[Image by Gerd Altmann from Pixabay]

Voice technology has come a long way in the last five years. Organizations and consumers have adopted voice technologies like text-to-speech, automatic speech recognition, natural language understanding, natural language generation etc. for mundane tasks through complex automation. In this article, I try to bring forth some insights around Voice Tech and possible business models.

Introduction
With any new technology, adoption and usage make the journey from emerging to being mainstream and voice is clearly crossing that chasm. Apple, Google, Baidu have reported hundreds of millions of devices using voice, and Amazon has 200 million users. Adoption is typically uneven, but the penetration levels of smartphones accelerate that journey when it comes to Voice Tech.

Given voice is not used in the same way by everyone, it creates opportunities to spread across context and experience continuums to find a sweet spot for deeper adoption. Use cases, context and method of access matter and playing a big role- voice adoption also differs by how people access it. While voice assistants are the media favorites, smart phones, in car assistants etc. are also very prevalent and scaling.

Altering Business Paradigms
Voice is literally changing how business works and customers think about products. Especially given we are in the middle of a global pandemic, the contact-less use cases have suddenly scaled by replacing touch intensive graphical user interfaces or buttons. With platform players like Siri, Alexa, Cortana, and Bixby creating broader use cases, the enterprise players like Nuance, Houndify etc. create niche opportunities with white labelled solutions.

The crowding of this market creates risk of rapid commoditization, but value is always created by context, user relationships and customer experiences which build the importance of skills like VUX (Voice User Experience) and VUI (Voice User Interface) specialists. Poor experiences can jeopardize use relationships, contaminate the brand, and impede adoption. Misunderstanding voice-based requests (poor ASR or NLU) can pollute the context itself e.g., answering an out of context question due to misinterpretation. Let us examine a few such improvements, these will enable various business models in the voice arena.

Boost Customer Interactions
One of the goals of voice technologies is to develop deeper personal relationships with the users and customers. The level of personalization possible with voice is much higher than the graphical user interface or the chatbot era. The sheer variety of systems, context and behaviors are the biggest challenge to automation or unified customer experience, voice can play a huge role in integration the technology ecosystem of hardware, software, and service together.

AI Enabled Automation
The convergence of Voice Tech and Artificial Intelligence create a myriad of opportunities as Voice Tech benefits from machine learning. AI is going to be embedded deep into the context enabling voice interfaces to easily leverage the scale. For examples, voice assistants can teach themselves how to solve problems better and quicker with your customers.

Sonic Marketing
Voice Tech is well on its path to be front and center of search engine optimization. It has started to migrate organizational thinking from text-based to voice-based SEO. As voice-based searches continue to increase, sonic marketing strategies must weave themselves around voice. Sonic marketing is more than search, it also spans voice content e.g., podcasts and sonic branding i.e., using audio to reinforce brand recognition. Voice-enabled ads provide advertisers with real-time measurement capabilities, enhancing the appeal of sonic marketing. Lack of in-house skills and knowledge make it a bit hard for early adoption of voice-based marketing strategies, but there is wide acknowledgement of the large opportunity it presents.

Voice Enabled Products
Voice has morphed from being a small feature on a smartphone to being able to control large ecosystem of products and services. They can eliminate the issue of multiple apps on multiple devices and siloed operation of ecosystems. Customers expect products to have native integration with their platforms of choice. All new generation products will have to be voice enabled for them to stay relevant and competitive.

The COVID-19 Catalyst
Coronavirus has provided an ideal catalyst to voice adoption. Given concerns on the spread, everyone from retail stores, to banks and hotels etc. have either created or planning contactless products, services etc. Voice technologies provide the perfect alternative to touching buttons or screens to interact and transact.

Business Models
Voice Tech is the unifying glue between a single ecosystem or multiple ecosystems enabling efficient distribution and monetization. Voice is also a way to quickly weave service economics around hardware or software. Voice can create voice enabled hardware infrastructure to drive larger adoption, create software modules to help developer communities to voice-enable applications or enhance existing services to provide better integration with products in the ecosystem.

Platform Based
Platform plays have been around for a while, it is about creating of a seamless interaction on a two-side marketplace and monetize transactions. Ability to gain critical mass on each side of the platform and engineer interoperability, interconnections through meaningful curation is the key. The hardware market is very disparate e.g., bulbs, plugs, home appliances etc. Voice assistants can make the integration seamless; the business model eliminates the need of many separate mobile applications into one voice interface. The key is to get as many hardware vendors to adopt the platform while driving adoption by the users leveraging platform business model. Monetization can be attained through the hardware vendor or the user, for now most cases are the former paying for services while user paying for voice enabled hardware like Alexa or Google Assistant.

Voice First Business Models
As voice adoption scales with voice only applications or voice enabled hardware, platforms will amplify reach, monetization, and scale by enabling third party solutions like marketing, analytics, security, automation etc. Almost all the Big-Tech players have embarked on creating such ecosystems to scale. KPIs to measure these ecosystems have been around # of skills e.g., Alexa, usage of skills, transactions enabled by ecosystems, # of ecosystems enabled e.g., smart home, smart car etc.

Transaction Based Business Model
The transaction-based business models have been popular for a while, we can see voice-based transactions scaling across value chains such as payments, advertising, marketing, food delivery etc. The basic economic value underpinning the business model is a piece of the transaction fee, this can get to significant levels of scale as ecosystems grow their reach. The transaction-based business model can scale across hardware, software and services creating new experiences across complete value chains for companies and consumers alike.

Voice-Data Interchange Model
The voice and data interchange are a great way to monetize Voice Tech. The difference between voice and data being most data generated by sensors today are monetized by sensor creator, but voice is monetized by parties other than the creators. In future, I see the advent of voice brokers aggregating queries from multiple sources and monetizing them as packaged intelligence. For examples, voice archives can become an archived investment to corelate with data patterns as augmented intelligence for better targeting etc. There is also a market for real time voice commentary, such as weather, disaster, traffic jam information etc. One of the emerging business models is micro- monetization of voice e.g., AaaS or “Answers as a Service”

Conclusion
Voice is a fast-growing interface enabling better experiences through automation, integration and weaving service economics around hardware and software ecosystems. Voice Tech is at the crossroads between multiple context, intelligence and experience continuums and will continue to scale and evolve. There do exist a few barriers and challenges to overcome such as voice privacy when it is aggregated by cloud players, lack of fully baked data sets in the right volumes to train models in the enterprise, voice security still being immature and limited open source communities etc.

By Nitin Kumar, CMC, CMAA

Keywords: AI, Emerging Technology, Mobility

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