Customer data is the bedrock of a successful sales and marketing strategy — it helps marketers optimise their customer engagement strategy and increase sales, by targeting the right audiences, at the right moment, and through the right touchpoints.  

Today, businesses with a digital presence tap on a diverse pool of data across sources to optimise their marketing efforts. In particular, one of the most common — and valuable — data sources is the third-party data that companies collect through consented cookies on their customers’ devices. These provide insights into their customers’ behaviour even after they leave the businesses’ websites and help marketers pinpoint the right avenues to target customers with the right ads. Little wonder then, the global data analytics market is forecast to grow to $6.4 billion in 2026.

However, with consumers demanding more robust data privacy policies, internet browsers have moved to block the collection of third-party data. Most notably, Google announced last year that its Chrome browser – the most used browser in the world – would no longer support third-party cookies, following in the footsteps of competitors including Apple’s Safari and Mozilla’s Firefox.

With the loss of a valuable data source, businesses have an urgency to find new ways of unlocking customer insights.

To this end, call recordings from contact centre operations represent a rich source of untapped first-party customer data for brands. After all, over 76% of customers are intuitively inclined to dial for customer service when faced with a problem, or when they have specific questions about a product not available in an FAQ — this consumer habit has been decades in the making, and has stuck even in this era of online commerce.

Call recordings thus provide unique insight into a brand’s customers and their preferences, helping to identify and index the causes of customer dissatisfaction, and helping businesses improve operational efficiency, and agent performance.

However, voice data is inherently messy and difficult to sort – customers don’t always speak with the clarity that allows for easy transcription. In addition, other challenges are involved in the process, such as analysing recordings, categorising and translating that data into actionable insights. How then can brands tap into this resource?

Intelligent technologies like voice AI can be the answer to this dilemma. With capabilities like natural language processing and speech recognition, voice AI is able to help businesses mine through existing call data, analyse them, and derive insights for growth. Here are 3 ways how Voice AI can help businesses with their customer data.

1. Ability to perform in-depth speech analysis, to improve existing processes

For most businesses, call recordings have to be transcribed into textual data before they can begin to be sorted and analysed. Yet, this process takes hours to complete, and human agents are prone to mistakes during transcription.

Using voice AI technology to convert audio data into text files can reduce the time taken and mistakes made. The textual data can also be run through analytical tools more quickly, enabling companies to unlock meaningful insights and improve customer interactions.

Beyond enabling seamless and accurate voice-to-text transcriptions, voice AI has the ability to perform in-depth speech analysis on voice data, including tone and language.

With advancements in natural language processing and speech recognition, AI-powered voice assistants can analyse calls and extract information on what approaches work best — from language and wording to tone and accents, and even pauses. Insights from data can be used to update scripts based on customer response, helping to ensure greater chances of success in future calls. Further, the raw audio data can be used to feed machine learning, which is the process that makes AI smarter over time.

2. Breaking down and sorting customer data

Having a large resource of data is only great if you can use it to gain actionable insights. With customer data coming from multiple touchpoints in the customer experience (CX) landscape, organisations are missing a valuable opportunity to use that data to reduce friction in digital interactions. Research from McKinsey also shows that businesses implementing AI solutions to analyse data have cost savings of between 20% and 30%, and customer satisfaction score improvements of 10%.

By using AI solutions to parse through and break down existing data silos, unify sources and analyse them, businesses are able to create unique and personalised customer profiles for their consumers, inspiring brand loyalty and increasing customer retention. And as voice AI gets more intelligent through training, it can also automatically categorise customers into different buckets, according to their preferences or personality during call interactions. This helps brands manage their portfolio more efficiently and target prospects more efficiently as well.

3. Real-time collection of customer data

Not only can voice AI help businesses make use of existing data, it can also help with the real-time collection of customer data, especially during calls. As the creation of high-quality structured audio data sets is directly impacted by the accuracy of transcription and the quality of the audio recordings, it is imperative that collecting audio data has to be done carefully.

By utilising voice AI agents, they are able to collect and sort caller data even as the call is happening, improving customer data collection and cleaning, resulting in quality audio data for further analysis. By going through the thousands or millions of recorded calls, voice AI can begin to see (or hear) patterns that may help companies streamline and optimise their processes. One day, voice AI may even be able to automatically classify and score every call, speeding up the process of analysing data to gain insights.

To learn more about how Voice AI can help your business with its data collection and analysis, contact us for a consultation or schedule a product demonstration.