Embracing AI in Media Planning and Buying
October 4, 2023
Like all ‘leaps forward,’ AI can provide significant positive change; AI-driven technology is already revolutionizing and disrupting business practices across industries. It is challenging what we believe is possible when human intelligence is optimized, not cannibalized, with next-generation technology.
In the dynamic and competitive media agency landscape, the adoption of new technology, like AI, can be the difference between growth and obsolescence. AI’s algorithms can drive greater success (create a competitive edge) via the ability to fuel more effective audience segmentations, predictive modeling, dynamic ad personalization, and real-time optimization. Technology media platforms (e.g., Google, Meta) have embedded AI in their solutions.
AI is continuing to ‘level the playing field’ for mid-sized and smaller media agencies with media agencies wielding significant scale (e.g., billings, resources, dollars in market). The continued shift to programmatic buying of media channels (including some linear) and automation are mitigating size in favor of technological and data prowess.
Many media agencies are utilizing AI in some manner, with larger agencies (e.g., holding company brands) well down the path of adoption (technology and partnerships). For those who have only ‘dipped a toe’ in the AI water, or have yet to onboard AI solutions, it is time to embrace and optimize your media planning and buying processes with AI tools.
To help guide your journey we have outlined how to approach integrating AI into your media planning and buying processes – from core benefits to considerations to a step-by-step integration guide. Specifically (links are clickable to connect with what interests you most):
- Core AI Benefits for Media Agencies
- Critical AI Tools for Media Agencies
- AI Considerations for Media Agencies
- Onboarding and Activating AI: A Step-By-Step Guide for Media Agencies
- AI Tools and Platforms for Media Agencies to Consider
- Take Action: The Time to (Significantly) Embrace AI is Now
Core AI Benefits for Media Agencies
With its advanced algorithms and analytical capabilities, AI empowers media agencies to better leverage vast amounts of data, automate processes, unlock strategic insights, streamline operations, and make data-driven decisions at scale… and with unprecedented precision.
ENHANCING DATA ANALYSIS AND INSIGHTS
- Audience Segmentation/Targeting: AI dives deeper into complex datasets, extracting actionable insights for more precise audience segmentation (demographic and behavioral) and messaging. The results are enhanced campaign performance, increased engagement, and improved conversion rates.
- Personalization: AI can deliver personalized experiences at scale by analyzing consumer preferences, historical interactions, and contextual cues to create tailored messaging and content. This level of personalization nurtures deeper brand loyalty, increases customer satisfaction, and drives better campaign outcomes.
- Campaign Optimization: AI techniques ‘fine-tune’ campaigns in real-time via continuous analysis of performance metrics. Algorithms can automatically identify patterns adjust, and optimize media placements, ad creative, and targeting strategies. This dynamic optimization enhances campaign efficiency, boosts ROI, and maximizes the impact of media investments.
INCREASING AUTOMATION AND EFFICIENCY
- Streamlined Media Planning and Buying Processes: AI alleviates labor-intensive tasks by automating various aspects of the media planning and buying process. From data gathering and analysis to media selection and negotiation, AI-powered tools enable agencies to expedite workflows, reduce manual errors, and allocate resources efficiently.
- Advancement of Programmatic: AI is changing how media agencies place messaging across digital channels. Programmatic platforms leverage AI to analyze vast amounts of data in real-time and enable automated media buying, bidding strategies, and audience targeting decisions to improve efficiency, scalability, and performance.
- Social Media Management: AI-powered tools automate and manage social media platforms more effectively – from sentiment analysis to identifying trends to content scheduling and community management.
- Content Creation and Curation: AI can create personalized, engaging content at scale. Natural language generation (NLG) algorithms produce compelling articles, product descriptions, and ad copy, while AI-powered recommendation engines curate relevant content for audiences to foster deeper engagement and drive content discovery.
- Optimal Resource Allocation: Utilize AI tools in making informed decisions regarding resource allocation via predictive analytics and machine learning models. They also identify high-performing channels, optimize budget allocations, and allocate resources based on data-driven insights, ensuring maximum ROI and campaign effectiveness.
