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“How do you prevent theft in the self-service checkout?”

Grocers and General Merchandise retailers are expanding self-service checkouts in mass to provide a more convenient customer experience.

But,

The economic hardships of high inflation, the increasing variety of payment options, and the massive turnover of employees promise that the big problem of theft will only get bigger.

Stores have many ways to improve loss prevention with anti-theft devices, security gates, and surveillance cameras.

The but the core challenge is:

How to promote convenience by increasing payment options and self-service kiosks and, at the same time, reducing theft?

The secret is:

If you start with a behavior-based point of view, you will be able to design more cost-effective and productive technology solutions.

Here’s how:

How Behavior Analytics Works:

Theft and convenience are outcomes of the same customer behaviors during the checkout:

#1 behavior is the start of the checkout process. For self-service checkouts designed for baskets, the process begins when the customer puts the shopping basket on the left tray of the self-service kiosk. If the basket is not placed correctly, it is a trigger.

#2 behavior occurs when the customer scans the products. When customers scan products, they put each item against the scanner. The system accepts the scan if the scanner recognizes the item’s bar code. That means the item has a defined image of the proper scan. If the scanned item is not in the correct position, it is a trigger.

#3 behavior comes from the scan process itself. It takes time to find the code bar and scan it correctly. If the scan time is less than 1 or 2 seconds, likely, the system did not recognize the item. Therefore, the Scan Time serves as a trigger.

#4 behavior is when the customer pays. For each item, you have data about the scan and payment. If there is a mismatch in the details or the transaction, it is a trigger.

#5 behavior reflects the end of the checkout. When customers pick up paid products from the right-side tray of the self-service kiosk, you can compare it to the data from the beginning of the checkout.

For example, you compare the weight of the left and right trays. You could also compare product images. Data mismatch is a trigger.

Once you identify and quantify the customer behaviors, the next step is technology.

Win-Win-Win Alignment

With the Win-Win-Win Alignment framework, you correlate behaviors, actions, and the problem-solution path in technology.

The technology solution's infrastructure is:

The self-service checkout process contains three technology solutions: a scanner kiosk, a payment system, and a camera mounted above the checkout zone.

The self-service kiosk has a tray on the left (start checkout), a display with a scanner kiosk, and a pickup zone with bags on the right side (finish checkout). And each of the trays has built-in weight sensors.

The payment terminal should be connected to the self-service kiosk. If not connected physically, then it must be networked in real-time.

There is a camera above the self-service area. The goal is to capture product images and anonymous data on customers’ locations and times.

Data Fusion Requirements:

The solution required the integration of technologies, including a self-service kiosk, data fusion from the Point of Sale, a Workforce Application, and a Computer Vision solution.

You’ll also need high accuracy in weight sensors in the kiosk trays, imaging of product recognition, and time-based accuracy of seconds to monitor the scanning process.

Most importantly, the AI system self-trains and self-corrects the measurements of each touchpoint and the sensitivity of triggers and alerts based on the retailer’s policies.

The Win-Win-Win Alignment framework is helpful here.

For example,

The #1 touch point is when the customer puts the shopping basket on the right end to start the checkout process (behavior). If the basket is not placed correctly (problem-solution path by weight and imaging), there is an alert to the store’s staff (action).

Go ahead and spell it out for the other behaviors.

PEACE for Profit

The PEACE for Profit is a method to design experiments to increase sales, conversions, and profits in physical environments.

PEACE stands for People (identifying behaviors), Engage (quantifying behaviors), Abandon (identifying context), Convert (quantifying context), and Ecosystem (design experiments). The default outcome is Profit.

You start with outcomes:

For loss prevention in self-service checkouts, the outcome is less shrinkage (less theft).

Next, you focus on inputs:

• Identify behaviors: The customer behaviors include putting the basket on the left tray, scanning each item, putting paid items on the right tray, making payment, and taking the paid items.

• Quantify behaviors: basket in the correct position, product scanned correctly; scan all products in the basket; within Optimal Scan Time, payment approved, and pick up all paid products. These are yes/no steps.

• Identify context: The complete checkout process, from putting the basket on the left tray to picking up the paid items from the right tray.

• Quantify context: Design a step-by-step process to checkout that includes all the touch points with yes/no conversations. “Yes” means the checkout was completed successfully. “No” triggers an alert to the associate to investigate.

• Design tests: The experimentation is to understand the sensitivity of the solution.

For example,

You may want to increase the sensitivity of the Scan Time until the false-positive triggers are less than 3%. As a result, the associate will respond to “incorrect” triggers in only 3 out of 100 scenarios.

Or,

You may want an accuracy rate of 98% for scanned product imaging.

And,

Of course, there are more aspects to this process.

For example,

You need to define the triggers, alerts, and responses for the store associates. Another is deploying sensors to detect theft inside the store.

In other words,

With Behavior Analytics, Win-Win-Win Alignment, and PEACE for Profit techniques, you can reduce theft and increase convenience in the self-service kiosks.

For example,

Your outcome objective could be reducing theft in the self-service checkouts by 70% or more during the first year of deployment.

That is just one example of In-Store Optimization.

Hat’s off to Mark Schuijer for the asking, “How to use technology solutions to prevent theft in the self-service checkout?”

Cheers,

Ronny Max

​

P.S. Struggling to increase your store sales? Not sure how technology improves the customer's experience? Worried about too much costly and ineffective technology in stores?

I’d love to help…in public 😊

Let me know what you’ve got going on and how I can help. I’d love to turn your question and my response into next week’s newsletter.

Hit reply to send me an email.

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Behavior Analytics Academy trains people and teams to increase conversions, sales, and profits in physical environments, such as stores, malls, banks, airports, and smart cities. Build Profitable Ecosystems. One Behavior at a Time.

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