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With a burgeoning stream of online choices, fostering customer loyalty is a constant challenge. Companies must become masters of the new “nonstop customer” experience. They will at times have to analyze the data on their customers' behavior for new opportunities, and at other times directly influence their customers' choices.

Customers continue to escape from traditional marketing channels into digital realms where they can become more knowledgeable and empowered than they have been in the past. So the nonstop-customer experience model uniquely places evaluation, not purchase, at its center.

The article offers a new rule number one of marketing is: know your customer's behavior on their path to purchase.

To understand what customer journeys are being taken by customers, the model groups loyalty behaviors into four general archetypes: emotional loyalty, inertia-based loyalty, conditional loyalty and true deal chasing.

The article proposes that marketing departments act on the insight they gain from analyzing their customers in terms of the four loyalty profiles in two ways: by sometimes reinforcing customer behaviors, and at other times redirecting them.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.