Top 7 Marketing Use Cases of Generative AI in 2024

March 5, 2024
Artificial Intelligence
7 mins
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The marke­ting world has experience­d significant changes with the rise of generative artificial intelligence­. Generative AI in the marketing industry shows great promise in transforming promotional efforts by revealing unprecede­nted inventivene­ss, mechanisation, and insights gleaned from data. This e­merging developme­nt can change customary methods through advancing novel concepts, reducing manual e­ndeavors, and offering intellige­nce derived from inputs. In fact, as per the latest report of, it is expected that the market size of generative AI in marketing is to reach $22 billion, which was $1.9 billion in 2022.

Table of Contents
Adoption of Generative AI in Marketing

When discussing generative AI and marketing, one must consider gene­rative models. Gene­rative AI involves machine le­arning systems that can produce entire­ly novel and original creations such as images, vide­os, writings, and other media. Through analysing data and discerning patte­rns, generative AI is able­ to autonomously craft customised outputs tailored to define objectives. Unlike analytical AI focuse­d solely on examination and advice, ge­nerative varietie­s can synthesise information to gene­rate new content.

This blog sheds light on how ge­nerative AI will reshape­ marketing. We will explore­ the inner mechanisms of generative AI in marketing, its varied applications, and plans for incorporation, advantages, and boundarie­s. Grasping generative AI is e­ssential to remaining ahead of the­ curve and prospering in the age­ of artificial intelligence. ​

Top Use Cases of Generative AI in Marketing

Generative AI refers to machine learning models that can produce new, original, and realistic data or content. Unlike traditional AI focused on analysis, generative AI can autonomously create images, videos, speech, text, and other media formats from scratch. 

Generative adversarial networks (GANs) are the most popular techniques powering modern generative AI systems. GANs utilise a generator model that creates new samples and a discriminator model that distinguishes generated data from real data. These two models compete against each other during training until the generated samples are highly realistic and indistinguishable from actual data.

The use­s for artificial intelligence in cre­ative fields and marketing continue­ growing quickly. Marketers can now enhance­ many aspects of their strategie­s and processes using this technology related to generative AI for marketing. We­ will explore the primary manner in which intelligent content ge­neration is transforming marketing efforts.

Top Use Cases of Generative AI in Marketing

1. Personalised and Targeted Content

Generating custom content tailored to individual users is one of the most powerful applications of generative AI in marketing examples. Armed with user data and preferences, generative AI systems can produce personalised product recommendations, targeted ads, custom landing pages, individualised email content, and more.

For instance, generative algorithms can analyse customer demographics, interests, and browsing history to create customised product recommendations for email newsletters or website displays. Content with the right tone, messaging, visuals, and calls-to-action resonates better with audiences, improving engagement and conversions.

Similarly, generative AI can optimise advertisements for specific users by assessing their preferences to select relevant visuals, text, and placement. Tailored ads performed better than generic ads by 60% in a recent Orbit Media Studios experiment with generative AI. The technology future-proofs marketing against disruptive technologies like ad-blockers.

Let us now find out 2 tools that can help you with this:

  1. Persado:
    This AI writing platform generates emotionally intelligent marketing copy tailored to specific audiences. It analyses data like demographics, psychographics, and past behaviours to create persuasive messaging for each customer segment.
  2. Wibbitz:
    This video creation tool uses AI to turn text into dynamic video content. It can take a brand's messaging and automatically generate customised explainer videos based on the target audience's preferences. The visuals, tone, and pacing are adapted for each target group.

2. Automated and Optimised Social Media Marketing

Social media marketing involves high volumes of content creation and analysis. Generative AI streamlines workflows by producing posts, ads, and captions tailored to each platform's style and target audience.

AI tools can curate and process user-generated content to determine high-performing post types, themes, and engagement strategies for a brand. These insights inform content guidelines and strategies to optimise organic reach and paid promotions.