IMPROVING DECISION-MAKING (IN REAL-TIME)
- AI-Powered Predictive Analytics Models: Leverage AI-powered predictive analytics to anticipate consumer behavior, campaign performance, and market trends. Through historical data and identifiable patterns, the models provide actionable insights for informed decision-making and proactive campaign strategies.
- Data-Driven Media Strategy: AI solutions fuel the development of data-driven media strategies by integrating a comprehensive understanding of consumer insights, market trends, and performance data to optimize the delivery of the right message in the right channel and at the right time.
- Chatbots and Virtual Assistants: AI-powered chatbots and virtual assistants provide instant, personalized support to brands and audiences to streamline customer interactions, resolve queries, deliver tailored recommendations, and improve customer satisfaction and brand relationships.
- Competitive Advantage through Intelligent Automation: Gain a competitive edge by automating routine tasks to allow for a focus on higher-value activities, such as strategic planning, creativity, innovation and client relationship management.
By harnessing the power of AI, media agencies can elevate their capabilities, drive better
results for clients, and be more competitive.
Critical AI Tools for Media Agencies
Key types of AI that can revolutionize the way media agencies traditionally operate include:
- Machine Learning (ML)
ML algorithms (autonomously) identify patterns, uncover hidden correlations, provide predictions, and generate actionable insights by analyzing historical campaign data, consumer behavior, and market trends. In turn, media planners and buyers can optimize ad placements, allocate budgets more effectively, and deliver highly targeted messaging, ultimately enhancing campaign performance and return on investment (ROI).
- Natural Language Processing (NLP)
NLP provides the tools to extract insights from the ever-growing breadth of unstructured text data to identify emerging trends, extract relevant keywords, and provide a comprehensive understanding of public opinion surrounding brands, products, or campaigns. Its algorithms analyze, interpret, and parse sentiment of textual content, including social media posts, customer reviews, and online articles to facilitate more informed data-driven decision-making to refine messaging strategies, craft engaging content, and respond to emerging narratives.
- Computer Vision (CV)
CV algorithms provide the ability to analyze and interpret images and videos at scale. Through object recognition, image tagging, and scene understanding, CV identifies visual elements within content, such as brand logos, objects, or demographics of individuals. This information is leveraged to tailor messaging by audience, ensure brand consistency across platforms, and gauge the impact of visual content within a campaign.
- Predictive Analytics (PA)
AI fuels predictive analytics and the ability to forecast outcomes (using historical data and patterns) enabling agencies to anticipate consumer behavior/preferences, campaign performance, engagement patterns, and market trends.PA models facilitate proactive data-driven decisions for optimal media placement, campaign scheduling, and budget allocation to deliver a competitive edge and maximize the effectiveness of initiatives.
- Deep Learning (DL)
DL is a subset of ML algorithms and is an additional tool in processing and understanding complex data sets. With its ability to analyze vast amounts of structured and unstructured data, DL algorithms excel at uncovering intricate patterns and relationships. With deeper audience insights, further personalization of ad targeting, and optimization of creative messaging is possible, resulting in enhanced engagement and higher conversion rates.
- Hyper-Personalization: AI algorithms deliver hyper-personalized content and experiences allowing for more highly targeted messaging, customized recommendations, and personalized advertising campaigns resonate with individual consumers on a much deeper level.
- Voice Recognition (VR): Voice recognition taps into the booming voice search market. AI-driven voice recognition algorithms optimize content for voice queries, develop voice-activated ads, and deliver seamless voice experiences, enhancing brand visibility and accessibility.
- Augmented Reality (AR) and Virtual Reality (VR): AR and VR technologies are (finally) poised to significantly impact the media landscape with enhanced, personalized, immersive experiences and interactive storytelling that captivates consumers in new and exciting ways.
These AI-enabled technologies provide media agencies with the tools to harness better and activate against data-driven insights to create more relevant, proactive, engaging, and impactful media experiences for their brands and consumers.
