Restaurant Analytics

The Most Important Restaurant Analytics in Quick Service Industry

In the quick service restaurant (QSR) industry, time literally is money. This becomes apparent when many fast food establishments like Tim Hortons and Dunkin' Donuts are able to directly link performance metrics, like service speed, to increases or decreases in profits, and other restaurant analytics.

In businesses where margins are small but volumes are large, small improvements, like being able to complete an order 10 seconds faster, make a huge difference over the course of a year.

Collecting and analyzing accurate performance data from each location has become a necessity in order to measure results and detect where improvements can be made.

The Missing Ingredient

When people in the industry hear about restaurant analytics, most of them think of exception reporting at point-of-sale (POS) terminals. But there is another source of data that is largely untapped: surveillance cameras. Video is the most context-rich source of data and a core part of contextual analytics.

In other words, if you just look at the transactions at the end of the day, you might be missing a big part of the story. By including video in your analysis, you get a much better understanding of what happened, as if you were there watching.

When all is said and done, restaurant franchise owners will only see a significant ROI from restaurant analytics if they leverage them to make better decisions in day-to-day and long term operations.

So here are three features of the Solink Restaurant Loss Prevention system that help restaurants make a big impact on their bottom line.

1. POS Exception Reporting

Each QSR location might have hundreds, or thousands, of daily transactions. Reviewing all of them for fraud would take hours. But by leveraging POS exception reporting, businesses are able to flag transactions that are irregular or potentially fraudulent.

POS exception reporting becomes more valuable when transactions are matched to video and analyzed for additional context.

One advantage is that you don’t need to look for the video evidence because it’s already matched with the corresponding transaction. Using video also makes it possible to detect additional POS exceptions, such as a customer not being present during a transaction (a scenario that often indicates internal fraud).

Other suspicious events that can be flagged are no sale transactions, voids and unauthorized employee discount redemptions.

2. Queue line and drive-thru monitoring

In a recent blog post, we talked about the importance of measuring queue lines and tracking how long customers wait at each step in the service process. To shed precious seconds (or minutes) off of service speeds, businesses need a reliable way to track customer wait times across all locations. This helps to identify bottlenecks and determine how each location is performing in comparison to the company/industry average.

3. Event-driven analytics and notifications

Event-driven analytics is a broad term that we use for any type of application that uses predetermined rules to detect important events which, if detected, trigger some form of action.

For instance, a rule can be assigned to detect in-store motion between the hours of 1am-4am (a likely time for a business to be closed). If this event is detected, our analytics platform will send a notification to the appropriate personnel so they can investigate the situation.

Another way to use event-driven analytics is to create rules around the queue lengths and customer time-to-service. If cars at a drive-thru, or customer waiting time, exceeds a certain number, management can be notified to open another register. This event data can be recorded and leveraged for future reference.

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