The surge of interest in artificial intelligence (AI) brought about by the launch of GPT-3 – the engine behind OpenAI’s ChatGPT bot – has gotten more businesses exploring possibilites in AI technology. One of the more obvious benefits of AI technology is its ability to analyze large amounts of data quickly and accurately. Voice AI pushes this boundary even further by converting unstructured voice data into structured datasets.
Voice AI can capture a wide range of data points, including the customer’s tone of voice, sentiment, and the specific words or phrases they use. This data can provide businesses with insights into customer preferences, needs, and behaviors, depending on how deep and well you dig into the information. One key ingredient in unlocking and enhancing customer interactions is what the AI training world calls intents.
Voicing out customer intents
Intents play a crucial role in AI technology, particularly in voice AI systems. Intents are essentially the goals or purposes of a user’s spoken or written input. They can be signalled by specific words or picked up contextually, and are used by AI engines to determine what action should be taken in response. In other words, intents help AI systems understand what a user wants or needs, allowing them to respond appropriately.
For example, if a user asks a voice assistant to play a song, the intent of their input is to request music playback. The voice assistant’s AI technology recognizes this intent and responds by playing the song that was mentioned. Similarly, if a user asks a bot for recommendations on a particular category of product, the intent of their input is to seek advice on product choices. The bot’s AI technology recognizes this intent and, taking cues from previous interactions, responds with personalized product recommendations.
Intents are typically defined using natural language understanding (NLU) models, which are trained on large datasets of human language to identify and classify intents accurately. NLU models use a combination of techniques, such as machine learning algorithms and semantic analysis, to interpret the meaning behind a user’s input and identify the intent accurately.
Getting intents right enables businesses to provide more accurate and personalized interactions between users and machines. By understanding users’ intentions, AI systems can provide more effective responses, improving the overall user experience and increasing user engagement.
Discovering what customers like
Voice AI can discover hidden customer preferences by picking nuances and taking note of them into customer profiles. These extra data points can help paint a more complete picture of customers, enabling businesses to better personalize customer journeys. Here are some ways voice AI engines can pick up hidden cues from call data.
- Tone of voice analysis – Just as human agents can sense a customer’s emotional state through their tone of voice, voice AI engines can also be trained to pick up these signals through changes in pitch or speed of talking. When a bot detects that a customer is agitated, it can switch to a more soothing or calming tone to reassure customers. If the customer shows a persistent pattern of getting agitated, this can signal a preference to speak with a human agent. Talkbots can use mellow speaking tones during initial interactions, then hand off the interaction to a human agent who has high empathy and emotional intelligence.
- Sentiment analysis – Voice AI technology can analyze the sentiment of customer interactions, categorizing them as positive and negative experiences based on how the customers respond. This information can provide businesses insights into areas where they need to improve, as well as how, when, and in which channels customers prefer to be spoken to.
- Natural language processing – By leveraging natural language processing, AI can attribute meaning to customer statements, even when they use colloquial language or non-standard grammar. As bots engage with customers, the AI engine begins to develop its understanding of certain expressions and can classify specific words or phrases as having positive or negative connotations. As the patterns are strengthened through various interactions with different customers, the bot can then refine its future interactions.
Building better customer profiles with data
All these tools enable AI assistants to collect data from every interaction. While humans may struggle to record, analyze, and interpret vast amounts of data from call recordings, voice AI engines can transcribe, process and analyze customer interactions in a matter of minutes. This analytic capacity enables businesses to identify patterns and trends in customer behavior that may not be picked up in a timely manner, or even altogether missed.
The ability of voice AI engines to collect data on customer interactions is proving to be more and more useful in building up robust customer profiles. helping build more robust customer profiles. This, in turn, helps create more personalized customer experiences and tailored marketing, which ideally improve customer satisfaction.
We are just starting to build our capabilities in AI technology. As large language models continue to become more sophisticated and AI neural engines more accurate, intent recognition will continue to improve. Ultimately, this will lead to even more seamless and personalized interactions between users and machines, creating experiences that customers will not just enjoy, but also remember.
