OLO has become the digital ordering backbone for a significant portion of the restaurant industry. If you run a multi-unit chain, there's a good chance your online ordering, mobile app, and delivery platform connections all flow through OLO in some form. It's reliable, it scales, and it integrates with most major POS systems, including Brink.
Having OLO isn't the same as getting the most out of it. For many restaurant groups, OLO works well as a transaction engine while its data sits largely untapped. The platform captures valuable information about guest behavior and order patterns, then leaves it unexamined because nobody has built the reporting layer to put it to work.
That's the OLO integration opportunity most operators are missing.
What OLO Data Actually Contains
Every OLO transaction generates a rich data record: what was ordered, when, from which platform, at which location, at what price, with what modifications, and with what outcome. Over time and at scale, that data paints a detailed picture of digital ordering behavior. It shows which menu items convert well online versus in-store, which platforms drive the most repeat customers, what time-of-day patterns look like for digital orders versus dine-in, and how digital order volume correlates with in-store operational pressure.
This information is genuinely useful for menu engineering, staffing decisions, marketing allocation, and delivery platform strategy. Accessing it in a meaningful way, though, requires connecting OLO to the rest of your data infrastructure rather than treating it as a standalone transaction processor.
The Integration Stack Around OLO
In a well-integrated restaurant operation, OLO sits in the middle of several critical data relationships. On one side, it connects to the POS. For Brink users, the OLO-to-Brink integration ensures that digital orders flow into the same sales record as in-store transactions, giving you a unified view of revenue regardless of channel.
On the reporting side, OLO data should flow into your analytics infrastructure alongside POS data, labor data, and delivery platform data. This is where custom integration work matters. OLO's API exposes detailed order-level data that the standard platform dashboards don't surface in an operationally useful way. Building a data pipeline that pulls this data, combines it with your other sources, and surfaces it through custom dashboards and KPI reports is what turns OLO from a transaction tool into a business intelligence asset.
On the accounting side, OLO order data needs to reconcile cleanly with your GL. Custom accounting exports that map OLO revenue by location, by day, by channel, and by tax category are essential for operators running at any meaningful scale.
What Good OLO Reporting Looks Like
When OLO is properly integrated into a restaurant's data infrastructure, a multi-unit operator can see things like: digital order revenue as a percentage of total revenue by location, average digital ticket versus in-store ticket over time, top-performing digital menu items across the brand, delivery platform performance broken down by location (DoorDash vs. UberEats vs. GrubHub vs. own-channel), and guest sentiment data from delivery platforms overlaid with order volume trends.
These aren't exotic analytics. They're the basic operational questions that restaurant leadership teams should be able to answer quickly. The reason most can't is that the data exists but hasn't been connected.
Getting OLO Right from the Start
If you're already using OLO and your reporting feels incomplete, start by mapping out what data OLO generates and how it currently connects (or doesn't) to your POS, your accounting system, and your analytics layer. Most operators discover that the gaps live in the plumbing, not the data itself. OLO is generating exactly what you need; the work is building the infrastructure to use it.
Talk to the Suntek team about OLO integration at SuntekSolutions.io/calendar.
