Adtech and martech companies are engaged in a fierce battle for audience attention. Customers are bombarded with thousands of ads and marketing messages every day, and the average attention span is plummeting, so it’s no wonder they tune out — or turn on ad blockers.

But it’s not all doom and gloom. The global adtech market is expected to grow at a rate of 22.4% through 2030, and martech’s projected growth rate is 18.5% through 2032.

This is driven by — and creating — unprecedented opportunities for the industries, and AI is one of the game-changing technologies making that possible. It can analyze data and spot patterns, helping business leaders make smarter, strategic decisions. Generative AI (gen AI) can learn from existing data to create new content. When used strategically, AI and gen AI can tackle adtech and martech’s key challenges while maximizing opportunities for growth.

Power advertisting and marketing success

Adtech and martech are primed to reap the benefits of AI and gen AI. Both industries create massive volumes of data that can feed AI algorithms to produce new insights. They also require expensive and time-consuming processes, such as A/B testing and lead scoring, that are perfect candidates for AI-enabled automation. Plus, both rely on the development of innovative strategies and content, which gen AI can enhance and optimize with data-driven insights.

Here are three key ways adtech and martech can use AI and gen AI to boost personalization, engage consumers and create more effective advertising and marketing strategies.

1. Optimize advertising and marketing content

Two out of five marketers spend at least half their budget on content. Maximizing the ROI on those dollars is crucial. AI can help adtech and martech companies optimize their strategies and processes to make their investments really pay off. 

• Content creation: Gen AI can produce text, images and videos that resonate with specific audiences. AI can analyze how well content is doing and suggest tweaks to make it better. 

• Automated creative testing: Gen AI can automatically create different versions of ads and marketing content, then see in real time which ones perform best. 

• Real-time bidding (RTB) optimization: AI models can sift through tons of data from RTB exchanges in milliseconds and adjust bids on the fly, leading to more cost-effective campaigns.

• Ad inventory valuation: AI can assess how specific ad inventory has performed in the past and predict its future value.

2. Improve audience segmentation

Traditionally, adtech and martech companies had to spend a lot of time and money analyzing data to segment audiences. Now, AI can automate and streamline this process, creating more detailed and accurate insights into audiences. 

• Precise audience segmentation: Generative AI allows marketers to use natural language to segment by using the full breadth of their data — combining behavior, preferences and demographics — enabling more accurate and relevant segmentation. With democratized access to insights, marketers can now build and refine segments independently, without relying heavily on technical teams, and empower faster, more-targeted campaigns.

• Real-time audience profiling: AI can continually update and refine audience segments with real-time data, letting advertisers adjust their targeting strategies dynamically for better results.

• Lookalike audience creation: Gen AI can craft highly accurate lookalike audiences by analyzing the traits of top customers, helping companies more accurately reach potential customers who have similar behaviors or interests. 

3. Enhance monitoring and measurement 

AI enhances measurement of advertising and marketing efforts with granular insights that give a clearer picture of performance.

• Spend recommendations: AI can recommend how to allocate ad and marketing spend across different platforms and channels, based on performance data and audience preferences.

• Cross-channel attribution: Gen AI helps cross-channel attribution by simulating and generating potential customer journeys based on historical data, giving marketers the ability to fine-tune attribution frameworks.

• Brand lift measurement: Gen AI generates synthetic survey responses and audience feedback patterns, giving marketers proactive insights into how to optimize campaigns for maximum brand lift before committing to a full launch.

• Marketing mix modeling (MMM): AI enables advanced MMM by generating alternative future marketing mix scenarios based on historical performance, allowing marketers to test and evaluate multiple strategies virtually, leading to more efficient media planning.

Modernizing your data strategy 

Gen AI facilitates a fundamental shift in how adtech and martech leaders work, strategize and engage with audiences. Every member of the C-suite will need to consider how AI will impact their business. To succeed in adopting AI, companies need a robust data strategy that ensures the data used to train AI models is high quality, relevant and accessible. 

Some important questions to ask before adopting AI:

1. How does this AI solution align with our overall data and business strategy and goals? And can the solution evolve with our business needs, as technology and the industry continue to evolve?

2. What is the total cost of ownership (TCO) for the solution or project, including additional costs like maintenance, training and future upgrades?

3. How confident are we in the quality, security and governance of our data foundation?

With those answers top of mind, business leaders can look at their current tools and strategies with fresh eyes and see where there is room for streamlining:

• Cost: The cost of building and maintaining an enterprise-grade data and AI stack for diverse platforms, tools and content; democratizing access to AI tools across teams; integrating AI technologies with existing platforms; and overcoming data silos between business units and partners can add up quickly — and take a long time to implement — when done using legacy systems.

• Complexity: Managing large volumes of diverse data types (e.g., videos, audio, metadata, etc.) across platforms incurs high costs due to data duplication and latency issues with a variety of service providers. Additionally, managing the expenses associated with large language models (LLMs) for content generation and audience analysis further escalates costs.

• Security and governance: Safeguarding personal data, overseeing AI tools and keeping up with regulations are key to maintaining trust and efficiency. Protecting intellectual property is also critical. As AI becomes more involved in content creation and distribution, effectively managing these security and governance issues, while ensuring consumer privacy and IP protection, is essential to reduce legal and financial risks and should be done carefully, with trusted partners.

A modern data strategy helps adtech and martech organizations prioritize data sharing and collaboration; break down silos; build an agile data infrastructure; and leverage third-party data, along with AI and native apps to enhance the customer experience.

By adding on a modern data platform, companies can reduce the complexity that comes with managing enormous volumes of complex, customized data, helping surface the information most valuable to driving innovation — and saving money.

The power of data and gen AI

These are just a few of the ways gen AI and a robust data strategy can help adtechs and martechs access and mobilize their advertising, media and entertainment ecosystem of data and solutions. Companies that tackle these challenges and make the most of new opportunities can make their ad and marketing efforts more effective and efficient, and stay ahead of the competition.

Ready to learn how Snowflake can help your adtech or martech organization harness the power of gen AI? Download our ebook, Gen AI in Advertising, Media and Entertainment: 4 Things You Need to Know.