Introduction

Artificial intelligence is no longer a futuristic concept; it is reshaping marketing strategies at an extraordinary pace. Brands today are exploring the question: What kind of data is generative AI most suitable for? Understanding this is crucial for marketers and founders aiming to scale campaigns efficiently. The right data not only drives creative outputs but also ensures campaigns resonate with the intended audience.

Generative AI in marketing allows businesses to create compelling content, personalized messages, and engaging visuals. Yet, not all data is equally valuable. Structured and unstructured data, customer behavior patterns, and historical campaign analytics all influence the quality of AI outputs. In this article, we’ll explore the types of data best suited for generative AI, illustrate real-life applications, and provide actionable strategies to harness AI effectively for marketing campaigns. KPMG

1. Understanding Generative AI in Marketing

1.1 What is Generative AI?

Generative AI refers to algorithms that can produce new content by learning patterns from existing datasets. Unlike predictive AI, which forecasts trends, generative AI creates original outputs like text, images, videos, and audio. This technology powers AI-driven campaigns that can scale content creation without compromising personalization.

For example, platforms like Hobo.Video leverage AI UGC and AI influencer marketing to generate videos and social content for campaigns, enabling brands to maintain high engagement without manual effort.

1.2 How Generative AI Uses Data

Generative AI requires large amounts of relevant data to produce meaningful outputs. It identifies patterns, learns from examples, and then generates new content aligned with brand objectives. For marketing, this includes:

• Past social media posts and engagement metrics
• Customer reviews and feedback
• Images, videos, and audio samples
• Market research and competitive analysis

The richer and cleaner the dataset, the more effective the AI-driven campaigns. Understanding the best data for generative AI ensures marketers invest in the right resources.

2. Structured vs Unstructured Data: Which Works Best?

2.1 Structured Data

When marketers hear the term “structured data,” they often think of neatly organized spreadsheets, CRM dashboards, or databases. This type of data is clean, quantifiable, and easy for machines to process, making it the perfect foundation for targeted campaigns. Examples include:

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• Customer demographics
• Purchase history
• Email engagement metrics

Generative AI can use structured data to segment audiences and generate personalized content. For instance, a fashion brand can generate tailored email campaigns based on purchase history and engagement rates.

2.2 Unstructured Data

Unlike structured data, unstructured information is messier but often richer in emotional and contextual value. It reflects the voice of customers in their own words, images, or behaviors insights that can be gold for creative storytelling. Unstructured data often comes from:

• Social media posts
• Video content
• Customer reviews
• Forum discussions

This data is especially valuable for AI marketing tools for content creation. Generative AI can analyze unstructured datasets to identify sentiment, preferences, and trending topics, then produce UGC Videos, ad creatives, or social content that resonates with audiences.

2.3 Combining Structured and Unstructured Data

The most powerful campaigns often combine both data types. Structured data guides targeting, while unstructured data fuels creativity. For example, AI-powered marketing tools can merge sales analytics with social listening data to generate influencer marketing campaigns that are both relevant and visually engaging.

3. Best Data Types for Generative AI in Marketing

3.1 Customer Interaction Data

Every click, comment, or message a customer leaves behind tells a story. These interactions form one of the richest data sources for generative AI, because they reveal real behavior instead of assumptions. By studying how customers engage, brands can create content that feels personal and relevant. The data includes:

• Produce personalized email content
• Generate chatbots with human-like responses
• Craft engaging social media captions

Brands that leverage this data often see higher engagement rates and better ROI.

3.2 Visual and Multimedia Data

In today’s attention-driven digital space, visuals often speak louder than words. Generative AI can turn raw images and videos into creative assets that capture audience attention. By tapping into multimedia data, marketers can build campaigns that are not only informative but also emotionally engaging. The data includes:

• Create video ads for UGC campaigns
• Generate product visuals for influencer marketing India
• Develop creative content for multiple platforms

Platforms like Hobo.Video utilize AI UGC tools to generate multimedia assets that maintain brand consistency.

3.3 Historical Campaign Data

Past marketing campaigns provide insights into audience preferences, timing, and format effectiveness. Generative AI can analyze this data to predict which type of content will likely perform well. This is part of best practices for AI-driven marketing data, allowing brands to optimize campaign strategy while reducing guesswork.

3.4 Trend and Market Data

Analyzing trends, competitor campaigns, and emerging topics allows generative AI to produce content that aligns with audience expectations. This type of data is crucial for:

• Influencer marketing campaigns
• Social media posts for viral potential
• Product launch strategies

Leveraging AI for creative marketing campaigns ensures relevance and timeliness.

