When a Voice Assistant Asks for Feedback: An Empirical Study on Customer Experience with A/B Testing and Causal Inference Methods

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

“…The choice of optimization is imperative to achieve desired outcomes. When employing AI to automatically elicit customer feedback [26] or mining insights from product reviews [41], the policy output is when to trigger elicitation and the similarity score of product reviews respectively. However, the desired policy outcome is an improved customer agility and experience.…”

“…Only five papers that studied the outcomes of AI policies were found in this analyzis. Three of which were formative [1], [5], [9] and the other two performed an online impact analysis [28] and cost-benefit analysis [26]. For industries looking at employing AI in their business, policy evaluations supports the business case and trust.…”

“…Sidaoui, Jaakkola and Burton [25] proposes a chat bot framework that is based around "narrative inquiry via data collection mechanisms such as storytelling and interviews" to improve primary experience gathering. Deng and Murari [26] suggests that Crowdsourced Voice Feedback is a more reliable, customer-centric, less expensive and instantaneous method of measuring CX when using Intelligent Voice Assistants (IVA). With the use of ML based causal inference methods, they discovered that prompting customers for feedback had no negative impact on CX; however, when the elicitation frequency increased the response rate decreased.…”