While launching appliedAI.com 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 any 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. Hear online fashion retailer Otto talk about pricing optimization: https://www.youtube.com/watch?v=zaronZcVwE4
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. One of the most bleeding edge examples in this area is Lowe’s autonomous retail service robot. It will explore how robots can meet the needs of both customers and employees. Meet the LoweBot: https://www.youtube.com/watch?v=hP3yfGHTXFo#action=share
More mainstream solutions in this space include leveraging images taken by employees to manage and analyze shelf space. Trax Image Recognition explains their solution in this space in detail: http://www.traxretail.com/technology/
Visual Search Capability: Leverage machine vision to enable your customers to search your products by image or video to immediately reach their desired products. In the today’s world too easy to ensure customer’s desires with the AI algorithms thanks to the lots of snapping and sharing images. See how Flipkart, one of India’s largest online retailers used visual search: https://www.youtube.com/watch?v=ci-BxEaHuhg
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.
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.
Neuromarketing provides a marketing research method that can be implemented even before a product launch. Let’s take an actual example explained in more detail here; two marketing researchers from Rotterdam made subjects view some trailers of movies while hooked onto an EEG (Electroencephalogram) headset. They later chose which of these movies they would like to see at home. They were also asked about their preference regarding the movies based on the movie trailers. Both the EEG data and the self-report data predicted their individual movie choice equally well. EEG data is much richer than stated preferences and the interesting fact is that only the EEG data of these subjects could accurately predict the box office success of the movie.
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. For example, according to ActionIQ, terms like “web personalization” or “email personalization” are outdated. A true personalization solution that can scale needs to leverage data from “any” channel and help advance the personalization of marketing & products across “any” channel. Vendors in this space first solve data related problems and provide an interface that is flexible, fast, and entirely self-service for Marketing and Analytics teams.
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 fits its context, making static images on the web come alive with your messages. See Gum Gum Sizzle Reel for examples
Recommendation personalization: Leverage customer data to reach customers with personalized recommendations via email, site search or other channels. For instance, Visenze provide visually similar product recommendations: https://www.youtube.com/watch?v=ci-BxEaHuhg&feature=youtu.be
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. For example SocialFlow allows you to reach your audience at the right time by analyzing your audience’s online habits
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. With the increasing demand in digital media to specify the targets will more easy for the brands in the competitive marketing.
We have put these use cases, along with some sample vendors operating in this space on https://appliedai.com/#!/marketing:
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 https://appliedai.com