Data as we know it is evolving. Previously limited to numerical and text form, data is now increasingly presented to businesses in the form of audio and video, especially with the accelerated use of telephone and video-call communication between businesses and customers.
Voice recognition AI holds more essential data than any other communication, conveying sentiment, and emotion, contextualising conversational analysis. Traditionally, this data has been branded as ‘challenging’.
Using human-powered efforts to analyse and apply audio data is time-consuming, eating away at business resources to produce often erroneous or inaccurate conclusions. Implementing AI-based voice recognition provides the opportunity to delve into the world of audio data and the crucial information it provides – information that can drastically improve the customer experience.
What is voice recognition AI?
In the context of understanding human experiences, reactions, and behaviours, there have been countless conversations surrounding the use of AI to detect visual indicators, such as facial expressions. However, indications through our voices are becoming more valued by businesses as digitisation becomes increasingly prominent.
Voice recognition AI is a term covering the use of various automated processes to effectively analyse, decode, and apply audio data efficiently, utilising features such as machine learning to keep these processes accurate and updated.
Systems such as NLP (natural language processing) innovatively applied to detect behavioural and language features. This insight into behaviour, language, and sentiment generates more developed insight into the customer experience. It can also be applied to greater sales enablement, anti-fraud strategy, customer protection, and regulatory compliance.
How can voice recognition be applied to the customer experience?
1. Understanding the impact of customer service
Achieving a more in-depth understanding of customer behaviour, actions, and the sentiment is crucial to building exceptional customer service. Collecting data on these factors through voice recognition AI allows businesses to structure their sales enablement strategy around the actual customer experience, rather than relying on more approximate assumptions.
While there have been some experiments using AI with other forms of data to provide this information, these processes are rarely transferrable to audio data. This is problematic – particularly as a business is increasingly conducted with customers remotely via phone calls or video links.
2. Effective and non-Invasive anti-fraud strategy
Voice recognition AI is also easily transferred to tackling other customer experience-related issues beyond the basics of positive, negative, or productive interactions. Fraud is an issue that has plagued a plethora of industries, and its presence is only increasing with digitisation. Businesses are actively pursuing a more comprehensive anti-fraud strategy.
Voice recognition AI also provides anti-fraud coverage without disrupting the customer experience. Customers can be severely impacted by fraud, whether because of successful attacks on a business or intrusive anti-fraud strategies. Outdated anti-fraud measures require more training and increased effort from customer-facing staff (particularly in contact centres), which can distract them from providing a positive customer experience.
Combining voice recognition with machine learning makes it possible to detect fraudulent intent as early as the first phone call, without customer service agents intervening. As a result, customers can continue to experience exceptional customer services while protected by a comprehensive anti-fraud strategy. Genuine customers will not be pushed away from a business due to invasive strategies.
3. Regulatory compliance
Increasingly complex and detailed, regulatory compliance applies to all businesses, especially those that need to utilise customer information to improve sales, experiences, and branding. Voice recognition AI provides the most efficient processing and organising of data within a business.
Processing and recording audio data has previously been viewed as particularly challenging and time-consuming. Automation removes several challenges, providing records often within seconds, which can then be used to demonstrate regulatory compliance. Efficient data storage also allows businesses to provide proof of compliance during disputes, potentially saving costs on fines.
How should companies be implementing AI?
One of the biggest challenges for companies in Europe who are looking to adopt AI technology is to ensure that their implementation complies with current and forthcoming European legislation insisting that the technology is explainable and free from bias.
The General Data Protection Regulations (“GDPR”) already allows an individual to object to decisions that are made completely by machine, or which automatically profile them, so a “black box” decision making process that cannot be explained (and overridden) will fall foul of this.
The EU’s framework for AI is focused on building fairer AI systems, but there is significant pressure from organisations such as the European Digital Rights to strengthen this to ensure much stricter debiasing of systems to protect vulnerable people. Anyone using or building a new AI system needs to be aware that the underlying data used to train the system, and the algorithms used to build it, has to be balanced (e.g. by age, sex and race), and fairly obtained.