A Custom CRM That Tells Lima Restaurants When Regulars Stop Coming

The regular who quietly stopped coming

Every restaurant in Allen County has them. The couple who takes the same booth most Friday nights. The contractor who phones in four large pizzas for the crew every other Thursday. The family that books a table for ten around the same birthday each year.

Here's the question: if one of them stopped showing up in August, would anybody on your staff know? In most places, no. The clues exist, but they're spread across the reservation book, the POS, a stack of to-go tickets and whatever your host happens to remember. Nobody connects them until someone says, months later, "Haven't seen those two in a while."

That matters more this year than most. The National Restaurant Association's 2026 State of the Restaurant Industry report found that roughly 4 in 10 consumers ate out less last year, and about 60% of operators saw weaker traffic. Sixty percent of operators also say they're focusing this year's technology spending on the customer experience. When fewer people are walking in, the guests who already like you are the cheapest tables to fill.

There's a second reason to keep your own records. Nation's Restaurant News, covering the same report, noted that 56% of consumers would rather order directly from the restaurant, and that operators' complaints about third-party delivery include limited access to data. A delivery app order may never give you a real name. A call-in or reservation that lands in your own system does.

What the CRM tracks, and where AI fits

A CRM is just a system that remembers people and what's going on with them. Most off-the-shelf ones are built for sales teams chasing deals, which is why they feel clumsy in a restaurant. A custom one is shaped around your actual shifts. For this one job, catching regulars before they drift, it stays small:

• A guest record that every reservation and phone-in to-go order attaches to: name, number, party size, usual order, allergies, seating preference.
• A visit history built from those entries, so the system learns each regular's normal rhythm, whether that's weekly, monthly or once a year.
• A drift flag. When a regular goes well past their usual gap, they land on a short list for the owner or manager.
• An AI step that turns a host's quick note ("no cilantro, window seat, anniversary in May") into clean fields, then drafts a short, personal check-in the owner reads and approves before anything goes out.

The AI is what makes this workable. Nobody at a host stand on a Friday night will fill out a twelve-field form. They'll type half a sentence, and the system does the filing. It helps on the phone, too. When a regular calls in a to-go order, whoever picks up can see the usual order and the allergy note instead of starting from scratch, which gets them off the line and back to the dining room sooner.

The NRA's explanation for why operators rate technology more highly than diners do is that many see it as a way to "take pressure off of staff and give employees more time to focus on guests." That's the test. If a tool adds clicks at the host stand, it was built wrong.

What Paglow delivers

We start by watching how your place actually runs: who takes reservations, where to-go orders get written down, what your POS already captures. Then you get three things.

The CRM spec spells out what the system tracks, who touches it, and which events kick off which actions. The data model defines the pieces (guests, visits, reservations, orders, notes) and how they connect, so the information stops living in five places. The build plan lays out the order of work, what plugs into tools you already pay for, and what it will cost to run each month. We design it to react when something happens, like a new reservation or a closed ticket, instead of checking constantly in the background, so a slow Tuesday isn't running up a bill.

If you can name three regulars you haven't seen since summer, that's the list this is built to catch.