Queue Management – Digital Mortar

Case Study

BACKGROUND
A Fortune 1000 apparel retailer wanted to improve customer experience at checkout.

EXECUTION
Blended edge-based 3D analytics cameras installed in two dozen locations with proprietary queue recognition machine learning in DM Queue.

LEARNING
Daytime part revealed that store teams were under-allocating to registers and not opening 2nd queues at key inflection points.

IMPACT
Reduced the duration stores were in the worst queue time performance zone by 30% and improved satisfaction by double digits.

Precise Queue Reporting

A flexible and customizable queue tracking interface. Easily and accurately surface line length, time, peaks and forecasts. Analyze any amount of historical data for single or groups of locations or get a window into what is occurring in real time.

Easily measure queue times in customizable windows.

Use day-time visualizations to quickly see when queue times peak.

Set fully customizable goals and monitor performance over time.

Bad Lines are a Major Driver of Dissatisfaction and Experience Friction

But how do you know if you’re doing a good job?

Good queue measurement lets you answer fundamental questions about operations and customer experience. How long do customers spend in line (not just at the register)? What days/times are problematic? How much do stores differ in performance? Which teams/employees are better and which are worse?

DM Queue provides simple, easy, powerful answers to all of these questions in a robust dashboarding package that’s totally integrated with our People Counting, Display Measurement and Shopper Journey platforms. So you can go from Door to Floor to Cashwrap in one powerful reporting system

Edge-Based ML Means It works.

Powerful video processing that can be quickly customized to your lighting and interaction needs
It’s practical.

No video to the cloud. No requirement for massive bandwidth. Easy single camera installation. Little or no impact on store infrastructure.

It’s privacy sensitive.

No face recognition. No PII captured or sent. No video capture off device at all. GDPR compliant with NO personal data added to your privacy burden.
It’s inexpensive.

Modern edge-based ML is remarkably inexpensive for the power it brings. Meaning measurement IS affordable.

Predictive Analytics and Alerting Transform Queue Management

The real magic of DM Queue isn’t in reporting though. Because the goal of DM Queue is to help you translate analytics into action. Built-in real-time alerting is your first level of defense against bad customer experience.

But let’s face it – by the time the line has gotten long, you’ve already got a problem. That’s why the DM Queue includes advanced statistical forecasting algorithms that integrate real-time door-count and line state data to forecast line lengths in 5, 10, 15 and 30 minutes.

That means your Store Managers can act BEFORE lines get bad.

Which may seem like the ultimate in Queue Management analytics. But it’s not. Because…

Queue Management and Gamification

Case Study

Precise Queue Reporting

Bad Lines are a Major Driver of Dissatisfaction and Experience Friction

Edge-Based ML Means It works.

It’s practical.

It’s privacy sensitive.

It’s inexpensive.

Predictive Analytics and Alerting Transform Queue Management

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