Artificial intelligence use cases/application areas in offline/digital marketing | by appliedAI

While launching we interviewed corporate leaders and all sizes of AI vendors, searched news articles, patents, venture capital financing and more to identify established and emerging AI use cases. We have identified about a dozen fundamental artificial intelligence use cases in marketing. We focused on core marketing activities such as optimizing pricing and placement, optimizing advertising/marketing, personalizing recommendations, collecting and leveraging customer feedback. We are always improving our structure and would love to hear your comments and suggestions.

Primary marketing activities and AI use cases in these activities are listed below. To get more information on each, please visit the relevant page to see references, videos and detailed explanations:

1-Optimize Pricing & Placement

Pricing optimization: Optimize markdowns to minimize cannibalization while maximizing revenues.

Merchandising optimization: Leverage machine learning and big data to optimize your online or offline merchandising

Shelf audit/analytics: Use video, images or robots on the retail area to audit and analyze your use of shelf space. Identify and manage stock-outs or sub-optimal use of shelf space.

Visual Search Capability: Leverage machine vision to enable your customers to search your products by image or video to immediately reach their desired products.

Image tagging to improve product discovery: Leverage machine vision to tag your images taking into account your users’ preferences and relevant context for your products.

2-Optimize Advertising

Neuromarketing: Leverage neuroscience and biometric sensors to understand how your content impacts your audience’s emotions and memory. Test your content in private until it achieves the desired effect.

Analytics: Connect all your marketing data and KPIs automatically. Act on your data to manage campaigns, trigger alerts and improve your marketing efficiency

Marketing personalization: Reach the right customers, at the right time, through the right device and channel with the right message. Surprise your customers with your personalized marketing increasing customer satisfaction.

Context aware advertising: Leverage machine vision and Natural Language Processing to understand the context where your ads will be served. Protect your brand and increase marketing efficiency by ensuring your message is in with the context of the webpage or app where the message will be shared

3-Personalize Recommendations

Recommendation personalization: Leverage customer data to reach customers with personalized recommendations via email, site search or other channels.

4-Connect & Leverage Customer Feedback

Social media monitoring: Leverage machine learning to optimize the channel, target audience, message and timing of your social media posts.

Social analytic & automation: Leverage Natural Language Processing and machine vision to analyze and act upon all content generated by your actual or potential customers on social media, surveys and reviews.

Social media optimization: Leverage machine learning to optimize the channel, target audience, message and timing of your social media posts

PR analytics: Learn from, analyze, and measure your PR efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue.

You can see all these use cases below in this rather complicated graph.

To get more information about these use cases including references, case studies, customer videos and information on vendors operating in this space, please visit us at or see our extended blog article on AI use cases in marketing.

Stay positive!

Cem Dilmegani

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