Why It’s Important
Access to deep insights or predictive analytics often required marketers to rely on specialised data science teams to sift through data and generate findings, which could be both time-consuming and resource-intensive. However, tools are now becoming increasingly simplified for non-tech staff so they can explore key metrics, such as customer lifetime value or likelihood of service discontinuation, and make data-driven decisions without needing specialised technical knowledge.
In addition, interpreting voice data often requires manual oversight or rudimentary keyword-spotting algorithms that could not capture the nuance or context of a conversation.
With NLU, enterprises can go beyond mere transcription to understand the meaning and sentiment behind spoken words. This enables organisations to gather more nuanced insights from voice interactions, like customer pain points or preferences, without manual analysis. However, NLU capabilities, which enable the tool to understand context and sentiment, could also be exploited to mine sensitive data if not properly secured. In addition, enterprises must be cautious about compliance with data protection regulations, like GDPR or CCPA, that govern the storage and handling of customer data.
Who’s Impacted?
- CMO
- Marketing teams
What’s Next?
- Robust encryption, access control mechanisms, and compliance features are critical to ensure the secure operation of any tool that deals with voice data analytics and customer data gathering tools. Ensure that these are observed when acquiring AI based solution.
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