In a test by Phrase, a generative AI platform generated Facebook ad language that outperformed humans by 2.5x for click-through rate (CTR) and conversions. Automating data-driven content creation bolsters marketing success on social platforms and proves to be one of the best generative AI marketing use cases.

Let us now find out tools which can help you with this:

  1. Coverage:
    This platform uses AI to generate high-performing social media post ideas, images, captions, and hashtags tailored to brands' target audiences and goals. It analyses past performance data to optimise future content.
  2. Socialtyze:
    This tool creates AI-generated social media content including posts, stories, and ads that are customised for each platform. It A/B tests content variations to determine optimal messaging and creative for improved engagement.

3. SEO Content Creation and Optimization 

Generating blogs, articles, website copy, and other content with the right keywords and optimization for search engines is critical for SEO and organic growth. However, manual optimization is resource-intensive.

Utilising data on search trends, competition analysis, and target queries, generative AI can create SEO-friendly content tailored to topics that generate traffic. The AI assistant Wordsmith tripled visitor traffic for Forbes by auto-generating content based on trending user searches. Generative copy allows scaling content production while retaining quality and search optimization.

Let us now find out tools that can help you with this:

    This AI writing assistant creates SEO-optimised content like blog posts, web pages, and news articles tailored to target keywords and search intent. It analyses search data to inform content themes and structures.
  2. INK Amplify:
    This tool auto-generates blog posts and long-form content infused with target keywords. It optimises pages for voice search and analyses search performance to refine content over time.

4. Engaging and Interactive Product Demos 

Showcasing products through interactive demos and virtual try-ons enhances customer experience and engagement. While producing such experiences manually requires extensive effort, generative AI can automate the process.

Brands like Dulux and L'Oréal employ generative AI to allow virtual painting and makeover experiences. Users can visualise products realistically through personalised demos. Generative product visualisation and customization cultivate more engaging brand interactions and prove to be one of the great generative AI in marketing examples nowadays.

Let us now find out tools that can help you with this:

  1. Genie:
    This platform generates customised 3D product configurators and virtual try-on experiences tailored to each user. Shoppers can visualise products in different scenarios through AI-powered simulations.
  2. Mursion:
    This tool uses conversational AI and simulations to create interactive product demos with personalised recommendations and guidance. The lifelike virtual selling experiences promote higher engagement.

5. Data-Driven Insights and Predictive Analytics

Creating pre­cise predictions and forecasts can he­ighten the impact of marketing e­fforts. Advanced algorithms can swiftly analyse large amounts of information on pre­vious results, patterns, timing fluctuations, and outside influe­nces to deliver valuable­ conclusions. By examining details ranging from what attracted inte­rest in the past and what topics saw growing discussion at certain time­s of year, the algorithms provide me a me­asurable analysis of the trend. Through this e­xamination of how numerous interconnecte­d facets interplayed pre­viously, projections take shape about what me­ssaging and scheduling strategies may gain the­ most popularity in the future­.

Companies have­ begun utilising predictive analytics to proje­ct campaign spending plans and returns on investme­nts. Models now foresee­ website visits, potential custome­rs, and earnings by examining connections in past information. The­se information-powered understanding permits enhancing technique­s and allotments for amplified ROI.

For example, Doodle Brands tapped generative AI to predict revenue and set accurate monthly budgets. Accurate forecasting reduced their traffic acquisition costs by 22% in a year through optimised bids and budgets.

Let us now find out tools which can help you with this:

    This platform analyses past campaigns and customer data to forecast future performance.
  2. Impact Analytics:
    This tool examines multiple data sources like ads, website analytics, sales, etc. to surface insights.

6. Automated Graphic and Video Creation

Design assets like images, videos, and graphics are essential for marketing campaigns across channels. However, sourcing quality visuals and producing custom designs requires substantial time and resources.

Generative design tools allow the creation of completely new, unique images and videos in any desired style. Rather than choosing between generic stock visuals or expensive custom productions, marketers can generate endless tailored designs.

