Sprinklr’s Product Insights helps you understand how customers feel when they interact with a product by capturing customer experience data at the point of occurrence. Sprinklr’s industry-leading artificial intelligence (AI) and natural language processing (NLP) models convert that data into insights that:
Enhance the product experience
Deliver superior brand value
Directly engage with customers to make them happier
Product Insights is relevant to all personas that engage with and derive value from customer feedback. Whether you are a marketer, an account executive, a product developer, or in a strategic role where you need to ensure all of your teams are aligned and armed with actionable data to delight customers.
What are the major use cases solved through Product Insights?
Product Design: Identify design issues and highly-rated features for both your own and competitors’ products.
Product Innovation: Identify new product ideas and improvements based on customer suggestions.
Top Attributes: Identify top attributes loved by the customers, and why.
Low-performing Attributes: Identify the attributes that are most disliked by customers.
Quality Issues: Identify quality issues early and rectify them at the manufacturing level.
Product and Brand Performance: Identify top and low-performing sub-brands/products / SKUs based on key product metrics.
Customer Reaction to New Releases: Monitor new product releases and learn which attributes are liked and disliked.
Unified Customer View Across Channels: Centralize customer feedback and data across channels, including offline data from brand surveys.
Track Key Product Metrics: Track key product metrics like star rating, experience across products and brands, and how they change over time.
Competitor Benchmarking: Understand what customers are saying about competitors’ products.
Promotion Assessment: Identify how customers receive offers and promotions. Are they talking about it?
Point-of-market Entry: Use usage patterns to identify new market segments or fulfill an unspecified user requirement.
Brand Positioning: Judge overall brand sentiment. Identify cases where the brand positioning is going wrong.
Inventory Management: Improve inventory management by identifying product availability issues, the popularity of sales channels, etc.
Retailer Management: Analyze customers’ voices for any particular retailer, offline or online, to streamline operations and maintain quality.
Crisis Management: Detect potential crises at an early stage through active monitoring of customer reviews and social data.
Crisis Mitigation: Identify cases where a user propagates false information about the brand/product before it gets out of control.
Smart Alerts: Use AI to get notifications when changes are detected in key product metrics like experience score, star rating, sentiment, etc.