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.