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The retail industry is inundated with buzzwords that describe the best way to engage with consumers — such as omnichannel marketing, customer journey, and a 360-degree view — to provide a more personalized and optimized experience. All of these buzzwords point to a common problem most retail marketers are aching to solve: How can they use customer data to create timely insight into what customers are doing in-store and/or online and convert that into strategies and actions that increase sales?
It’s an age old question with new age solutions, and the retail industry is now leveraging artificial intelligence (A.I.) to usher in a unique way of thinking about this problem. In fact, a new chart shows there are 45 A.I. companies focused on retail across a range of verticals, including recommendations, merchandising, search, conversational commerce, and especially multichannel marketing.
Using omnichannel retailing, the ability to connect with a customer both online and offline, is ideal because omnichannel customers have a 30 percent higher lifetime value than single channel customers. With 73 percent of customers willing to leave a brand that doesn’t personalize their experience, retailers understand that data is king and can help fuel the personalized experiences their customers are demanding.
By collecting behavioral data of customers across all of the channels on which they interact with the brand, retailers can have hundreds or even thousands of characteristics at their disposal that describe the intent and propensity of their customers, including future purchasing behavior. But the vast amount of collected data and algorithm permutations creates a new challenge that far outstrips the ability of even the best marketers to analyze and uncover the most optimized ways to interact with customers. Enter A.I., which can learn on its own in real time via machine learning.
The future of retail technology lies in solutions that are powered by machine learning, which can provide fast and intelligent automation as well as dynamic scalability. Machine learning unleashes powerful self-adapting algorithms to uncover latent patterns of behavior that are difficult or impossible for decision-makers to discover on their own. These algorithms — regressions, decision trees, neural networks, model ensembles, etc. — can automatically sift through thousands of customer behavioral characteristics to find the few that actually drive purchase behavior. The result is one or more predictive models that encapsulates this behavior in a concise, mathematical form that can improve marketing programs, optimize messaging, and increase engagement with customers.
With recent advances in machine learning tools, accessing the power of A.I. is becoming more mainstream. A.I. tools are coming to market across a range of retail services, especially where they can be individually specialized in understanding and solving a specific problem — something that first-generation A.I. can do relatively quickly.
For example, A.I. chatbots have become the rage of 2016, allowing customers to message and shop with an A.I. assistant. However, many consumers have been underwhelmed, showing some solutions are still in the early days. On the other hand, A.I. tools for marketing and scheduling, as well as self-driving vehicles, have shown really impressive results in applying machine learning to the problems they are solving.
X.ai is a good example of A.I. saving hours of time scheduling meetings. It benefits from Amy Ingram, an A.I. personal assistant that emails and coordinates schedules with humans or other A.I. assistants. Comma.ai is showing that A.I. can be used to leapfrog previous complex algorithms, powering self-driving vehicles by mimicking data from actual drivers.
To stay ahead of the curve, retailers need to embrace continuous evolution to keep up with the needs of their shoppers. While there is rarely a silver bullet solution to any problem, retailers are using adaptive solutions by leveraging machine learning technology. For retailers, using this type of technology can mean gaining an immediate edge over the competition that will be further optimized over time with new and additional data, creating a sustainable benefit rather than plateauing.
Successful retailers should use this new way of thinking, and new technology powered by machine learning, to quickly provide a better experience for their shoppers which can lead to a more optimized business with increased revenues, lower expenses, and higher margins.
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