For Self-Checkout, Location, Location, Location — And Data — Boost Conversions  

For retailers new to self-checkout (SCO) or those that have used it for years, there’s a gold mine in the most basic SCO data that includes whether consumers are paying by card or cash, the basket size and how lines build up at the register or at the kiosk.

Data tells a tale, in other words. But no two stores are exactly alike, and automating the retail experience comes as a balancing act between examining the data and observing what’s happening in the field (OK, the checkout line) in real time.

In an interview that ran in this space earlier this year, Carl von Sydow, director of self-service at Diebold Nixdorf, told Karen Webster that data, used wisely, can lay out the road map to an optimal checkout experience.

In a follow-up conversation with Webster, von Sydow recounted that “the store data we are talking about, first of all, is not complicated or complex — it’s pretty simple information.”

That data, he continued, includes time stamps, the number of items in a basket and whether certain items are age-restricted (requiring staff intervention for age verification and causing a negative impact on the throughput).

“It’s pretty simple,” he told Webster, “but it’s all about how you interpret the information.” And all too often, retailers may be not be translating data into practice as they eye proper implementation of a pilot for, and eventual deployment of, self-checkout solutions.

The Customer Type

“Self-service appeals to customers who come in with the intent to buy just a few items, are in a hurry and want to leave quickly,” he told Webster.

“If you look at the store data and can identify a group of customers with small baskets — fewer than five items, and perhaps paying with a card — you have good potential to increase conversion rates for these types of customers,” he said.

Theoretically, then, the retailers would look to add more self-service options to that location. But simply bringing the self-service option to the store is not enough.

As von Sydow told Webster, data discovery never stops, especially if aided by on-site observation.

He noted that in one pilot of self-checkout, a retailer logged 25 percent adoption rates in the first 30 days.

That’s an impressive uptake, on its face. Yet the retailer, cautious of a widespread deployment, had been operating self-checkout lanes only part of the day — those lanes had been closed mornings and evenings. Upon opening those lanes during the entire day, the retailer found that customers immediately moved to those lanes, boosting conversion rates even further.

Other data showed the value of opening more lanes to self-service, as 65 percent of the traffic that had moved through manned lanes showed basket sizes that were in fact 10 items or less — ideal for self-service.

According to von Sydow, the data, combined with real-time analysis, showed that this particular retailer could easily double its self-checkout throughput.  Beyond pilots, even when reviewing current or old self-checkout implementations,  companies still find insights in the data to show new ways they could be serving their customers.

Location, Location

If the mantra in retail is “location, location, location,” that same mantra applies to self-service checkouts, too.

As von Sydow related to Webster, different retail models have different layouts and staffing needs, which of course means that retailers must be cognizant of just where they place self-checkout machines.

He related work Diebold Nixdorf did with one retailer where the data showed low utilization of two self-checkouts housed within a bank of six machines. The two units that were underutilized where the middle ones — and it turns out consumers could not readily tell when they were available to be used.

“This wasn’t a technology issue but a layout issue and customer service issue,” said von Sydow, and it was easily fixed by staggered layouts and signage.

Ideally, he said, self-checkout solutions should be visible as soon as a customer enters the store, so that he or she is mindful of that option throughout the shopping journey.

The Convenience Store Opportunity

Smaller store footprints lend themselves well to self-checkout options, said von Sydow.

“Convenience stores are an interesting store format right now,” he told Webster.

The argument that convenience store transactions are cash-heavy enough to make a case against self-checkout options simply doesn’t hold true.

The data shows that, even with 65 percent of payments done with bills and coins, there’s still ample opportunity to use credit and debit cards.

Convenience stores show particular sensitivity to conversion rates.  After all, the consumer who peers through the convenience store window and sees a line at a manned checkout may opt not to come in at all.

But technology has made it possible for “hybrid” models to exist, where manned lanes can become self-checkout lanes with the aid of software deployed on premise.

“The maximum capacity at checkout, at any given time, increases dramatically,” he told Webster.

The Human Factor

Webster brought up the contention that automating checkouts will dehumanize the retail experience.

To that argument, von Sydow countered that automation, in general, should never be about simply reducing labor efforts and costs, done chiefly through headcount reduction.

He said taking staff away from the register and letting them interact with customers elsewhere in the store lets them focus on what he called “high-value tasks.” As he said, by the time the customer gets to the checkout — whether at a manned terminal or a self-service kiosk — the decision to buy has already been made, and there likely is no chance to boost the customer’s basket size.

You want to move the valuable resources you have from the checkout into the store and utilize them during the decision-making process, which in turn will increase customer service,” he told Webster.