AI Considerations for Media Agencies
When adopting AI tools, it is important to consider the Ethical (data privacy, algorithmic biases, maintaining transparency), Skillset (training, specialists, balancing the human touch), and Resource (data, hardware, software, systems, talent, legal) challenges and needs.
A. ETHICAL CONSIDERATIONS
- Data Privacy/Security: AI’s access to vast amounts of data it reviews/assesses raises data privacy and security concerns. Review and evolve your agencies stringent data protection rules in handling sensitive consumer information to address AI related potential threats.
- Bias in Algorithms: AI algorithms can develop biases depending upon the data with which they are trained. Be mindful of the potential biases that can manifest in algorithmic decision-making, such as gender or racial biases, and implement a rigorous testing, evaluation, and ongoing monitoring process to avoid.
- Transparency: Clearly communicate how AI is used in processes, including data collection, targeting methods, and decision-making algorithms, and identify of any AI-created content.
B. SKILLSET CONSIDERATIONS
- Train Employees on AI: Invest in training to upskill your employees on AI concepts, tools, and applications to foster innovation, ensure AI tools are used effectively, drive successful integration into existing workflows, and convey the potential pitfalls of misuse.
- Hire AI Specialists: Recruit AI specialists, as operations more and more rely on AI, to provide strategic guidance for proper adoption and usage.
- Balance of Automation and Human Expertise: While AI handles repetitive tasks and data analysis at scale, human critical thinking, creativity, and strategic decision-making are invaluable to overall success.
C. RESOURCE CONSIDERATIONS
- High-Quality, Reliable Data: Accurate AI outputs require data cleaning and preprocessing to ensure data integrity and to mitigate the impact on AI-driven decisions of any potential biases.
- Hardware, Software, and Storage: Implementing AI technologies requires robust hardware, software, and storage capabilities. Evaluate your infrastructure needs –high-performance computing, cloud-based platforms, and scalable storage solutions – to ensure internal processes can handle large volumes of data.
- Integration with Systems: Assess your tech ecosystem for compatibility and interoperability between existing platforms and new AI tools. Plan and execute integration with consideration for data flow, security, and efficiency.
- Legal Alignment on Implications: AI-driven media planning and buying can have legal implications, such as compliance with data protection regulations and intellectual property rights. Work closely with legal teams to protect the agency and mandate the responsible use of AI technologies within operations.
- Continuous Monitoring, Evaluation, and Adaptation: The bandwidth to have an ongoing assessment of AI performance, explore new applications, and stay updated on emerging AI trends and technologies to keep ahead of the competition and deliver impactful results.
Onboarding and Activating AI: A Step-By-Step Guide for Media Agencies
Integrating AI into your media operations requires a strategic and well-planned approach to is essential for success. Key steps include:
- Identify Goals: Clearly define goals and objectives for the role of AI in augmenting your media planning and buying (e.g., improved audience targeting, predictive analytics).
- Consult with Experts: Enlist guidance from AI experts in selecting the most appropriate AI tools (suitability, capabilities, and potential impact) for achieving the identified goals.
- Create Engagement Guidelines for Employees: Clear, concise rules of on employees engagement with AI are critical to protect the company and its clients from potential legal and ethical issues. Governance details should be captured in an Acceptable Use Policy (AUP) and made available to all employees. Central tenants for your AUP can be found in The White House’s AI Bill of Rights proposed outline (www.whitehouse.gov/ostp/ai-bill-of-rights/).
- Address the ‘Fear’: Employees are concerned their position will be replaced by AI. Clarifying the role of AI as a tool for augmenting their efforts, not replacing them, will go a long way.
- Ensure Data Cleaning Process: High-quality and clean data is essential for accurate AI outputs. A robust data-cleaning process to eliminate errors, inconsistencies, and irrelevant information from datasets is necessary for AI algorithms to receive accurate inputs and achieve optimal performance.
- Provide (Continuous) Learning: Train employees on AI’s benefits, challenges, and utilization. Foster a culture of ongoing learning encouraging employees to stay updated on the latest AI trends, technologies, and best practices.
