Brenden Sommerhalder

Vice President, MQO Research

Brenden is an innovation-minded "people insights hunter" with over 15 years of leadership in research, decision-making, and marketing strategy development. He has worked with clients across public, private, and not-for-profit sectors in a variety of industries such as government, utilities, consumer packaged goods, telecommunications, hospitality, retail, pharmaceuticals, and media. As Vice President at MQO Research, over the past year Brenden has led the research and development of an internal suite of A.I.-powered tools and services, and led the successful "go-to-market" of the firm's client-facing SaaS platform: the MQO A.I. Hub. As a “techie” and “tinkerer” at heart and a hardened “quant” at mind, Brenden has always been quick to adapt and adopt the latest tools available to solve the task at hand while protecting the integrity of the work through careful planning and rigorous validation.

Session: Act Like an Expert Market Researcher: A.I. Wrangling and the Obvious Need for Experts

It has been well over a year since ChatGPT turned the world's attention to the incredible developments in A.I. capabilities -- and we're all still here. This session will demonstrate how A.I. has not only failed to supplant experts, but rather further solidified our role in delivering high-quality and high-impact insights for our clients. We’ll take a step toward the deep end about the nuances of A.I. models and their configurations, prompt engineering, fine tuning, and functional case examples demonstrating how they can be added to professional workflows. We'll discuss an array of A.I. model variables and how, as expert practitioners, we can manipulate these parameters to suit our needs. Whether it's adjusting a model's knobs such as temperature, frequency penalty, or nucleus sampling, or strategies like sequencing A.I. operations to break up a task, we will demonstrate with hands-on examples how proficiency with A.I. is a unique tool for expert and innovation-minded researchers. As professionals, we have always maximized our effectiveness by bringing to bear a deep understanding of the subject matter and our clients’ needs combined with the tools at our disposal. When it comes to using A.I., this interaction effect is amplified: Getting these tools to do good work requires not only being able to critically assess and iterate their output, but also to set them in the right direction in the first place.