Technology
How AI will revolutionize the retail customer experience
Michael Ouliel, founder and CEO of BlackSwan Technologies, shares why he believes the untapped potential of retail artificial intelligence lies in consumer-focused applications.
March 31, 2021 by Michael Ouliel
Retail is on the brink of a new era defined, once again, by technological advancement. In a matter of years, today's futuristic technologies will become commonplace, with artificial intelligence transforming both back-office business operations and the day-to-day customer experience in ways once hard to imagine.
Foreshadowing retail's digital revolution, the industry's spending on artificial intelligence is estimated to grow to $7.3 billion per year by 2022, up from an estimated $2 billion in 2018.
Most retailers are concentrating on the competitive advantages offered by embracing AI to automate time-consuming, routine tasks and increase operational efficiency. A recent report found 74% of the AI/machine learning technology used in retail targets back-end operations, while only 26% directly interacts with customers. Yet, according to IBM, intelligent automation has the greatest impact in retail when focused on improving the customer experience.
As retailers decide where to allocate their upcoming tech investments, they should consider the value of AI — not only in automating back-office tasks for increased operational efficiency, but in personalizing and simplifying the shopping experience of each individual consumer. Here are a few of the ways in which retailers have begun to use AI to offer a more individualized customer experience:
Location-based predictions
According to Accenture, 83% of consumers are willing to share their data if it will result in a more personalized experience. How can retailers most effectively leverage this data?
Advanced enterprise AI systems enable retailers to aggregate and analyze the data of individual consumers in order to implement ultra-personalized and timely marketing campaigns. For instance, by utilizing location-based analytics, companies can accurately determine when a customer is in close proximity to a particular retail location, and ensure that they receive a notification regarding promotions that a store is having.
By increasing consumer receptivity with highly relevant content, retailers maximize the value of their per capita ad spending. One global brand, a customer of BlackSwan Technologies, described the insights they gained from this type of analysis as "eye opening," and they leveraged the insights to exceed corporate forecasts of revenue growth.
Location-based analytics can also streamline supply chain processes to reduce shipping times. Amazon, for example, recently received a patent for "anticipatory shipping," which predicts what a shopper is likely to order and sends it to the shipping center nearest to them before they even order it. This is a case of "back-office" innovation improving "front-office" user experience. Although dominant industry players like Amazon are often the first to adopt cutting edge practices like these, they are both accessible and equally beneficial to smaller retailers looking to gain a competitive edge.
Tailored recommendations
Location-specific information, of course, is not the only valuable data that can be leveraged to augment the customer experience.
Other data points, such as past purchase behaviors, demographic characteristics, and price-point or style preferences can be analyzed by AI software to accurately predict the goods and services most likely to appeal to specific customers. Accurately segmenting customers based on these and other key characteristics allows retailers to identify and target new product trends that align most closely with the tastes of defined customer populations.
In fact, abundant computing power, combined with billions of accessible data points about consumers plus the latest AI techniques can support the identification of hundreds to thousands of micro-customer behavioral segments and personalize offerings accordingly.
Comprehensive customer service
One of the most difficult and costly elements of scaling a retail enterprise is keeping up with the demanding and unpredictable, yet critically important customer service component. Helpfully, this is one of the most effective applications of customer-facing AI. By utilizing AI to process and learn from past interactions, retailers can tailor conversations with individual customers, drawing upon concerns or preferences those individuals have expressed in the past.
For example, an AI application can generate alerts for customer service representatives regarding past issues with a certain client when a new conversation with that customer is initiated.
Natural language chatbots can also be taught to understand this historical data, allowing shoppers to have personalized conversations with customer service "reps" at any time of day or night, without the hassle of waiting on hold. Increasingly, retailers are incorporating "sentiment analysis" to be sensitive to the state of mind of the customer. Some startups are taking the chatbot experience even further, developing bots with realistic facial expressions that offer a more responsive customer service experience with all the convenience of automation.
The untapped potential of retail AI lies in consumer-focused applications like these, which are capable of transforming the customer experience through highly tailored recommendations and predictions, as well as always-accessible service.
By harnessing consumer data with AI that's applied pervasively across the enterprise, retailers can provide unmatched services to an unlimited number of customers, without increasing labor costs. This is a critical advantage for companies seeking to expand their operations without sacrificing profitability.
Michael Ouliel is the founder and CEO of BlackSwan Technologies
About Michael Ouliel
Related Media
Subscribe
Get the latest news and resources from Retail Customer Experience.
Recent Posts
