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HomeNewsAIOmer Cohen, of Skeep, on AI-driven shopping experiences, zero-party data, and e-commerce...

Omer Cohen, of Skeep, on AI-driven shopping experiences, zero-party data, and e-commerce discovery

Omer Cohen is Co-Founder and CEO at Skeep, a Tel Aviv-based software company whose AI-driven technology delivers compelling personal experiences to e-commerce site visitors. Skeep integrates with many of today’s most important e-commerce platforms, including Shopify, Magento, Amazon, Woo Commerce, and others.

Omer and I chat about how different kinds of shoppers require different personalized experiences and how Skeep provides customized user experiences for each segment. We delve into AI, and the degree to which smaller e-commerce brands can take advantage of AI-driven systems such as the one Skeep offers, given that they typically do not capture as much user data as larger brands do. We discuss e-commerce challenges that Generative AI * — the type of AI used by Skeep — can be deployed against.

We discuss issues relating to privacy regulation in the U.S. and the EU and discuss how “zero party data” — data that the consumer fully consents to sharing with brands — can provide valuable intelligence to brands without impinging on user privacy.

Omer and Skeep’s team were recently at NRF 2023 in New York and there they provided the e-commerce community a chance to test-drive the latest beta version of Skeep. Omer is clearly enthusiastic about the future of AI-enabled e-commerce and is excited by what the future brings in 2023 and beyond.

* By the way, for those unfamiliar with the term “Generative AI,” here’s what ChatGTP has to say about the topic:


Generative AI refers to a type of artificial intelligence that generates new data, such as images, text, or audio, that is similar to or mimics existing data. The most common types of generative AI are Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). GANs consist of two neural networks: a generator that produces new data and a discriminator that attempts to distinguish the generated data from real data. VAEs, on the other hand, learn to approximate the underlying probability distribution of the input data and can then generate new samples from that learned distribution. Generative AI can be used in applications such as computer graphics, language generation, and music composition.



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