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Ai-assisted media buying & planning: A strategy for advertisers

Artificial intelligence (AI) is spurring a new generation of process evolution. At Allscope, integrating AI into planning and buying not only boosts efficiency, but improves targeting, optimization, and measurement.

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Media buying and planning play a key role in marketing success. Agencies have applied technology to improve buying and planning processes going back to the Madmen era.

The Role of AI in Media Buying and Planning

  1. Enhanced Targeting Capabilities. AI can uncover untapped media opportunities for media planners and buyers. Machine learning algorithms can analyze consumer behavior, segment audiences, and predict outcomes. For example, natural language processors can review social media dialogue and identify topics and keywords used by specific audience groups. Predictive analytics models built into CRM platforms, like Salesforce or Optimove, can generate sales propensity scores based on prior purchases or customers’ browsing behaviors. Additionally, AI can analyze past media plans, reviewing historical data, audience demographics, and campaign performance. Applying this technology to scale “look-alike” target audiences allows Allscope to examine target behaviors across thousands of datapoints instead of scaling audiences based on demographics, only.

  2. Real-Time Optimization. We use AI algorithms to continuously analyze data and adjust media placements in real time. This ensures that our ads reach the right audience at the right moment, maximizing impact and minimizing waste. Additionally, we have automated related  sophisticated tasks, like monitoring ad performance or adjusting bids on real-time bidding platforms. This frees agency resources for higher-level decision-making and complex problem-solving.

  3. Attribution. AI improves our cookie-free multitouch attribution (MTA) by recursively parsing all the touchpoints to better calculate how each one impacts KPI’s. Instead of relying on fixed weights for different points on the customer journey, this approach is purely data driven and results in more accurate attribution that continues to evolve over time.

  4. Modeling. Similarly, AI helps us build better models for determining return on ad spend based on the relationship between changes in spending level and results over time. Instead of building a single media mix model (MMM), we use AI to iterate through thousands of variations until determining the best statistical fit.


Challenges and Risks

  1. Data Privacy Concerns: Implementing AI requires handling sensitive data. Ensuring compliance with privacy regulations is crucial.

  2. Integration with Existing Systems: Integrating AI tools with legacy systems can be complex. Companies must plan for seamless integration.

  3. Ethical Considerations: As AI becomes more prevalent, ethical concerns around its use need to be addressed. Transparency and fairness are essential.


The Future of AI in Advertising

Newer AI technology is reshaping media buying and planning by offering efficiency, precision, and real-time insights. AI offers powerful tools for processing massive quantities of data, streamlining repetitive tasks, and punching out iterative options. However, AI remains only as good as the data it is trained on and the people applying it. Today’s AI does not replace strategic thinking, higher level decisioning, or creativity in the planning process. Today’s AI augments human intelligence but it does not replace it.



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