The Ultimate Pocket Guide to Voice AI for Telephony/Mobile Ordering

Restaurants today are facing a barrage of challenges that require them to adapt with cutting-edge technologies. 

More and more customers expect an Amazon-level experience, one that includes personalized shopping and lightning fast convenience, so restaurants are increasingly turning to technology to address gaps, boost profits, and enhance the customer experience.

Voice AI, which has already been widely adopted in consumer goods, is one such technology used for telephony/mobile ordering that’s undergone significant investment and is now poised to make waves in the restaurant industry. Keep reading to learn everything you need to know about adopting Voice AI for your telephony/mobile ordering. 

Voice AI Is Poised to Solve Restaurant Pain Points

Although every restaurant is different, there are pain points affecting the entire industry—and staffing is one of the top challenges. 

For one, the turnover rate in the restaurant is astronomically high compared to other industries. For restaurants, the average employee retention is 110 days, meaning that on average, they have to replace their entire team of servers, hosts, and other customer-facing employees more than once every year! 

Secondly, annual labor costs have gone up by 21%, placing further strain on staffing operations. With the cost of employee turnover averaging around $5,864 per person for a typical front-line employee, staffing can be a serious drain on operating costs.

Meanwhile, 42% of customers order by phone, QSR drive-thru orders account for approximately 50-70% of all revenue, and 60% of customers will order more online than in-person. Together, these metrics point towards the untapped potential of leveraging Voice AI to streamline telephony/mobile ordering and raise profits. 

How Restaurants Can Leverage Voice AI for Telephony/Mobile Ordering

While recent advancements in Siri, Alexa, and Google Assistant have made voice AI feel new and groundbreaking, the development of these capabilities has been going on since the 1990s. In the 2010s, Voice AI experienced explosive growth, with more sophisticated and reliable systems integrating into various industries and use cases. Advances in natural language processing and machine learning, combined with expansive computing power and countless voice samples that “train” AI, have made AI altogether more sophisticated and reliable.

As the technology improved, restaurants who wanted to stay ahead of the competition began using Voice AI for a wide range of applications, such as taking reservations and processing payments. The pandemic accelerated the adoption of Voice AI in restaurants as many businesses sought to reduce person-to-person contact. 

However, since the pandemic, restaurants have been struggling with the growing demand for to-go orders. There’s never been more ways for customers to place orders, but now, many are facing a demand they can’t handle. Kitchens are taxed and when that happens, restaurants fail.

That’s why Voice AI is being increasingly used to elevate the customer experience while letting restaurants assign employees to the tasks that matter most: preparing and serving great food.

Here are five ways that restaurants can leverage Voice AI:

1. Drive-thru. AI can understand and interact faster than any human-to-human interaction, increasing your drive-thru speed of service. 
2. Telephony. “Own the phone” by automating phone-in orders and reservations as well as handling order disputes. Best of all, the customer never gets put on hold, text interaction is easy and the unique phone # tracks repeats and preferences.
3. Digital Orders. Embed Voice AI into any computing device UI including apps, websites and kiosks. Voice AI never misses offering upsells while 65% of employees do.
4. Issue Resolution. 5% of drive thru orders create issues for customers that cause them to call the store. Voice AI handles problems, keeping employees focused on critical tasks.
5. Status. Order and seating status is easily updated via speech and text communication automations.

How Restaurants Can Use Voice AI to Stand Out From Their Competitors

With Voice AI, customers never have to experience the feeling of waiting in line. They can order what they want, how they want, and when they want whether that’s via drive-thru or in a restaurant’s mobile app, all while receiving personalized recommendations and upsells. 

What is perhaps most advantageous for restaurants, however, is that Voice AI allows them to create an amazing customer experience at scale. When restaurants scale with Voice AI, they can accomplish more with not incremental costs but decreasing costs! By saving money, your profits go up—and considering the staffing challenges facing restaurants and the fact that Voice AI technologies are constantly improving, it pays to be an early adopter. 

Conversational Voice AI: The Technology That Makes Telephony/Mobile Ordering So Powerful

Restaurants can effectively leverage Voice AI because of its numerous capabilities, transforming the way we interact with technology to improve our daily lives. This is possible because Voice AI isn’t a single technology but several that, when combined, allows for amazing customer experiences. 

In the next section, we’ll highlight key AI technologies to understand, but right now, we’ll discuss Natural Language Processing (NLP) because it’s a crucial one to driving value for restaurants. NLP is a field of AI that focuses on enabling machines to understand and interpret human language. It uses machine learning algorithms and statistical models to analyze and understand natural language data, such as text or speech. NLP can be used to perform a wide range of language-related tasks, such as language translation, sentiment analysis, and text classification. 

While NLP is a broad field of AI, Conversational Voice AI is the specific application of NLP that allows humans and machines to have a spoken conversation with each other. This is the technology that supports:

• Ordering
• Reservations
• Customer Issue Resolution
• Status Updates

Key AI Technologies to Understand

In addition to Natural Language Processing, there are other technologies that are important to understand. This may seem like a lot of technical information, but it’s important to develop a working knowledge of these terms:

1. Automatic Speech Recognition (ASR). This technology converts spoken words into text. ASR systems use complex algorithms to analyze and decode spoken language, converting it into written words. 
2. Text-to-Speech (TTS). This technology converts text into spoken words. TTS systems use advanced algorithms to generate natural-sounding speech from written text. 
3. Wake Word Detection. This technology identifies when a user is addressing a voice assistant. Wake word detection enables voice assistants to listen for specific words or phrases that signal the start of a user’s request. 
4. Decision Engine Personalization. Supports personalization rules and interaction from external sources and adaptation from NLP interaction. 
5. Dialog Management. This technology manages the flow of a conversation between a user and a voice assistant. Dialog management enables voice assistants to ask questions, clarify intent, and provide relevant responses based on the user’s input.
6. Natural Language Understanding (NLU) Services. NLU services are used to interpret the meaning of spoken or written text. This includes understanding the relationships of parts of text, identifying intent in a message. 
7. Core Data Services and Database Technologies. These vary somewhat from standard restaurant online ordering, point-of-sale and back office systems. Although all restaurant technologies are tending to cloud architectures, modern and relevant Voice AI technologies have been born on cloud and skew heavily into more flexible and distributed technologies. 
8. NoSQL Databases. NoSQL databases, such as MongoDB and Cassandra, are often used in Voice AI applications due to their ability to handle unstructured data and their high scalability. 
9. Graph Databases. Graph databases are used in Voice AI applications requiring complex data relationships and recommendations, e.g., personalization and recommendation engines. 
10. In-Memory Databases. In-memory databases, such as Redis and Apache Ignite, are used in voice AI applications that require fast and real-time processing of large amounts of data. 
11. Integration Technologies. Superior Voice AI experiences that are interactive and deliver actionable results rely on internal system and external system data with real-time API technologies such as Webhooks and JSON.

Voicify Your Telephony/Mobile Ordering

Voicify is working with some of the largest players in the restaurant industry to reimagine the food ordering experience with voice. By “voicifying” the food ordering experience for speed and accuracy, restaurants are able to increase revenue and elevate the customer experience.

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