Michael Tambe, Head of Data Science for Amazon Advertising Field Sales, discusses the limitations of prescriptive analytics in sales and the importance of generative AI in retail. He emphasizes the establishment of infrastructure for generative AI and the integration of physical and digital elements in the retail industry. The podcast also explores the challenges and uncertainties of conversational AI.
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Quick takeaways
Prescriptive analytics in sales should shift towards a more conversational approach using generative AI.
Generative AI can customize recommendations by incorporating real-time information and second-party data in a customer-centric sales strategy.
Deep dives
The Limitations of Prescriptive Analytics in Sales
The guest, Michael Tamby, highlights the challenge of implementing prescriptive analytics in sales. While prescriptive analytics can provide recommendations and task lists for sales reps, it often doesn't account for the fact that customers set the agenda. The perspective should shift towards a more conversational approach, where generative AI can be utilized. The conversation emphasizes the importance of second-party data, which includes information that customers convey to salespeople during conversations that may not be captured in traditional data collection processes.
Applying Generative AI in Sales Processes
Generative AI can play a crucial role in customizing and adapting recommendations for sales reps. By enabling a more conversational and flexible approach, generative AI can incorporate real-time information and second-party data, allowing recommendations to be tailored to the specific needs of each customer. This approach involves moving away from rigid, linear processes and embracing a more adaptive and customer-centric strategy in sales.
Potential Applications of Generative AI in Retail
In the retail industry, the utilization of generative AI can optimize existing customer relationships. By summarizing and sorting insights from various metrics and signals, generative AI can provide sales reps with human-digestible summaries that account for different aspects of a customer's profile. Additionally, the podcast discusses the evolving nature of the retail industry and the need for traditional retailers to adapt to a mix of digital and physical experiences. It highlights the importance of collecting data and engaging customers in a conversational manner, while also considering the psychological factors that may influence customer acceptance of AI-driven technologies within retail spaces.
Today’s guest is Michael Tambe, Head of Data Science for Amazon Advertising Field Sales. Mike has led data science efforts in sales and marketing and leading edge companies like Amazon Ads and LinkedIn. Through these experiences he’s become an advocate of enterprises building a “data driven go to market engine.” He joins Emerj Senior Editor Matthew DeMello on today’s podcast to talk about what that means, along with the challenges and possibilities of new emerging AI capabilities. If you’ve enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!
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