How does conversational analytics software work?

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Graphic showing the steps in which conversational analytics software analyzes customer experience data to extract meaningful brand insights
Identify objectives
The first step to getting relevant insights from social listening is zeroing in on your objectives. For example, ask yourself: Are you using data analysis to meet long-term goals like enhancing customer albania phone number data experience or short-term goals like click-through rates (CTRs) to encourage a trial or purchase? Having this perspective helps you narrow your focus into high-priority areas and results that will best support your goals.

Gather relevant data
The more relevant the data source, the more accurate your insights. For example, choose industry-specific channels like TripAdvisor if you’re in the hospitality industry, or Yelp and GMB if you’re a local business.

These sources, along with customer interactions with virtual agents and chatbots on your websites, social media DMs and consumer forums like Reddit, will give you the critical brand insights you need.

Process the data
Filtering noise from tons of customer conversations and social chatter to get the right insights is the next step. Conversational analytics tools use AI tasks like NLP and named entity recognition (NER) to identify important topics and themes that emerge from data analysis. NLP and NER are also essential for sentiment analysis, so you can dig into conversational data to measure customer experience and brand health.