QSRs Discover Their Voice
VoiceSignals #4 — Musings on Voice tech news
The market value from quick-service restaurants (QSRs), fast food chains, drive-thru establishments, and deliveries is estimated to be worth $273 billion. A market that is bound to expand as more people eat out, while many eateries are actively working to meet consumers where they are by offering more ordering solutions and addressing their patrons’ increased appetites for speedy and convenient tech-enabled experiences. McDonald’s recently acquired Apprente, a startup building conversational agents that can automate voice-based ordering in multiple languages, in order to bring voice technology to drive-thrus; while Domino’s, KFC, Pizza Hut and Wingstop have enabled their customers to use in-app voice-activated ordering, while others have integrated with major virtual assistants — such as Cortana, Alexa or Google Assistant — to accomplish that task. According to a PYMNTS Digital Drive Report consumers are most likely to use voice technology to seek out restaurant information, with 53 percent indicating they would like to use such solutions to search for restaurants based on menu information. 49 percent use voice offerings to find information about specific restaurants, and 44 percent use it to find restaurants based on their cravings.
Obviously there are several hurdles the industry will have to overcome before the trend can be fully adopted. Consumers are used to visualizing what they order, first, and then using their voice to order. It can also be frustrating when misunderstandings happen or you can’t place an order with your specific expectations and wording, like ‘leave the onions out’ instead of ‘without onions’. Strides in voice technology are happening every day, like voice assistants with screens or integrated new skills in understanding distinct voices and dialects(Alexa); while software companies are building dedicated voice ordering interfaces for restaurants, over Google Home, Alexa, and the mobile, to enable eateries to integrate the technology fast and cost-effectively.
The bigger disruption will come from what these companies will do with all the voice data they will have collected. Food ordering usually involves a lot of information and feelings, for example how we feel at the moment…hungry, peckish, famished, frustrated, impatient; where we are; what we’re doing; and what we crave. For brands, it won’t just be an order on the teller anymore, it will be a full conversation where they will anticipate what we’ll order, predict what we desire, and suggest completely new products that fit our personality. It will revolutionize how we eat and how we produce our food. 🥤🥤
Tell Us How You Really Feel
A study by the Association for Psychological Science explored the correlation between human expression and the true feelings they convey. While there is an inevitable link between the two, our deeper emotions linger far below the surface, tucked away from the prying eyes that may be analyzing our face at the time. A simple smile may mask one’s sadness while a forced frown stifles a giggle just simmering behind a set of clenched teeth. Facial expressions can be manipulated when the occasion demands it.
Voice is more complex. It includes the words and the language being used for speech, but it also includes a wealth of other information like emotional cues that make it very difficult to manipulate and control at all times. Feelings like sadness, exhaustion, and anger can be discerned just from the tone of voice, revealing exactly what we feel. We might want to say we’re feeling ‘fine’ but our voice will betray what our mind and body are really feeling.
So why don’t you start by telling us how you feel?
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Can Emotional Analytics Help Businesses Get in Tune with How their Customers Are Really Feeling?
Herb Greenbaum from CRMxchange, a platform focusing on call center software and customer experience, interviewed Behavioral Signals’ CTO, Alexandros Potamianos, Callminer’s CTO, Jeff Galino, and Steve Kraus, VP of Marketing at Cogito, on emotion analytics as a software offering.
He notes how emotion analytics solutions have the capability to extract insights from all customer touchpoints and channels across the organization. These products employ historical data and real-time information to identify customer patterns and trends, providing the background for an agent to tweak the dialogue over the course of the call. Data and real-time information help companies to determine the right offer to generate to retain customers, thereby reducing escalations and churn. This makes emotion analytics a weapon of mass instruction for businesses seeking to gain an edge by learning what makes their customers tick.
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The Intersection of Voice & Emotion
Julie Daniel Davis, the host of the podcast Voice in Education, asks Rana Gujral in Episode 32: What is best for children when it comes to Voice and AI? Rana talks about global classrooms and new tools that can completely disrupt education; either that includes teachers tackling tedious administrative work or allowing children who live far from schools to follow the program without missing out on education. He explains why AI will never replace teachers, but how AI will empower them to do their job better; by offering better support, new tools and applications, and allowing students to learn independently in a way that is conducive to how they think.
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AI and Natural Language in M&A
Ned Gannon, President of DFIN’s eBrevia subsidiary, explores how AI is proving invaluable in the legal world of mergers and acquisitions, especially in contract analysis. As AI becomes more adept at analyzing contracts, very significant levels of efficiency can be realized: the software can review thousands of pages in seconds to detect contract irregularities and extract information, reducing contract review time by 67%t. And because AI is not limited by human reading and comprehension speeds, software is able to scan and analyze many more documents for risks than what would otherwise have been reviewed due to time or budget constraints.
It will be interesting to track how legal will be integrating other facets of NLP, including NLU, in the future.
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Written by Vicki Kolovou for Behavioral Signals
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