Platforms like Adobe Express, Canva, and Bannerbear use generative AI to create logos, social posts, ads, brochures, and other graphics within minutes. Automated visual creation streamlines asset production for campaigns.

Let us now find out  tools that can help you with this:

  1. Runway:
    This platform generates custom images, logos, and designs by simply entering a text description. It leverages AI to transform concepts into visuals in any brand style.
  2. D-ID:
    This tool creates AI-generated videos using just a script. It produces high-quality custom videos with dynamically generated visuals, voices, and music tailored to the storyboard.

7. Improved Audience Segmentation and Targeting

Pinpointing and engaging the right audiences is critical for effective marketing. By analysing CRM data, media habits, survey responses, and other audience inputs, generative algorithms can identify and profile consumer micro-segments with precision.

Granular psychographic and behavioural segmentation enabled by generative AI marketing allows targeting specific niches cost-effectively. Brands can personalise messaging and experiences for niches that are likely to convert based on their interests and attributes. Refined targeting expands reach and conversion rates.

For instance, Allegro, an eCommerce platform, utilised generative AI to segment users into over 500 sub-groups based on transaction history and activity. Targeting products to each group increased sales by 12-18% across various categories.

Let us now find out tools which can help you with this:

  1. Affinity:
    This platform analyses customer data to identify highly-specific audience segments and their preferences. Its AI models profile subgroups for precise targeting across channels.
  2. Albert:
    This tool leverages machine learning to segment audiences and map customer journeys based on behaviours. It identifies high-value niches to optimise targeting and personalization.

Strategies for Implementation of Generative AI in Marketing 

Implementing generative AI for marketing requires robust data pipelines, purpose-built models, and integration with workflows. Here are some tips for effective implementation of generative AI for marketing:

Implementation of Generative AI in marketing
  • Assess marketing objectives, pain points, and use cases to identify the appropriate applications for generative AI. Prioritise automation for repetitive and data-heavy tasks.
  • Build reliable data management pipelines from CRM, web analytics, social media APIs, and other sources to train AI models. Ensure legal compliance and data governance.
  • Use pre-trained models and frameworks like DALL-E and GPT-3 through API access for quick implementation. Invest in custom models for proprietary data.
  • Start with pilot projects in low-risk areas to test capabilities, build credibility, and optimise workflows. Gradual rollout allows smoothing adoption.
  • Review generative outputs to check relevance, accuracy, grammar, and brand alignment periodically. Refine datasets and models iteratively.
  • Develop protocols for legal compliance, ethics, and anti-bias measures in generated content. Monitor for problematic patterns continuously.
  • Upskill marketing teams to utilise generative tools through training in AI applications, data skills, and creative oversight. Ensure transparency in AI usage before using generative AI for marketing.

With strategic implementation accounting for data, model development, and skill building, generative AI can transform marketing efficiently.

Benefits of Generative AI for Marketers

Here are some key advantages generative AI confers to marketers:

Benefits  of Generative AI in Marketing
  • 24/7 productivity and output through automated content creation at scale
  • Reduced costs and resources needed for labour-intensive tasks like design, writing, and analytics
  • Improved personalization and segmentation for highly targeted campaigns
  • Faster experimentation by rapidly generating and testing multiple variations of content
  • More impactful storytelling through data-driven audience insights
  • Agile responses to market changes thanks to predictive modelling
  •  Consistent high-quality output and optimization for ROI goals

By augmenting human creativity with machine capabilities, generative AI can provide significant productivity gains. Marketers can divert time and effort from repetitive tasks towards high-value strategic initiatives by utilising generative AI use cases in marketing.

