Every January, the National Retail Federation (NRF) hosts a major trade show in New York City. I have attended as a partner of Springboard Retail for four of the last five years. It has been a unique opportunity to peek into the latest in retail technology. Inevitably, the technology featured at the show works its way down from enterprise to small business over the course of several years.
This is a trade show full of buzzwords and acronyms that are sometimes bewildering. Tech companies from startups with names that are at once catchy and forgettable to behemoths like Salesforce show off their latest and greatest. It is a place for these companies to woo new customers and investors.
Independent retailers are easily lost in a forest of big box teams browsing the show floor. Usually the ones who visit are headquartered close to NYC or have a very specific interest in coming. Having attended this show as an independent retailer, half the battle is figuring out which vendors will give you the time of day. Most will politely demonstrate a product even if you aren't the 50+ store chain they are hoping to land.
NRF lends itself to being a show where you can kick the tires on cutting edge technology even if it may be years before you consider adopting it as a practical matter.
Allow me to guide you as the ghost of indie retail tech past, present, and future as seen through the lens of Retail's Big Show.
These are technologies that are now so intrinsic in the fiber of retail technology that they are hardly mentioned as a feature anymore.
At 2015's NRF, companies were singing "big data" from the rooftops. I sat in on several demos way out of my price range involving sophisticated analytics platforms and business intelligence solutions. Fast forward to 2019 and that technology has dropped in price by 10x with companies offering small businesses monthly plans ranging from $50-$100 per month. "Big data" as a marketing term is virtually non-existent. A quick search on Google trends confirms that interest in this term is 50% of what it was in 2015.
"Big data" arguably is a distant ancestor of the latest industry darling: machine learning.
Cloud & SaaS
SaaS stands for "Software-as-a-Service", a model in which users pay a subscription for access to software that is usually hosted offsite. I would venture to guess that nearly every retailer is using some sort of SaaS product. Dropbox, Google's G Suite, Microsoft's Office 360, Mailchimp... The list is virtually endless. You can find a cloud service to do just about anything.
Having cloud as an attribute is more of a requirement than a feature now. Tech companies big and small are continuing to build out their cloud offerings. Even more traditional companies are offering cloud options to try and prevent their customer base from looking elsewhere.
Google Trends confirms that search interest in "Cloud Computing" is 50% of what it was five years ago.
(Side note: Does anybody know what caused the spike in May 2017?)
Let's continue our journey by looking at tech that has evolved from pipe dream to practical.
I know, I called this a "Technology Past" only moments ago. Yet its continuing impact on small business is undeniable and represents the largest cost-saving opportunity for most organizations whether they realize it or not.
Looking to replace an in-house server? Evaluate your cloud options first before dropping $10k+ on some new shiny hardware. Microsoft Azure and others have evolved to provide virtually every conceivable service for a monthly fee that is significantly lower than the cost of buying your own hardware.
Evaluating POS? Your top contenders are likely all going to be cloud-based or hybrids (cloud-based with offline mode).
Cloud computing is woven into the very fabric of doing business these days.
Traffic counting technology goes back to the early 2000s in the form of infrared people counters. As the tech moved to the cloud several years ago, most solutions were cost-prohibitive to small business at the time. Now you can get a cloud-connected traffic counter that you can configure yourself for under $100 per month.
Multiple counters can be used to analyze customer movement within your store, providing priceless data for store layout and more.
Conversion rate is one of the most important metrics for online businesses and now it is within reach for brick-and-mortar retailers of all sizes.
Camera-Based Facial Recognition & Product Identification
One of the most impressive demonstrations at the NRF show was a booth that applied facial recognition to the customer experience. Imagine a customer walking into your store, being recognized immediately by cameras, and receiving a "ping" on your phone that a VIP has walked in. The technology exists and is being implemented in major chains.
Amazon's "boundless store" was on display as well, demonstrating customers simply picking up what they want to buy, walking out, and getting charged the correct amount.
There were dozens of booths featuring powerful cameras and object identification. There are so many possibilities here. Picture a future where restock orders are automatically generated by cameras which identify "outs". Or a customer walks up to your checkout and has their rewards account immediately associated to the transaction based on facial recognition. It's not too far away.
Machine learning has been an up-and-coming buzz term for many years now. It is now reaching its peak. Machine learning is likely going to be as high-impact of a change as the shift to the cloud has been.
Take a look at the Google Trends graph which shows interest nearly double 2017:
Machine learning in its simplest form involves "training" a machine based on historical data so that it can predict probable outcomes of future events. For example, Amazon recently switched to a machine learning algorithm to suggest restock quantities for its third-party sellers. They claim a 20% increase in sales by applying these principles.
It sounds like the stuff of science fiction but it is coming, and fast. The major cloud computing providers are starting to open up machine learning resources for cheap to free for developers everywhere. Which means it is matter of time before some entrepreneur packages this for small business. The applications are nearly endless and the massive resources of the biggest technology companies in the world are being thrown behind machine learning initiatives.
Where "big data" was about data aggregation, machine learning is about taking action on that data.