Each day, an estimated 2.5 quintillion bytes of data are generated by individuals (that’s a thousand raised to the power of six–or eighteen zeroes). That’s the sort of figure we generally don’t see except in reference to cosmic bodies.
Akin to the study of astrophysics, human activity generates usable data that is beyond a realm one organization—let alone one individual—can readily comprehend much less have hope to utilize. ‘Big Data,’ in fact, explicitly refers to data too vast for any one processing software or observer to ‘deal with.’
Like the observer effect in quantum mechanics, how a datum is understood or utilized depends upon the person beholding it in ways that are too complex to totally understand, but we can still do what we can to pin down our intentions and set our goals when examining the resources available to us.
Similarly, when a retailer has a set of data on (e.g.) consumer preferences captured by AI on the store floor, what they do with that is inseparable from A) how developed their understanding of their own goals are and B) how efficiently and effectively they can organize that data.
It is because data is today generated at a rate exponentially greater than at any time in human history that properly leveraging that data has come to define the winners in the race for digital innovation.
Specifically, building socioeconomic pressures such as rushing, historic inflation (near 10% YoY in multiple countries), labor shortages, an increased rate of ecological disasters, and the threat of an official recession all call for the leveraging of new AI and ML-powered tools to affect efficiency gains, a strategy that has become the steady marching beat of the retail industry as a whole.
Indeed, at a time when omnichannel engagement has become essential to capturing the ever-evolving customer, having these efficiency tools in place becomes especially important when consumer spending goes flat or dips or you need to rush stagnating stock out the door (correspondingly, second-hand ‘thrift’ retail is expected to grow some 15-20% annually for the ‘next several years’).
For retailers who want more eyes on the ground to help their storefronts (e.g.) better manage stock and support their employees remotely, software and networking technologies have emerged to allow for just that.
Poor Visibility as Top Barrier to Understanding the Day-to-Day Storefront
Retailers who want to know more about what is going on in their stores are likely aware of a number of roadblocks to doing so even if they already have data collection services in place, from low staffing to training barriers to a lack of documented communication tools and poor surveillance protocols.
For Australian-based co-founder and CEO of ReStore for Retail, Ben Chamberlain, that once meant finding a way to respond to rapidly evolving situations on the store floor when tasked with assisting a chain in liquidating stock that had to go ASAP (an issue many American retailers are currently struggling with as the effects of COVID-19 continue to be sifted through).
ReStore’s parent company, Hilco Global, is the largest liquidator of retail store assets in the world. The approach was to algorithmically suggest promotions, create a plan, and then provide a timeline wherein the objective was to have fully liquidated and monetized the overstock.
“We would start with some level of discounting and then graduate it on a weekly basis, managing the inventory on the way down to ensure that every single item was optimally sold,” he recounts.
Chamberlain consistently had a problem, however: keeping ‘eyes on the ground,’ as it were, so that store conditions were properly monitored and promotional efforts were correspondingly responsive.
“What I found was that the tools I had available to me to understand what was going on in the stores were incredibly unhelpful,” notes Chamberlain.
“If I wanted to simply understand how the stores were looking, the best way for me to do that was just to visit the store. However, I can’t be at every store at once,” he continues, noting the strain this placed on staffing and work hours.
“It again all breaks down into ‘how do I get visibility more efficiently.’” Like the core struggle with ‘big data,’ Chamberlain and his team had to break down the overwhelming task in front of them into manageable chunks that were salient to their objectives.
To Leverage the Cosmic Pie that is Big Data, You First Have to Capture (and Share) It
“As we looked at this as a business, we said ‘there has to be a better way of doing this,’” recounts Chamberlain.
His answer would eventually lie in the same kind of technologies that are currently driving tech transformations across the globe, a tool that leveraged the ubiquity of networked cellular devices and advanced comms tech to pool data on storefront conditions and share them seamlessly with managers.
Developing it slowly and using it initially to aid in addressing aforementioned issues with store visibility in high-intensity liquidation efforts, Chamberlain’s team would eventually talk to retailers and ask what else they’d like to see.
“Sales, visual execution, and so on were what we heard. The tool got to the point at the end of last year where it had real marketability,” he notes, eventually becoming the cornerstone for ReStore for Retail.
“We’ve created an ecosystem to deal with all of that information to access that information more conveniently,” he continues, noting that it has developed a variety of applications, including helping employees to feel “more connected to management than before” with a better understanding of how they fit into store goals.
Meanwhile, store managers use ReStore primarily to do the kind of real-time storefront evaluations (and comparisons) that formed the tool’s core in its inception, helping them to see (e.g.) what their other store’s promotions are for that day (and how well they appear to be doing) without having to step foot through a threshold.
Like with any system that manages large amounts of data, proper organization is part-and-parcel to the accessibility and usability that make ReStore’s tool work, utilizing goal-setting or what ReStore calls ‘the Four Pillars,’ focal points around which performance is most commonly evaluated.
The tool has even developed a rating system to form what can be thought of as a sort of aggregate evaluation platform for data that otherwise would be disparate, spread out, inaccessible and incomparable.
Since its formal rollout, the tool has moved away from liquidations toward ‘high-intensity retail’ and other functions that have little-to-no tolerance for latency while wrangling the sheer volume of data stores generate.
Data collection and sorting technologies like ReStore’s will no doubt continue to proliferate in the age of exponentially growing data. As ever, though, the real question is what those tools do for the quality of human life.
Technology should, first and foremost, take work off people’s plates. The hope is that such tools do just that, helping store associates and managers cope with the intense workload that is both managing a store’s traditional functions and tracking those functions in a data-literate way.