Eric Duerr is CMO of predictive marketing platform Rocket Fuel.
What are the main ways AI/machine learning will impact marketers and their work in the next year or two?
We’ll see a larger divide of AI-fluency among brands and agencies and a new generation of predictive marketers. Those that master AI, will be able to adapt to the ever-increasing data availability by applying machine learning to harness and take action on this data. Companies will have full-fledged decisioning engines that will help them predict which experiences will resonate with an individual in a specific context, at an exact moment in time and deliverer that experience in real-time via an individualized brand experience.
In summary, what will be the long-term impact of AI/machine learning on marketing?
When implemented over a significant period of time, AI/machine learning reduces overhead, provides more accurate forecasting, better brand and direct response performance, increased brand relevancy, 1:1 brand personalization, dynamic insights, and measurable testing. In short, AI will be the most disruptive wave to hit marketing in a very long time.
How is AI/machine learning incorporated in the work you’re doing?
Rocket Fuel uses AI to evaluate unique consumer interactions and data to help brands to create more meaningful experiences. Our Predictive Marketing Platform, which is powered by AI, enables real-time interaction management for brands and agencies across the larger scope of marketing technology.
AI powered to our Predictive Marketing Platform allows:
- Agnostic data aggregation and identity management
- Dynamic user propensity scoring, interaction identification, and action response
- Response intelligence, insight curation, and recursive user intelligence
In your experience, is AI/machine learning already affecting what brands do, or are awareness and adoption still very limited?
AI is still nascent in terms of adoption. Many brands are just now learning what AI for marketing truly can do. Some brands are leveraging AI in various forms, but it’s commonly deployed as purposed-based for customer experience versus marketing efficiencies. Ultimately, awareness is high, and confusion/complexity is high, so adoption is still low.
