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VoiceEnabledAssistants(VEA) are entering human lives in every possible way, both in-home (mobiles, smart speakers, thermostats, lights et al.) and out-of-home (cars, work places, hotels, schools et al.). VEA are personal digital assistants present in devices that are enabled for voice interaction (to perform specific tasks for the users), could be the simplest definition1. Very high potential is predicted for voice assistants’ usage by Google2 (estimates: 50% Voice Search by 2020, USD 18.3 B by 2023) as well as by Gartner3. Smart speakers are far exceeding these estimates4, near 38Bn units by 2026.
Using only their voice, users are now able to ask (search) and hear (access) flight options, banking details etc., as well as make purchases in some cases. 80% of the respondents believe, “voice control will soon become part of daily life”, reducing dependence on touch or type (Bing and eConsultancy, 2019). With the emerging voice interactions, several services and applications i.e., Skills on Amazon Alexa, Actions on Google Home et al. (coining term Vapps for Voice applications) hitherto available on websites and mobile have started to engage their consumers on voice platforms as well, leading to Voice engagement (VE). With strong literature support, (Moriuchi, 2019) inferenced that (customer) engagement is above involvement or participation and studied that it impacts loyalty positively. Businesses are starting to observe initial contribution through VE (i.e., business transaction OR monetary transaction) which is to be termed as Voice Commerce (V-Com).
Organizations collate data emanating from their digital initiatives (social media, e-commerce, content et al.) & devices (mobiles, pc, speakers, IoT devices et al.) to create actionable information, feeding their business intelligence(BI) initiatives. Big Data Analytics(BDA) is offering transformative benefits in real-time customer service, dynamic pricing, personalization etc., resulting in higher conversion rates and better productivity (Akter & Wamba, 2016). For an overview on critical factors that are at play in BDA, see (Sivarajah, Kamal, Irani, & Weerakkody, 2017). All this is leading to the brighter perspective of improved service innovation models which are reflecting at firms like Google, Amazon, Netflix and a lot more.
It is important to note that in the proposed 3 tiers of BI&A (Business Intelligence and Analytics), voice analytics was not listed as a part of the emerging research analytics [list included: Big Data, Text, Web, Network and Mobile Analytics] though sensor-based content was included in BI 3.0 (Chen, Chiang, & Storey, 2012, p. 1169). In accordance, voice analytics should not be mixed up with speech analytics applying linguistic and semantic analysis to understand context, sentiment of the speaker. A sound case was built for improving customer service by (Scheidt & Chung, 2019). Similar posit should be developed for voice customer experience (Voice-CX) with voice as The engagement channel.
BDA context will be arrogated to Voice commerce as another channel of e-commerce. Here the data will be captured from sources as voice, voice notes, speech recognition, voice-ID and TTS-STT (text-to-speech & speech-to-text) et al. when observing V-Com in the ubiquitous commerce or U-commerce (Kumar, Joshi, & Saquib, 2015).
The primary objective of this paper is to establish linkage between voice engagement and business intelligence. The outlined route is to understand how voice as a channel is being leveraged and if that engagement and resultant data will help add to voice business intelligence(VBI).
With the understanding of the customer engagement (CE), context of e-commerce and BI (business intelligence), this work works the foundation for voice business intelligence (VBI). This paper presents the case for Voice, as voice engagement in both commercial and non-commercial forms, in both direct and indirect ways, will lead to generating behavioural data, feeding VBI.