Database architecture for future AI — Brian McKenna // DMi Partners
- Part 1Leveraging AI in email tools — Brian McKenna // DMi Partners
- Part 2 Database architecture for future AI — Brian McKenna // DMi Partners
Show Notes
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02:30Data cleanliness for AI powered email marketingIn a future AI-driven email decision-making landscape, data accuracy will be paramount. Organizations must assess current data reliability, discard unreliable data, and establish systems to collect data in a reliable and actionable fashion in the future.
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03:59Data quality and AI challengesThe importance of good data for businesses remains, but with the evolution of AI, potential issues with data cleanliness become more significant. The impact on results and potential risks could be more pronounced as we move towards greater automation in email creation.
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05:04Data concerns in AI and email marketingAs clients move beyond the initial phases of AI use in email marketing, there is uncertainty around the data being made accessible to these models. Concerns revolve around the data source and the degree of confidence in the data provided to language algorithms.
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06:24Database architecture for LLMsDespite the rise of AI, cleaning and organizing data so its easier to pull into the decision trees of an email journey or an email remains crucial. The introduction of AI amplifies the scale at which we can leverage clean data for more personalized emails.
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07:59Data collection for AI driven email marketingCurrently, data collection focuses on data relevant to email content decisions. However, in the future, it will involve collecting and feeding comprehensive click-based data to the LLM to enable automated decision-making and model optimization.
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09:54The future of analytics and email marketing attributionThe future involves collecting more data to connect email marketing with offline purchases and behaviors. Analytics will potentially reduce our current reliance on panel data and pave the way for better attribution analysis of email impact.
Quotes
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"If we're thinking about a future state where we'll be leveraging AI to make decisions on emails, we must ensure all that data that is being fed into it is accurate." - Brian McKenna
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"Today, if we have clean data, we're going to use it to deliver much more personalized emails. With AI, it's a magnitude of scale. We could leverage it for clients to quickly and efficiently deliver better emails." - Brian McKenna
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"Think through how we can collect more data that can be leveraged to tie any engagement with our emails back to offline purchases, and other behaviors. That is going to be a path forward." - Brian McKenna
- Part 1Leveraging AI in email tools — Brian McKenna // DMi Partners
- Part 2 Database architecture for future AI — Brian McKenna // DMi Partners
Up Next:
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Part 1Leveraging AI in email tools — Brian McKenna // DMi Partners
Brian McKenna, VP of CRM at DMi Partners, delves into artificial intelligence in email and database architecture. From optimizing send times to product feed recommendations based on subscriber data, AI is already an integral part of our email marketing efforts. However, the spotlight is on GPT tools, promising an automated content delivery system where AI can scale delivering the right content to the right subscriber at the right time. Today, Brian discusses leveraging AI in email tools.
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Part 2Database architecture for future AI — Brian McKenna // DMi Partners
Brian McKenna, VP of CRM at DMi Partners, delves into artificial intelligence in email and database architecture. As we proceed toward a future where our marketing emails are thoroughly automated by AI, data cleanliness must be a top priority. Ultimately, feeding large language models inaccurate data can lead to misinformed decision-making, reduced personalization, and diminished effectiveness in reaching and engaging the target audience. Today, Brian discusses database architecture for future AI.
