Receptiviti
Analyze customer sentiment, track experience, and gain AI-powered insights for effective customer segmentation.
What is Receptiviti?
Receptiviti is an AI-powered sentiment analysis tool that helps businesses understand what customers think and feel across written feedback, reviews, messages, and support conversations. It processes customer communication to identify emotional patterns, satisfaction levels, and underlying concerns, then uses this data to segment customers into groups with similar behaviours and preferences. The tool is designed for customer service teams, product managers, and marketers who need to move beyond simple ratings and understand the 'why' behind customer feedback. With a freemium model, it's accessible to smaller teams while offering paid plans for organisations that need deeper analysis and integrations.
Key Features
Sentiment analysis
Automatically detects emotional tone in customer communications, moving beyond positive/negative to identify frustration, confusion, or enthusiasm
Customer segmentation
Groups customers by behaviour, sentiment patterns, and experience characteristics for targeted engagement
Experience tracking
Monitors customer sentiment over time to spot trends, improvement areas, and early warning signs of churn
Multi-channel input
Processes text from emails, support tickets, reviews, social media, and survey responses
API and integrations
Connects with existing customer platforms and CRM systems to embed sentiment analysis into workflows
AI-powered insights
Provides contextual analysis and recommendations based on patterns across customer feedback
Pros & Cons
Advantages
- Frees teams from manual reading and categorising of customer feedback, saving time on analysis
- Freemium model allows small teams to test and validate the approach before committing to paid plans
- Works across multiple communication channels, so you can analyse feedback from wherever customers engage
- Helps identify customer segments and problem that aren't obvious from numerical data alone
Limitations
- Accuracy of sentiment analysis can vary depending on language nuance, sarcasm, or industry-specific terminology
- Requires integration work and data preparation to get value; the tool works best with consistent, structured feedback data
- May require significant volumes of customer feedback to produce meaningful segmentation and insights
Use Cases
Customer service teams monitoring support ticket sentiment to prioritise urgent or at-risk customers
Product managers analysing feature requests and complaints to understand what matters most to different customer groups
Marketing teams segmenting customers by satisfaction level and experience perception for targeted campaigns
Churn prevention: identifying customers showing negative sentiment patterns before they leave
Voice of customer programmes tracking satisfaction and experience across the entire customer lifecycle