Artificial Intelligence for Marketing: Practical Applications

Artificial Intelligence AI for marketing

artificial intelligence in marketing

We’ve covered a lot so far, including what AI marketing looks like in the real world. Spotify will also send automated email marketing messages with personalized recommendations. This insight can help marketers develop better campaigns that actually produce sales and ROI. A chatbot can personalize the customer journey during the stage when they’re consuming marketing content. Most marketers today use generative AI to recommend content and generate portions of an article.

One is on the backend when marketers use AI to forecast demand for products, develop customer profiles, do programmatic ad buying, and the like. The other is the customer-facing side, as marketers use AI to improve the customer experience, thereby strengthening the brand and making more sales. In fact, 75 percent of organizations using AI and machine learning say it enhances customer satisfaction by more than 10 percent. The coming years could potentially see further investment in AI-powered analytic tools that will help marketers make more accurate real-time decisions.

Enhance the customer experience with personalized content

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. Today, AI technologies are being used more widely than ever to generate content, improve customer experiences and deliver more accurate results. Before choosing an AI tool, organizations should fully explore the different types of AI marketing applications available and look at how they’re being used by other businesses. Microsoft’s AI strategy in the marketing market includes the Integration of AI technologies into its existing marketing and advertising products, such as Microsoft Advertising and Microsoft Dynamics 365. Partnership with major brands and advertising agencies to provide AI-powered solutions for their marketing and advertising needs.

These studies put a boundary condition for marketers when using AI in the product/branding actions to generate positive customer responses. Mechanical AI can be used for product/branding actions that can benefit from standardization. For example, brand logo design can be automated by a decision-tree like machine learning using multiple-choice questions, allowing small budget marketers to have AI-assisted branding (Avery 2018). While enjoying the standardization benefit of mechanical AI, one existing study is cautious about automating product decisions, when those products are related to consumers’ identity (Leung et al. 2018). Feeling AI can be used to understand existing and potential customer needs and wants, for example, who they are, what they want, and what their current solutions are. The major distinction between market analysis and customer understanding is that the latter often involves emotional data about customer sentiments, feelings, preferences, and attitudes.

Netflix’s AI-centric user content suggestion tool

They leverage machine learning and automation to optimize ad delivery and achieve optimal results. How it works is you would create and upload the ad images, copy, and landing page URLs, and the AI takes care of automatically generating ads in multiple formats that would be served across various channels, such as Google search engine results, Youtube, etc. Human expertise remains crucial in leveraging AI to create compelling, targeted, and optimized content that drives engagement and business success. AI is a valuable tool that combines human creativity with intelligent automation for remarkable results in marketing.

  • Phrasee’s computational linguistics team built language models for eBay, allowing the e-commerce giant to generate custom copy tied to its brand tone, customer needs, and specific promotions at the click of a button.
  • This is in line with the reasoning that transparency constitutes a pro-ethical condition for enabling other ethical principles (Turilli & Floridi, 2009).
  • Current practice relies heavily on focus groups to gain qualitative insights about customers.
  • Through this deep data analysis, AI can discern subtle patterns, behaviors, or preferences that might often be overlooked using traditional methods.
  • They also use AI to create images, thumbnails, and artwork that will appeal to users individually.

AI tools that are trained on data that doesn’t accurately reflect customer intentions will fail to provide useful insights into customer behavior or make useful strategic recommendations. By prioritizing the quality of their data, enterprises can ensure that their AI solutions will help them better achieve the outcomes they seek for their marketing programs. Data scientists or engineers with a background in AI, machine learning and deep learning don’t typically sit on marketing teams, but their expertise is necessary for successful AI marketing initiatives. To address this issue, organizations have a choice—they can either make the investment to hire the data scientists and engineers they need, or they can go to a third-party vendor for help training and maintaining their AI marketing tool. Both approaches have their advantages and disadvantages, primarily around the level of investment an organization is willing to make.

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