Measuring the value of artificial intelligence in improving search and chatbot outcomes

Jeff Larche and Josip Lazarevski
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Jeff Larche: Director, Analytics and Personalization, TA Digital, USA
Josip Lazarevski: Senior Product and Data Science Architect, TA Digital, USA

Applied Marketing Analytics: The Peer-Reviewed Journal, 2021, vol. 7, issue 2, 154-160

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.

Keywords: artificial intelligence; machine learning; return on investment; technological domestication; Adobe Analytics; Google Analytics; data visualisation; brownie charts (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:aza:ama000:y:2021:v:7:i:2:p:154-160

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