Going beyond demo & psychographics to understand human motivations — Bryan Gernert // Resonate

Show Notes

• 01:08
  Resonates journey and use of AI

  Resonate was started 14 years ago in order to go beyond basic demographics to understand people at scale and why they make decisions. Theyve been leveraging AI for over a decade and witnessed an acceleration in AI's significance due to the explosion of generative AI.
• 03:15
  Resonates integration of large language models into its solution

  Resonate has developed a capability where people can ask their platform and their data to tell them what is most important about any audience and receive relevant data. Although it's in the alpha stage, the feature is expected to be available to all customers within 90 days.
• 05:18
  How Resonate approaches data collection and privacy

  Resonate developed its own first-party ground truth of the US population via opt-in long-form surveys linked to real-world interactions and demographics. Their AI infrastructure helps to keep track of how whats important to consumers is changing.
• 10:14
  Contextualizing behavior data

  Behavioral data and other data sets serve as signals for Resonates models. Furthermore, they go a level deeper to understand the contextual nature of customer engagements in terms of whats most important and relevant to them during the engagement.
• 11:57
  Leveraging consumer data for meaningful insights about their values

  Resonate contextualizes its behavioral data based on what consumers engage with and their interests. They leverage NLP to understand consumer actions and preferences and figure out what matters most to consumers, how theyre changing, and make predictions.
• 13:13
  Leveraging unstructured data for decision making

  Resonate collects a vast amount of unstructured data, including free text data, which they organize using natural language processing. This data is obtained from diverse sources, and the focus is on leveraging unstructured information to inform decision-making and modeling.
• 14:07
  Resonate's approach to data analysis and precision modeling

  Resonate is currently using generative AI and LLM models, but these models are primarily focused on understanding and contextualizing data. They use a combination of XGBoost and others to do precise modeling based on data input.
• 15:46
  Overcoming early challenges in scaling data collection and infrastructure

  Resonate initially faced challenges in data collection scalability and the cost of setting up its own hardware. But the evolution of hardware, software, and distributed computing has since provided the foundation necessary for Resonate's data-driven approach and AI capabilities.
• 18:39
  Accelerating data driven marketing with AI insights

  AI and data-driven marketing will enable more advanced starting points for tasks like segmentation and creative work. The knowledge for such tasks will be integrated into solutions, bringing marketers closer to the finish line in executing data-driven strategies and understanding consumer values.
• 20:37
  Staying relevant in an AI and data driven marketing landscape

  To stay relevant as knowledge workers, individuals should adopt the "Four E's" approach: Embrace AI and technological advancements, Explore new possibilities and trends, Experiment with different approaches, and Execute strategies to adapt to the evolving landscape and maintain expertise.