The future of AI marketing applications in retail

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Over the past few years, e-commerce has been slowly and steadily transformed by artificial intelligence (AI). The world has watched Amazon go from bookseller to voice-activated, product-recommending, Internet of Things behemoth over the last 15 years, and — for many reasons — e-commerce has been an area of great AI adoption, particularly in marketing.

That being said, the retail space (which encompasses a much broader array of businesses beyond just e-commerce) often doesn’t get the same degree of limelight when it comes to AI use cases. I’ve set out to display some real use cases that highlight how and where AI is pushing the boundaries in the retail sector.

While the applications below are by no means a complete list, it serves as a nice general overview of the possibilities in this space, along with related startups and technology companies working on those specific applications.

We’ll begin with what is arguably one of the most import broad areas of AI retail marketing, segmentation.

Customer segmentation for AI in retail

Personalization is a key word when it comes to improving the shopping experience, and marketers are becoming much more savvy about using AI to collect data to create a more complete view of each customer. AgilOne is an example of how AI can help marketers optimize their communications as it continually learns from user behavior.

The technology is designed to integrate a wide range of customer data derived from multiple digital and physical channels. It uses the data to analyze and predict customer behavior, allowing marketers to fully understand their customers and engage with them in an authentic way.

Other companies expanding this technology include SiteZeus — technology which collects big data to make location-specific predictions, forecasts and decisions — and Sentient, which encourages users to have real-time conversations with virtual salespersons who adapt their products and inventory in real time to match customers’ changing preferences and needs.

AI solutions for customer segmentation will continue to become more and more sophisticated, allowing marketers to integrate vast amounts of data to make the customer journey more personalized, and more efficient. I’ve written in greater depth about the importance of personalization and segmentation in the future of AI and marketing in this previous MarTechToday article.

Image recognition

In the near term, machine vision may open a new array of possibilities for online consumers, such as allowing them to search for products using images. For example, if you need a new office chair, you could upload a picture of one you would like to buy, or even a picture of your desk and have a chair matched to it. You may also be able to use an image you found via Google search to help you find the product you desire.

Companies like CamFind are experimenting with image recognition. The app allows you to take a picture of any object, and the mobile visual search technology will tell you exactly what the object is, without your having to type in any questions or details.

Other companies developing machine vision for possible retail use include Tagalys, which uses image data tagging and image recognition engines to make product recommendations, and Findally, which uses image recognition and machine learning to turn images into text data.

Retail virtual agents

From chatbots to sophisticated systems that can interact with humans, this field of AI is bound to advance quickly in the near term, as it’s already showing many applications in the e-commerce industry. Chatbots are currently used in customer service and support but are also being developed to help customers make purchases.

[Read the full article on MarTech Today.]

Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.

About The Author

Daniel Faggella is an email marketing and marketing automation expert with a focus on the intersection of marketing and artificial intelligence. He runs TechEmergence, a San Francisco-based market research and media platform for artificial intelligence and machine learning applications in business.


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