Understanding customer behavior is increasingly important to stay competitive and capture market share. Here’s how connected devices are bringing customer insight to a new level.
Understanding customer behavior has always been important to capture market share and to foster loyalty. But today, as products become commoditized and various industries teem with competition, understanding customer behavior is key to survival in a crowded field.
There is no shortage of tools and systems designed to understand your customers' behavior—from customer relationship management systems to marketing automation software to customer personalization tools and more.
But understanding customer behavior takes data and computing power. Data analysis in real time and heavy processing aren’t well attuned to the cloud computing architecture. For data- and compute-intensive enterprise tasks, we need alternative architectures to cloud computing.
Organizations have taken this quest to the street or, more accurately, to the enterprise edge. At the edge, devices, applications and data can be accessed without a lengthy trip to the cloud. This enables faster processing and, potentially, more secure access to data. As a result, smart connected devices such as Internet of Things (IoT)-enabled sensors and mobile devices—as well as artificial intelligence (AI)-driven applications—can be better exploited. Enterprises can get faster access to data and analyze that data more quickly as they make critical business decisions.
The edge has been an invention of necessity, and its evolution an inevitable consequence of its logistical impact. As hundreds of thousands of cloud-based applications have sprung into being as the cloud became the platform of choice, billions of IoT devices have made their way into the world. This huge imbalance of growth rates has given rise to two great deficits on the IoT side: too little processing power where things are really happening, and too little time to wait for centralized computing architecture to process the results of these data-intensive processes.
IoT devices have greatly expanded the role of mobile computing. They have come to define it, really, to the point that many, if not most, people make more use of apps while on their feet than while sitting down. This increased mobility and the need for speed have triggered a migration of processing resources away from the cloud and out to the edge, where they’re badly needed.
It’s hard to overstate the impact of this increased computing power; it enables enterprise decision making to be agile and fleet-footed at headquarters, but also in the field. The spread of sensors, smart cameras and other diverse endpoints in stores, warehouses, factories and even private homes (as well as the devices customers carry) is essentially redefining IT. Our understanding of IT systems, and the applications they give rise to, is morphing all around us. To some degree, this evolution has been self-determining; but as we introduce new infrastructure into existing systems – that is, as we extend them to the edge – the potential to guide that evolution is increasing by leaps and bounds.
We’ve seen warehouses and factories become self-monitoring, regulating their own energy consumption and performing self-maintenance; now they’re moving toward self-optimization. Inventories, now digitally monitored, can soon be entirely self-managed. Point-of-sale—that is, how the transaction takes place—is an increasingly automated experience for the customer; it is destined to be self-scaling and, when appropriate, customer-individuated, with amenities tailored to the buyer.
The collection of data that enables understanding customer behavior, decision making and preferences has long since been exploited to put specific recommendations in the customer’s path. Anyone who uses social media or who sees purchase suggestions derived from their recent online browsing history is aware of this trend.
But alongside these artificial intelligence (AI)-driven recommendations, we’ve seen an array of additional innovations to optimize customer experience. Digital wallets accelerate payments and can be biometrically matched to owners; smart mirrors assist in a wide range of in-home personal choices, proliferating options; out in the world, beacons are waiting to greet us, modifying in-store experiences as we approach.
All of these devices accumulate oceans of data about us, and we need the edge to process it all. But it’s the processing of this particular data, about how we interact with the world, that’s the game-changer enabled by the edge. It’s giving us more windows into the customer mind, and we need AI to tease out what it means and how to work with it.
IoT can capture passive, unconscious behaviors and identify deep patterns that can be turned into actionable information. In a way, it can know what the customer wants even before he or she wants it. Moreover, these windows offer a view that’s simply better than what came before.
Of course, using AI and smart connected devices to influence customer behavior is a delicate line to walk. Enterprises have to be careful stewards of this kind of influence—as well as of the data about customer behavior that they collect. Otherwise, businesses run the risk of manipulating customer behavior purely for their own ends rather than educating customers and providing customer choice. Careful stewardship of customer data also requires companies to ensure that they are compliant with Global Data Protection Regulation (known as GDPR), Payment Card Industry Data Security Standard (known as PCI-DSS) and other standards.
But understanding customer behavior can go further. We already know that smart connected devices can gather and assess the physical motion of workers in a factory or warehouse and use that information to optimize processes in those environments. It can do the same in a store.
Shoppers have distinct browsing and buying behaviors. When a customer enters a store intent on purchasing a single item, knowing exactly which one he or she wants, that shopper tends to walk quickly and directly to the item. When someone shops with a list, though, he or she will migrate from one department to another, occasionally vulnerable to an unscripted impulse buy. Still other customers might wander a store with only a vague plan and may end up buying just about anything.
All these behaviors and the physical patterns of motion and action underlying them can be used to optimize the retail experience, from the placement of specials and kiosks to the physical layout of a store itself to the presentation of items on shelves to the display presentation––all AI-derived and driven by customer-centric data.
Some stores’ enterprise AI is doing clever things, such as offering deals on baby supplies to customers it guesses might be pregnant. As connected devices are used for understanding customer behavior, connected devices and personalized marketing enable a store to identify a customer who has recently entered a store, traveled a particular pattern through certain aisles, and is then offered a digital coupon on his or her phone just at the moment he or she arrives at the aisle where that product is shelved.
IoT now bridges the gap between the physical world and the digital universe that accommodates that world. AI directs the traffic on that bridge. We can expect the sophistication of these connections to deepen rapidly. Smart cameras and beacon technology in a store can optimize these environments, based on their awareness of motion; they will soon study body language and facial expressions and implement benign contrivances to turn a bad customer experience into a good one. An entirely new class of demographic is emerging that will optimize and personalize the experience of the customer beyond what browser/click-tracking or social media monitoring could ever have achieved.
The edge is where understanding customer behavior emerges, and exploitation of this truth will yield more effective marketing, customer service, communication, apps and business processes.
Cloud computing architecture no longer suffices for the volumes of data, devices and users on the network. If users want speedy data, rich experiences with virtual reality and data analysis, some of this data processing needs to move to the edge. This facilitates processing closer to devices, applications and users, with less delay. AI-infused experiences will also accelerate, with mobile devices offering much more contextual and knowledge-driven applications.
For customers, this may bring shopping experiences that are rich, diverse and contextual in the moment. Enterprises will reap the benefits of these kinds of AI-enabled experiences, but only if they can safeguard customer data and prevent personalization from becoming intrusive, excessive or creepy.
Scott Robinson is director of business intelligence at Lucina Health in Louisville, Ky.