Retail/eCommerce

Personalized Shopping Assistant with Sentiment Analysis

Challenge:

Retailers often face difficulties in personalizing shopping experiences due to diverse customer preferences and fragmented feedback across multiple channels.​

Solution:

Implementing a Generative AI-powered Personalized Shopping Assistant using Databricks’ unified data analytics platform enables the integration and analysis of customer behaviour, preferences, and sentiment from various sources, including reviews, social media, and purchase histories.​

  • Real-Time Product Recommendations: Leveraging Databricks' machine learning capabilities, the assistant provides tailored product suggestions to individual customers based on comprehensive data analysis.
  • Dynamic Pricing Strategies: Utilizing sentiment analysis and demand forecasting within Databricks, the assistant offers dynamic pricing options that reflect current market conditions and customer sentiments.

Advantages/Benefits:

  • Increased Sales: Personalized recommendations have led to a 20% improvement in conversion rates.
  • Enhanced Customer Satisfaction: Sentiment scores have risen by 15% due to more relevant and customized shopping experiences.
  • Operational Efficiency: Cart abandonment rates have decreased by 25%, indicating a smoother and more engaging customer journey.