Measuring the value of artificial intelligence in improving search and chatbot outcomes
Abstract
This paper recommends that the best way to measure the success of artificial intelligence used in internal search or chatbots is not to use the data driving these improvements, but rather to tune a standard user behaviour measurement system to the metrics that matter and use carefully constructed experiments to assess the return on investment. The paper also maintains that digital marketers can learn a great deal about their customers and products from these enhancements, including what products need the most online marketing help. Through this process, the marketer can learn as much about the system as the system is learning from its users.
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Author's Biography
Jeff Larche is Director of Analytics and Personalisation at TA Digital. He has worked at some of the world’s largest digital agencies to design, build and operate customer analytics and personalisation systems. He has embraced the growing complexity of digital marketing while working to make the process simpler to manage and turn into insights.
Josip Lazarevski is Senior Product and Data Science Architect at TA Digital. He is a masters-qualified data scientist with experience in various industry sectors. An astute analyst, strategic thinker and creative problem-solver, Josip possesses the ability to focus on the detail and distil complex challenges into tangible, quantifiable execution strategies to meet business data objectives.
Citation
Larche, Jeff and Lazarevski, Josip (2021, September 1). Measuring the value of artificial intelligence in improving search and chatbot outcomes. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 7, Issue 2. https://doi.org/10.69554/KARK8296.
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