- Develop Implementation Plan: IT, Data, and media teams need to collaborate in developing a detailed implementation plan covering data integration, infrastructure requirements, scalability, and system compatibility.
- Employ Agile Implementation: Embrace agile methodologies and iterative approaches to reflect changing trends and technological advancements in AI. Break down complex projects into smaller, manageable tasks to implement AI solutions more efficiently and flexibly.
- Collaborate and Partner: Identify and create affiliations with AI providers, research institutions, and startups to tap into the expertise and resources needed to leverage emerging AI trends effectively.
- Thought Leadership and Industry Involvement: Participate in industry events, conferences, and thought leadership initiatives to stay connected with the latest trends and developments. Share insights, contribute to discussions, and showcase expertise to position yourself as leaders in the new, AI-driven media landscape.
- Address Legal Compliance Needs: Review existing and assess new contracts and ensure alignment with legal requirements and considerations related to AI integration (e.g., data privacy, intellectual property rights, and compliance with regulations).
- Address Ethical Considerations: Proactively address and develop guidelines for potential biases, ensure transparency in AI-driven decision-making processes, and establish guidelines for responsible AI use.
- Monitor and Evaluate AI Performance: Establish metrics and feedback loops for continuous monitoring and assessing of AI’s effectiveness, identification of areas for refinement, and exploration of new applications aligned to trends and your evolving goals.
- Ensure a Balance with Human Critical Thinking: While AI technology offers tremendous benefits, it should not replace the unique insights and creativity people bring to the process. Toyota refers to this as “autonomation” (autonomous and automation), which translates into “automation with a human touch.”
AI Tools and Platforms for Media Agencies to Consider
In our experience, comprehensive research for the right AI tools is critical in identifying the right tools for your needs that are truly AI fueled. The following are examples of AI capabilities that address core needs of media agencies.
For employees proven AI tools to consider include:
- OpenAI’s ChatGPT (https://chat.openai.com/chat): A dialogue-driven interface using advanced AI techniques to provide clear, instant, and humanlike text-based answers to questions or queries based on natural language.
- OpenAI’s Dall-E 2 (https://openai.com/dall-e-2/): An AI system that can create realistic images and art from a description in natural language. From almost any query, it produces content in a range of styles and can also add and/or remove elements from existing images.
- Jasper (https://www.jasper.ai/): An AI content generator that creates all types of content in a variety of languages (26 to be exact). With the Chrome extension users can create content directly within social media platforms.
- Omneky (https://www.omneky.com/): An AI-driven, cross-platform creative tool designed to develop personalized ads powered by OpenAI.
For media agency processes AI tools to consider include:
- Mint AI (mint.ai): Focuses on helping consolidate resources, processes, workflows, and information into a single system to better support their advertising operations. Utilize AI to provide predictive recommendations for media plans, channel-by-channel, in real-time.
- Mediatool (mediatool.com): An intuitive interface, providing collaboration features, media buying capabilities, budgeting, reporting, and topline presentations.
- Hive AI (thehive.ai): Leverages AI to evaluate emerging market trends, competitive positioning/benchmarking, and measures share-of-voice in (near) real-time across platform channels.
- Sherloq by IBM Watson (sherloq.app): Seamlessly integrates AI and ML with a brands website and marketing campaign data to allow visits converting to be rated/valued. Sherloq’s AI uses this data to ‘learn’ which visits are more valuable than others to optimize plans.
Take Action: The Time to (Significantly) Embrace AI is Now
The future of media planning and buying is AI-powered. Integrating AI into your media planning and buying processes is a critical necessity for staying competitive in today's rapidly evolving marketplace. With the right strategies, education, and a commitment to ethical and responsible AI use, your media agency can harness the full potential of AI to be more efficient and effective.
Your journey to meaningful AI adoption may be complex, but the rewards are substantial – enhanced campaign performance, improved ROI, streamlined processes, and the ability to navigate the evolving media landscape with confidence.
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