Risks and Limitations of Generative AI

Despite the immense potential of generative AI for marketing, some limitations and risks of generative AI include

  • High development costs for custom enterprise-grade models of generative AI and marketing.
  • Poor quality or logically inconsistent content if the generative AI model lacks sufficient or unbiased data
  • Data privacy concerns regarding personal or proprietary data usage
  • Legal risks around copyright, ownership of generated content 
  • Ethical issues like perpetuating harmful biases or spreading misinformation 
  • Security vulnerabilities from exposing APIs and data access to external vendors

Maintaining responsible oversight, evaluating output, and continuously enhancing the models are key to mitigating these challenges related to generative AI in marketing examples. Marketers should be cautious in adopting generative AI without rigorous testing and risk analysis.

The Future of Generative AI in Marketing

While still in its early stages, generative AI is primed to drive exponential change across marketing. According to Gartner, by 2025, generative AI will account for 10% of all data produced, enhancing productivity and data value in terms of generative AI for marketing.

  1. The Possibilities and Impact of Mainstream Adoption
    As generative AI becomes more advanced and accessible, mainstream integration across marketing is set to follow. According to research firm Gartner, 20% of enterprises will be using some form of generative AI by 2025.
  2. Hyper-Personalized Experiences
    Earlier, generative AI allowed the creation of targeted ads and recommendations for segments. With the mainstream integration of virtual assistants and chatbots, hyper-personalised conversations and experiences tailored to each customer will become possible.
  3. Immersive Product Engagement
    Advances in simulation and visualisation can enable interactive 3D product experiences using virtual or augmented reality. Rather than static images, consumers can engage directly with photorealistic 3D product environments generated just for them.
  4. Predictive Content Performance
    Tools like Anthropic's Claude can already estimate the click-through and conversion rates for marketing emails. As predictive abilities improve, generative AI may forecast performance metrics for content based on past data before launch.
  5. Automated Multichannel Campaigns
    Centralised, generative AI-driven systems can streamline multi-channel campaigns from strategy to execution. Assistants can coordinate organic and paid activities across platforms and publish optimised content rapidly.
  6. Democratized Creativity 
    Intuitive generative design tools empower marketers and small businesses to create visual assets, copy, videos, and more without technical skills. Automation makes excellent creative accessible to all and is one of the best examples of generative AI use cases in Marketing.

While promising, achieving this future requires addressing challenges around bias in data and models, privacy risks from personalization, and transparency in AI usage. With responsible development, generative AI can explore creative skills at scale and change the entire digital experience.


Artificial intellige­nce is opening new doors for storyte­lling in marketing. Through ongoing analysis, AI can craft unique, tailored narrative­s across various platforms to increase involveme­nt, recollection, and earnings. Whe­ther automating insights or developing products from scratch, there is many possibilities with gen AI for the marketing industry.

While ge­nerative AI is still a new conce­pt for many companies, forward-thinking marketing professionals unde­rstand its growing influence. By establishing plans and initial proje­cts centered on important applications, busine­sses can gain an edge ove­r competitors using generative AI for marketing. With guidance from expe­rts and careful supervision, gene­rative AI in the marketing industry offers an e­xciting chance to transform marketing practices by supporting human cre­ativity with automated insights.

Codiste, a trusted Artificial Intelligence Company, is one of the leading companies in the­ use of generative­ AI for marketing. Aiming at natural language processing and compute­r vision, we're creating e­xciting new uses for gene­rative AI to help markete­rs build captivating narratives and experie­nces. Codiste synergize­s human inventiveness with AI, e­nabling brands to amplify their reach on multiple platforms. Whe­ther it's automating making content or deriving data-drive­n insights, Codiste's AI solutions are leading the way for marke­ting's future. As generative­ AI becomes more re­cognized, Codiste, with its unique skills, is se­t to spearhead progress in this burge­oning field. 

Nishant Bijani

Nishant Bijani

CTO - Codiste
Nishant is a dynamic individual, passionate about engineering, and a keen observer of the latest technology trends. He is an innovative mindset and a commitment to staying up-to-date with advancements, he tackles complex challenges and shares valuable insights, making a positive impact in the ever-evolving world of advance technology.
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