Originally posted on RetailITInsights.com.
Everywhere you look companies are talking about using artificial intelligence (AI), chatbots, or virtual assistants as part of their e-commerce strategy. More often than not, these types of technologies are still in the “hype” phase: a lot of speculation, but not much implementation.
AI has yet to become mainstream in practice, but we have become really good at collecting information. The proliferation of devices, digital touchpoints and social media platforms has led to an explosion of rich data. And while true AI is the promise that we’ll no longer be required to analyze this raw data, we still need technology to turn data into actionable insights.
For businesses, data informs contextual marketing to provide a frictionless experience and increase loyalty. As the development of flashy new technologies chugs along, data is truly the key to unlocking a world of potential for both businesses and their customers. Using data, no matter where it comes from, is the future of retail.
Ignore the Nomenclature
AI and machine learning seem to be retail’s latest buzzwords. Some analysts estimate global revenue from AI will reach $36.8 billion by 2025. But it’s not the first time we’ve seen this excitement, even in retail. There was a huge push in the 80s and early 90s around AI’s application in retail.
Despite the resurgence of these technologies today, no one is applying cognitive computing in the way it’s depicted in science fiction yet. When you strip away the bells and whistles, what we’re really doing is getting closer to understanding the algorithms that can analyze feedback and make better data-driven decisions. When it comes to AI and machine learning what we’re talking about is gathering data, using that data to make more intelligent decisions, and driving experiences to new levels. Understanding the efficacy of data is the crux of retail success.
Clarke’s Third Law states any sufficiently advanced technology is indistinguishable from magic. Put simply, when technology becomes advanced enough, similar to the way AI is depicted in movies, it will appear so streamlined, like wizardry. We haven’t reached “magic” yet, but the technology is getting close. This is the result of two big changes over the last couple years: first, an abundance of recorded data and, second, ease of collection. Customers are increasingly more comfortable sharing their information because they spend so much time online. With the resulting surge in available data, we can do a better job of incorporating our learnings into more engaging, frictionless brand touchpoints.
Create A Frictionless Experience
We live in an “I-want-it-now” world. Data algorithms, at their best, work to deliver a commerce experience without really feeling like you’re doing an exchange. For example, consumers like to reap the value from a service and pay for that service in a seamless way — such as Netflix and Uber. In other words, it’s frictionless experiences fueled by data that customers want.
Today’s consumers are less concerned about what they want six months from now than what they need in two days. That’s instant gratification. And more than ever before, we have better data and computational power to deliver instant, frictionless experiences. We’re on the cusp of a very cool inflection point.
Guide Contextual Commerce
While not as sexy as AI robots, data is changing commerce in unprecedented ways for retailers who use it well. For today’s retailers to make an impact, personalization is key in every part of the customer experience. In practice, few retailers effectively use data to make personalization a reality. Regardless of AI’s progress in the market, retailers must lean into data to fine-tune and target their messaging.
One such example is contextual marketing, where a retailer uses data to pull in factors such as time of day, language, purchase history, device, and location to create a meaningful and more targeted message for consumers. These personalized messages feel like a one-on-one exchange, empowering the consumer with relevant information.
One obvious concern is consumers are wary of highly personalized messages. Just look at when Facebook used pictures of “friends” in ads. It was creepy and didn’t work. Or when Target got caught for mining data to find out when customers were pregnant. Also creepy. But when done tactfully and in a way that brings value, personalization won’t feel so creepy.
Experimentation with contextual commerce will lead to the sweet spot where merchants can provide the ultimate personal experience without intruding on privacy. Retailers need to make the offers so compelling they don’t create cognitive dissonance. Eventually, we’ll see a decrease in concern about privacy, as long as the offers sustain relevance and deliver value. Making messages relevant to the consumer is immensely valuable for all parties involved.
Call it what you will: AI, machine learning, cognitive computing. But the most important piece? The data that forms the foundation of these technologies. Ultimately we’re going to land on an industry term that describes what we do with AI and machine learning rather than the technologies themselves. Put in retail terms, “AI” is the new “omni-channel.” AI is enjoying the same hype that omni-channel once did because it sounds sexy. But the real value of these technologies only proves itself when we apply it to better understand the customer.