Retail’s Big Show, NRF 2018, wrapped up at New York City’s Javits Center earlier this month with more than 35,000 retailers and industry partners in attendance. Billed as the retail industry’s premier annual event, this show definitely shines a spotlight on technology. As the New York Times described it, “it was a conference about shopping that looked more like an expo for tech.”
Artificial intelligence, augmented reality, data analytics, and automation become more than just buzzwords. Industry leaders are beginning to show how these once-vague ideas are being ingrained into all sorts of technologies. All in an effort to help retailers compete in today’s digital-but-still-physical world.
For our part, we showcased our in-store technology by creating scenarios that retailers encounter everyday. Arriving at our booth, retailers entered a mock chocolate store where they could shop for refreshments and custom chocolate gift bags. Qtrac iQ queue management technology monitored, tracked, and reported their movements as they waited in line for refreshments, and Qtrac VR mobile queuing technology allowed visitors to order chocolate gift bags from their phones and then browse the booth while their order was being prepared. It was definitely our most exciting NRF show to date.
Here are some of the key themes and takeaways from the show along with how we experienced their connection to queue management.
Analytics has been the rage at NRF for several years now. There is a seemingly endless number of companies offering ‘retail analytics.’ But the idea of what this really means in the minds of retailers themselves is becoming better defined. As companies try to realize their vision of having the “Google Analytics” of brick-and-mortar, they’re learning more about what they really need to gain that competitive edge. Brick-and-mortar retailers are leveling the playing field with their online retail counterparts by reaching for analytics that help them track how shoppers shop and how to identity and remove any friction along the consumer’s path to purchase.
Did you say friction? Nowhere do we see the potential to disrupt an otherwise satisfying shopper experience more than in the queue. Poorly run waiting lines can be the demise of a business. Queue analytics can help retailers identify those points where shoppers turn away.
Realizing the ROI of analytics requires that retailers can use the information in real time to effect the shopper experience and to optimize service efficiency. Retailers understand that data is only useful if it can be put to use. To this end, automation is key. We have to let the data and analytics do the work of re-routing customers to where they can be served fastest and directing associates to activities or areas where they are needed most.
Consider that customers are won and lost in the queues/waiting lines that exist throughout a retail environment. Waiting for a fitting room, waiting to exchange an item, waiting to pay for an item, waiting for an associate to help… Queue management analytics with real-time automation and alerts configurable to a retailer’s unique thresholds--these are the types of analytics solutions that can have a real impact on retail.
Omnichannel remains a focus of retailers at NRF as they continually look for ways to better connect the online and physical store experiences. We saw many solutions targeting the omnichannel goals of retailers. One area of interest is the buy online, pick up in store (BOPUS) trend. Terry Lundgren, Executive Chairman at Macy’s, speaking at NRF, described significant growth in BOPUS. Supporting this statement, a July 2017 survey by JDA Software Group found that half of all internet users had bought online and then picked up the item in a store at least once in the past 12 months. That was up from just 35% in 2015. This growth is said to reflect consumers’ desire for an omnichannel experience. The same study found that 54% of internet users said physical stores were their preferred channel.
Mobile queuing systems can improve the in-store pickup experience by giving customers the opportunity to avoid the waiting line when they arrive in-store to claim their goods. And the store will surely benefit from an increase in impulse sales from those customers who choose to browse the store while they wait for their purchases to be retrieved.
As Laurence Haziot, a global managing director at IBM, remarked, “each individual is a segment in itself.” Personalization was everywhere at NRF and we wholeheartedly agree with Debbie Hauss, Editor in Chief of Retail Touchpoints who remarks, “retailers and solution providers are at the same turning point in personalization strategies that they were in omnichannel strategies a few years ago: they know a focus on personalization will deliver results, and now they need to figure out how to strategically realize that goal.
The race is on.
At the Lavi booth, retailers showed a lot of interest in our virtual queuing system for its ability to not only personalize the service but to let customers shop and engage with the retail store on their terms.
A common theme throughout the halls of NRF and within the retail industry as a whole, is the idea of freeing store employees from manual tasks and utilizing them in areas where customer service can be improved. Solutions from mobile POS and endless aisle solutions to inventory automation and robotic stock checks, focus on empowering talented associates to use their skills to serve and sell. These solutions were plentiful at NRF 2018.
For our part, we showcased how our queue management technology can give associates visibility into customer traffic, customer information, and customer preferences, all while automating queuing-related functions. By empowering store associates in this way (via a mobile device), retailers are able to impact the customer experience and become more efficient.
As usual, NRF 2018 delivered. Retailers have a lot to be excited about as solution providers step up to the challenge of helping them succeed in an increasingly dynamic environment. What were your key takeaways? How will you use what you learned to improve the critical area of customer queuing?