking, and a DoorDash order show up as 4 anonymous transactions even though they’re actually from the same guest.
Before setting up multi-touch attribution, you’ll need to make sure that, at minimum, your POS, reservation platform, delivery app, and loyalty program are all feeding into one unified guest profile.
For example, Eureka! Restaurant Group was splitting email across two platforms and data across 5. As a result, it was difficult for Eureka! to attribute revenue to their email program, or segment based on guest preferences and activity.
So, the restaurant group switched to a B2C CRM with pre-built integrations for platforms already in their stack: Shopify, Thanx, Eventbrite, and Toast. Now, they can track performance more clearly. Eureka! knows, for instance, that one campaign drove thousands of dollars in Eventbrite ticket sales.
Restaurant predictive analytics: forecasting repeat visits
Predictive analytics uses historical purchase and behavior data to forecast future guest behavior. It includes metrics like next order timing, predicted LTV, and churn risk.
When you can predict this kind of behavior, you can get ahead of it by sending messages that are more likely to resonate closer to when they’re likely to happen.
Here are 3 ways you can use restaurant predictive analytics to boost revenue:
- Predicted LTV estimates what a guest will spend over time, so you can design different offers for high spenders vs. guests who love a discount. That way, you’re not offering discounts to people who would spend more anyway.
- Churn risk flags guests whose orders and/or visits are slowing, so you can win them back before your relationship goes completely stale.
- Next order timing predicts when someone is likely to order again, so you can send nudges or offers close to when they’re likely to act on them.
How Pi Co. integrated their guest data with their POS system
Pi Co. used to run their email marketing through their POS system while text messages were handled by a separate platform. Both channels clearly resonated with the pizza chain’s audience, but their initial set-up wasn’t scalable.
Having email and SMS in separate platforms made it hard to keep their opt-in data up to date. Sending an SMS campaign could take days of manual collaboration and list review.
Pi Co. franchisees recommended Klaviyo B2C CRM, and the platform’s pre-built Square integration was a selling point. It meant the pizza franchise could streamline their tech stack into two core, integrated platforms.
Now that they’re using Square and Klaviyo together, Pi Co. can enhance their CRM strategy with robust omnichannel automations and targeted, real-time campaigns. They use a drag-and-drop flow builder to create cross-channel flows, including a win-back flow and a thank-you flow that rewards repeat purchasers.
“Because Klaviyo and Square are integrated, we look at them as one solution,” says Marc Askenasi, president and CEO of Pi Co. “Using them together makes life super easy for a company of Pi Co.’s size and growing. Everything is centralized for us, and we can track marketing impact all the way through to conversion without a ton of additional legwork.”
Activate your restaurant analytics with Klaviyo
Klaviyo is the autonomous B2C CRM that brings together real-time guest data, analytics, marketing automation, customer service, and built-in agentic AI, so that restaurants can measure and act on their data from the same place.
With Klaviyo, you can:
- Act on your data, faster. Access pre-built recommendations and flow templates to act on your real performance data without starting from scratch.
- Build advanced reports. Use unlimited groupings, custom profile properties, and combined flow and campaign reporting without a business intelligence tool.
- Customize attribution. Measure attribution by joining two or more metrics or filtering for specific events across your POS and online store.
Start measuring and acting on your data from one place with Klaviyo. Get a demo