4. Real-Life Applications in Marketing

4.1 Influencer Marketing

Influencer campaigns thrive on fresh, relatable, and visually engaging content. But creating such content at scale is a constant challenge for brands. This is where generative AI steps in, helping marketers design posts, videos, and captions that match an influencer’s unique style while staying true to brand identity.

• Create AI UGC videos
• Generate post captions
• Adapt content for different demographics

This approach is widely used by top influencer marketing companies in India, enabling brands to scale without losing personalization.

4.2 Social Media Content

Social platforms are relentless, they demand a steady stream of fresh, eye-catching posts to keep audiences engaged. Generative AI makes it easier for brands to meet this demand by producing diverse content formats tailored to each platform’s audience behavior and trends.

• Carousel posts
• Short-form videos
• Engagement-focused graphics

For example, campaigns on Instagram can use AI to produce content optimized for interaction with famous Instagram influencers and top influencers in India.

4.3 Email Marketing

Generative AI can create personalized emails for each segment using structured data like purchase history or engagement metrics. The result is higher open rates, click-through rates, and conversions, aligning perfectly with data-driven marketing strategies.

4.4 Paid Ads and Campaigns

AI-powered marketing tools can generate ad variations, test headlines, and optimize visuals using both structured and unstructured data. This reduces human workload while maintaining creative authenticity, a strategy many Indian D2C brands now adopt.

5. Leveraging Data for AI-Driven Campaigns

5.1 Segmentation

Segmenting audiences using structured data like demographics and behavior improves AI output relevance. Generative AI can then generate content specific to each segment. Segmentation is the backbone of data-driven marketing. By breaking audiences into smaller, more specific groups, brands can make their campaigns far more targeted and relevant.

5.2 Personalization

Personalization is key in influencer marketing. Using interaction data, generative AI can produce content that feels tailored, creating a stronger bond with consumers. Today’s consumers expect more than generic content—they want experiences that feel designed just for them. Generative AI helps marketers deliver this kind of personal touch at scale.

5.3 Content Diversity

AI-driven campaigns benefit from a variety of content types. Combining text, visuals, and video ensures campaigns are engaging across all platforms, particularly when working with top influencers in India or AI influencer marketing strategies. Relying on a single content type is risky in today’s crowded digital space. Generative AI enables brands to diversify their content strategy, ensuring they connect with audiences across multiple formats and platforms.

6. Metrics for Evaluating AI-Generated Marketing Content

1. Engagement Rates: Likes, shares, comments, and view time
2. Conversion Metrics: Click-through and sales rates
3. Campaign ROI: Comparing AI-driven campaign performance vs manual campaigns
4. Sentiment Analysis: Feedback from comments, reviews, and reactions

Using these metrics, brands can continually refine best practices for AI-driven marketing data and achieve better outcomes.

7. Challenges in Using Generative AI

• Data Quality: Poor data leads to irrelevant outputs.
• Bias in AI: Training data must represent diverse audiences.
• Computational Resources: High-quality generative AI requires significant processing power.
• Skill Gaps: Teams need to interpret outputs and adjust campaigns effectively.

Addressing these challenges ensures generative AI delivers maximum value for marketing campaigns.

8. Future Trends in Generative AI Marketing

• Increased adoption of AI for UGC Videos in campaigns
• Real-time content generation for social media trends
• Hybrid strategies combining generative AI with predictive AI for campaign optimization
• Enhanced personalization in influencer marketing India

Brands partnering with platforms like Hobo.Video can stay ahead by integrating AI tools intelligently, balancing creativity and data-driven decision-making.

Conclusion : Key Takeaways

• Generative AI is most effective when fed structured and unstructured data from customer interactions, trends, and multimedia sources.
• Combining historical campaign data with market insights enhances content quality.
• Best practices include segmentation, personalization, and content diversity for AI-driven campaigns.
• Platforms like Hobo.Video make it simple to leverage AI for UGC Videos, influencer campaigns, and creative marketing strategies.

Understanding what kind of data is generative AI most suitable for empowers marketers to optimize ROI, reduce manual workload, and create campaigns that resonate.

About Hobo.Video

Hobo.Video is India’s leading AI-powered influencer marketing and UGC company. With over 2.25 million creators, it offers end-to-end campaign management designed for brand growth. The platform combines AI and human strategy for maximum ROI.

Services include:

• Influencer marketing
• UGC content creation
• Celebrity endorsements
• Product feedback and testing
• Marketplace and seller reputation management
• Regional and niche influencer campaigns

Trusted by top brands like Himalaya, Wipro, Symphony, Baidyanath, and the Good Glamm Group.

If you’re a brand ready to break the mold and achieve hypergrowth, we’re already on your wavelength.Let’s build something powerful together.

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