What is call analytics and how is AI giving marketers more power to measure inbound call data?
A new article on Martech.org reports that the ability to track calls is a core use case of call analytics technology. However, advances in machine learning and artificial intelligence (AI) are driving more sophisticated applications.
Call analytics software manages the inbound phone channel (including both landlines and mobile phones), handling tasks from assigning call tracking numbers to measuring, monitoring, analyzing and reporting the resulting caller data and campaign results. These platforms provide call tracking, recording, routing and attribution tools to enable these functions.
The ability to track calls is a core use case of call analytics technology. However, advances in machine learning and artificial intelligence (AI) are driving more sophisticated applications, including the following:
Call analytics software manages the inbound phone channel (including both landlines and mobile phones), handling tasks from assigning call tracking numbers to measuring, monitoring, analyzing and reporting the resulting caller data and campaign results. These platforms provide call tracking, recording, routing and attribution tools to enable these functions.
The ability to track calls is a core use case of call analytics technology. However, advances in machine learning and artificial intelligence (AI) are driving more sophisticated applications, including the following:
- First-party database-building: As marketers lose access to third-party cookie data, first-party data sources such as phone calls are becoming more valuable in brand efforts to build privacy-compliant customer databases. Call analytics platforms facilitate the scaled collection and analysis of caller data.
- Customer journey attribution: Call analytics platforms provide online-to-offline attribution across media channels, helping marketers understand the role that each customer touchpoint plays in a conversion. The result is more efficient resource allocation and more relevant messaging based on customer preferences.
- Marketing campaign optimization: Call analytics platforms connect calls to the search keywords, social display ads or webpages that drove them. Marketers can use unique phone numbers for each website visitor to understand which pages and elements are driving the highest quality calls, as well as which ones are causing visitors to leave. Call data, including demographics, product interests and buying stage, can also be used to optimize search bids or make on-the-fly changes to campaign messaging and creative.
- Audience segmentation and targeting: Call analytics platforms record and transcribe calls, then apply AI-based models to the results to determine the characteristics of the highest-performing callers or leads. Using the data, marketers can build personas or look-alike audiences to create high-performing customer segments.
- Personalized, intelligent routing for lead generation: Call analytics platforms use machine learning to score and route calls based on factors including call source, geography, demographics, purchase history or intent. Tools such as whisper messages arm sales reps with known customer information that personalizes the caller experience.
- Sales rep coaching and development: Many call analytics platforms include automated sales performance and evaluation tools to provide scoring/grading systems, script optimization and real-time alerts that flag lost opportunities.
- Integrations with chat applications and SMS messaging: Like phone calls, online chat and messaging are key channels for customers to interact with businesses, so some players are extending their experience with conversational analysis to popular messaging apps as well as site-specific chat and SMS.
